svEPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-95/130
August 1995
Development of Computer
Supported Information
System Shell for Measuring
Pollution Prevention
Progress
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CONTACT
Harry Freeman is the EPA contact for this report. He is presently with the newly organized
National Risk Management Research Laboratory's new Sustainable Technology Division in
Cincinnati, OH (formerly the Risk Reduction Engineering Laboratory). The National Risk
Management Research Laboratory is headquartered in Cincinnati, OH, and is now responsible for
research conducted by the Sustainable Technology Division in Cincinnati.
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EPA/600/R-95/130
August 1995
DEVELOPMENT OF COMPUTER SUPPORTED INFORMATION SYSTEM SHELL
FOR MEASURING POLLUTION PREVENTION PROGRESS
Rada Olbina
Postdoctoral Fellow
Center for Environmental Management
Tuft University Medford, MA 02115
and
Cynthia Flowers
Research Fellow
Science/Engineering Education Division
Oak Ridge Institute of Science and Education
Oak Ridge, TN 37831-0117
Project Officer
Harry Freeman
U.S. Environmental Protection Agency
National Risk Management Research Laboratory
Cincinnati, OH 45268
NATIONAL RISK MANAGEMENT RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OH 45268
Printed on Recycled Paper
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DISCLAIMER
The information in this document has been funded wholly or in part by the
United States Environmental Protection Agency. It has been subjected to peer
and administrative review, and it has been approved for publication as an EPA
document. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
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FOREWORD
The U.S. Environmental Protection Agency is charged by Congress with
protecting the Nation's land, air, and water resources. Under a mandate of
national environmental laws, the Agency strives to formulate and implement
actions leading to a compatible balance between human activities and the
ability of natural systems to support and nurture life. To meet this mandate,
EPA's research program is providing data and technical support for solving
environmental problems today and building a science knowledge base necessary
to manage our ecological resources wisely, understand how pollutants affect
our health, and prevent or reduce environmental risks in the future.
The National Risk Management Research Laboratory is the Agency's center for
investigation of technological and management approaches for reducing risks
from threats to human health and the environment. The focus of the
Laboratory's research program is on methods for the prevention and control of
pollution to air, land, water, and subsurface resources; protection of water
quality in public water systems; remediation of contaminated sites and ground
water; and prevention and control of indoor air pollution. The goal of this
research effort is to catalyze development and implementation of innovative,
cost-effective environmental technologies; develop scientific and engineering
information needed by EPA to support regulatory and policy decisions; and
provide technical support and information transfer to ensure effective
implementation of environmental regulations and strategies.
This publication has been produced as part of the Laboratory's strategic
long-term research plan. It is published and made available by EPA's Office
of Research and Development to assist the user community and to link
researchers with their clients.
E. Timothy Oppelt, Director
National Risk Management Research Laboratory
iii
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ABSTRACT
Basic elements and concepts of information systems are presented:
definition of the term "information", main elements of data and database
structure. The report also deals with the information system and its
underlying theory and design. Examples of the application of information
systems for solving primarily environmental problems are presented.
Aspects of measuring pollution prevention progress are discussed. The
application of system analysis and the definitions of system inputs and
outputs are analyzed. Information system applications are considered as tools
for measuring pollution prevention. Types of pollution prevention measurement
and the usage of normalization as well as financial and management issues are
briefly addressed.
A framework for an industrial production and waste generation tracking
system is outlined. A model of the system is presented and parameters are
precisely defined. A cost analysis of the system is carried out and examples
of cost analyses suggested for selection of industrial technologies are
discussed.
Based on the tracking model, an information system shell has been developed
and its application for waste minimization and for measuring pollution
prevention progress at an industrial facility is discussed.
Finally, the computer programming of the information system shell using
commercially available database management systems is presented.
iv
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TABLE OF CONTENTS
Disclaimer ^
Foreword iji
Abstract -....' „ ^v
List of Figures . .'.'!!' ix
List of Tables . '•••.....!."!'!!.".'!.'!'!! x
Acknowledgements xi
Introduction . . „ j
Conclusions ..... 3
1. Identification of Concepts of Information Systems. 4
Definition of the Term Information 4
Data and Potential Information Storage ..... 6
Specification of the Information Application 6
Main Elements of Database Structure . 6
Data Classification . . 8
Data Handling 9
Numerical Data Handling 9
Numerical Data Recording and Processing ......... 10
Evaluation of Numerical Data .... 11
Definitions of Information Systems ... 12
Information Paradigm 13
Systems Theory ! '! .' 14
Information System Design as Problem Solving 18
Available Information System Building Tools ....... 19
Information System Objectives and Design ..... 20
References . . 27
2. Examples of Information System Applications. 30
Introduction 30
Water Management ^ 30
Strategies for Managing Water Quality . ! ! 30
Management of Water Resources in the Changing
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Economic System in Russia 32
Groundwater Management . . . . 32
Wellhead Protection Areas and Management of
Environmental Resources . ... 32
Irrigation and Drainage Systems Rehabilitation ..... 34
Water Quality Implications of Nonpoint Source Pollution 34
Wastewater Management 34
Storm Sewer System Management 34
Wastewater Treatment Process Control 35
Sewer Flows Forecast 35
Waste Management .36
Waste Recycling, Source Reduction and Disposal 36
Waste Management - Cleanup Activities 37
Site Characterization and Remediation Activities 38
Environmental Impact Assessment .... ........ 38
Nonpoint Pollution from Agriculture ..... . . 38
Development of Emissions Inventories and Emission Modeling 40
Environmental Impact Assessment for Gold Mining 41
Oil Spills Accidents 42
Environmental Management .43
UNEP Global Environment Monitoring Systems 43
Decision-Making for Environmental and Natural Resources 44
Environmental Protection and Energy Conservation 45
Environmental Quality and Land Use . 45
Environmental Protection in Germany 46
Environmental Information Systems Inventory 46
Industrial Process Control and Total Quality Management . i 47
Utility Industry Application . . ..... 47
Development and Implementation of an Information
System for Process Control at Inco's
Copper Cliff Smelter Complex 48
Offsites Management Systems in Oil Refineries 48
References 52
3. Aspects of Measuring Pollution Prevention Progress 55
Introduction 55
vr
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Application of System Analysis 55
System Inputs and Outputs Definition ........ . -. . -.'. . . .55
Database, Simulation and Information System Application . . 57
Pollution Prevention Measurement Types and Normalization 57
Financing 58
.Management Practice 58
References 59
4. Development of a Model of an Industrial Production and Waste
Generation Tracking System . . 53
Introduction . 63
Industrial Production and Waste Generation Tracking System 63
Development of IPWGTS Model 64
Cost Analysis of IPWGTS . .... 67
Cases of Industrial Production and Waste Generation
System for Decision-Making . 70
System Costs for Decision Making . 70
References . 74
5. Development of Information System Shell for
Measuring Pollution Progress .... 75
Introduction . 75
Structure and Function of the Information System Shell . . 75
Use of Information System Shell ................... 77
6. Computer Programming of the Information System Shell . .79
System Overview 79
Quantitative Analysis . . . . 79
Cost Analysis . 79
Information System Shell ....... ..... 81
Goals and Requirements 81
Implementation 81
Data Entry 82
Data Entry - Bulk Information ............ 83
Data Format 83
Calculation with Data Entry . 84
vii
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Automatic Data Entry 84
Existing Data Entry 86
Module Design 86
Data Entry - Specific Information 89
Input Material Information 89
Cost Analysis Information 89
Waste Streams 90
Data Storage and Manipulation 91
Data Storage - Archive Database 91
Module Design 93
DM Database Design 94
Data Queries . , .95
Module Design . 95
User Interface Design 98
Forms and Controls 98
Overall System 100
Common User Activities 101
Using the ISS System 103
Development of Dynamic Forms 104
Constant Forms 104
Quantitative Analysis 104
Cost Analysis 105
Future Work , 105
vm
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LIST OF FIGURES
Figure 1. Concepts of information theory and definitions
of the term "data/information" 4
Figure 2 Physical structure of a database in term
of logics and semantics 7
Figure 3. Open system concept 15
Figure 4. A methodological approach to information
system design 21
Figure 5. Schematic presentation of system analysis
application to industrial production process 56
Figure 6. Industrial production system's inputs and outputs .... 57
Figure 7. Industrial production and waste generation
tracking system 64
Figure 8. Cost analysis of an industrial production
and waste generation tracking system 67
Figure 9. Scheme of an information system shell .76
Figure 10. Schematic presentation of the ISS databases 77
Figure 11. Control flow graph of the ISS system 99
Figure 12. Screen transition graph - quantitative analysis
display functions 100
Figure 13. Screen transition graph - cost analysis
display functions 100
IX
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LIST OF TABLES
TABLE 1. Summary of information system applications ....... 49
TABLE 2. Definition of IPWGTS model parameters 66
TABLE 3. Costs and revenues of industrial production
and waste generation tracking system 68
TABLE 4. Summary of calculation requirements and results of
data entry 86
TABLE 5. Queries created dynamically by DM database at
start-up 95
TABLE 6. Queries created dynamically by DM database
for cost analysis 96
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ACKNOWLEDGMENT
We thank the U.S. Environmental Protection Agency's National Risk Management
Research Laboratory, Cincinnati, OH for sponsoring the project.
Special thanks to our colleagues Harry Freeman, Jim Bridges, Ivars Licis and
to Pollution Prevention Research Branch for their understanding and support.
xi
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INTRODUCTION
The growing importance of pollution prevention (P2) as an environmental
strategy has increased pressure to develop systems for measuring P2
achievements. The U.S. Environmental Protection Agency defines P2 as the use
of materials, processes, or practices that reduce or eliminate the creation of
pollutants or waste at the source. It includes practices that reduce the use
of hazardous materials, energy, water, or other resources and practices that
protect natural resources through conservation or more efficient use. The
idea underlying the promotion of P2 is that it makes far more sense not to
generate waste than to develop extensive processing/treatment schemes to
insure that the waste poses no threat to the quality of the environment1. The
Pollution Prevention Act2 charges the U.S. EPA with responsibility for
developing national P2 goals, and for devising a scheme for measuring national
P2 progress. Industrial firms are under pressure, in part from the public
disclosure of toxic release information required under Susperfund Amendment
and Reauthorization Act Title III, to practice P2 and to communicate successes
to the public. All of these activities must occur within a recognized frame
of reference or measurement scheme to be meaningful3. In the Report to
Congress, Pollution Prevention Strategy4, the EPA states that establishing
clear and measurable indicators of progress in P2 serves a number a purposes,
such as helping to focus the efforts of each sector of society, and making it
easier for the public and Congress to understand and track progress in
reducing pollution. The EPA seeks continually to improve the quality of the
data gathered under the Resource Conservation and Recovery Act and the Toxic
Release Inventory, to improve the effectiveness of both programs in tracking
progress in preventing pollution at industrial facilities. In addition, work
is being carried out to:
(i) develop a more comprehensive database to measure pollution
prevention in sectors other than manufacturing;
(ii) identify the most useful indicators and units for measuring
pollution prevention at the source;
(iii) determine whether the same indicators and units can be used across
different sectors; and
(iv) identify relationships between plant-level measurements of
pollution prevention and combined effects of prevention by
multiple sectors at regional and national levels.
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The Chemical Manufacturers Association's Pollution Prevention Code of
Management Practices5 states that each company should strive for annual
reductions, recognizing that changes in production rates, new operations, and
other factors might result in increases. The goal is to establish a long-
term, substantial downward trend in the amount of waste generated and
contaminants and pollutants released. Quantitative reduction goals will be
established that give priority to those pollutants, contaminants and waste of
greatest health and environmental concern. The Code asks each member company
to establish a P2 program that.includes the following tasks:
developing a quantitative inventory at each facility of waste
generated and releases to the air, water, and land, measured or
estimated at the point of generation or release.
measuring progress at each facility in reducing the generation of
waste and in reducing releases to the air, water, and land, by
updating the quantitative inventory.
To implement P2 programs, industrial facilities must first measure the
environmental impacts of their facilities. The industrial facility should
set up boundaries of the observed system (unit operation, process, facility,
etc.); identify and quantify system parameters (inputs and outputs toxicity
and quantities) and their relationships; then collect, process and evaluate
data, and make cost analyses. Finally, facility managers will encourage the
implementation of P2 programs that will improve efficiency and reduce impacts
on the environment; and reduce costs of facility operation and maintenance.
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CONCLUSIONS
The analysis of available literature shows that the number of information
system applications for solving environmental problems has significantly
increased in the last three years. However, these applications are still
concentrating on "end-of-the-pipe treatment" technologies rather than on the
improvement of industrial operations responsible for generating environmental
pollution in the first place. Furthermore, these applications proved to be
very complex and site specific, and most of them incorporate graphic
presentation of environmental impacts resulting in so called geographic
information systems (GIS) which require teams of experts in different fields,
as well as sophisticated software and hardware. Data collection, processing
and evaluation remain the biggest challenge in widespread use of information
systems.
The main aspects in dealing with pollution prevention measurement are
applications of system analyses (including system inputs and outputs
definition), databases, computer simulation and information systems, pollution
prevention measurement types and normalization, financing and management
practice. However, to introduce pollution prevention and to be able to
measure its progress in any industrial process that generates waste, the
quantity of waste at the point of its generation and the costs of generating
waste have to be identified.
Tracking system for quantification of parameters in industrial production
and waste generation system is presented based on the basic theoretical
approach for the system understanding. Cost analysis of such a system is
carried out. A scheme of an information system shell for measuring pollution
prevention progress in an industrial facility is developed. Cost analysis is
a part of the information tracking system. It is intended to help facility
managers to measure and track quantities of waste generated, managed,
released, processed, recycled and disposed. Knowing the cost of their
operations, facility managers could recognize the opportunity to introduce
pollution prevention measures and improve efficiency of their facility.
The information system shell has been programmed based on the idea of
generality. It has been designed incorporating the common Windows interface.
It consists of three main sections: data entry, data storage and manipulation,
and display functions. The display functions are created and enabled based on
the input of the user. In addition, the same system can be used to study more
than one process; the design of the system is not based on the characteristics
of the first data sets entered.
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SECTION 1
IDENTIFICATION OF .BASIC CONCEPTS OF INFORMATION SYSTEMS
DEFINITION OF THE TERM INFORMATION
The term "information" is defined by two main theories6 -- semiotic
(information theory of symbols and characters which further includes
syntactic, semantic and pragmatic theories) and statistic (information theory
of signals transmission). Figure 1 shows schematic presentation of the
relationship among information theories and the definition of term
information.
DEHNmOKSOF
DATA / WFORUATION
THEORY OF
INFORMATION
Figure 1. Concepts of information theory and definitions of
the term "data/information"
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The terms data and information are not always separately defined with
different meanings; usually they are used as synonyms. However, in this
report, the following definitions are adopted: data are sets of messages
which have a certain meaning, while information is a set of data which
receivers use for removal or reduction of uncertainty, as well as the basis
for undertaking definite actions6.
Data consist of a collection of symbols or characters arranged in some
orderly way to serve as a vehicle for information. Information is the meaning
derived from data and represents the semantics - the relationship between a
symbol and the actual object or condition that is symbolized. The impact of
the objects or conditions on the receiver represents the pragmatic level of
information. It deals with the relationship of information and the user.
Introducing parameters such as value and usefulness of information, this
theory increases the capability for prediction and/or anticipation of future
events. According to these theories, data/information is defined as follows:
(1) relationships among characters which respond to certain rules in order
to provide meaningful information7;
(2) collections of symbols or characters arranged in some orderly way to
serve as the vehicle for information8;
(3) data which have been recorded, classified, organized, related or
(4)
(5)
(6)
(7)
(8)
(9)
interpreted within the framework so that meaning emerges ;
10
the enlargement of knowledge
the extent to which one's knowledge is enhanced upon receiving the
.11.
information ;
communication of facts or instructions11;
the part of a sentence or news, which has news value for the receiver
and which enables problem solving7;
event that reduces the amount of uncertainty of the system10;
numerical quantity that measures uncertainty in the outcome of an
experiment to be performed12;
(10) the amount, in the simplest cases, to be measured by the logarithm of
12.
the number of available choices ;
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(11) the quantity that is transported between two or more objects or systems
when the objective is not to transport energy, mass, momentum, or charge
(or any other conserved quantity of physics)
13
Data and Potential Information Storage
In the process of applying information, all relevant information is not
used simultaneously. For example, a decision-maker usually does not gather
all the information necessary to make a decision. Potential information is
gathered which requires that some form of intermediate storage be used. Data
covering long periods of time may be necessary to undertake some manipulations
and decisions. Therefore, data must be stored until needed14.
Specification of the Information Application
The specification of application is necessary because information has
meaning only when associated with any kind of action to be taken, such as15:
- decision making,
- planning,
- monitoring (as performance or quality control indicator),
- problem solving,
- knowledge creation.
These elements also implicitly include the specification of events to be
observed, and the. time and place of observation14.
Main Elements of Database Structure
A database, according to Birplla et al .6 is a selected set of data
relevant to a specific field of application, stored in some common computer
memory and organized in a such a way that its elements (data) may be accessed
by users with simple user-friendly dialogues (Figure 2).
According to the Mcmillan Dictionary of Information Technology16, the
database structure is independent of programs that use the data and a common
control approach is employed in adding, deleting or modifying the data.
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Data models specify how separate pieces of information are related in a
database. Information is broken down into data structures. The most basic of
these structures are records and fields. A record can be simply defined as a
collection of fields or attributes. At the logical (file) level, records are
the basic units of data storage. Fields are the elementary data items. A
field can be defined both in terms of its:
DATABASE
The highest unl of the physbal
structure of data, which include*
bgc and semantic*.
RLE Ofl SCOPE
The common element of th« logical,
semantic, and physical structure of
data, which relates to the more
specific field of application.
DATA BLOCK OR RECORD
The specific element of the physical
structure of data adapted to the
characteristics of the machine
processirg the data.
DATA SYLLABLE
The specific element of the logical
and semantic structure of data which
represents the data relationship
concerring the application of an
indvUua) object suitable to user
cognition.
DATA RELD OR CONCEPT
The specifc ebmant of the logical
and semantic structure of data
which defines particular basic data
or concepts.
SYMBOL OR BYTE
The common element of logical.
semantic and physical structure
of da!a which idantliet the location
in the memory where one symbol
can be placed.
BIT
The simplest unit of the informed ion
in the physical structure of the data:
an element
Figure 2. Physical structure of a database
in term of logics and semantics
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syntax -- the information that a field contains which conforms to a
certain type, such as real number, an integer, a character or a
string;
semantics -- the meaning of information within a fields which
corresponds to the concept represented by the field. Thus, if the
field refers to a quantity of some measurement, the number in that
field should be a realistic representation of that quantity.
Although complex data models can be defined simply, in the essence of
every data model lie structures that are equivalent to records and fields,
whether or not they are referred to by those names".
The approach in the organization and structuring of formatted databases
needs to encompass two contradictory requirements:
- user needs or the way users define and use data,
- machine (computer) requirements or the way data are memorized in order
to optimize machine processing.
On the basis of these requirements, two different structures of the same
database may be distinguished:
- logical-semantic structure (user aspect) which completely ignores the
way in which data are physically stored in the computer memory, giving
attention only to the meaning and the logic connection dictated by user
need;
- physical structure (machine aspect) which ignores the meaning of the
data and takes into consideration only the formal characteristics such
as data length, type of symbols or codes, etc.
Data Classification--
In order to reduce the large amount of different types of data to a
manageable order, the classification of data, and formation of general ideas
about groups of facts (or concepts) is applied.
