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CONTENTS
Disclaimer iv
Acknowledgments v
Acronyms and Abbreviations vi
Glossary vii
1. Introduction 1
2. Smart Data Infrastructure 2
3. Smart Data Infrastructure and Technologies: Information Inputs 4
3.1 Continuous Monitoring 4
3.2 Level Monitoring 5
3.3 Flow Monitoring 6
3.3.1 Physical Flow Monitoring 6
3.3.2 Alternative Flow Monitoring Technologies 6
3.4 Rainfall Monitoring 7
4. Collection System Optimization 8
4.1 Capacity Management Operation and Maintenance and l/l Control 10
5. Real-Time Control Systems 10
5.1 Components of an RTC System 12
5.1.1 Supervisory Control and Data Acquisition Systems 12
5.2 Real-Time Decision Support Systems 13
5.3 Level of Control 14
5.4 Guidelines for Applying RTC 16
5.5 Key Considerations for RTC Systems 17
6. Data Management and Sharing 18
6.1 Big Data Management 18
6.2 Data Sharing 18
6.3 Real-Time Public Notification and Transparency 19
7. Data Analytics 19
7.1 Data Validation and Filtering 20
7.2 Key Performance Indicators 21
8. Data Visualization and Decision Support Systems 22
9. The Future of Data Gathering Technology for Wet Weather Control and Decision-Making 23
10. References 25
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APPENDIX A: CASE STUDIES
Buffalo, New York: Real Time Control of Inline Storage A-l
Falcon Heights, Minnesota: Predictive Flood Control System A-3
Hawthorne, California: Real-Time Monitoring to Prevent Sewer Overflows A-5
Louisville, Kentucky: Real-Time Control for Integrated Overflow Abatement A-6
Newburgh, New York: Real-Time Control to Monitor Discharges for Reporting/Public Notification A-9
Philadelphia, Pennsylvania: Real-Time Control to Manage Retention Pond Discharge A-10
San Antonio, Texas: Real-Time Control for Cleaning Optimization A-12
San Diego, California: Stormwater Harvesting Augmentation Analysis A-14
South Bend, Indiana: Real-Time Control and Real-Time Decision Support A-16
Washington, DC: Real Time Controls for Rainwater Harvesting and Combined Sewer Overflow
Control A-18
Wilmington, Delaware: Real-Time Control to Reduce Combined Sewer Overflow Discharges A-20
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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Disclaimer
The material and case studies presented in this document are intended solely for informational
purposes. This document is not intended, nor can it be relied on, to create any rights enforceable by any
party in litigation with the United States. Case studies used in this document are unique and site-specific,
and they may not be as effective as demonstrated. This document may be revised or updated without
public notice to reflect changes in the technologies and to update and/or add case studies. The U.S.
Environmental Protection Agency (EPA) and its employees do not endorse any products, services, or
enterprises.
Mention of trade names or commercial products in this document does not constitute an endorsement
or recommendation for use.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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Acknowledgments
Many stakeholders and subject matter experts contributed to this document, including:
• Jeff Wennberg, City of Rutland, Vermont
• Missy Gatterdam, Metropolitan Sewer District of Greater Cincinnati
• Edward D. Speer, CDM Smith
• Marcus Quigley, Opti
• Hari Vasupuram, Opti
• David Drake, SmartCover
• Tim Braun, Emnet
The document was developed under EPA Contracts EP-C-11-009 and EP-C-16-003.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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Acronyms and Abbreviations
CFR
Code of Federal Regulations
CMOM
Capacity Management Operation and Maintenance
CPU
Central Processing Unit
CSO
Combined Sewer Overflow
DSS
Decision Support System
EPA
U.S. Environmental Protection Agency
FOG
Fats, Oils, And Grease
GUI
Graphical User Interface
ICS
Industrial Control System
loT
Internet of Things
l/l
Inflow and Infiltration
IT
Information Technology
KPI
Key Performance Indicator
LTCP
Long-Term Control Plan
MMSD
Milwaukee Metropolitan Sewerage District
MSD
Metropolitan Sewer District (Louisville)
MSDGC
Metropolitan Sewer District of Greater Cincinnati
O&M
Operation and Maintenance
PLC
Programmable Logic Controller
PWD
Philadelphia Water Department
RTC
Real-Time Control
RTDSS
Real-Time Decision Support System
SAWS
San Antonio Water System
SCADA
Supervisory Control and Data Acquisition
SSO
Sanitary Sewer Overflow
WWTP
Wastewater Treatment Plant
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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Agent-Based Control: System with locally interacting components that achieve a coherent global
behavior. Through the simple interaction of buying and selling among individual agents, a desirable
global effect is achieved, such as fair allocation of resources.
Big Data: Data sets that are so large or complex that traditional data processing application software is
inadequate to deal with them.