Data generation17 requires four steps to be useful beyond the moment of
observation. The steps involve:
- classification of data - the basic problem of relating observation to
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anticipated situations;
- establishment of procedures for recording data in a manner that will
allow retrieval, yet be sufficiently simple to enable the operation to
be repeated;
- summarization of classified and recorded data;
- specification of the collection procedure of the system.
Classification reduces the complexity of the available materials, provides
the basis for identification by grouping similar facts together, provides a
record of experience, and relates classes of events14.
Three major characteristics of any classification system17 are:
- classes must not overlap - i.e., they must be mutually exclusive;
- the classification system must be exhaustive - requiring that each item
be classified and placed in some distinct category;
- the basis for classification must be significant (oriented towards
specific goals) and in accordance with some previously determined
pattern.
Data Handling--
Since data are past-oriented, manipulation is necessary to adapt them into
information relevant to the present and future. Based on the works of
1ft
Johnson, Kast and Rosenzweig , it can be stated that the system connecting
the information and its application must be designed to gather relevant facts
and screen out unwanted or unusual data. Selected data may become information
for decision making. However, it is more likely that additional processing
is necessary before meaningful information emerges.
Numerical Data Handling--
Numerical data, as usually understood in physical sciences such as physics
and chemistry, are numerical representations of the magnitudes of various
quantities19.
Data in science and technology are usually the result of experiments or
observations carried out by researchers. In some instances the primary
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objective of the research is to obtain the data, but more frequently the data
are generated for some other purpose (e.g., as a base for the establishment of
legislation and regulations of a specific field of human activity). As a
result, some potentially valuable data are not published at all. It is,
however, desirable that such data be submitted to, and stored in, appropriate
databases to facilitate later utilization
20
Data handling is taken to include all of the steps of intellectual and
physical manipulation involved in:
- recording the results of observation (in the laboratory, pilot-scale
level, industrial production or field),
- interpreting and refining those results,
- publishing and disseminating the report, and improving accessibility of
that report,
- re-analyzing and evaluating the results where necessary, and compiling
them,
- delivering the data to a user for final application in solving some
problems, or in decision making, planning, and in creation of new
knowledge19.
Numerical Data Recording and Processinq--
The trend today is to obtain data in digital form, thereby facilitating
the extensive capabilities of modern computers and telecommunication systems.
As computer technology advances, there is a growing tendency toward the
automation and computerization of data, including the handling, collection,
storage, editing, retrieval, and dissemination of data.
The analysis and interpretation of data is becoming diffused throughout
every part of science and technology. It includes the need to:
- assign a correctly assessed error to a single datum derived by
measurement,
- determine the best function and parameters to represent a relationship
between variables,
- detect and eliminate bias and false-effects introduced by the
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instrumentation or the method of performing an experiment,
-, test hypotheses and statistical inference.
An important function of the analysis of data is to provide a good
estimate of quantities on which the observed data depend. Powerful
statistical methods have been developed to perform this task. Periodically it-
becomes, necessary to evaluate all the new data available and to ensure that
any adjustments made to data sets are consistent with the operational
characteristics of the system which is subjected to errors of measurement.
Statistics is used to represent the interdependence of the measurement and to
connect the previous data set with new measurements, in order to derive a new
data set of new characteristics.
Evaluation of Numerical Data--
The critical evaluation of data refers to those processes involved in
assuring that retrieved data meet certain standards for accuracy and
dependability. Kieffer21 states that a short definition of reliable data
would be data which are presented with error bars which were chosen so that
the probability of the 'true' value lying outside of these limits of error is
extremely small. Branscomb22 suggests that in carrying out a critical
evaluation, a meaningful quantitative statement can be made about the probable
presence of systematic errors in the data. This statement must be based on a
set of objective criteria for assessing the likely presence and effect of
systematic errors.
The processes of data evaluation procedures can be summarized23 as: (1)
examination and appraisal of the data, and assessment of experimental
techniques with associated errors, (2) re-analysis and recalculation of
derived results, and (3) selection of 'best' values.
The proper assessment of experimental techniques is a fundamental factor
in the evaluation process, involving: (1) a study of the experimental design,
(2) the way in which instruments have been used, and (3) the problem of
systematic errors.
The end result of any evaluation process should be the presentation of
'best' or recommended values together with quantitative estimates of the
uncertainties in these values. The 'best' values derived by the critical
evaluation process are only the best values at the time reviewed. Any
selection of recommended values is subject to change in the light of improved
measurement or evaluation24.
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DEFINITIONS OF INFORMATION SYSTEMS
There are many definitions of the term information system. The following
refer to the definitions of a computer-supported information system:
• a set of hardware, software, and informational facilities that permit
the accumulation, classification, storage and retrieval of a large
amount of information. An information system not only stores data, but
also provides facilities for assigning meaning to it, and hence,
provides information. The information system consists of three major
components:
- a large repository for data called a database,
a means of accessing the data,
a means of processing the data for analysis and reports25;
• any means for communicating knowledge from one person to another, such
as by simple verbal communication, or by completely computerized methods
of storing, searching, and retrieving information9;
• systems concerned with collection, storage, processing, transmission,
distribution, retrieval and utilization of information26;
• a system whose goal is to provide information and information services
for its environment. This definition implies that an information system
must encompass at least two subsystems, one consisting of the system's
collection of information, the other providing the information
services27;
• an application of the computer that provides for the routine processing
of data. It is made of databases, application programs and manuals, and
machine procedures. The databases store files (subject of the system).
The application programs provide the data entry, updating, query and
report processing of the system. The manual procedures document how to
obtain the necessary data for input into the system and how to
distribute the system's reports and forms. The machine procedures
instruct the computer how to perform the system's processing activities
in which the output of one program is fed as input to the next
program28;
• a system designed to solve a variety of data, information, and
knowledge-based problems. Information systems provide analytical
12
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support to users. They help allocate and evaluate resources, plan and
simulate large events and processes. This extremely important
distinction defines the range of application of modern information
systems. Recently their applications have been expanded from data-
oriented to analytical computing (search, identification,
classification, categorization, planning, evaluation, prioritization and
decision-making)29.
According to Nichols14, the general function of information systems is to
determine user needs, to select pertinent data from the infinite variety
available in an'organization's environment (internal and external), to create
information by applying the appropriate tools to the data selected, and to
communicate the generated information to the user.
A special type of information system is the geographic information system
(GIS) which can be defined as an electronic data storage system or database
connected to graphic tools for mapping and illustrating data that they
.30
31
contain . According to Bruckman et al. a GIS is a specialized data
management system designed for the entry, analysis, management, and display of
data commonly found on maps. The GIS is an effective tool for determining the
relationships among demographic, natural resource, land use, and air and water
quality objectives. For example, a GIS can overlay spatial data of various
types and compute populations affected by air quality, or it can map emission
estimates and land cover for a visual relationship, analyze them spatially,
and report the resulting statistical relationships. A GIS can provide maps
and tabular summaries of results (e.g. areas of implementation, results, ratio
of cost to effectiveness) or maps of progress to date compared to
environmental goals.
Information Paradigm
A possible way to look at information systems is through the so-called
information paradigm27. In the information paradigm real systems are
distinguish as parts or aspects of reality to be investigated as a whole, in
order to know or eventually control them; information systems describe the
real systems in the past, present or future for the purpose of knowing and
controlling. Information system functions then include collection, storage,
processing, retrieval, transmission and distribution of data by people and
machines. An important aspect of the information paradigm is the so-called
recusion principle, which specifies that the information paradigm also holds
for the sub-systems, i.e., for all real systems and information systems within
the system considered, and that it does so at all levels. This view has
13
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important consequences for modelling information systems, and designing models
of information systems or real systems. The information system in this view
must always be considered in combination with the real system of which it is a
representation, and special attention is to be paid to the dynamic behavior of
this combination.
The system under manipulation becomes more and more complex. Therefore, the
behavior of information systems, and their interaction with the real systems,
becomes harder and harder to predict. Nevertheless, that is exactly the
intention: the task is to achieve certain goals in a real system (e.g., just-
in-time delivery in a transportation chain, or 100% of the airplane-seats sold
without having to reject any customers), to try to develop information
systems, modify the real systems, to define the interfaces between these two,
and to achieve these goals. In order to understand the objectives, and
subgoals derived from them, and comprehend the dynamic behavior of both the
current and alternative systems, the discipline of information systems is to
offer new approaches and new techniques.
Systems Theory
Systems theory as an approach to studying organizations and their behavior
has its roots in physics and biology27. Its application to the study and
development of computer-based systems gained particular importance and
attention with the development of cybernetics, defined as a science of
communication and control concerned especially with comparative study of
automatic control systems. Many papers have been published describing systems
and their fundamental properties, characteristics, environments, components,
relationships, and regulatory capabilities.
According to Nordbotten27, the most basic concept is that of a system, which
can be initially defined as "a group of units so combined as to form a whole
and to operate in unison". A system is composed of a group of units. These
units can be considered as subsystems. The system is also a component of some
supersystem. In addition to the system under consideration, the supersystem
is composed of one of more units (subsystems) that form the environment for
the system.
t
When analyzing a particular system, it is necessary to fix attention on that
system and then to identify27:
1. its supersystem, i.e., that system of which it is a component unit,
2. the environment with which the system interacts, and
14
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3. its component subsystems. i-
Subsystems 'so combine as to form a whole'. There must be a system
structure that relates the component subsystems into an organized whole: the
system. The system structure is defined by the relationships that exist among
the subsystems. The relationships among the system components define order,
sequence, interdependency, and time relationships. The relationships define
the sequence of actions and activities within the system. An important task
of a systems analysis is to identify and evaluate the performance of these
actions.
The primary goal for the system defines its reason for existence, that end
toward which the system strives. Further, when the subsystems are goal-
fulfilling at their subgoal level, the system's goals can be greater than the
sum of the subgoals. This axiom is central to the interest in determining the
primary goal(s) for the system before considering the subsystems goals.
Consequently, the system can be defined as a structured, interrelated set of
components (subsystems) whose combined goals act to achieve the primary goal.
Systems are of two types: closed and open. A closed system is one that is
entirely self-sufficient, with no necessary interaction with its environment.
However, information systems exist to provide information services for their
environment and thus can not be considered closed system. On the contrary, an
open system is one that is dependent on its interaction and relationships,
with its environment. The primary goals of the system are defined in relation
to this interaction. These environmental interactions or relationships fall
into three classes (Figure 3):
1) input, consisting of stimuli, which can be taken from resources,
directives, queries and/or information directed toward the system, and
which can be used in some fashion by the system;
2) output, consisting of the system's reactions to the input stimuli, which
may take the form of results, products, and/or system state information
available to the environment. Outputs are released to the environment,
possibly initiating some feedback.
3) feedback, consisting of messages and reactions to the outputs from the
system. The feedback commonly generates new or modified input to the
system, and as such is a special type of input relation.
In analyzing an open system, defining the primary system goal(s), the
system may be considered as a 'black box' or undefined entity, and the
15
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attention can be concentrated on the environment's interactions with the
system.
The topic of this text is concentrated on the following:
1) open systems,
2) goal-directed systems,
3) the interaction of systems with their environment, and
4) a structured set of interrelated subsystems which systems are composed
of.
The lowest-level subsystems are considered to be the atomic elements if the
system. The atomic elements of a system are either elementary processes or
system objects. Elementary processes perform some action on the system
objects and can be combined to form the action(s) or task of the subsystem.
system boundaries
"black box"
INPUT
SYSTEM
OUTPUT
-\ Feedback \-
Figure 3. Open system concept
The three basic system components important for understanding of
information system concepts are:
1) processes, or actions performed on objects as required by the system
goals,
2) objects, or things of interest to the system (synonym for the system
object is entity), and
3) relationships, which exist between objects and processes.
16
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System processes are the actions of the system. Systems objects or
entities are of interest to the system, as defined by the system goals.
are described by characteristics or attributes. There are two types of
attributes:
They
1} descriptive attributes, whose values give descriptive or identifying
properties of an individual object, and
2) associative attributes, whose values identify a relationship between
two separate objects.
System objects can be grouped into classes, where a class is defined as a
group of individuals having some common attribute(s) of interest to the
system.
The third basic system component is the relationship set. Relationships
exist between system processes, between system objects, and between processes
and objects. Nordbotten27 recognizes that there are two kinds of relationship
relevant to information system concept:
1) structural relationships, which define the organization or structure
of the system processes and objects, giving groupings and sequences,
and establishing the place, within the whole system, of each of the
subsystems, and
2) communicative relationships, which define the interactions between
the system and its environment and among the system's subsystems.
The structural relationships commonly coincide with the movement of
objects, such as material resource flow through the system and in this way,
representing the system's external relationships. Communicative relationships
exist between the system and its environment and between the system's
subsystems. They define the system's paths for exchange of information in
terms of the inputs to each unit and the expected or required outputs. It is
among the communicative relationships that the system goals can be found.
Further, the set of communicative relationships defines the information
transfer of the system. This information flow can be changed over time in
response to the changes in the external system goals. Consequently, the major
task of system analysis is to identify these relationships and evaluate them
with respect to the current or anticipated goal set.
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Information System Design as Problem Solving
Designing and building of information systems can be regarded as a process
of solving an ill-structured problem. In most cases the information system is
developed to learn about or control the real system. According to
Nordbqtten27, to solve a problem of information system design, the process of
creating an information system is divided into the subprocesses of problem
perception, conceptualization, specification, development of proposed
solutions, solution choice and implementation.
Developing information systems and problem solving are closely related
activities. In problem solving and, therefore, in information systems design,
conceptualization plays an important role. When a problem solving case is
looked at, the structure of the decision making process can be used as a
framework. This process consists of the following steps: (1) intelligence,
and the analysis of possible courses of action, (2) choice, (3) selection of
an alternative course of action from those available, and (4) implementation,
efficiency and effectiveness of the choice.
An information systems designer tries to solve problems in the real system
that is being controlled. A problem can be thought of as a state in which the
problem owner is in doubt how to identify one or more desired outcomes. A
problem is defined as a situation meeting the following conditions:
(i) there is a decision maker;
(ii) the decision maker has a desired result (a goal);
(iii) there are two or more alternatives to achieve that goal. The
alternatives have differing efficiencies;
(iv) the decision maker is in doubt as to choose from the alternatives;
and
(v) the environment that cannot be controlled by the decision maker
has an influence on the result of the decision.
Problems may be considered as well-structured or ill-structured. A problem
is defined as well-structured when it meets the following requirements, and as
ill-structured if it fails to meet one of the requirements:
1. the set of action alternatives is finite and identifiable;
18
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2.
3.
the solution is consistently derived from a model that shows a
good correspondence; and
!
the effectiveness or the efficiency of the action alternatives can
be numerically evaluated.
In problem solving two more types of models, known as models as-is and
models to-be can be identified. Analysis of the existing situation results in
a model as-is. The construction of the model as-is is achieved by going
through the sub-cycle problem as perceived - (conceptual model-empirical
model), as often as needed to reach a degree of correspondence that will
suffice. The construction of models to-be is achieved by going through the?
sub-cycle conceptual model-empirical model solution as often as needed to
reach a satisfactory degree of validity. • ,.
Available Information System Building Tools
In order to structure the ideas about what an information system should
do, diagraming techniques were introduced27. Data-flow diagrams, entity- .
relationship diagrams, activity graphs, and precedence diagrams are some of
the multitude of techniques that were advocated. Essential in most of these
diagramming techniques is the idea of decomposition: the aim is to cut up an
information system into manageable and understandable pieces. These pieces,
or components, may be of a very high level of abstraction.
One major problem of these diagramming techniques is the need of different
techniques in order to get a somewhat complete view on a particular system.
An additional problem is that the different techniques harmonize badly, which
makes consistency-checks extremely difficult. A weakness is that they mostly
give only a static representation of the information system under
consideration.
In the growing interest in information systems design, a great number of
automated tools have appeared on the market to support the design process,
although the first attempts in this field originate from the 1950s. New tools
usually support a particular design methodology. A tool that is able to
create and maintain a dynamic representation of systems is simulation. A
number of available simulation computer languages have a powerful animation
(picture) function. In that way they are capable of giving insight in the
dynamic aspect of the empirical model that is being studied.
19
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Information System Objectives and Design
Holloway32 stated that the objectives of an information system can be
summarized as follows:
- clear communication with the user concerning the objectives and role of
the information system in the field of application, and commitment to
apply the appropriate resource once priorities have been set;
- a clear sense of direction based on the above objectives and information
system role;
- information system functions organized so as to support growing needs
such as user computing, data resource control and application
architecture; and
- appropriate levels of skills and resources, as well as adequate
planning, to meet demands.
Figure 4 shows a methodological approach to information system design32:
- first level (Level 1) assists in "application/construction" and database
implementation. If a methodology has only these two components, it can
be said that it facilitates the efficient translation of an application
design into the software program having capabilities to integrate
application design and database design. This suggests another level is
needed to aid design;
- second level (Level 2) suggests that there is an application design and
database design, and it can be said that it facilitates the efficient
translation of user requirements into a high-capability software program
connected with powerful database management system. This suggests that
before a design is started, it is necessary to understand user
requirements;
- third level (Level 3) helps identify and clarify user requirements; and
- fourth level (Level 4) determines user requirements which consist of
information systems analysis and conceptual data modelling. Information
system analysis considers the processes and the data used by the
processes, by prototyping the requirements with the user.
20
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METHODOLOGY
LEVEL 4
LEVEL 3
LEVEL 2
LEVEL 1
W FORMATION
RESOURCE PlANffiNO
PRODUCT EFFECTIVENESS
Figure 4. A methodological approach
to information system design
When a methodology has all four levels, it can be said that it fosters
product effectiveness in supporting interrelated applications which share
common data. In modeling a system, its dynamic composition needs to be
described by the way it accomplishes work, not just its static structure.
dynamic composition of a system can be described in-terms of activities,
processes and events. This applies to virtually all systems, especially
information systems, whose accomplishment of work depends on the interaction
of computer, software and human activities. These interactions are analyzed,
designed, implemented and maintained. The following analysis and design
approaches can provide advantages to the system during information system
development :
The
1.
2.
3.
more realistic representation of real world behavior leading to
improved information system analysis and design;
inclusion of important system evaluation measures such as cost,
productivity and reliability as components of the modeling tool;
more rapid development of information system projects by
identification of feasible alternatives through simulation;
4. improved maintenance of current operating information systems through
21
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simulation of proposed changes; and
5. personnel and hardware/software productivity improvements achieved by
extensive sensitivity analysis of important variables.
The construction of a dynamic model of a real-world information system
includes the specification of the operational parameters and behavioral
relations of the information system, the assignment of probability or other
distributions to necessary input variables, and the simulation of the
alternative system design.
The performance-related variables might include the following:
Costs
Development
Implementation
Operations
Personnel
Hardware
Software
Supplies, etc.
Physical Productivity
Total capacity . ,
Per unit processing time
Personnel utilization time
Hardware utilization time :
Database activity rate
Reliability
Error rates of hardware, software and personnel
System downtime rate
Organizations confronted with building an information system usually have
a staggering amount of information for planning, implementation and
development of this technology.
According to Flagg34, in general, the form of an information system follows
an understanding of the organization's functional objectives. The following
seven steps are recognized as important to the success of any complex
information system building. According to the author, the steps follow a
logical progression, but the distinction becomes less clear as the information
system building process matures.
22
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1) Orientation of Decision-Makers and Staff. This step involves a formal
orientation and introduction to an information system, and includes:
• discussion of various information system uses and functions;
• implications to an agency's organizational structure;
• operation, staffing, cost and personnel requirements;
• performance expectations and general implementation time frames;
• questions raised by the technology (legal, political, economic,
scientific);
• relational and object-oriented data model information; and
« a hardware/software demonstration.