Cloud: Large-scale, offsite data storage facilities.
EPA SUSTAIN: Framework for the placement of best management practices in urban watersheds.
Gray Infrastructure: Engineering projects that use concrete and steel.
Green Infrastructure: Projects that depend on plants and ecosystem services.
Internet of Things: Process in which hardware is connected to a network (the internet) so that it can
better communicate with other systems.
Long-Term Control Plan: Written strategy required by the Clean Water Act for communities with
combined sewer systems to reduce and/or eliminate combined sewer overflow discharges in the long
term.
Machine Learning: Data analytic method used to devise complex models and algorithms that lend
themselves to prediction. This is also known as predictive analytics. There are many algorithms available.
Model Predictive Control: Model-based control strategy that predicts the system response to establish a
proper control action. This strategy explicitly uses a mathematical model of the process to generate a
sequence of future actions within a finite prediction horizon that minimizes a given cost function.
Real-Time Control: The ability of water infrastructure (valves, weirs, pumps, etc.) to be self-adjusting or
remotely adjusted in response to current weather conditions.
Smart Water and Smart Data Infrastructure: The ecosystem of technology tools and solutions focused
on the collection, storage, and/or analysis of water-related data.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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1. Introduction
Rain and snowmelt (referred to as wet weather
conditions) can significantly increase flows at
wastewater treatment facilities, creating
operational challenges and potentially affecting
treatment efficiency, reliability, and control of
treatment units at these facilities.
Current approaches to wet weather control rely
primarily on gray or green infrastructure, or a
combination of the two. In recent years,
however, municipalities and utilities have been
considering how they can take advantage of
technological advances to improve their
operations and infrastructure. These advances
include:
• Faster computer processing and network
speeds, providing ready access to reliable
information for informed decisions.
• Smaller, more accurate, and less expensive
sensors.
• Low-cost storage of large quantities of data.
• The advent of the "internet of things" (loT),
allowing sensors to be connected over large
geographic areas.
• Smaller, higher-capacity batteries and
photovoltaics, reducing dependence on
permanent hard-wired power sources.
• Wireless transmittal of acquired data,
reducing the need for continuous or dial-up
hard-wired communications systems.
This document focuses on how municipalities,
utilities, and related organizations can use
advances in technology to implement "smart
data infrastructure" for wet weather control-
that is, how they can use advanced monitoring
data to support wet weather control and
decision-making in real time or near real time.
Case studies about communities that have done
this across the country are included as
appendices and referenced where applicable
throughout the report.
What Is in This Document?
This document summarizes key aspects of utility
operations where smart data systems can provide
significant benefits. It is organized as follows:
Section 2 presents an overview of smart data
infrastructure, its relationship with green and gray
infrastructure, its benefits, and a general
"roadmap" for implementation.
Section 3 describes technologies applied
specifically to wastewater collection and
stormwater systems and key considerations for
selection, design, implementation, and operations
and maintenance requirements.
Section 4 describes the use of smart data
infrastructure to promote collection system
optimization, as well as long-term control plan
implementation, modification, and development.
Section 5 discusses the use of real-time control
systems to maintain and meet operational
objectives.
Section 6 discusses data management, data
sharing, and public notification when using smart
data systems.
Section 7 describes data analysis in smart data
systems, including data validation/filtering and
the use of key performance indicators.
Section 8 discusses data visualization and decision
support systems.
Section 9 discusses the future of data gathering
technology for wet weather control and decision-
making.
Appendix A includes 11 case studies about
communities across the country that have
implemented smart data infrastructure
technologies.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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2. Smart Data Infrastructure
Smart data infrastructure is the integration of
emerging and advancing technology to enhance
the collection, storage, and/or analysis of water-
related data. These solutions can generally be
grouped into a framework that consists of
hardware, communications, and management
systems.
• Hardware includes the devices that
measure and collect water-related data,
such as level meters, flow monitors, valve
actuators, and pump-run monitors.
• Communications refers to networks,
including wireless communications, that
migrate data from the hardware to the
systems that perform analysis.
• Management refers to the software tools
and analytical solutions that perform
analysis and provide actionable information.
It also includes data visualization to give
managers real-time information for
decision-making and to communicate with
the public.
Smart data infrastructure leverages hardware,
communication, and management analytics to
provide real and tangible benefits to utilities,
including:
• Maximizing existing infrastructure and
optimizing operations and responses to be
proactive, not reactive.
• Providing savings in capital and operational
spending.
• Improving asset management and
understanding of collection and treatment
system performance.
• Improving long-term control plan (LTCP)
implementation, modification, and
development.
• Meeting regulatory requirements.
• Prioritizing critical assets and future capital
planning.