2) Organizational Assessment. This step involves identifying existing
operations, funding mechanisms, functions and missions, and includes:
• determining the types of graphic-based applications conducted and
documenting how the graphic information is used;
• identifying existing graphics data support personnel;
• understanding what, how and how well information flows between sections,
departments and other agencies;
• determining data formats, scales and media;
• identifying how an information system could assist specific projects and
programs;
• identifying various automation solutions (both software and hardware)
that may be appropriate;
• identifying and considering various organizational scenarios for
information system placement, including possible system designs, service
levels and staffing; and
• analyzing information system costs and benefits.
3) Functional Objectives and Implementation Plan. This step involves
creating and documenting specific information system objectives and
functions, and includes:
• establishing automation priorities and time frames related to programs
and projects;
• developing a plan to establish and further information system processes
both internally and externally;
• designing the general process for information system decision-making and
production;
• establishing a training plan;
• formalizing data development and maintenance agreements with outside
organizations; and
23
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• providing answers to questions such as how the agency will handle data
requests, how the agency will work with other agencies, whether the
agency will seek to collect revenue, how legal issues will be resolved,
who will have access to the data and how data integrity will be
maintained;
4) System Design. This step involves designing the system specifications
that will support an organization's program and project goals, and
includes:
• designing hardware and software environments and schematics;
• describing required interfaces;
• developing benchmarks for hardware and software purchases;
• establishing a plan and chronology for procuring necessary hardware and
software;
• describing the user interface;
• designing information access levels and security; and
• describing the databases.
5) Project Requirements. This step involves selecting a pilot project
and evaluating the data and funding required to complete it, and
includes:
• identifying project goals and the primary project impetus, e.g.,
scientific, political, etc.;
• determining data quality objectives, constraints and critical decision
pathways;
• identifying the information and base themes needed to complete the
project;
• assessing existing data formats;
• determining who will be responsible for the information;
• assessing whether the information exists or developing additional
information;
• procuring necessary funding;
identifying a project team;
defining the roles and duties of each team member;
determining if extra training or outside help is needed;
deciding if additional hardware or software is needed;
• establishing quality assurance/quality control procedures; and
• setting checkpoints and target dates for completion.
6) Detailed Database Design. This step involves the design, development
and output of the application information, and includes:
24
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7)
evaluating program/project heeds;
reviewing available software;
designing data layers;
designing and defining coding schemes, global variables and conventions
for object names;
creating data tables and related tables, as well as data and project
directory structures and documentation for all project-related work;
defining data characteristics and relationships;
designing processes for data input and conversion, updates, maintenance
and archival; and
adding to data documentation.
Application and Development. This step involves the design,
development and output of the application information, and includes:
assessing specific user needs;
designing and testing macro programs and user-interface menus to most
efficiently meet project goals;
developing a user-friendly interface;
establishing hierarchical structure charts;
creating logical data query and analysis screens;
reconciliating spatial and data inconsistencies for all data layers;
eliminating redundancy;
fixing problems;
reviewing problem solutions;
re-evaluating project goals and accepting feedback form project
manager(s);
• automating and outputting all data sets, including digitizing, entering
and importing files;
• creating and outputting graphic files;
• developing a prototype;
• creating test plots and a trial run of the acceptance test;
• checking, adjusting and cleaning outputs using quality assurance/quality
control procedures;
• running the acceptance test;
• documenting and reviewing all procedures;
• maintaining all data;
• procuring additional funding; and
• obtaining, finally, bliss.
According to Essnik35, an information system is a system that is created to
support a larger system of which it is a component. That larger system is to
be studied, as seen from a set of specific perspectives, in order to delineate
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and structure a variety of informatory and decision making support to a number
of user groups. The development of information system is, in essence, the
transformation of a model of a part of reality into a model of the target
information system. For this reason the abstract model is the representation
('what is relevant to be depicted within the information system') and
embedding specification ('who will use the system for what') of the target
information system. Most of the methods do not make it clear how they
perceive these relationships, although such a. treatment is essential to
understand the way of modeling and the perspectives that are encompassed (or
rather that are disclosed).
As an effect, three basic 'axioms' are to be incorporated in the
information system development strategy:
1. the information system is part of the observed system ('the component
paradigm'), an abstract model of the reality ('the model paradigm'),
and support system for the observed system ('the controlled system
paradigm');
2. the information system can be seen as a simulation instrument of the
observed system in order to contribute to the steering of the behavior
within the observed system. Because the observed system itself is a
dynamic system, the information system model should reflect the
dynamic properties of the observed system;
3. the problem of information system development is to be seen as the
evolving generation of a specific and partial real-world model, and
the transformation of that model into an information system model.
The generation of the evolving model of information system is a cyclic
process in which the information system is seen at different levels of
abstraction.
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8. Galliers, R; Information Analysis - Selected Readings, Addison-Wesley
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9. McGraw-Hill Directory of Scientific and Technical Terms. 1984.
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Databases, Object-Oriented, Deductive, Hypermedia Technologies, John
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Mcmillan Press, Ltd., 1985. 790 pp.
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Galliers, Addison-Wesley Publishing Co., 1987. 285pp.
18. Johnson, Kast and Rozednweig, 1963. In: Information Analysis -
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19. Rossmassler, S.A. Presentation of Data in the Primary Literature. In:
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SECTION 2
EXAMPLES OF INFORMATION SYSTEM APPLICATIONS,
INTRODUCTION
The application of elements of information science (databases, simulation,
expert systems artd information systems) as a tool for planning, decision-
making, monitoring and problem solving has steadily grown as more capable and
user-friendly computer software programs become available on the market!
Below are summaries of available published literature explaining the
application of information systems for solving of environmental and industrial
production problems. As can be seen, the majority of information systems use
geographical mapping system as an integral part, so that geographical
information systems (GIS) are predominant in this review.
In order to discuss the application of information systems, environmental
protection and industrial process areas are divided in the following
categories: r
- water management,
- wastewater management,
- waste management,
- environmental impact assessment,
- environmental management, and
- industrial process control and total quality management.
WATER MANAGEMENT
Strategies for Managing Water Quality
Nonpoint source load data generated for a recent project funded by the
Galveston Bay National Estuary Program (GBNEP) was used to develop strategies
for managing Galveston Bay's water quality1.
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The project involved defining the drainage areas contributing runoff to
the bay and developing a current land use map for those areas. A GIS
hydrological model was developed to calculate runoff from the drainage areas,
and a water quality model was designed to calculate the total load of a given
water quality constituent entering the bay. Nonpoint sources include a wide
array of diffused pollutant types and sources from land drainage, human
activity and major storm water outfalls. Pollutants include sediments,
nutrients (total phosphorous and total nitrogen), BOD, oil and grease, heavy
metals, synthetic organics and fecal coliforms.
ARC/INFO, a GIS software developed by Environmental Systems Research
Institute, Inc., served as the fundamental tool for the entire Galveston Bay
nonpoint source assessment. The CIS allowed researchers to store, manipulate
and process several hundred megabytes of electronic data required for the
n'onpoint source calculation. Hydro!ogic and nonpoint source load models were
incorporated into the system so flow and water quality calculations could be
attributed to different geographic regions.
Three main GIS mapping types were developed:
1) GIS watershed/subwatershed mapping; 2) GIS soils mapping; and 3) GIS
land use mapping.
The first dealt with mapping of two drainage delineations: watershed and
subwatershed. For this study the area was divided into watershed boundaries
which were digitized into a GIS database from maps. The second, mapping
concerned soil types within the project area and these were mapped using the
county soil surveys published by the Soil Conservation Service (SCS). The
third mapping is the product of a land use database which was developed from
interpreted Landsat satellite imagery.
For GIS nonpoint source modeling purposes, each pixel in the land use
database was associated with a subwatershed and a soil map. A soil
type/watershed composite polygon map was obtained by overlaying the soils maps
and the subwatershed map layers in the GIS. The soil type/subwatershed
composited polygons were transformed to pixels through an ARC/INFO
transformation process. A software utility was developed to overlay the soil
type/subwatershed pixels and the land use pixels, and to output data
aggregated by the land use category, subwatershed and soil type attributes of
each pixel in the study area.
A GIS runoff and water quality modeling was tested based on raingage data.
A GIS model for calculating runoff from the study area was developed using the
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Soil Conservation Service's method. The runoff calculation model was used to
calculate the runoff from the whole basin. For each land use class, typical
concentrations - Event Mean Concentrations (EMCs) - of each constituent were
estimated from available nonpoint source data. A nonpoint source load
calculation model also was developed. The load model requires calculated
runoff volumes and EMC values for each pollution parameter based on land use.
The project's GIS mapping are expected to be the foundation for future bay
projects that require intensive mapping effort.
Management of Water Resources in the Changing Economic System in Russia
Belyaeva et al.2 describes the cooperation between the Water Problem
Institute of the Russian Academy of Science and the Tennessee Valley Authority
to develop a joint project demonstrating the use of GIS in managing water
resources under the changing economic system in Russia. The purpose of the
project is to improve decisions by better organizing, analyzing and presenting
water resource data and management options. Results to date include
development of a conceptual approach and review of existing data. The project
area includes the Upper Volga River Basin which encompasses the Moscow
metropolitan area. Data are being managed at three levels. Initial
conclusions indicate a great potential for this technology application, but
many social and economic obstacles due to the current political situation are
also apparent.
Groundwater Management
Hall and Zidar3 explain the cost-effective microcomputer technology for
groundwater data management, data analysis and the delineation of wellhead
protection zone. The data was designed to be transported to a UNIX platform
and integrated in the GIS.
Wellhead Protection Areas and Management of Environmental Resources
According to Rifai et al.4, the article exemplifies one of many potential
links between ground-water models and GIS. An example demonstrates through a
GIS user interface for delineating wellhead protection areas (WHPAs) the
tremendous opportunities presented by GIS for management of environmental
resources.
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The project developed a GIS database and modeling interface to implement
ground-water protection strategies by state arid local government and
regulatory agencies. The specific objectives were to:
- collect and incorporate the data relevant to developing a wellhead
protection program from the various federal, state and local agencies
into the GIS database;
- develop an automated linkage between the GIS database and a ground-water
model to delineate WHPAs around public water-supply wells for the City
of Houston;
- evaluate the various WHPA model parameters to address the effects of
parameter input on the size and shape of delineated WHPAs;
- show how GIS helps delineate WHPAs; and
- develop a prototype ground-water management system {using the
interactive GIS/modeling capabilities). This management system allows
area! enlargement, incorporation of more data, or use in other areas
which are developing a similar ground-water protection program.
%
A geographic information system promotes efficient and effective
management of ground-water resources, the GIS database combines data from
numerous sources into one system and spatially relates the data to enhance the
decision-making process of wellhead protection programs.
The main advantages of this type of system are:
(1) the data are contained in one*database and accessed from one system
and are therefore easily transferable;
(2) the system can be used by several governmental or regulatory agencies,
promoting data sharing and interaction between the different agencies;
(3) any change in the public water-supply wells or the sources of
contamination and land-use patterns can be assessed quickly; and
(4) decision-makers are able to spatially conceptualize and easily relate
all the available data. All these factors allow for more informed
decisions regarding ground-water protection and management strategies.
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Irrigation and .Drainage Systems Rehabilitation
Allen5 presented the application of CIS for solving of water irrigation and
drainage problems. Topics include the planning and rehabilitation of
irrigation and drainage systems, managing conflicts between irrigation and
drainage systems and urban development, urban-agricultural transfers and
exchanges, and economic and regulatory influences on conservation. Other uses
include: managing conflicts between wetland resources and irrigation
projects, developments in surface irrigation, groundwater management,
hydrology and management of intermountain groundwater basins, aquifers,
evaporation ponds, watershed hydrology, and the potential effects of climate
change on water resources.
Water Quality Implications of NonpointSource Pollution
Heidtke and Auer6 explain the magnitude and water quality implications of
non-point source phosphorus loadings to Owasco Lake in New York, and they use
data supplied by GIS to establish the specific land use, soil texture and
surface slope attributes within each of the hydrologic sub-basins comprising
the overall watershed. Data are evaluated through the application of a
methodology which links geographic characteristics, long-term average runoff
loads and a set of critical lakewide water quality response parameters. The
GIS-generated attribute matrices provide a much more accurate depiction of
critical geographic characteristics known to impact nonpoint source runoff
loadings, thereby improving the reliability of current and projected
phosphorus loads to Owasco Lake.
WASTEWATER MANAGEMENT
Storm Sewer System Management
The City and County of Denver's Wastewater Management Division (WMD)
realized that an adequate funding base was needed to maintain its
deteriorating storm sewer system7. A revenue system existed for the sanitary
sewer system but not for the storm sewer system. City Council approved a
revenue system that required property owners to pay an equitable share for
maintaining the storm sewer system. A method was needed that reasonably
predicted the amount of water runoff from any given property. A billing
system was devised that determined a charge by taking the percentage (or
ratio) of impervious area to the total parcel area. Initially, a group of
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field inspectors measured impervious areas on properties using a hand-held.
wheel. However, it was estimated that it would take approximately 80 years to
measure the entire city. In 1986 HMD solicited information from technical
contractors on the development of a computerized information system that uses
aerial photography. ,
A system was designed and developed that requires individuals to collect
data on a workstation that provide on-screen digitizing capabilities. The
workstation uses specialized hardware and software that combines the
technologies of photogrammetry, image processing and GIS. .The system
initially used the Kork Geographic Information System (KGIS) and an ORACLE
relational database management system. Later the system was converted to
ARC/INFO, a GIS software developed by Environmental Systems Research
Institute, Inc., Redlands, CA, and the workstation was upgraded to Microvax
3800. . .
The collection system operates by converting a portion of a black-and-
white aerial photography to an image that appears on a high-resolution color
monitor as a 256-level gray-tone image. The GIS calculates the area of the
features instantaneously, providing ,a basis for determining the ratio of a
particular land parcel bill. The calculations are converted into a
transpprt.able ASCII file and moved across the ETHERNET network for billing.
WMD is correcting the GIS databases for future billing. The process
includes registering photographs, modifying line and annotation information,
and verifying data in the billing database. ,
Wastewater Treatment Process Control
Arnold8 presents process control with consideration of environmental
aspects or so-called "phase model of production". It is introduced as a
semantic tool for a structured visualization of complex processes and
exemplified in wastewater treatment. Based on the elaborated information
model, various engineering applications that determine the plant's
instrumentation or requirements of field communication are illustrated. ,
Prospects for process control in relation to the environment are presented.
Sewer Flows Forecast
Shamsi and Scheinder9 discuss the applicability,of GIS to help forecast
sewer flows and determine whether a watershed has the ability to meet the
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county needs (Allegheny County, PA, USA). The watershed is expected to
undergo significant development in the future. The main issue appears to be
the concern about the watershed hydraulic capacity to convey future flows.
An analysis of the terrain, hydrography, soil associations, land use,
census properties, and locations for major trunk and interceptor sewers was
conducted to develop the watershed GIS. The software programs used in the
project were primarily ARC/INFO, and ERDAS, an image processing and raster-
based GIS from ERDAS, Inc., Atlanta, GA, USA. Additional programs were
written whenever needed for data format changes or for creation of a product
for which the methodology was not available in either of the commercial
packages.
The final GIS analyses were performed on raster information layers. The
color, raster maps were produced: (1) existing service areas, (2) present land
use, (3) census tracts and (4) soil associations.
The present land use map shows that despite the existing developments, a
substantial watershed area is still undeveloped. Future subareas outlined are
based on the assumption that each parcel of land ultimately will be developed.
The U.S. Environmental Protection Agency's Storm Water Management Model (SWMM)
simulates dry weather (sanitary) flow and wet weather (storm water) flow on
the basis of land use, demographic conditions, the hydraulic conditions in
the watershed, meteorological inputs and conveyance/treatment
characterizations. Based on these, SWMM can be used to predict combined
sewage flow quantity and quality values. SWMM is well suited to this study
and is the model of choice for most combined sewer overflow feasibility
studies. The capacity analysis was performed by comparing the maximum
simulated flows in various sewer segments on the interceptor to their full
flow capacities, defined as the maximum flows that can be conveyed by sewers
without surcharging, estimated from sewer size, slope and roughness.
WASTE MANAGEMENT
Haste Recycling. Source Reduction and Disposal
Portland's Metro is a regional government agency that serves three Oregon
counties. The agency is charged with responding to a population of 1.4
million on a variety of regional and environmental issues. In recent years,
the public's interest and participation in recycling has grown to challenge
Metro's staff10. One area directly impacted is the agency's Recycling
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Information Program. A GIS-based call response system is one way the district
is managing its response.
Metro's Recycling Information Program is a regional clearing house for
recycling, waste reduction and disposal information. The program offers a
telephone hotline, which receives more than 80,000 calls annually, and
provides information to businesses, governmental agencies, schools and the
public. The typical information requested is the name, location and hours of
a recycling or solid waste disposal facility that handles a specific material;
directions to a facility; or instructions for preparing recyclable materials.
Thus, the information has to be retrieved quickly to respond to caller
questions. Often this involves locating the appropriate facility nearest the
caller from among the area's more than 280 recycling facilities. This is done
by the help of fully integrated computer system.
In order to improve the efficiency of its operations, Metro has
incorporated a call response system. The system developed is based on
ARC/INFO GIS software and is compatible with the Regional Land Information
System (RLIS) used by Metro's Transportation Department.
Basically, the system links Topologically Integrated Geographic Encoding
and Referencing (TIGER) files and the base maps of Metro's RLIS with a series
of databases containing recycling information. The various databases store
information about individual recyclers and haulers, curbside recycling,
business recycling, yard debris recycling and processing, household hazardous
waste disposal, and general recycling data.
The system supplies information for referrals t,o other government agencies
and community resources, records complaints and referrals, and provides maps
of ZIP code areas and main streets. The system allows Metro staff to compile
a variety of reports: monthly reports can be generated by ZIP code, by county,
and by type of request to help review and monitor the program's activities.
Waste Management - Cleanup Activities
Salzmann11 reported on GIS applications in the environmental field
particularly in waste management. According to the author there has never
been such a wide-scale effort in subsurface investigations, from a corner gas
station underground storage tank (UST) removal (tank yank), to Superfund
cleanup efforts across the country. The environmental industry also is
changing its purpose, shifting away from Remediation Investigation (RI) and
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moving into actual clean-up operations.
The role of GIS technology is proportional to the scale of the clean-up
efforts. For example, a UST job by itself does not call for a GIS
application. However, a GIS is essential if many of these operations occur
within a municipality and the local government needs to monitor cleanup
activities. Increasingly, the technology is employed to detail where a
responsible party's contamination is located, and how much contamination can
be defined legally on that property.
Geological software alone could be directly applied to the hazardous waste
business. A true GIS solution would manage the automated mapping/facilities
management (AM/FM) infrastructure needs of the site's residents; store,
display and model the contaminant; and forecast the contaminant's
transportation.
Site Characterization and Remediation Activities
Ganter and Cole12 discuss the establishment of a Facility for Information
Management Analysis and Display (FIMAD) to support site characterization and
remediation activities of the Department of Energy's Environmental Restoration
Programs (ER). An important first step for these programs is establishment of
necessary infrastructure to support the massive clean-up efforts that will be
required. FIMAD is designed to support management, research and documentation
of all ER activities, through a series of workstations available to task
leaders. Within the workstation environment, GIS and Relational Database
Management System (RDBMS) tools are currently provided.
ENVIRONMENTAL IMPACT ASSESSMENT
Nonpoint Pollution from Agriculture
According to Mertz13, each year more than 1 billion tons of sediment wash
from agricultural lands into U.S. waterways. With the runoffs and sediment
come pesticide residues and nitrogen and phosphorus from fertilizers, further
deteriorating water quality. Actually, agriculture is one of the biggest
contributors to nonpoint pollution.
Advances in combining GIS with modeling capabilities offer a powerful,
efficient opportunity to target regions creating the most nonpoint pollution.
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This technology is being used in Pennsylvania and the Chesapeake Bay drainage
basin. Farms located in Pennsylvania's Susquehanna River Basin contribute
nearly one-third of the nitrogen and one-quarter of the phosphorus pouring
into the Bay. States participating in the federal Chesapeake Bay program are
committed to reducing nitrogen and phosphorus input 40 percent by the year
2000.
Researchers working in this program spent a year defining watersheds in
Pennsylvania and identifying the processes and parameters that contribute to
nonpoint pollution. Ultimately, their GIS-basecl model ranked the nonpoint
pollution potential of 104 watersheds in Pennsylvania, providing a map for
targeting funds.
The project's one-year time frame forced the researchers to use existing
data. Seven data layers were scaled and subdivided into 1 hectare grid-cells.
These layers were adapted from a number of existing data sets:
1. Watershed boundaries
2. Land use data
3. Animal densities (N and P were calculated by developing nutrient
loadings)
4. Topography (latitude/longitude)
5. Soils data (types and percentages of individual soils)
6. Precipitation layers (daily precipitation)
7. Rainfall-runoff (22-year average)
An Agricultural Pollution Potential Index (APPI) was developed from the
data layers to rank the 104 relative nonpoint pollution potential. Runoff
Index, Chemical Use Index, Sediment Production Index, and Animal Loading Index
were developed also.
The 104 watersheds were ranked according to their potential for nonpoint
pollution. Only the pollution potential of agricultural lands were assessed.
Results showed that watersheds with the greatest agricultural pollution
potential were located in the southwestern and northeastern sections of
Pennsylvania, despite the fact that agricultural production was conducted on
less than 40 percent of the land within those watersheds.
The GIS-based computer model is accurate on a statewide basis but it is
not reliable if the attention is focused on the small, individual watershed.
Further, the watersheds were divided with the highest nonpoint pollution
potential into subwatersheds. Extra details provided a clearer picture of
watershed nonpoint pollution potential. The GIS-based model may widely affect
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management of nonpoint pollution in the entire Chesapeake Bay drainage basin.
The model was also used to evaluate agricultural nonpoint pollution potential
in the Chesapeake Bay drainage region.
GIS will remain an essential tool for decision makers. More and more
local people are starting to realize that CIS is an indispensable tool for
setting priorities and concentrating programs. GIS-based computer models
provide scientists with pictures, an important communication tool. According
to the researchers, one of the real values of GIS is the visual mapping so
that people can easily see problems.
An additional layer is already in the works. Groundwater information is
being prepared for Pennsylvania. Other layers that would be incorporated into
the sy:tem include detailed pesticide/chemical use information, urban
pollution potential, and mining effects.
Development of Emissions Inventories and Emission Modeling
Many studies have been performed to address the ozone nonattainment
problem in the United States31. More recently, many of these investigations
have relied upon the use of advanced photochemical models. Pollutant emission
rates are a key input to these models. Four different types of emissions
estimates are required:
- base year emission estimates,
- periodic annual updates to the base year emissions estimates,
- reasonable further progress projected emission estimates, and
- emission estimates for input to photochemical and other models.
The emission database needed for photochemical modeling involves the
preparation of spatially and temporally resolved emission estimates. The
development of an emissions modeling database requires the spatial management
of large quantities of emissions data and emissions estimates. GIS have been
used to develop a more flexible, user-friendly emissions modeling system.
An emission model is an integrated collection of equations and other
computational procedures that are encoded for computer-based calculation of
emission estimates. Emissions data refer to the information, typically stored
in a database and commonly referred to as an emission inventory, that is input
to an emissions model to produce emission estimates, by source category or
source within given classification.
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An emission modeling system {a group of emissions models executed in ..
specified sequence) further processes the emission estimates and generates
speciated, spatially and temporally resolved values for input to photochemical
and other regional air quality models.
GIS can be used to facilitate the development, of modeling of emissions
data, emission estimates and the quality assurance of.these emissions-related
information. This paper presents several different techniques that illustrate
how GIS can be used to help:
- develop emission data,
- process emission data to produce emission estimates, and
- perform quality assurance functions on emissions data and estimates.
Environmental Impact Assessment for Gold Mining
One of the largest gold.mining companies in the USA, Independence Mining ,
Co. (IMC), is looking to expand its exploration and development operations ori
3,000 additional acres in Jerritt Canyon, Elko County, Nevada14. To develop
an environmental impact statement to substantiate its expansion plans for
rigorous federal requirements, IMC is merging global positioning system (GPS)
and GIS technologies, and hiring numerous experts in mapping, air and water
quality, soils, vegetation and wildlife biology.
According to the author, the IMC is trying to extract minerals in an
environmentally sound manner. Mill, roads and disposal, site were designed to
minimize the mine's impact on designated threatened species. The IMC has been
accumulating more data about the impact of its operations. In cooperation
with U.S. Forest Service, the IMC is working to create a detailed Cumulative
Effects Analysis (CEA) as a tool to try to quantify cumulative effects and to
move away from qualitative assessments. As a part of EPA's EIS requirements,
CEA will provide a baseline for environmental norms before the mine expansion
and carefully monitor changes in the total environment during,mine operations.
The refined information in IMC's CEA will be entered into a GIS for storage,
analysis and cartographic display. The transfer of survey data into the GIS
also would be. time-consuming, requiring manual digitizing and separate entry.
Meanwhile, the GIS would be only as useful as the field data supporting it.
After surveying the options, IMC chose GepLink, a computerized GPS/GIS
mapping system, to provide the missing link between field'data collection and.
GIS map creation. The system basically quantifies ecological and other mine
information and puts it into a GIS.
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011 Spills Accidents
The applicability of CIS to deal with oil spills in general and
particularly in Florida's most ecologically sensitive habitats and popular
beaches near St. Petersburg is discussed
15
Florida Department of Environmental Protection (DEP) has been designing a
GIS application to help manage spills; the accident occurring in August 1993
near the mouth of Tampa Bay provided the test of the application's design.
The U.S. Geological Survey (USGS) topographic maps are annotated with
Environmental Sensitivity Index (ESI) for shoreline types, wildlife-resources
areas and strategies. The ESI ranking of shorelines is critical because it
cartographically indicates the vulnerability of specific shorelines to an oil
spill.
The existing ESI are updated and the information integrated into a GIS to
facilitate more frequent updates and real-time analyses. One of the tasks
also was to provide the DEP Office of Coastal Protection with the capability
and technical support to facilitate oil-spill contingency planning, response
and damage-assessment responsibilities.
A Florida Marine Spill Analysis System (FMSAS) was initiated. The
principal goal of the project is to design an application that integrates a
variety of information (digital maps, images and tabular data) with targeted
analytical routines needed to implement an oil-spill response strategy focused
on resource protection. Additional requirements are to implement a selected
set of these conditions for pilot study area and in Florida Keys to develop a
strategy for expanding the prototype to a state-wide, operational system.
The functional requirements of and basic format for the FMSAS database
design were tested. In addition, the needs assessment and database and
application designs determine data requirements and guide a thorough inventory
and evaluation of coastal data available in Florida. The FMSAS prototype is
used with 10 different databases (including data on marinas, habitats, and
threatened and endangered species) to generate a resource-at-risk report.
Experience gained from this and other exercises are used to further refine the
evolving FMSAS design and prototype.
Many data resources were combined to provide maps for simultaneous
evaluation and monitoring aspects of the response efforts. To meet those
requirements, additional data have to be acquired and integrated in GIS. GPS
receivers are. used to record locations of the vessels and the changing
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perimeter of the spill. The GPS files are imported immediately into CIS and
incorporated into maps. The maps includes information such as road networks,
navigational aids and the locations of temporary rescue headquarters. Scanned
USGS quadrangle images are rectified and used as base maps to provide maximum
annotation quickly. The various databases and images are combined to create
different maps: those showing the changing locations of spill boundaries and
resources at risk are used by command center and media and field workers,
while those showing information for determining environmental sampling
strategies are used to create response and damage assessment.
The question of whether GIS can contribute to oil spill management was
answered in the Tampa Bay accident. The conceptual design and physical
characteristics of the system are further refined. The plan for further
incremental development prioritizes key databases. The challenge is to
assemble and automate the data for each region of the state before a spill
occurs there. DEP is exploring the possibility of cooperative agreements with
other agencies and organizations to foster a collective investment so FMSAS
can be shared and improved without redundant expenditures. The long term goal
is to continue FMSAS development to provide .greater protection for Florida's
natural resources.
ENVIRONMENTAL MANAGEMENT
UNEP Global Environment Monitoring Systems
UNEP (United Nations Environmental Programme) has established GEMS (Global
Environment Monitoring Systems) to assess the state, trends and problems of
the environment. This task requires a well-coordinated, high-quality data
collection program. The task of assembling, storing and disseminating data in
geographic form was given to the GRID (Gl.obal Resource Information Database).
Both GEMS and GRID are elements of Earthwatch, the environmental assessment
side of UNEP and UN. According to Hebin and Witt16, conventional methods of
data collection, distribution and analysis are not suitable for large-scale
multi/interdisciplinary studies. So GRID initially undertook a pilot study
during which a variety of activities were performed. The most prominent were
global data set collection and a series of case studies in the developing
world, as well as the development of a comprehensive environmental database in
digital form for the African continent.
GRID'S task-oriented activities and objectives include database
management, GIS/IP (Geographical Information System/Image Processing)
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technology transfer and GRID system development in response to the
requirements of the UN Conference on Environment and Development's "Agenda
21," which called for strengthened GRID to promote decision-making
information.
UNEP/GRID's long-term goals are to facilitate access to all major existing
global and regional environmental databases; to make available to all UN
agencies and intergovernmental organizations compatible GISs; and to promote
the use of databases and GIS/IP technology in national environmental
assessment and management.
There are two UNEP-funded GRID centers: GRID-Nairobi and GRID-Geneva.
There are several nationally funded GRID centers (Arendal for Norway, Tsukuba
for Japan, Warsaw for Poland, then in Nepal, Fiji, and in Brazil) and some are
in process of being established (Denmark, Germany, Russia and Ukraine). The
idea is for the whole to form an interconnected network for data exchange and
data management. The intention is to connect all GRID centers with ,
communication links to be able to transmit data to and from them.
To fulfill GRID'S task, data are collected from a wide variety of sources
such as statistical tables, satellite images, and digital data. The data are
stored in the computer systems after being ingested, reformatted and
georeferenced to a standard coordinate system and projection. Data processing
includes use of GIS and IP analysis systems from many public and private
sources.
GRID also uses a wide variety of software packages including ARC/INFO,
which is used primarily for digitizing and GIS functionality; NASA's Earth
Resources Laboratory Applications Software (ELAS) and Land Analysis System
(LAS); IBM's Image Analysis Executive (IAX);. ERDAS', image processing software
ERDAS; Clark University's IDRISI program for both GIS and IP capability;
TYDAC's SPANS for modeling purposes; and various database management, graphics
and word processing systems.
Decision-Making for Environmental and Natural Resources
Gracia and Hecht17 report on a program prepared to develop an effective
decision-support tool for the Naval Undersea Warfare Center (NUWC) at Keyport,
Washington and its Environmental and Natural Resources Division. The program
goals are twofold: (1) to improve the data assimilation or data fusion
function that must be performed by the Center's environmental managers through
use of graphic GIS-based site characterization; and (2) to improve the overall
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environmental data management process.
Common to all hazardous waste reclamation programs is a requirement to
follow the CERCLA (Comprehensive Environmental Response, Compensation and
Liability Act) which requires sites to be screened by applying a Preliminary
Assessment and Site Inspection evaluation. Sites meeting predefined criteria
are subject to a detailed Remedial Investigation/Feasibility Study (RI/FS) to
gather information to support a selection of clean-up alternatives and cost-
effective remedial actions. Authors of NUWC believe GIS offers a marked
improvement in the way restoration is typically conducted and managed.
Initial project results indicate that GIS will provide solutions to the
complex data management problems associated with environmental restoration
programs. The early results indicate that the spatial analysis and display
capabilities of GIS can improve the ability of environmental restoration
managers .to visualize and evaluate site conditions and the scope of their
sampling plans and programs. The research will continue to address site
characterization, data fusion and data management functions. Near-term
project emphasis is being focused on two areas: (1) applying predictive
modeling techniques to support the risk assessment process and subsequent
decision-making activity, and (2) integrating GIS with other data systems and
analytical tools to enhance decision-support use.
Environmental Protection and Energy Conservation
Cassitto describes the use of information and communication technology as
being increasingly applied to enhance environmental protection and energy
conservation. Automation and control systems allow energy recovery schemes to
be used in the most effective manner, while intelligent buildings use
computerized information systems to regulate energy use and the internal
environment. The use of telemanagement systems to monitor and optimize
natural gas supply in cities and water supplies and wastewater treatment is
also described. Information systems are being applied to air quality
monitoring networks, to improve environmental pollution control, and to manage
traffic problems worldwide. Efforts to apply communication technology to
improve energy and environmental services in Europe (Italy, Norway, Germany),
Asia (Turkey) and New Zealand are highlighted.
Environmental Quality and Land Use
An unidentified source from Israel19 describes the implementation of
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policies concerning environmental quality in land use and the application of
Information science. In the past 15 years, environmental protection has been
achieved through policies the have been incorporated into the land-use
planning system by environmental advisors to national, district, and local
planning authorities, by environmental impact statements (EIS), and through
the use of computer technology. The EIS system is the formal means by which
environmental considerations are incorporated into planning decisions. The
EIS is a statutory system which deals with a restricted number of major
development schemes such as airports, industrial plants, and solid waste
disposal sites.
The development of the GIS allows the analysis of geographic data for
environmental purposes. GIS can be used to identify areas of environmental
impact and to choose alternative sites.
Environmental Protection in Germany
Page20 presents the review of the state of data processing and applied
informatics in environmental protection in West Germany (FRG). The main
results of an empirical study of environmental computing by the FRG's
Environmental Federal Office are cited. Subject and technically oriented
aspects are discussed, and a final evaluation of the development state of
environmental software is presented. The informatics approaches discussed are
mainly non-standard database applications, interactive ecological simulators,
and expert system approaches.
Environmental Information Systems Inventory
Kokoszka21 discusses an attempt to automate environmental information in
order to develop an extensive network of databases. The U.S. EPA for example
has established an Information Systems Inventory (ISI) that consists of more
than 600 EPA (regional and headquarters) systems, databases and models. A
brief description of the basic application of each database is included. The
databases are arranged for discussion according to the following categories:
* Air
* Contractors
* Emergency Response
* Facilities
* Hazardous Waste/Solid Waste
* Laboratory QA/QC
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* Physical/Chemical Properties
* Superfund/CERCLA
* Toxic Substances/TSCA
* Toxicity
* Treatment
* Water
INDUSTRIAL PROCESS CONTROL AND TOTAL QUALITY MANAGEMENT
Utility Industry Application
Epner and Parmenter22 describe the application of GIS in the utility
industry. This industry has embarked on management and structural
transformations aimed at providing higher quality service at a competitive
price to keep old customers and win new ones. Under these conditions, quality
management programs are fast becoming the norm in the utility industry. These
programs come under a variety of names, but total quality management (TQM) is
best known.
For utilities, accurate, up-to-date and easily accessible information
provide the foundation for delivering quality service to the customer. As the
amount and the complexity of information grows, more and more utilities are
converting their information sources to integrated AM/FM/GIS (Automated
Mapping/Facilities Management/Geographic Information System) databases.
However, the critical need for database accuracy and the high cost of data
conversion present enormous hurdles to overcome before the benefits of
AM/FM/GIS can be realized.
The philosophy behind TQM is the following: by improving the process of
production or service delivery using well-defined tools, long-term
improvements in quality and productivity can go hand-in-hand. TQM transforms
the approach to quality by focusing on process. According to the authors, 80
percent of quality problems are designed into the process and thus are
management issues, while only 20 percent are due to individual job
performance. Thus, 80 percent of quality problems can be eliminated by
developing a better process, which at the same time eliminates the inspection
and rework required to correct the problems.
Without good, detailed and objective data, one cannot assess process
performance. With good data, facts replace options, conflicts can be
minimized, real problems can be identified and solutions can be monitored.
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Process data may come from a variety of sources. Computers can provide
data by measuring time or automatically tallying errors found in automated
inspections. Whatever the data source, management must make the source's use
clear to everyone involved. First, there must be a well-defined purpose for
collecting data. More data are not necessarily better; they may not show
anything new and they will take time and effort to collect. Second,
management must reassure team members that the data are for process analysis,
not individual performance evaluation. To ensure the optimal use of data for
process analysis, data should be kept in computer spreadsheet programs. An
efficient, accurate database must be in place and operational. TQM, applied
from the beginning of AM/FM/GIS development, brings users and conversion
specialists together to create a smarter conversion process. Using TQM
analysis tools and sharing data and brainpower, this process can be made even
more efficient as it proceeds.
De\
/el opment
jind
Implementation
of an
Information
Svstem
for
Process
Control at
Inco's Copper Cliff Smelter Complex
In recent years, Inco has implemented major productivity programs with the
purpose of decreasing unit costs. Key activities at the Smelter Complex have
dealt with improvements in operating and maintenance practices, energy
conservation, redefinition of administrative structures and employee
involvement. Metallurgical process control has played an important role in
the development of more efficient operating practices with high specific
throughput. This has permitted a reduction in the number of operating units,
allowed the removal of redundant pieces of equipment and has led to a
reduction of maintenance and operating costs. Landolt et al. discuss the
strategy and approach followed in the development and implementation of
information systems for process control. It includes descriptions of process
assessment work in the plant, modelling, implementation of improved practices,
and development of information system for operators and supervisors.
Offsites Management Systems in Oil Refineries
Computerized offsites management systems in oil refineries offer a unique
opportunity to integrate advanced technology into a coherent refinery ^
information system that contributes to benefits-driven optimal operations .
According to Valleur, the information system can improve oil refineries by
improving business opportunities, oil movement and advanced technology, and
project scoping and sizing.
48
-------
The following business opportunities have been recognized by applying
information systems:
• increased complexity of day-to-day refinery operations with more
products and more complex commercial specifications;
• recognition that offsites must not deoptimize what advanced process
control has generally achieved on process units;
• efficient short-term scheduling requiring flexibility on the offsites;
and
• availability of new equipment such as. low cost, reliable on-line
analyzers and emergence of commercial software packages to monitor,
control, and schedule oil movement operations.
Advanced instrumentation and computing technology is used in conjunction
with information systems to improve:
• mixing techniques and tank status,
• accuracy and reliability of on-line analyzers, and
• expert systems to help operators in selecting options and diagnosing
incidents.
Project scoping and sizing the system's engineering effort are difficult
because of their combinational nature and integration requirements with other
systems such as laboratory, oil accounting and scheduling. Applying
cost/benefit analysis and thorough economic evaluation based on information
system relationships would select the optimal investment.
TABLE 1. Summary of information system applications
No.
1
2
Area of
Application
Water Management
Water Management
Research Objective
Water quality monitoring
Decision-making improvement
IS Type
GIS
hydrogeo logical
model
GIS managing
water resources
Year of
Development
1993
1993
Author(s)
Rifai et al.
Belyaeva et al.
49
-------
Area of
Application
Water Management
Water Management
Water Management
Water Management
Uastewater
Research Objective
Cost-effective groundwater
data management
Water quality monitoring
Irrigation/ drainage system
management
Water quality monitoring
Cost-effective storm sewer
IS Type
GIS
GIS
GIS
GIS
GIS
Year of
Development
1993
1993
1993
1993
1993
Author (s)
Hall & Zidar
.
Rifai et al.