• Providing the ability to better optimize
collection system storage capacity to reduce
peak flows and the occurrence of overflows.
• Enabling effective customer service and
enhancing public notification.
Smart data infrastructure can be used to inform
operational decisions that ultimately improve
the efficiency,
reliability, and
lifespan of physical
assets (e.g., pipes,
pumps, reservoirs,
valves). According to
Global Water
Intelligence
Magazine,
implementing digital
solutions by
consolidating
monitoring, data
analytics,
automation, and
control could
potentially generate
up to $320 billion in
cost savings from
the total expected
capital expenditures
and operating
expenses for
different water and
wastewater utilities over the five-year, 2016-
2020 period (GWI 2016).
The potential cost savings and other factors,
such as regulations related to water quality, will
likely stimulate the water industry to invest in
smart data infrastructure and increasingly adopt
the management of data-driven monitoring and
control systems in the operation of various
combined sewer, separate sewer, and municipal
separate storm sewer systems.
Physical Layer
:\ 3
Hardware
v y
r "\
Communication Layer
Automation & Control
V J
Management Layer
QQl
Softwares Analytics
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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Better Information
with improved
monitoring
r ^
Effective O&M Action
with increased
automation
k. A
Clean Insight
with sophisticated
analytics
Informed Decision
with data-driven
decision support
In the future, data feeds and cognitive
computing could significantly assist system
managers by providing near-instantaneous
support information for many of the routine and
immediate response decisions that must be
made in both the municipal and industrial
sectors. Transformation may help water and
wastewater utilities take advantage of
innovations and opportunities in future
operation and maintenance (O&M)
(see Figure 1).
Figure 1. Better information and data can lead to
more effective O&M
Roadmap for Implementing Smart Data Infrastructure
There are few, if any, insurmountable technological barriers to implementing the various technologies described
in this document. Real-time control technology (Section 5), for example, has been around for nearly 30 years.
While its implementation in collection systems remains relatively limited, the effectiveness of real-time control
technology has been proven in many successful applications in wastewater treatment plants (U.S. EPA 2006).
When selecting technology and level of complexity, it is important to understand the utility's priorities and needs
(e.g., O&M, information technology, security, data usage requirements). It is also important to remember that
smart data infrastructure is scalable. Utilities can start small, applying technology that is compatible with the
utility's existing capacity to ensure full acceptance and utilization of that technology, then move toward a more
comprehensive approach with higher degrees of performance.
Regardless of the size or age of their infrastructure, utilities can benefit from this general roadmap for
implementing smart data infrastructure:
1. Vision for a utility of the future: Imagine how data, assets, and technology could be leveraged to benefit the
utility.
2. Schedule: Understand the capacity and timeframe for staff to accept change.
3. Technology evaluation: Validate data, prove benefits, and understand delivery.
4. Detailed planning: Seek funding and develop an implementation plan.
5. Phased implementation: Deploy the technology and associated platform.
6. Continuous improvement and innovation: Evaluate phase 1 performance and adapt the planning if
necessary.
Key considerations for developing and implementing the roadmap include the following:
• Ensure organizational commitment for staffing and budget needs. There will be initial investment, as well as
annual costs associated with the adoption of a technology.
• Communicate to ensure buy-in and support from all levels of management and foster strategic partnerships.
• Establish clear authority, roles, responsibilities, and communication channels.
• Define performance expectations.
• Educate and integrate team members early in the project.
• Provide continuous training and technical support to build the existing workforce's capacity and attract a new
generation of workers.
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Smart Data Infrastructure for Wet Weather Control and Decision Support August 2018
3. Smart Data Infrastructure and Technologies:
Information Inputs
Smart data infrastructure can generate highly
informative data sets to support wastewater
and stormwater collection system decision-
making. These data sets help to answer critical
questions that allow operators to maximize the
effectiveness and efficiency of system operation
(Figure 2); however, the usefulness of the data
generated relies on accurate and relevant
information inputs.
The following sections describe specific
strategies and technologies for generating
useful wastewater and stormwater collection
system data, including key considerations for
selection, design, implementation, and O&M.
These strategies and technologies include:
• Continuous monitoring (Section 3.1)
• Level monitoring (Section 3.2)
• Flow monitoring (Section 3.3)
• Rainfall monitoring (Section 3.4)
Information
Insights
Decision
Action
Notification
Descriptive analytics: what's happening?
Diagnostic analytics: why did it happen?
Predictive analytics: what will happen?
Prescriptive analytics: It will or has happened.
What to do about it?
Preemptive analytics: what steps to take?
Regulatory and public notification
Figure 2. Operational process supported by information inputs
3.1 Continuous Monitoring
Continuous monitoring refers to permanent
monitoring systems that report data back to a
central system for use. The physical quantities
to be monitored in a wastewater and
stormwater collection system for proper
operation and control are relatively basic and
typically consist of flows, water levels, and
rainfall conditions for dry and wet weather
operations. In addition, equipment (such as
pumps, gates, and valves) status needs to be
monitored to ensure safe O&M.