Allen
Heidtke & Auer
Smith
Water Management
Wastewater
[ Management j
Wastewater
Management
Wastewater
Management
1 Waste Management
II
Water quality monitoring
Cost-effective storm sewer
system maintenance
Wastewater treatment
process control
Wastewater flow forecast
Recycling information
program
GIS
GIS
IS
GIS
GIS-based call
responsive ,
system
1993
1993
1993
1993
1993
Heidtke & Auer
Smith
Arnold et al. '
F1 — —
Shams i &
Scheinder
Himes ,
10
15
16
Impact
Impact
Impact
Impact
Assessment
anagement
ianagement
imental
nent
n mental
nnent
nmental
ment
nmental
program
Superfund cleanup
Superfund cleanup
Nonpoint pollution
potential ranking
Ozone no-attainment p
Gold mining environme
impact
Oil spill management
GIS
GIS
1994
1991
GIS
1993
GIS emission
model
1991
GIS
1993
GIS
17 Environmental
Global environmental
monitoring system
GIS
Salzmann
Ganter & Cole
Mertz
Bruckman
Rodbell
1993
1993
Friel'et al.
Hebin & Witt
18
19
20
Environmental
Management
Environmental
Management
Decision-making for
environmental and natural
resources
GIS
Environmental
Management
Environmental protection
and energy conservation
IS energy
consumption
Environmental quality and
land use
GIS
1993
1990
1989
Gracia & Hecht
Cassito
Unv., Israel
50
-------
No.
21
22
23
24
25
Area of
Application
Environmental
Management
Environmental
Management
Industrial
Process Control
and Total
Quality
Management
Industrial
Process Control
and Total
Quality
Management
Industrial
Process Control
and Total
Quality
Management
Research Objective
Environmental protection
Environmental protection
Total quality management
(TQM)
Process control
Oil refineries off-sites
management systems -
benefits driven optimal
operations
IS Type
IS
IS
GI:S
6IS
IS
Year of
Development
1988
1992
1993
1988
1993
Author(s)
Page
Kokoszka
Epner &
Parmenter
Landolt et al.
Valleur
51
-------
REFERENCES
1. Rifai, H.S., Newell, C.J. and Bedient, P.B. GIS Enhances Water Quality
Modeling, GIS World, August 1993. pp. 52-5.
2. Belyaeva, T., Higgins, J.M., Kirpichnikova, N., Lanzova, I. and
Hagerman, J.R. GIS Application to Water Quality Management in the
Upper Volga River Basin: Joint TVA/Russia Project. In: Proceedings of
the IAWQ 1st International Conference on Diffuse (Nonpoint) Pollution:
Sources, Prevention, Impact, Abatement, Water Science and Technology,
Vol. 28, No.S-5, 1993. pp. 119-27.
3. Hall, P. and Zidar, M. Use of Geographic Information Systems and
Models on Personal Computer and Workstation Platforms in the
Development of Wellhead Protection Program, Salinas Valley,
California. In: Proceeding of the Symposium on Engineering
Hydrology, 1993. pp. 377-82.
4. Rifai, H.S., Hendricks, L.A., Kilborn, K. and Bedient, P.B. A
Geographic Information System (GIS) User Interface for Delineating
Wellhead Protection Areas, Ground Water 31(3): 480-8, 1993.
5. Allen, R.G. Management of Irrigation and Drainage Systems: Integrated
Perspective. ASCE. 1993. 1204 pp.
6. Heidtke T.M. and Auer, M.T. Application of CIS-Based Nonpoint Source
Nutrient Loading Model for Assessment of Land Development Scenarios
and Water Quality in Owasco Lake, New York, Water Science Technology,
28(3-5):595-604, 1993.
7. Smith, L. Wastewater Management Project Provides Timely Revenue, GIS
World, August 1993. pp. 38-40.
8. Arnold, M., Ingendahl, N. and Polke, M. Process Control with
Consideration of Environmental Aspects, (Techno!ogiepark Herzogenrath,
Rheinisch-Westfaal. Tech. Hochsch., D-52134 Aachen, Germany). CherrL.
Tech. (Leipzig), 45(5):363-74, 1993.
9. Shamsi, U.M. and Schneider, A.A. GIS Forecasts Sewer Flows, GJS
World, March 1993. pp. 60-4.
10. Himes, D.O. and Fadhl, M. GIS Facilitates Regional Recycling Efforts,
GIS World, September 1993. pp. 37-9.
52
-------
11. Salzmann, A. Environmental Industry Needs Hybrid CIS, GIS World.
February 1993. pp. 18.
12. Ganter, J.H. and Cole, G.L. Geo-Based Integrated Data Analysis for
Environmental Restoration Programs, Cartography and GIS/LIS Tech Pap.
In: 91 ACSM ASPRS Annu Conv Technical Papers - ACSM-ASPRS Annual
Convention, Vol.2., 1991. pp. 122.
13. Mertz, T. GIS Targets Agricultural Nonpoint Pollution, GIS World.
April 1993. pp. 41-6.
14. Rodbell, S. GPS/GIS Mapping Accurately Assesses Environmental Impact,
GIS World. December 1993. pp. 54-6.
15. Friel, C., Leary, T., Norris, H., Warford, R. and Sargent, B. GIS
Tackles Oil Spill in Tampa Bay, GIS World. November 1993. pp. 30-3.
16. Hebin, G. Witt, P.V. Global Environment Monitoring System (GEMS),
United Nations Environmental Programme, 1993. pp. 34.
17. Gracia, J.M. and Hecht, L.G., Jr. GIS Improve Visualization,
Evaluation Capabilities in Superfund Cleanup, GIS World. February
1993. pp. 37-41.
18. Cassitto, L. Use of Information and Communication Technologies in the
Field of Environment and Energy Conservation, OECD/et all Cities & New
Technology Conference, November 1990. pp. 201-18.
19. Unv. Environmental Planning,Israel Environment B, 12(4):2-8, 1989.
20. Page, B. Environmental Computing Status and Research Perspectives,
Computer Techniques in Environmental Studies. In: '88 2nd
International Conference, 1988. pp. 597-608.
21. Kokoszka, L.C. The Guide to Federal Environmental .Databases,
Pollution Engineering, 24(3), 1992. pp. 8.
22. Epner, M,., Parmenter, B., 1993, Competitive Utility Environments
Require Total Quality Management Techniques, GIS World, March 1993.
pp. 38-41.
23. Landolt, C.A., Steenburgh, W.M. and Hloang, T. Development of a
Process Control and Information System at Inco's Copper Smelter
53
-------
Complex, Copper '87, (Univ. Chile, Fac. Cienc. Fis. Mat., Santiago,
Chile), Vol. 4, 1988. pp. 603-23.
24. Valleur, M. Integrate Offsites Management with Information Systems,
TECHNIP, Paris, France, Hydrocarbon Processing, November 1993, pp.
63-6.
54
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SECTION 3
ASPECTS OF MEASURING POLLUTION PREVENTION PROGRESS
INTRODUCTION
Measuring pollution prevention (P2) is'a significant challenge. The
difficulties are conceptual--how do you measure waste that has not yet been
created and practical--what units to use, and whether to track money saved or
waste avoided? Another question involves the use of measurement data: is it
desirable or even possible to compare P2 results of different companies and
different industries ?
Further, why should P2 be measured? How should accomplishments be
measured? How can pollution prevention measures be incorporated into the
larger environmental data reporting scene? Are we trying to measure overall
national progress in reducing waste or merely local progress in reducing
discharges to local air, water, and landfills; to measure the physical amount
of waste reduced or reductions in toxicity and other adverse environmental
effects; to measure the efficiency of a single industrial plant or to be able
to compare across plants, products, or economic sectors2?
An analysis of available literature2'3'4'5'6'7'8'9-10 shows that the main
aspects in dealing with P2 measurement as summarized by researchers from
industry, academia and R&D institutions are:
(1) application of system analysis;
(2) system inputs and outputs definition,
(3) database, simulation and information system application;
(4) pollution prevention measurement types and normalization application;
(5) financing; and
(6) management practice.
(1) Application of System Analysis
Research carried out in industry has shown that efforts are concentrated on
measuring releases before and after some waste and/or wastewater minimization
actions have been applied. Thus, system analysis is a commonly recognized
55
-------
technique in dealing with industrial production systems (Figure 5). Detailed
process diagrams10, a simple block flow diagram11 or process flow diagrams •
are applied to analyze the system under consideration. Boundaries (unit
operation10, product unit definition12 or facility boundaries14) of this
dynamic system14 are determined ("black box"8 approach). Furthermore, time of
system observation is considered. Mass balance12'13'15'16 appears to be the
usual approach in quantifying system parameter relationships.
jjstta bounds
(unit optralioH, p
MASS IN
MASS
"itact box"
"*" , dynamic tjitim
roetss) ' '
FLOW DIAGRAM ASD
MATERIAL BALANCE
Industrial ,
Production
' Process" _':
MASS OUT
5 IN = MASS OUT
\
\
\
\
\
\
Timeframe '
Figure 5. Schematic presentation of system analysis application to industrial
production process
System Inputs and Outputs Definition
System inputs17 (raw and other materials) entering into the industrial
production processes as well as system outputs17 (products, by-products ,
process losses10, waste16, emissions7, discharges8, nonproduct outputs ,
releases18) are in most cases precisely identified and quantified1 . The terms
describing products and by-products are commonly used and their meaning widely
understood. However, it seems there are many different definitions concerning
the amount that is generated, lost, emitted, and/or released into the
environment, as shown in Figure 6. It appears that considerable work on
standardization of these terms is needed to clarify precise meanings.
56
-------
INPUTS
' OUTPUTS
1
1
RAW . . .^ plj
MATERIALS r fl
OTHER
MATERIALS
S'DUSTRIAL 1
PROCESS j
1
|
1
1
1
t
->
PRODUCTS
BY-PRODUCTS
PROCESS
LOSSES,
WASTE,
EMISSION'S,
DISCHARGES,
NONPRODUCT
OUTPUTS,
RELEASES
(TO AIR, WATER,
LAND)
Figure 6. Industrial production system's inputs and outputs
(3) Database. Simulation and Information System Application
Computer-supported databases15'16'19'20 are used to organize and process data
on inputs- and outputs that characterize any industrial production system.
These databases allow establishment of tracking matrix19 and/or loss tracking
system1 for bill of materials, shop production, and product routing15. A
computerized information system is applied to provide material accounting22,
inventory control (for purchasing minimal quantities of raw materials23) as
well as computerized integrated manufacturing (CIM)21. Computer-supported
simulation is used for direct observation of system behavior under a variety
of conditions, while optimization24 has proven to be a powerful tool for the
scheduling of resources such that the overall efficiency of the system is
achieved.
(41 P2 Measurement Types and Normalization
According to some theoretical research12'15'15'16 a material balance is the
most commonly used approach for measuring P2 progress in industry. Terms such
as material accounting system10'13, inventory of production and emissions6,
purchase records, material inventory, product records and specifications'"
22
are
57
-------
also being used. Some companies have developed tracking matrices19, while
others use input, by-product and emission reduction indexes12, as well as so
called point loss measurement. Normalization of data to present more
meaningful P2 measurement has been used in many different ways. Some
practical application showed industries compare data on system inputs and
outputs with previous year data and adjust for production5, while others use
material use efficiency expressed in percentage or economic efficiency21.
Absolute and normalized pollution levels25, as well as absolute and normalized
pollution reduction25 are also being used in practice. Independent
variables12, regression theory12, and autocorrelation12 approaches are applied.
The quantity of waste generated is presented per unit of product22.
Production index21 and normalization per unit of product at each step of
production15, is used.
(5) Financing
The financial aspects of P2 are considered to be very important7'17'21'22.
Cost analysis22 serves as a basis for P2 project selection in industry.
Activity based costs (ABC)23 and/or economic feasibility analysis7 are
applied. In industry, the placement of environmental costs are recommended to
be made into separate accounts26 to serve as a powerful tool for resource use
optimization.
Management Practice
.21
20
27
Total Quality Management-- zero losses or 100% efficiency"' --is only a
different term for the introduction of P2 practice into industrial production
based on material balance. So called Total Quality Management Structure
and/or Total Quality Management Program (TQMP) are also being introduced'
Measuring effectiveness7 by the ratio of waste reduction and production is
also being used. Important considerations for industrial management
concerning introduction of P2 measurement are time frame28, costs28,
priorities28, and implementation period29. Material accounting could therefore
be considered as a standard management procedure for P2 measurement.
58
-------
REFERENCES
1. Freeman, H., Harten, T., Springer, J., Randall, P., Curran, M.A.,
Stone, K. Industrial Pollution Prevention: A Critical Review,
Pollution Prevention Research Branch, Risk Reduction Engineering
Laboratory, U.S. EPA. J. Air Waste Manage. Assoc. 42(5): 618-56,
1992.
2. Rappaport, A., Management Tools to Support Pollution Prevention, A
Project Proposal, Tufts University, Center for Environmental
Management, Boston MA and U.S. EPA, Risk Reduction Engineering
Laboratory, Cincinnati, OH, 1992. 10 pp.
3. Measuring Pollution Prevention Progress, Proceedings, EPA/600/R-
93/151, U.S. EPA, Office of Research and development, Risk Reduction
Engineering Laboratory, Cincinnati, OH 45268, 1993. 97 pp.
4. Evaluation of Measures Used to Assess Progress in Pollution
Prevention, U.S. EPA, Pollution Prevention Office, Pollution
Prevention Division, Washington, D.C., Center for Economic Research,
Research Triangle Institute, Research Triangle Park, NC, 1990. 52
pp.
5. Craig, J., Baker, R.D., Warren, J.L., Evaluation of Measures,Used to
Assess Pollution Prevention Progress in Industrial Sector, Final
Report, EPA Contract No. 68-W8-0038, RTI Project No. 233U-4633-1FR,
1991. 58 pp.
6. Harper, P.O., Application of Systems to Measure Pollution Prevention,
Pollution Prevention Review, 145-53, 1991.
7. Raftelis, G.A., Financial and Accounting Measures as Part of
Pollution Prevention Assessment, Environmental Finance, 129-50, 1991.
8. Willis, D.G., Pollution Prevention Plans -- A Practical Approach,
Pollution Prevention Review, 347-55, 1991.
9. Facility Pollution Prevention Guide, EPA/600/R-92/088, U.S. EPA, Office
of Research and Development, Washington, D.C. 20460, 1992. 143 pp.
10. Pojasek, R.B., Cali, L.J., Measuring Pollution Prevention, Pollution
Prevention Review, 1991. pp. 119-29.
59
-------
11. Gemar, C., How to Conduct a Waste Minimization Study?, Pollution
Prevention Review, 1992. pp. 277-86.
12. Greiner, T., Measuring Toxic Use Reduction at the Production-Unit
Level, Massachusetts Office of Technical Assistance. In: Measuring
Pollution Prevention Progress, Proceedings, EPA/600/R-93/151, U.S. EPA,
Risk Reduction Engineering Laboratory, Office of Research and
Development, Cincinnati, OH 45268, 1993. pp. 79.
13. Hearne, S.A., Aucott, M., Source Reduction versus Release Reduction:
Why the TRI Cannot Measure Pollution Prevention?, Pollution Prevention
Review, Winter 1991-92. pp. 3-17.
14. Hirschhorn, J.S., Waste Reduction Measurement Project at IBM,
Hirschhorn & Associates, Inc. In: Measuring Pollution Prevention
Progress, Proceedings, EPA/600/R-93/151, U.S. EPA, Risk Reduction
Engineering Laboratory, Office of Research and Development, Cincinnati,
OH 45268, 1993. pp. 29-30.
15. Saminathan, M., Sekutoeski, J., Williams, G., The Use of Systems
Analysis for Measuring Pollution Prevention Progress in the
Manufacturing of Electronic Components. In: Proceeding of the IEEE
International Symposium on Electronics and the Environment, Arlington,
VA, May 10-12, 1993. pp 1-6.
16. Roberts, N., Andersen, D., Deal, R., Caret, M., Shaffer, W. Computer
Simulation, Addison-Wesley Publishing Company, 1983. 562 pp.
17. Consoli, F.J., Life-Cycle Assessment (LCA): Current Perspectives,
Presentation, SCOTT Paper Company. In: Southern States Conference on
Hazardous Waste Minimization, Pollution Prevention and Environmental
Regulations Mississippi, Coast Coliseum Center, Biloxi, MS, September
22-24, 1992.
18. Industrial Pollution Prevention Opportunities for the 1990s, EPA
600/8-91/052, U.S. EPA, Office of Research and Development,
Washington, D.C. 20460, 1991. 60 pp.
19. Price, R., Implementing and Tracking Waste Reduction Progress at
DuPont, Waste Reduction Programs, DuPont Corporation In: Measuring
Pollution Prevention Progress, Proceedings, EPA/600/R-93/151, U.S.
EPA, Risk Reduction Engineering Laboratory, Office of Research and
Development, Cincinnati, OH 45268, 1993. pp. 52-3.
60
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20. Zosel, T., 3M Corporation's Pollution Prevention Measurement Program,
3M Corporation. In: Measuring Pollution Prevention Progress,
Proceedings, EPA/600/R-93/151, U.S. EPA, Risk Reduction Engineering
Laboratory, Office of Research and Development, Cincinnati, OH 45268,
1993. pp. 64-70.
21. Pojasek, R.B., Production Efficiency as a Measure of Pollution
Prevention, GEI Associates. In: Measuring Pollution Prevention
Progress, Proceedings, EPA/600/R-93/151, U.S. EPA, Risk Reduction
Engineering Laboratory, Office of Research and Development,
Cincinnati, OH 45268, 1993. pp. 50-1.
22. Hawes, T., Implementing Pollution Prevention Measurement: Practical
Consideration, Polaroid Corporation In: Measuring Pollution
Prevention Progress, Proceedings, EPA/600/R-93/151, U.S. EPA, Risk
Reduction Engineering Laboratory, Office of Research and Development,
Cincinnati, OH 45268, 1993. pp. 23-6.
23. Nagle, G., Pollution Prevention Systems and Issues at Bristol-Myers
Squibb Corporation, Bristol-Myers Squibb Corporation In: Measuring
Pollution Prevention Progress, Proceedings, EPA/600/R-93/151, U.S.
EPA, Risk Reduction Engineering Laboratory, Office of Research and
Development, Cincinnati, OH 45268, 1993. pp. 49.
24. Ciric, A.R., A Proposal for Synthesizing Waste Minimal Chemical
Processes Using Multi-Objective Optimization Methods, Department of
Chemical Engineering, University of Cincinnati, Cincinnati, OH 45221-
0171, 1991. 17 pp.
25. Karam, J.G., Craig, J.W., Curry, G.W., Targeting Pollution Prevention
Opportunities Using the Toxics Release Inventory, Pollution
Prevention Review, 1991. pp. 131-43.
26. Lizzote, R.P., Financial Planning in Pollution Prevention, Texas
Instruments Incorporated. In: Measuring Pollution Prevention
Progress, Proceedings, EPA/600/R-93/151, U.S. EPA, Risk Reduction
Engineering Laboratory, Office of Research and Development,
Cincinnati, OH 45268, 1993. pp. 33-4.
27. Mannion, D., Integrating Source Reduction with Total Quality
Management, Foxboro Corporation. In: Measuring Pollution Prevention
Progress, Proceedings, EPA/600/R-93/151, U.S. EPA, Risk Reduction
Engineering Laboratory, Office of Research and Development,
61
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Cincinnati, OH 45268, 1993. pp. 35-46.
28. McEntee, D., Pollution Prevention Assessment at Simpson Tacoma Kraft,
Simpson Tacoma Kraft. In: Measuring Pollution Prevention Progress,
Proceedings, EPA/600/R-93/151, U.S. EPA, Risk Reduction Engineering
Laboratory, Office of Research and Development, Cincinnati, OH 45268,
1993. pp. 47-8.
62
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SECTION 4
DEVELOPMENT OF A MODEL OF AN INDUSTRIAL PRODUCTION AND
WASTE GENERATION TRACKING SYSTEM (IPWGTS)
INTRODUCTION
Waste characteristics and quantities of waste generated, managed and/or
released in the environment in'a given time have to be determined to implement
waste minimization and/or pollution prevention measures in any industrial
production process generating waste. Cost analysis of such a system could
enable one to estimate waste generation and management costs and could help
production managers to evaluate the system and to introduce measures to reduce
or minimize waste at the point of its generation, or to prevent release of
pollutants into the environment.
Industrial Production and Waste Generation Tracking System
A framework for the determination of the main parameters of an industrial
production and waste generation tracking system1'2 was defined (Figure 7). It
is based on the following main production process variables:
1) raw materials (rm),
2) other materials entering production process (v),
3) produced products (P),
4) generated waste (y).
This generated waste may be:
a) managed (g) by applying waste management,
b) released (z) into the environment, causing environmental
pollution.
Managed waste (g) may be further processed to be:
c) used as secondary raw material and/or energy (s),
d) finally disposed of as processed waste (residues) in a special (or
secure) landfill (d).
63
-------
= Ur
RAW AND
OTHER
MATERIALS
PRODUCTION
PROCESS
r = rm + v
y =
SECONDARY
RAW
MATERIALS
AND/OR ENERGY
REUSE AND/OR
RECOVERY
s = R M (1-U) r
PRODUCTS
WASTE
GENERATION
RELEASE
(EMISSION,
DISCHARGE
AND/OR SPILL),
WASTE
MANAGEMENT
g = M (1-U) r
PROCESSED
WASTE
FINAL
DISPOSAL
d = M (1-R) (1-U) r
Figure 7. Industrial production and waste generation tracking system
Development of IPWGTS Model
Based On the works of Baetz et al.3, a model, enabling calculation of
quantities of waste generated in an industrial production was developed and
defined in Table 2.
During an industrial process at time t, a production factor U, correlating
quantity of raw materials r (r - rm + v, where v represents "other materials"
not defined as raw materials entering production process. An analysis of
large data set on raw materials consumed, products produced and waste
generated in different industrial production sites* enabled recognition of
additional parameters including "other materials" entering production in time
t. For example, paints and lacquers in "white goods" and furniture production
- those materials are usually not defined as raw materials) and products (P)
has the value 0 < U £ 1 is defined as:
U • P/r
The quantity of products then may be expressed as:
64
-------
P « U r
while quantity of waste generated is
• y - (1 - U) r.
Managed waste is further quantified by a waste management factor M which is
defined as the ratio between waste managed and waste generated:
M - g/y
and can have a value 0 * M * 1. The quantity of waste managed by storage,
collection, transportation, processing and final disposal is then
g * M (1 - U) r
while the quantity of waste released (emitted, discharged and/or spilled) into
the environment is
z « (1 - M) (1 - U) r
If waste is further processed by physical, chemical, thermal and/or
biological processing to recover secondary raw materials and/or energy, then
processed waste is determined by the waste recycling factor R:
R.- s/g
having the value 0 < R <, 1. The quantity of waste recycled into secondary raw
materials and/or energy by waste processing is
s « M R (1 - U) r
while the quantity of waste to be finally disposed is
d « M (1 - R) (1 - U) r.
Knowing quantities of raw and other materials (rm + v) entering the
observed system and quantities of products (P) produced, quantities of waste
generated (y) could be calculated. If the quantity of waste managed (g), by
the waste generator is known, it is possible to predict quantities of material
lost (z) through release (emission, discharge, and/or spill). Finally, if the
waste generator recycles managed waste into secondary raw materials and/or
energy (s), then the quantity of waste to be disposed (d) can be determined.
65
-------
TABLE 2. Definition of IPWGTS model parameters
Parameter
t
rm
V
r « rm + v
U - P/r
P - U r
y - (1-U) r
H *
g/y - g/d-u) r
g « H (1-U) r
z
R -
s
d •
- (l-M) (1-U) r
s/g - s/M (1-U) r
-MR (1-U) r
H (1-R) (1-U) r
Definition
industrial production time in which system was
observed;
quantity of raw material entering industrial
production in time t;
quantity of other materials (not defined as raw
materials) entering industrial production in time
t;
quantity of raw and other materials entering
industrial production in time t;
production factor after time t. U = 1 represents
total production, while U = 0 represents zero
level of production and therefore, total waste
generation;
quantity of products produced in time t;
quantity of solid, liquid and/or gaseous waste
generated in time t;
waste management factor after time t. M = 1
represents total waste management while M
-------
RAW
MATERIALS
COST.
c,
INDUSTRIAL
PRODUCTION
PRODUCTS
SALE
REVENUE,
PR,
WASTE
GENERATION
SECONDARY RAW
MATERIALS AND/OR
ENERGY REUSE
AND/OR SALE
REVENUE,
PR8 PR,
RELEASE COST
(EMISSION,
DISCHARGE AND/OR
SPILL),
WASTE
MANAGEMENT COST,
c.
PROCESSES WASTE
FINAL DISPOSAL
COST.
C7
Figure 8. Cost analysis of an industrial production
and waste generation tracking system
Cost Analysis of IPWGTS
A cost analysis of an industrial productionrand waste generation tracking
system is defined2 in Table 3 and general scheme presented in Figure 8. The
cost analysis discussed is an example but does not necessarily include all
costs associated with environmental issues and/or industrial production
process. However, the idea of approaching the problems of assessing costs at
the facility level of industrial production is shown.
67
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TABLE 3. Costs and revenues of industrial production
and waste generation tracking system
COSTS
ri» r2» r3» •*• rn
Pr1» Pr2» Pr3> ••• Prn
1*1 * Pr1>/2* Pr2' r3 * Pr3>
• * • > rn Prn
Ci
Ip Ij* ^3' "• ^n
W.j > Wg > Wj , • > « > WR
1 * u 1 * w l*w
1 1* 2 2' 3 3' **"'
ln*"n
C2
m,,, m2, nij, . .. mn
Pm1> Pm2» Pm3> ' ' * Pmn
^1» ^2» ^3> **• ^n
«"l * P«1 * 51' m2 * P.1 * 52> %
* PnS* «3» "- mn* P«* 5n
C3
°1> °2» °3' •" °n
Pol* Po2> Po3» ••• Pon
°1 * Pol* °2 * PoH' °3 * Po3>
•••» °n * Pon
c*
quantity of raw materials (by type)
unit price of raw materials (by type)
cost of raw materials (by type)
total cost of raw materials
number of employees by employment
classification
wages per employees by employment
classification
labor cost per employment classification
total labor cost
number of machines (by type)
unit price of machine (by type)
depreciation rate of machine (by type)
cost of machine depreciations
total depreciation
number of miscellaneous production
factors (by type) (e.g. commercial and/or
financial, insurance activities,
transport, energy, etc.)
unit price of miscellaneous production
factors (by type)
cost of miscellaneous production factors
(by type)
total cost of miscellaneous production
factors
68
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COSTS
Z1» Z2» Z3» ' " Zn
Pr1» Pr2» Pr3» ••' Prn
Z1 * Pr1> Z2 * Pr2> Z3 * Pr3»
7 * n '
' •* ' > zn Prn
C5
9l» 92» 93> ••• 9n
Pg1» Pg2> Pg3» ••• Pgn
91 * P9V 92 * Pg2> 93 * Pg3'
••" 9n * PQn
C6
dr, d2, d3, .. . dn
Pd1> Pd2> Pd3» •'• Pdn
d1 * Pd1> d2 * Pd2' d3 * Pd3>
••" dn * Pdn
C7
quantity of gaseous, liquid and/or solid
raw materials lost by release to air, ,
water and/or soil/land
unit price of raw materials (by type)
cost of raw materials loss (by type)
total cost of raw materials loss
quantity of gaseous, liquid, and/or solid
waste managed (by type)
unit cost of each increment of waste
management (.temporary storage,
collection, transportation and
processing)
cost of each increment of waste
management .
total cost of each increment of waste
management
quantity of processed waste for final
disposal (by type)
unit cost for processed waste final
disposal (by type)
cost for processed waste final disposal
(by type)
total cost for processed waste final
disposal (by type)
REVENUES
XT > Xg> Xj, .... . Xn
P'xl* Px2> Px3' ••' Pxn
X1 * Px1'^X2 * Px2» X3 * Px3'
' ' ; » Xn Pxn
PR,
quantity of products (by type)
sale price of products (by type)
revenues on sale of products (by type)
total revenues on sale of products
69
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REVENUES
s^» s2> s3> ... sn
Ps1> Ps2> Ps3' •'• Psn
S1 * P81»^S2 * Ps2> S3 * Ps3»
• • • » Sn Psn
PR2
&1> 6g, 65, ... 6n
Pe1» Pe2> Pe3» '"Pen
61 * Pel* P2 * Pe2> e3*Pe3»
•••> en*Pen
PR3
quantity of secondary raw materials (by
type) recovered by waste processing
sale price of secondary raw materials (by
type) recovered by waste processing
revenues realized on reuse or sale of
secondary raw materials (by type)
recovered by waste processing
total revenues realized on reuse or sale
of secondary raw materials (by type)
recovered by waste processing
quantity of energy recovered by waste
processing
sale price of energy recovered by waste
processing
revenues realized on reuse or sale of
energy recovered by waste processing
total revenues realized on reuse or sale
of energy recovered by waste processing
Note: The assumption that n raw material types, n employee classifications or n machine types-, etc. are
involved In the industrial production process is not correct. There are actually n raw material
types, k employee classification types and f machine types, etc. This notation, however,
complicates parameter identifications; consequently, n does not represent the same number in every
case.
CASES OF INDUSTRIAL PRODUCTION AND WASTE GENERATION SYSTEM COSTS FOR DECISION-
MAKING
System Costs for Decision-Making
Costs defined C1? C2, C3, C4, (Figure 8) are usual costs in any type of
industrial production and they can vary, but they are inevitable. Revenue
realized on production sale, PR,, has to cover all these costs and provide a
profit for successful continuation of the production.
70
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However, the costs caused by waste generation, C5, C6 and C7, are rarely
precisely defined, quantified and evaluated in terms of price in the
production process. Little is known or appreciated about true costs
associated with generating waste in industry5.
For example, costs C6 depend on the level of legislation and regulations of
the country where an industrial production is under consideration. However,
these costs have to be added to one of the basic cost of industrial
production, C5, caused by material release (and thus material loss) to the
environment. Therefore, even if legislation does not define permissible
levels of waste releases to air, water and/or soil/land and does not include
taxes on waste generators, a manager of an industrial production can still
calculate these costs.
Roney approaches the same problem from a total production quality
management point of view; he stated that hazardous waste is a result of bad
quality and is a significant part of manufacturing costs. He suggested that
high class manufacturers have to eliminate it for these reasons alone -- they
have to cut their emissions to be allowed to keep their factories open while
environmental pressures close down the others.
A similar situation exists with revenues obtained on reuse or sale of
secondary raw materials and/or energy recovered by waste processing. Cost
analysis can identify profitability of waste processing for recovering
secondary raw materials and/or energy, or for finally disposing of processed
waste into landfill. This analysis did help .environmental regulators to
encourage waste recycling by introducing high taxes on waste to be finally
disposed of in a landfill.
Finally, the industrial production and waste generation system under
consideration can be generally viewed according to the following three cases:
Case I - ,
In countries where legal sanctions against environmental pollution are
absent, industrial technologies (production processes) are operated and/or
typically selected maximizing profit, i.e., the difference between revenue and
cost, as it is shown in the following expression (i):
(i) max {PR, - (C,
C2 + C3
C4)}
71
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Case II
In countries where sanctions against environmental pollution have been ,
legislated, industrial technologies are operated and/or selected taking into
account:
(i) max (PR, - (C, + C2+ C3 + C4)}
as well as additional technologies for reduction of waste generation (by
processing waste into secondary raw materials and/or energy), maximizing the
difference between revenues and costs, as shown in (ii):
(ii) max {(PR, + PR2 + PR3) - (C5 + C6 + C7)}.
Case III
However, an overall approach to the solution of environmental problems
caused by industrial productions generating waste is suggested by operating
existing and/or selecting industrial processes through:
1. industrial production technology,
2. technology for reduction of waste generation (waste processing into
secondary raw materials and/or energy), and
3. improvement/optimization of industrial production by introducing
pollution prevention measures,
which maximize the total profit:
(iii) max {(PR, + PR2 + PR3) - (C, + C2 + C3 + C4 + C5 + C6 + C7 + C8)}.
In the expression (iii) C8 (total costs of activities related to
improvement/optimization of industrial production for environmental
production) is incorporated. These pollution prevention costs should enable
optimal industrial production under reduction of waste generation. In this
case costs of materials loss, C5, as well as waste management and processed
waste final disposal costs, C6 and C7, could be minimized or even eliminated.
Using this or similar cost analysis could help industrial
managers/operators to evaluate their industrial production processes and to
72
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concentrate on improving/optimizing production processes, instead of
concentrating on managing waste and/or wastewaters generated by these
processes. In fact, one dollar spent on improvement of'the processes
efficiency means in a long run, one dollar more of revenue realized on sale of
products. Finally, for industrial managers/operators, applying pollution
prevention strategies means observing their processes from different points of
view which could result in improved efficiency and reduced costs.
73
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REFERENCES
1. Olbina, R., Cizerle, A., Krnel, D. Specialized Bibliographic Database
on Waste and Wastewater Management (COS/ISIS Information Storage and
Retrieval System), on-line IZUM. University of Ljubljana, Faculty of
Science and Technology, 1991. 4000 documents.
2. Olbina, R. Computer Supported Modelling of Hazardous Waste Management,
Dissertation, University of Ljubljana, Faculty of Sciences and
Technology, 1991. 251 pp.
3. Baetz, B.W., Pas, E.I., Vesilind, P.A. Planning Hazardous Waste
Reduction and Treatment Strategies: An Optimization Approach. Waste
Management & Research, 7(2): 153-63, 1989.
4. Glazar, S.A., Dolnicar, D., Olbina, R. Computerized Information System
for Special Waste Management. Module 5: Factual Database for Waste
Generation Quantities in Slovenia. University of Ljubljana, Faculty of
Science and Technology, 1990. 930 records.
5. ICF Consulting Associates Inc. Economic Incentives for the Reduction of
Hazardous Wastes. 707 Wilshire Boulevard, Los Angeles, CA 90017, 1985.
79 pp.
6. Rooney, C., Reducing Waste, American Paint and Coating Journal, Jan 9,
1989. pp. 36-40.
74
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SECTION 5
DEVELOPMENT OF INFORMATION SYSTEM SHELL FOR MEASURING P2 PROGRESS
INTRODUCTION
The objective of this part of the research was to build a generic
computerized information system shell (ISS) for measuring pollution prevention
progress in any industrial facility generating waste. It comprises a real
data model of an industrial production and waste generation tracking system
(IPWGTS). The IPWGTS model precisely defines the observed system parameters
and their relationships (Section 4).
The essential part of the information system shell is a model of an IPWGTS.
An IPWGTS model was tested in selected industrial production processes of an
industrial sector, and real data were used for ISS building, model testing and
improvement.
An ISS could provide industry, research organizations, and governmental
institutions with a powerful tool for implementation of pollution prevention
strategies and introduction of efficient waste reduction/minimization
practices by calculating quantities of waste and/or wastewater generated in
industrial sectors and by applying cost analysis. After introducing process
optimization while minimizing waste and wastewater generation in production
processes, ISS provides measurement of P2 progress.
Commercially available database management systems (DBMS) for information
system shell building applicable on IBM PC are analyzed and appropriate DBMS
are selected.
Structure and Function of the Information System Shell
The information system shell includes databases on the following data
(Figure 9):
(i) codes, types, quantities and cost of raw and other materials
entering production process;
(ii) codes, types, quantities and revenues of products;
75
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(ill) codes, types, quantities and cost of waste and wastewater
generated by the process;
(iv) codes, types, quantities and cost of waste and wastewater
management system;
(v) codes, types, quantities and cost of material loss through
emission, discharge and spill;
(vi) codes, types, quantities and cost/revenues of secondary raw
materials/energy recovered by waste processing and/or wastewater
treatment processes; and
(vii) codes, types, quantities and cost of processed waste final
disposal.
Using developed parameter relationships of IPWGTS it is possible to
calculate the quantities of waste and wastewaters generated by the observed
industrial process in a selected time interval. Data evaluation could be
carried out by using the U.S. EPA's Toxic Release Inventory data as well as
direct measuring and monitoring of releases.
8-M*(1-U)*r
Databast
on raw and
other materials
quantites
i
Datab.
wastage
quart
iseon
•derated
tities
«"f
Comparison
Database
on products
quantites
P-U*r
Database on
actualy
measured
released
quantities
y-(1-U)*r .^
^^^ • 1 r
Database on
was!*
managed
quantities
MX —
y
Database on
waste released
quantities
"VsV. z-(1-M)*(1-(J)*r
_ t^
Database on
secondary
material and/or
•nergy recovered
quantities
H±g
Database on
processed
waste disposed
quantities
*-M«R*(1-U)*r
d-M*(1-R)*(1-U)*f
Figure 9. Scheme of an information system shell
76
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The IPWGTS model provides among others the following data:
(1) production factor determines the overall production process
efficiency;
(2) waste management factor determines the level of waste and wastewaters
management and estimates the waste and wastewater management system
efficiency;
(3) waste recycling factor determines the level of recyclability and
estimates the secondary material and/or energy recovery efficiency;
Use of Information System Shell
ISS is programmed in a way that allows the use of existing data in any
industrial facility. A facility manager can select a unit operation, several
unit operations, a part of facility and/or a whole facility as the IPWGTS for
testing. A manual for the use of ISS is available describing data preparation
and import into the system, as well as the steps of using the ISS. Once data
are collected and imported, all calculations are provided by the ISS. Further,
quantitative and cost analyses and the determination of the efficiency of the
observed system can be done quickly and precisely. The ISS provides daily,
weekly, monthly, quarterly and yearly time frames (Figure 10).
MCUSmuu. SECTOR
ISC| I DATE; WEEK |
RAW MATE mil
• Us
Ui
Figure 10. Schematic presentation of the ISS databases
77
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Finally, ISS allows schematic, tabular and graphic presentation of the
system parameter relationships. After the estimation of the facility
efficiency, the management team could decide which waste minimization and/or
pollution prevention measures should be applied, if any. If improvements have
been selected and implemented, the ISS can be used again and measure pollution
prevention achieved.
78
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SECTION 6
COMPUTER PROGRAMMING OF THE INFORMATION SYSTEM SHELL
SYSTEM OVERVIEW
The structure of the ISS is based on the parameters and relationships of an
IPWGTS discussed in Section 4. The ISS has been developed using Microsoft
windows and requires MS Access 2.0 and MS Excel to run. For fast performance,
it is recommended that ISS be used on a 486DX computer with around 66 MHz!
However, a 386 computer with Windows will also work.
Quantitative Analysis
The information used within the ISS can be broken into seven categories: raw
materials input, other materials input, products, waste generated, measured
releases, and managed waste (Figure 7). Each of these categories contains
measurements of a specific parameter quantity taken on a daily basis. The ISS
is based on the relationships that exist among these seven categories through the
model.
There are three factors used to correlate quantities within this system: the
production factor, the waste management factor, and the recycling factor (Section
4). Each of these can be computed within the ISS on the basis of available data.
By implementing these formulas, the ISS is able to calculate parameters
quantities. For example, in an industrial production process where only the
inputs and the product are closely measured, the total amount of waste generated
can be predicted but the management and recycling factors cannot. In addition,
these formulas could aid in determining if the methods of parameter calculations
are producing correct results.
Cost Analysis
In addition to calculations of the quantities of input and output of
materials in a process, this ISS also provides the capability to make a cost
analysis. The cost analysis incorporated in the ISS is based on the model of
IPWGTS and its parameter relationships and their unit prices. There are eight
79
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cost types recognized within the ISS (Section 4):
Cl: Total cost of raw materials: 2(r,. p5), where r = quantity of raw
materials by type and p = unit price of raw materials by type;
C2: Total labor cost: 2(1, \a.), where 1 = number of employees by
employment classification and w = wages per employee by employment
classification;
C3:
C4:
C5:
C6:
C7:
C8
Total depreciation: 2(111,. £, pf), where m
type), p » unit price of machine (by type), S
machine (by type);
number of machines (by
depreciation rate of
Total cost of miscellaneous factors: 2(0, p,-), where o = number of
miscellaneous production factors (by type) (e.g., commercial and/or
financial, insurance activities, transport, etc.) and p = unit price
of miscellaneous production factors (by type);
Total cost of activities related to improvement/optimization of
production: 2(i, p,.), where i = number of activities related to
improvement/optimization of production for environmental protection
and p = cost of activity related to improvement/optimization of
production for environmental protection;
Total cost of raw materials loss: 2(z, p,), where z - quantity of
gaseous, liquid, and/or solid raw materials lost by releases to
environment and p = unit price of raw materials;
Total cost of waste management: 2(g, p,-), where g = quantity of
gaseous, liquid and/or solid waste managed (by type) and p = unit
price of each increment of waste management;
Total cost of processed waste final disposal: 2(d,- PJ), where d =
quantity of processed waste for final disposal (by type) and p =
unit cost for processed waste final disposal (by type).
As data are collected, the user will be able to input new data as it
arrives. At all times, the ISS will determine, from the available data, if
any other types of quantities can be predicted, thereby offering the user an
improved view into the industrial production process under study.
80
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INFORMATION SYSTEM SHELL
The structure of the ISS can be divided into three sections: data entry,
data storage/manipulation, and data display. These sections are bound
together through the user interface.
Goals and Requirements
One of the requirements the ISS must fulfill is to be user friendly. By
being user friendly, the ISS is responsible for the following:
- organization of data in a format that allows easy comprehension by user;
- the display of correct values throughout system;
- quick and timely responses; and
- a system that is easy for the users to learn.
In addition to being user friendly, the ISS is expected to be applied to a
wide range of industrial processes and domains. In order to meet this
requirement, the system has been designed in a shell-like fashion. The core
of the system consists of table definitions and functions that are used for
data input, storage, manipulation, and display. Depending on the data
entered, the core system will produce different storage and displays specific
to the process being studied at that time.
Another requirement of this ISS is that it functions successfully not only
for the ideal situation, (having all data at hand from the beginning), but
also for the bare minimum, which in this case means only data on the raw
materials, and other materials, and products to the system. The next
requirement is that the system should be able to handle not only new data, but
also new data of different types. This will not only lead to more storage
types, but also to new areas within the user interface.
Implementation
The ISS is being developed on a PC machine and it entails two software
packages: Excel 5.0 (A Microsoft spreadsheet package) and Access 2.0
(Microsoft DBMS). In order to form a more coherent system, Visual Basic 3.0
(Microsoft) has been used to connect these packages.
81
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The data entry section of this system has been set up as an Excel
application. The design has taken the liberty of assuming that the majority
of users by this time have implemented storage of data on industrial processes
within spreadsheet packages. Excel has been used because of its conversion
capabilities, its general user friendliness, and its compatibility with other
software packages. In addition to data entry, the majority of calculations
needed by this system are done within Excel due to its speed. The
calculations are usually made up of multiplications and division operators.
The cost calculations used within this system are not calculated within Excel.
Access 2.0 is a database management system (DBMS) package produced by
Microsoft. In the ISS system, it is used for data storage and manipulation.
This ISS has been based on two databases: Archive and DM databases. The
Archive database is used strictly for data storage. The DM database is used
for data manipulation and is dependent on the Archive database. The system
has been split into two databases in order to conserve storage space and to
allow, in future implementations, the choice of different Archives as the
basis for the DM database (i.e., allow the user to choose the production
process archive to use at a given time). The Archive has been set up as the
primary storage facility of the system, therefore, each unique process studied
will have its own Archive database. The DM database references the data in
the Archive database. The ISS will contain only one DM database that is
independent of the type of process studied. The DM database is made up of
pieces of code (SQL) that create information by organizing data relationships
in different ways. This new information is stored as tables known as queries.
These queries, rather than the original tables, are used by the user
interface.
The user interface has been designed and implementation has begun using
Visual Basic. This has been done to enable an easier transition between the
software packages and a more coherent display. In addition, Visual Basic
offers more control than interfaces designed and implemented in either Excel
or Access. Visual Basic blends well with these application packages because
all use similar data structures and objects within the programming language.
Data Entry
The data entry for the ISS can be divided according to the amount of data
that they accept. The bulk of the information needed for the system to
operate is entered using an Excel application. This application accepts and
formats the daily measurements of all materials that course through the
system. In addition, the Excel macro is responsible for calculating the
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predictive values used within the system. However, in addition to this bulk
information, the system also requires specific information about each
parameter and outside source that may affect the parameter relationships
inside the process. The amount of this type of data is usually not as large
as the primary data and can therefore be entered into the ISS by the user
through the user interface.
Data Entry - Bulk Information--
Bulk information is used to refer to the daily data collected throughout
the system. These data contain information on quantities of material flowing
through the industrial production process. Because the structures of the ISS
are dependent on the user input, the data entry section of the system is made
up of macros and functions written in Excel Basic (similar to Visual Basic) to
accept formatted input and perform the needed calculations, and extra
formatting procedures to allow easy transition of data between Excel
and Access.
Data Format—In order for the system to be successful, data entry must follow
certain formatting guidelines. It has been assumed that each type of data is
stored in a separate file which conforms to the following guidelines:
1. all data on parameters quantities will be given on a daily basis;
2. all data conversions will been performed before data enter into the
system;
3. the first row in each column in a table will contain a unique title
for that column. The title can be a parameter name or some type of
representation symbolizing that parameter, and
4. the incoming spreadsheets will be formatted in the following ways:
- the first column in every sheet contains the date;
- no overall daily totals are given on any sheet;
- for parameters named (managed waste, measured releases, managed
releases, and secondary raw materials) the sheets will contain
four columns: date, waste, wastewater contamination, and air
emission. If these titles are not supplied, the system will
automatically assign them in that order.
83
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As the user is entering the needed files, the application will be
performing calculations and additional formatting based on the type of data
being entered. For example, when the sheet containing the amounts of raw
materials is entered, the application will compute the daily totals and assign
variables needed for transfer into the archive database.
Calculation with Data Entry--When the user has completed entering data, the
application will develop additional worksheets based on the types of entered
data. Based on the user inputs, the system will determine what extra types of
calculations can be made. The minimum required types of data for the ISS to
function are: raw and other materials (inputs), and products. The user will
not be allowed to continue the system until these three types of data are
entered into the system. If a system is given only these three types of data,
the application will create a worksheet containing the predicted amounts of
waste generated based on the production factor computed from the inputs and
the products quantities.
As for the rest of the data types, the user is responsible for entering as
much information as possible. It is hoped that through the use of this
system, in addition to improving the efficiency of the observed production
process, the user will take the initiative to improve techniques needed to
measure, collect and handle these types of data. Based on this second goal,
in future sessions with the information system, the user will have the ability
to incorporate new types of data that may not have existed at the beginning of
the ISS application. In adding new types of data to the system, the data
entry will be able to perform different calculations used within the system.
For example, given the quantities of waste generated and waste managed, it
will be able to predict the quantities of waste released using the previously
calculated managed waste quantity.
Automatic Data Entry—The user is sent directly into the Excel application
from the Setup window in Visual Basic. The user is given a choice between
manual and automatic data entry. Manual data entry allows the user to enter
data directly from the keyboard. This option will probably not be viable due
to the size of data files. Therefore, the data entry application will not
begin until the user presses the "Automatic" button on the top screen.
Automatic data entry will prompt the user through the process by asking for
the type of data and the file where that data is stored.
Once data entry begins, the automatic window, known as a dialog frame in
Excel, will remain on top of spreadsheets throughout the data entry stage. It
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contains a set of click buttons which represent different data types. As the
user loads these different data types, the dialog frame will disable the data
type click buttons that have already been used. In this way, the dialog frame
facilitates the ease and correctness of data entry by keeping track of what
type of data has already been entered, and keeping track of what types of data
are yet needed. Since certain types of data require different computations,
calculation errors made by the application can be limited by specifying the
type of data entered. In addition to highlighting a click button, the user is
prompted for the full path name of the file containing the data. After these
two inputs are given, the user can load the file by pressing the "load" button
on the dialog box. Once this button is pressed, the background of the screen
will display the file being loaded into Excel and the resulting calculations
that the application will perform on the given sheet.
N
The following steps represent the summary of data entry procedure:
1. press "Automatic" Button on Top Form. (Dialog Frame will appear);
2. click on type of data to be entered;
3. enter full path name of file containing that type of data;
4. press the "Load" button: the file will be loaded and required
calculations will be performed (e.g., total columns for raw material
input, other material input, products, and releases);
5. repeat steps two through four until all available data or at least the
minimum required data are entered;
6. press "Complete" button signaling completion of data entry.
Application will perform predictive calculations based on the data
given; and
7. press "Back to main" button on the tool bar within Excel to return to
user interface.
After entering data spreadsheets, the user continues the Excel application
by pressing the "complete" button on the dialog frame (step 6). This button
will signal to the application that data entry has been completed and based on
the types of data that have been entered, additional spreadsheets are created
by the application (Table 4). At the conclusion of these calculations, a
message box stating that the data entry has been concluded will appear. At
this point, the user is ready to exit the data entry application.
85
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TABLE 4. Summary of calculation requirements and results of data entry
Given:
Raw material Inputs,
products
Raw material Inputs,
products [releases] ,
Raw material Inputs,
products [releases] ,
other material inputs,
other material inputs,
managed waste
other material inputs,
managed releases
Raw material Inputs, other material inputs,
products [releases], {managed waste/releases},
secondary raw materials
Output :
Waste calculation
Waste calculation, production
releases, management factors
Waste calculation, production
management factors.
factors, managed
factors, managed waste,
Waste calculation, production factors, managed
waste/releases, management factors, final disposal and
recycling factors.
Exiting Data Entry--After completion of extra calculations performed and
entering the available data types, control will return to the user, and the
user will be responsible for exiting the application by pressing a toolbar
button. The toolbar button will perform the data transfer between Excel and
the Archive database in the background. Exiting the data entry application in
this manner allows the user who may be familiar with the capabilities of Excel
to "play" with the data, i.e., explore different tabular and graphical
functions within Excel using the data created by the application. To get a
full understanding of this data however, the user is referred to the
discussion on the formulas used in the calculations section.
Module Design—The following are the basic divisions of the modules used
within the data entry section. Only the major procedures and functions have
been included in this listing:
Data Entry:
AUTO BUTTON CLICKO:
HAN BUTTON CLICKO:
CANCEL_BUTTONO:
BACKTO_ MAI HOI
controls the user screen in general cases;
sets up "Automatic" dialog box and displays it to user,
called from "Top" sheet;
sets up "Manual" dialog box and displays it for user.
Also called from "Top" sheet;
cancels the Excel application;
assigned to a box on a tool bar. It is used for the
exiting data entry section after data entry is completed
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Automatic Input:
LOAD BUTTON_CLICKO:
DETERMINE, ADATAO:
COMPLETE_BUTTOM_CLICK()I
by user. BACKTO_MAIN will saved the entered data, and
then call and Access function based in the Archive
database to transfer the data into the database.
controls the automatic data entry;
loads the user defined file into the workbook. It also
calls functions to perform calculations based on the
type of data that the new worksheet contains. Finally,
it keeps track of what types of data have been entered
and adjusts the dialog box accordingly;
determines what, if any, types of additional data have
been entered into the system. Waste managed, releases,
and managed releases are considered as additional data;
signals completion of data entry. It first checks for
the three minimum data types required; if all three are
not present, it issues a warning to the user and does
not allow the user to exit the data entry section. It
then transfers control over to the control_calc
procedure;
Manual Input:
controls the manual data entry and dialog box.
to the automatic input module described above;
Similar
Total Calculation: computes the daily totals for all sheets;
COMPUTE TOTALS<):
WASTE_CALCULAT10NS<):
CONTROL_CALC(>:
computes daily totals for all sheets and adds
information needed for data transfer. Waste Calculation:
used to compute the various predictive calculations;
creates a new worksheet containing calculated waste
values. These include the production factor, total
inputs, and total waste using data from raw materials,
other inputs, and products worksheets. It is called
every time new data is entered into the system;
using the information from the automatic entry and the
additional data determination function, it controls the
calculation of further predictive calculations dealing
with waste management;
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Managed Haste and Releases: computes the predicted values of waste managed
and released. It will create at least two new
tables depending on given input. Only called
when either managed releases or managed waste
values are given;
cALc_MFACTORSo/cALc_HFACTORS2o: calculates management factors. One is used for
calculations when managed waste values are given
and the other is used when managed releases are
given;
CALC_MRELEASES<>:
CALC MUASTEO:
CALC_HANAGED_RELEASES()I
CALC MANAGED WASTEO:
calculates managed release amounts when managed waste is
given;
calculates managed waste amounts when managed releases
are given;
controls the calculation and creation of two new sheets:
managed releases and the management factors used;
controls the calculation and creation of two new sheets:
managed waste and the management factors used;
Calculate secondary material
and final disposal: computes all of the needed information for secondary raw
materials and final disposal amounts. It will create
two new tables: one containing final disposal amounts
according to types of waste, and one containing
recycling factors used in this calculation. Can only be
called when either managed wastes or managed releases
are input to the system;
RECYCLING FACTORSQ:
D!SPOSAL_FORMULASO:
RFACTOR_TABLE<):
DVALUE TABLEO:
enters the formulas needed for computing recycling
factors;
enters the formulas needed for computing final disposal
amounts;
takes computed values and moves them into a separate
table;
takes completed disposal values and moves them into a
separate table; and
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CREATE_NEW_VALUES<>:
controls the creation of two new worksheets: d_rfactors
and d_final_disposal.
Data Entry - Specific Information--The data entry implemented through the user
interface is responsible for accepting a relatively small amount of needed
data. The specific information collected by this system includes: information
on individual materials input used within the process, costs associated with
labor, machinery, miscellaneous factors, and measurements of different waste
streams (Section 4). Within the system, only the material input information
must be entered before the analysis screens can be viewed. The other
information can be entered into the system either at the beginning or during
the ISS testing. Like the bulk information, the ISS system can successfully
be used without entering all types of available data.
Input Material Information—The specific information on the input materials
within the process include the following:
* name - filled in by system according to bulk data entry;
* inventory code - code used by company or organization for the given raw
or other input materials;
* unit of measurement - unit from daily measurement;
* section of process where material occurs - the section of the process
where the material was measured and filled in by the system; and
* unit price - unit price of the material for a single unit.
In order to enter the analysis display section of ISS, the information
must be entered into system. The data entry screen is found through the setup
screen. For a new process, this data can only be entered after the bulk data
entry is completed. However, if a previous process is being tested, this
information can be updated once the user enters the setup screen. However,
there is a warning: only one unit cost can be used for calculations. That is,
if the unit cost of a certain parameter goes up or down over time, the ISS
cannot accept more than one cost per parameter. The cost analysis cannot take
into account parameter price changes over time.
Cost Analysis Information—In order to form a. complete cost analysis study,
extra information about the process must be taken into account. This extra
information includes data on labor, machinery, and miscellaneous factors
Section 4). This information is not required for the ISS to successfully run;
however, it will offer the user a more complete view of the process under
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study. These data entry screens follow basically the same format: the user is
allowed to update the information contained within the tables if desired, but
is then required to specifically tell the system that a new piece of
information is going to be entered. The following is a listing of the
information collected for each of these types of data:
Labor/Employee: Description of job type.
Job type number.
Number of individuals employed at this type of job.
Salary for this type of job.
Machinery Depreciation: Description of machine type.
Machine type number.
Number of machines of this type used in process.
Cost for one machine of this type.
Depreciation rate of machine.
Miscellaneous Factors: Description of activity.
Type of activity.
Number of activities of this type.
Unit cost of the activity.
The format of the data entry windows for this type of information is
prepared. The buttons on the bottom of the screen include arrows to view and
update all records within the table. An update button, when pressed will
clear the text boxes on the screen allowing for new information to be entered.
The update button must be pressed only after the new information is entered
into the system to record the new pieces of data. The cancel button is used
to close the window and return to the system.
Waste Streams—Finally, the last type of information that can be entered into
the system deals with measured quantities of specific waste streams. This
type of information has been separated from the other data entry because in
many systems this type of information may not be available at such a specific
level. If this type of information is available, it can be used to form a
more complete cost analysis of the process and potentially offer more
opportunities to apply pollution prevention strategies.
ISS accepts waste streams into three main groups (names are chosen to be
easily distinguished): vent flow, liquid waste stream, and waste stream. The
data entry for specific information pertaining to the contents of these three
waste streams expects the measurements for each type of waste stream to be
stored in its own Excel spreadsheet file maintaining the same format as the
data types entered through data entry for bulk information. The data entry
screen for this type of information is also prepared. The user is asked to
type the full path name of the data file and the type of waste stream that the
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file contains. When the ok button is pressed, the ISS will automatically find
these files and enter them into the archive database and perform the needed
data manipulation.
DATA STORAGE AND MANIPULATION
Data storage and manipulation in ISS is accomplished using two separate
database files: the Archive and the Data Manipulation (DM) database. The
Archive database is only used for storage of the information needed to study
the process. No manipulation is done within this database. The DM database
contains the necessary functions and queries to allow the user to study the
observed process. The DM is a read-only file; it uses attached tables from
the Archive database as its source of data. This has been done to limit the
possibilities of data corruption.
Data Storage - Archive Database
The data storage function in ISS is accomplished using a single database
file. This file is referred to as the Archive database. It is the foundation
of the Information System Shell. The Archive database contains only the data
tables and the macros used to control the acceptance of data from the data
entry sections of the system. The user will never directly access this
database. Once information is entered into the system, it is saved by the
Archive database. This information can then not be changed or deleted by the
user. This limitation has been implemented to ensure data integrity and
eliminate potential errors by users. The Archive database is the only data
file saved at the end of a testing session in order to be used in another
testing session of the ISS.
An empty Archive database contains 13 tables. These tables are empty in
the beginning; only their data dictionary is defined. Depending on the
number of different types of data entered into the system, the tables
corresponding to these types of data will be filled. The system, based on the
user input, will determine which tables are to filled. In addition to filling
the pre-defined tables the Archive database creates three new tables each time
a new process is to be studied. These three tables, containing raw material
inputs, other material inputs, and products quantities are defined
dynamically, since they are created at run time. The following is a list of
the tables defined and created by the Archive database:
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1. Daily raw material inputs - created dynamically
2. Daily other material inputs - created dynamically
3. Daily amounts of products - created dynamically
4. Daily amounts of measured releases
5. Daily amounts of calculated wastes
6. Daily amounts of managed waste
7. Daily amounts of managed releases
8. Daily amounts of secondary raw materials
9. Daily amounts of final disposal
10. Daily values of management factors according to type of waste
11. Daily values of recycling factors according to type of waste
12. Material Information
13. Current name of industrial process and company using this system
14. Labor/Employee information
15. Machine Depreciation information
16. Applied Pollution Prevention Techniques
17. Miscellaneous Production factors
18. Waste Stream information: Vent Flow
19. Waste Stream information: Waste Stream
20. Waste Stream information: Liquid waste stream
For the data types numbered 1 through 10, the user will enter data into
this database indirectly through Excel. By pressing the "back to main"
button, the Excel application has been programmed to open the Archive database
and then transfer the data into the Archive. This process has been automated
to eliminate potential user errors and increase the system's user
friendliness. For data formats that remain constant throughout different
processes (e.g. waste, managed waste, managed releases, management factors,
etc), the Archive database simply appends the information into the existing
table. If however, the data is of type numbered 1, 2, or 3, that may change
or be dynamic among different processes. The Archive will first check to
determine if a table already exists. If a table does not exist, the
application will create a new table and transfer this data into the table. If
the table does exist, the application will append the data into the table.
For the data types numbered 11 through 20, data will be entered into these
tables through the user interface using the methods for specific information
(described in section Data entry). Data table number 12, material information
is a special case. The contents of this table are based on the information
entered in the Excel macro as well as the information collected from the user
directly. The Archive database is responsible for taking the bulk information
and extracting chemical names and data types to enter into the material
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information table. The rest of the required data is entered by the user
through the interface of the system. The material information table has been
designed in this way to improve the quality of data by enforcing consistency
throughout the data and reducing the data entry performed by the user, thereby
reducing the possibilities of error and data corruption.
The following describes the actions that the Archive database takes
to develop a file for a given process:
1. Open Archive database.
2. Determine if all needed tables exist.
3. If all needed tables do not exist:
- Create needed tables;
- Transfer data from Excel into Archive;
- Fill chemical information table;
4. If the tables do exist:
- Transfer data from Excel into Archive;
5. Close Archive.
Module Design
Control:
contains the driver program for the Archive database;
CONTROL_ARCHIVEO: determines if tables exist and takes the necessary actions
depending on the result of determination.
Get Names:
FILL_CHEMICALO:
GET_NAMES<>:
fills the input table with chemical information;
contains the code to fill the input table successfully;.
inputs the field names of the imported tables containing raw
material inputs, other material inputs, and products, and enters
them as records in the input table under the chem-name field.
In addition, this procedure will also enter the original table
name where the given material is originally located;
Input Archive: enters the user data into the appropriate tables either by
creating new tables or appending the information into the
already existing tables;
returns the number of tables within the Archive database at a
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APPENO_ARCHIVE<):
APPEND_CALCO:
CREATE_TABLES():
given moment.
created;
Used to determine if all tables have been
used when tables already exist within the database and data must
be appended into the tables;
enters data into the tables containing "calculated" values as
well as tables with consistent format among processes. It
appends the data into the existing tables.
creates the needed tables for loaded data. These tables will
not remain constant between different types of processes and are
therefore considered to be dynamic.
DM Database Design
The DM database is responsible for the manipulation of the parameter tables
in order to emphasize informative relationships occurring within the data. The
DM is dependent on the Archive database for its data only. It is not dependent
on a given process and the specific contents of the data represented by the
Archive. Different Archive databases can be "plugged into" the DM and still
provide a successful system operation.
In order to maintain data integrity, the user never directly views this
database. All creation requirements have been automated so that the user may
simply press a button and wait for the system to complete.
The following is a summary of DM database creation:
1. attach tables from Archive database;
2. create Queries using attached tables;
3. close Database and return control to user.
The informative tables, known as queries, that are created within this
database are based on data referenced in the Archive database. These
references are read-only, thus reducing the chance of data corruption.
Information is created through these tables in DM rather than within the user
interface in order to take advantage of the speed of the DBMS package. By
creating these queries within DM, while there may be a lag time at the
beginning of execution while the queries are formed, the remaining system will
run smoothly and at a higher speed.
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Data Queries
Data queries are only developed once during a testing session with ISS.
These queries must be recreated every time ISS is run in order to maintain a
DM that matches the Archive database used. The user is able to change the
Archive used at the beginning of the system, but is not given such an option
for the DM database. Recreating the queries in the DM does not take long and
reduces the amount of memory needed for the system.
The DM database maintains 59 queries. The DM contains queries that
organize the data into tables according to time frame requirements: there is a
separate table for each type of data according to time frames: daily, weekly,
monthly, and quarterly. While this may seem to take up much space, it allows
a speedup in the interface by having the data in a format ready to be
displayed rather than forcing the interface to both prepare and display the
data, slowing the system down. In addition to time requirements, these
queries are used to organize data for the display functions. For example, in
displays containing all input material information, a join on the raw input
material and other input material must be created.
Not all of these queries are created dynamically because certain data
types have been carefully laid out and formatted so the contents are always
constant, regardless of the data type. Measured quantities of release is one
example of this type of data. All tables containing release data will be made
up of five attributes: date, air emission, wastewater, solid waste, and total
waste. However, certain data types are not constant throughout uses, i.e.,
for given processes, the contents of the data types vary. The dynamic data
types that force queries to be created dynamically are raw input material,
other input material, and product material. These tables can consist of a
varied number of attributes because each attribute represents a material.
Table 5 gives a listing of all queries created dynamically at start-up.
TABLE 5. Queries created dynamically by DM database at start-up
Basic Table
daily raw material input
daily other material
input
daily product
daily raw input material
and daily other input
material
Daily Query
da i ly_raw
daily_other
daily_product
daily_inputs
Weekly Query
w raw
w_other
w_products
w-inputs
Monthly Query
m raw
mjDther
m_products
m_inputs
Quarterly Query
q raw
q_other
q products
q_ inputs
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In addition to these queries if the user enters specific information on
materials within one of the three waste streams, the DM data base will create
twelve queries in order to allow the user to view information according to
time frame requirements (Table 6). This is based on the same reasoning used
in the creation of the queries for input and products materials, that each
process may contain a different amount of materials within each of these waste
streams.
TABLE 6. Queries created dynamically by DM database for cost analysis
Basic Table
daily vent waste
flow
daily liquid waste
flow
daily solid waste
flow
Daily Query
daily_ventflow
daily_1iquidwaste
daily_solidwaste
Weekly Query
w_ventf low
w_liquidwaste
w_solidwaste
Monthly Query
m_ventf low
m_liquidwaste
m_so1idwaste
Quarterly Query
q_ventf low
q_liquidwaste
q_solidwaste
Module Design
Attach Tables: determines what tables are to be referenced from Archive
database;
ATTACH_TABLESO: checks if Archive database is referenced for correct data. If
not, it will create the reference from DM to the Archive
database;
TABLE_ATTACHEDO: determines whether a specified table is already attached by
trying to select it in the database window. It is called from
Attach Table;
Starting
runs all needed query creation procedures during start-up;
DYNAHIC_QUERIESO: functions as the driving program for creating the needed
queries.
Time frame provides the individual functions that are used to create the
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DARY_QUERYO:
INPUT_QUERYO:
MONTHJJUERYO:
QUARTERJHJERYO:
WEEK_QUERY(>:
Cost Ana1ysis2
COST_SQL< ):
FIND_PRJCEO:
DAILY_LOST AMTSO:
WEEK_LOSTO:
MONTHJ.OSTO :
QUARTER LOSTCK
dynamic queries;
creates the SQL code for queries containing daily information.
These new tables contain an updated format to be compatible
with the other time based queries for the same data material;
creates the SQL code for queries containing input information,
which combines data from raw material input tables and other
material input tables;
creates the SQL code for queries containing monthly sums for
the materials used within the process;
creates the SQL code for queries containing quarterly sums for
the materials used within the process;
creates the SQL code for queries containing weekly sums for
the materials used within the process.
creates the queries containing specific information about
materials within the specified waste stream. Queries then
used as a basis for the time frame queries and cost
analysis display.
creates the SQL for queries containing cost information for
materials lost during production;
determines the price of a given material;
creates the SQL code for queries containing the daily sums for
the materials measured within the waste streams. It is
created to improve compatibility.
creates the SQL code for queries containing the weekly sums
for the materials measured within the waste streams;
creates the SQL code for queries containing the monthly sums
for the materials measured within the waste streams;
creates the SQL code for queries containing the quarterly sums
for the materials measured within the waste streams.
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User Interface Design
ISS is a windows program created using Visual Basic for its user
interface. Visual Basic has been used because of its compatibility with Access
and Excel and ease of use. The interface serves as a unifying front end for
the two applications being used.
Forms and Controls--
A form is an individual window used to either display system output or
accept user input. Controls are text boxes, buttons, click boxes, etc., that
appear on forms. These items accept user input. Each time the system is
used, it is called a session.
In order to develop an overall idea for the entire system, Figure 11 is
given. This diagram is a control flow graph of the ISS system. By following
the transfer of control, users can determine where they are within the system.
The dotted boxes around the Access modules represent hidden systems
programming used to create information system. The user will never directly
see these modules. Finally, display functions represent the hierarchical
structure of the screen transition diagrams shown in Figures 12 and 13.
Overall System--The system is based on user input. Therefore, the development
of the user interface has been based on the fact that some of the forms used
for display will be dynamic. The only forms that are dynamic correspond to
the dynamic queries; all forms dealing with the display of raw input
materials, other input materials, products, or any combination of these three
must be created dynamically. To accomplish this dynamic property within the
user interface, grid controls have been implemented in all forms. These grid
controls are similar to spreadsheet pages. By using grid controls, multiple
columns and rows of data can be developed based on the user input. In
addition to these grid controls, code modules have been developed to extract
the needed data from the database and fill these grids.
Data entry in this system, as discussed above, is split between an
Excel spreadsheet application and the forms within the user interface. These
data entry functions have been placed within the system at the beginning of
the flow of control. It is hoped that the user will enter in all relevant
data at one time, at the beginning of the session.
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(^ Start System J
Start TJvough
Figure 11. Control flow graph of the ISS system
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I I
OftwMsUrW
I ln(X*
RjwM**n*t
Product*
Mdwtal*
1
Product v».
Figure 12. Screen transition graph - quantitative analysis display functions
Cost Analysis
._J
Depreciation
LaborCost
ZLH
Raw Material Cost
i:
Cost of Acllvt et
related to
Improvement I
1
L_
Other Production
Costs
[Waste Management
_±!_J
Figure 13. Screen transition graph - cost analysis display funcions
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The user interface has been designed to. allow as much freedom as possible
in determining what to study while maintaining some control over the flow of
events. The following lists some events that must be controlled and ordered
in a particular way:
1.
2.
3.
4.
5.
Data entry of bulk information must be done before the archive database
can be created, as well as the creation of the DM database.
Material information can only be entered after the bulk information has
been stored within the archive database.
DM database can only be accessed after the bulk information has been
stored.
Time frame requirements can only be set after the queries within the DM
database.have been created.
Analysis display functions cannot be accessed until all costs within
material information have been completed.
This control has been enforced by enabling and disabling certain control
buttons at given points within the execution. This type of enforcement is
also used within the display functions to limit the user to the displays
containing information to be viewed.
Common User Activities--Throuahout this ISS system, a set of activities exists
that the user will expect to be able to complete. These activities relate to
studying the available data and forming some conclusion based on the apparent
trends. When attempting to study trends, it is sometimes beneficial for the
user to be able to summarize or break the data into different sets according
to elapsed time (i.e. a trend may become clearer when data is viewed monthly
rather than simply daily). Therefore, within this system, these activities
have been implemented using common controls and menu bars on each form.
Moving through the System: in order to move through the system, from whatever
form has the current focus, the user can specify the next display by clicking
on hot spots or control buttons located on the form itself. Another way to
move from form to form within the system is to use the menu bars located on
each form. Within the pull down menus, the user is offered the data type to
view, and based on what type the user chooses, the display for that data type
is brought up.
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Grouping Data According to Time: when the user is faced with attempting to
determine trends within the data, it may be easier to view the data according
to a different time frame. The time frames within this system include:
daily, weekly, monthly, and quarterly. This option has been implemented on
these forms in a control button known as the time period button, and the View
pull down menu. The time period button located on the form will always
display the current time frame (the default is Daily). If the user decides to
change the time frame, if using the time period button, the system will change
the time frame according to the order daily, weekly, monthly, and quarterly.
If the user is interested in viewing quarterly information, and does not want
to spend the time going through the other time frames, the user may go
directly to the quarterly time frame by using the View pull down menu. When
using the pull down menu, the current time frame will appear with a check
beside its listing. When either the time period button or View menu is used,
both are updated to display the current time frame in use.
In addition to these common activities, the menu bars implemented on these
forms allow the users to perform additional activities. The File pull down
menu offers the user two additional choices other than Close: Print to print
the current form, and Exit to quit the system entirely. The Graph pull down
menu enables the user to go to a common graphing form where the user is given
the choice of what types of materials to graph, based on the form that opened
the graphing capability. Each menu contains a Help pull down menu.
Closing Forms: when moving around the system, the user will be forced to close
certain forms when the screen becomes too cluttered. The cancel button and
Close menu option under the File pull down menu can be used in this case. The
cancel button on a form, when pressed, will close the form and open the form
that appears above it in the tree structure of the transition diagram (Figures
12 and 13). The Close option on the other hand, will only close the current
form and not open another form.
Studying Individual Pieces of Data: while the display forms will show only
one data set at a time, it becomes obvious that in order to make any type of
decision based on these data collections, all of the data must be able to be
viewed by the user. Because this system is based on databases, two arrow
buttons have been implemented on these forms to be used in traversing the
data. When each button is pressed, the form will display either the next data
set or the previous data set to the user. These buttons have been programmed
to sound a beep if the user attempts to move to a data set that does not exist
either because the user has reached the last data set in the table, or has
reached the first data set in the table.
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Using the ISS system--
Starting ISS: in order to begin a session with ISS, double click the ISS
icon. The first form that will appear deals with the Archive database. This
window offers the choice to either use a Previously developed Archive file, or
create a new Archive. Assuming that this is the first time the user is
running ISS, a new Archive is chosen. If. a previous Archive database were -
chosen, the summary found below would still be followed, but rather than
offering the user the ability to add new data in step 2, the button will
appear with a label "Add Data". This button, as well as the "Material
Information" and "Work System" button will be enabled, allowing the user the
choice of either continuing with the current Archive as is, or adding new data
to It. • .'-..',
The following is a summary of the actions that must be taken by the user
in order to set up a study for a new process:
1. From Setup menu form, click on grey factory button. This will lead to the setup form.
'2. Enter bulk information through Excel application by pressing the "New Data" button.
3. User will be in Excel application. Complete data entry within Excel application and return to
interface.
4. Wait until .computer has stopped working.
5. At this point, the "Material Information" button and the "Work System" button .will be enabled (i.e.,
the user will be allowed to click on these buttons and receive a response). Enter material
information by clicking on respective button.
6. Once the material information has been entered, click on the "Work System" button to create the DM
database. Wait until computer has stopped working.
7. At this point, the Date Range box will appear on screen. The user is to choose the date range for
the current session.
8. The setup form has now been completed, to continue on to display functions or specific information
data entry press the "OK" button.
At Step 7, the user is asked to determine a date range for use within this
session. This date range will serve as a filter for the rest of the system;
only data falling within that date range will be displayed for the user. The
user is allowed only to set the date range at this time. The date range form
is prepared. It is made up of two list boxes: the first one, the start list
box will contain a listing of dates within the system. When the user
highlights and clicks on a given date, the end list box will contain all dates
following the start date. This has been done to eliminate the possibility of
the user entering an ending date that may come before the start date. If the
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user chooses to do so, all data can be viewed by highlighting the check box
located on the form. This form will not disappear until the user enters
either a starting and ending date or checks the all data box. At this point,
the system will continue on to the display functions.
Concluding a session with ISS: the system can be terminated only through one
form, the Exit form. This form is reached through the setup menu. The Exit
form will ask the user for a full path name to save the current Archive
database. The current Archive must be saved under a new name in order to run
the system again. Once the Archive database is stored under a new name it can
be used in future sessions of ISS.
Development of Dynamic Forms--
In this system, most of the forms can be designed and created before run
time. However, as in the DM database, those forms which are based on raw
material inputs, other material inputs, and products must be created at run
time. This has been implemented in order to make the system able to
accommodate a variety of processes. The user interface creates these screens
based on usage. If the user does not reference forms, the forms are not
created. The forms display the chemical name, the amount of the chemical, the
unit of measurement, the unit cost, and then the total price of the amount of
specific chemical. The number of rows displayed is determined at run time.
ConstantForms--
All other forms not based on raw material input, other material input,
products data can be developed during design time. These forms deal with
waste and release amounts. Appendix ISS - Visual Basic Interface gives a
diagram of each form and the modules that control it.
and
Quantitative Analvsis--
The quantitative analysis section is based on the diagram in Figure 7.
This figure represents the relationships among all types of data involved
within the process. This diagram has been implemented with "hot spots" that
allow a user to position the cursor on top of an area and when the mouse is
clicked, a new form will open for the user. "Hot spots" are located on top of
each box describing a type of data material. By clicking on one of these
boxes, the user will be sent to the corresponding display form. In addition
104
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to these "hot spots", there is a listing of common comparisons made with this
type of data. The user is free to view those display forms by double clicking
on the desired description. The appearance of this form will change according
to the amount of different types of material entered into the system. The
"hot spots" corresponding to material data that has not been supplied or
calculated for the current session will be disable-appearing grey to the user
and performing no action when clicked upon. This has been implemented in
order to aid user in testing and limit the possible choices that could be made
by the user. The hierarchical structure of the system is shown in Figure 7.
This type of structure has been implemented in order to facilitate ease of
learning and high retention rate for the user.
Cost Analvsis--
The cost analysis of this system will follow the same pattern found in the
quantitative analysis. The basis for this section is shown in Figure 8. It
is similar to the form used by the quantitative analysis in its use of "hot
spots" and the limitation of user choices according to available data, but
cost analysis contains different types of information. However, it does not
offer the user the ability to view common relationships among the different
types of data. Within the cost analysis however, data entry screens can be
brought up to update information dealing with labor and employees, machinery,
miscellaneous factors, and applied pollution prevention techniques.
Future Work
With the completion of this ISS, the true test of any software package is
its acceptance by the users. The system will be distributed to interested
users in order to get feedback. It is hoped that this system will be valuable
to the user. The following is a listing of subject areas and potential
extensions of this work.
Data Entry - A major concern throughout this project has dealt with the
measurements of materials within different parts of the system. Because this-
system has been designed to deal with many data types, not all of which are
required by the system to successfully execute, formatting requirements have
been enforced. Future work in this area can include:
* A better way of formatting data coming into the system. This method
should be developed and improved upon based on user feedback.
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* A better way of determining and defining the types of data coming into
the system. A prime example is in the waste streams: for some
processes, all three types of waste may be measured, allowing the system
to simply create a total for these three wastes. Other organizations
will measure only the total amount of waste coming out of the process
rather than separating it into the three types of waste. Not only does
this affect the methods of accepting this data into the system, it also
plays a role in calculating the predictive formulas based on these
measurements.
Output of System - At this time, the only output that ISS produces is seen
in its display functions. In the future, a report capability could be added
to this system. At this time, the report function could be implemented as a
call to the DM database. This would involve creating dynamic reports within
the DM database. It is not clear how much time the creation of these reports
may require.
Display Function - Through actual use of this system, it can be improved
by adding display forms for relationships among the data that may not have
been included in the system. These relationships may be specific to a process
or organization, but can be included within this system if needed.
Updating the System - As mentioned above, display functions could be added
to the system if appropriate. However, as an extension to this system, a
"create your own relation" could be added. This would allow the user to
create a useful relationship within the system. This would allow the system
to grow according to the needs and requirements of the organization.
106
*U.S. GOVERNMENT PRINTING OFFICE: 1995-650-006722069
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