Continuous monitoring when combined with
proper data analytics and effective visualization
can generate significant O&M savings by
providing real-time insight into system
conditions, which allows operators to prioritize
asset management with effective targeted
maintenance. Some examples include level
trend detections that trigger alarms for
equipment maintenance (e.g., cleaning),
proactive inflow and infiltration (l/l) risk
assessment, and data-driven work scheduling
and asset management.
Continuous Monitoring in Practice
Milwaukee Metropolitan Sewerage District
(MMSD) is using continuous monitoring to
monitor the performance, value, and health
of green infrastructure throughout the city.
MMSD is monitoring 11 separate sites,
including installations in public rights of way,
allowing managers to see the combined and
individual performance of green roofs and
bioretention cells in real time. Every storm is
recorded, performance can be reported in
aggregate or by event, and the data can be
used to fine-tune maintenance intervals and
maximize performance.
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Key considerations for continuous monitoring of
wastewater collection systems include the
following:
• The nature of wastewater systems presents
a harsh and largely variable environment for
monitoring equipment.
• The selection and installation of equipment
needs to consider physical and hydraulic
conditions, humidity, grit, sedimentation,
debris, and corrosion, as well as confined
spaces and maintenance access. For
example, permanent monitoring equipment
should meet explosive zone classifications.
• The advertised measurement accuracy of
any sensor may not represent actual
performance; as such, it will need to be
calibrated/verified.
• Maintenance requirements, as well as
hydraulic and physical conditions around
the monitoring equipment, should be
considered to balance out the increase in
cost and complexity to provide accurate
measurements. For example, forgoing some
level of accuracy by selecting equipment
with easier maintenance needs can ensure
more reliable readings.
3.2 I I Monitoring
Multiple technologies are used to monitor water
level in wastewater infrastructures. The most
common types of sensors are pressure
transducers, ultrasonic level meters, microwave
meters, and capacitive probes. Other discrete
devices for specific level detection, such as
floating devices and vibrating level sensors,
could be used in some cases. The most
important criteria for selecting a specific
technology will depend on the environment and
infrastructure configuration where level must be
monitored. More precisely, conditions such as
the presence of turbulences and sedimentation
in the water or the presence of fats, oils, and
grease (FOG); foam; and obstacles in the air
space above the monitoring location must be
considered to select appropriate technologies.
Pressure transducers need to be submerged in
the water where the level must be monitored;
they are therefore convenient for applications
where sedimentation is not a significant issue.
They are typically used where water can be
turbulent at the location of measurement.
Stilling wells are usually recommended to install
pressure probes away from potential debris in
the water flow and for easier maintenance.
Ultrasonic level meters are also very common in
wastewater applications and consist of installing
a probe mounted above the water surface. They
are usually preferred whenever space is
available above the location where monitoring is
needed. Multiple makes and models are
available on the market. Ultrasonic sensors are
recommended where minimal obstacles, FOG,
or foam is present above the surface of the
water. The sensor must be mounted far enough
from sidewalls to avoid bad readings due to
ultrasonic soundwave reflections.
When monitoring space is small or when FOG
can be found in the air above the water surface,
Doppler radar microwave meters are
recommended because they use a narrower
signal beam that improves the reliability of the
measurement.
Capacitive probes are particularly suitable for
multi-point water level monitoring and are
preferred when a high spatial resolution (of a
few millimeters) is necessary (e.g., for a reliable
evaluation of stored volumes in big and flat
storage facilities). The main advantages of these
probes are that the sensors are easy to clean
and can handle temperature and pressure
variations. However, these sensors can
significantly disturb the flow and should not be
used in small pipes.
In general, sensors located above the water
surface have less O&M, but are subject to
corrosion and may experience issues with ice in
cold environments.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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For locations where monitoring the water level
is critical, redundant sensors based on different
technologies are recommended. This strategy
would consist of using, for example, an
ultrasonic meter and a pressure sensor in a
storage facility to ensure water level monitoring
in all conditions and to maximize the availability
of measurements for safe infrastructure
operation.
3.3 Flow Monitoring
Operators can use several technologies and
methods of flow monitoring to better
understand the characteristics of their collection
systems.
3.3.1 Physical Flow Monitoring
Typical commercial flow meters available on the
market include ultrasonic Doppler devices,
acoustic Doppler sensors, transit time effect
sensors, and newer technologies such as
Doppler radar sensors and laser Doppler meters.
Flow meter technology has been developed to
fit a variety of applications; submerged and
"non-contacting" devices (sensors located above
the water surface) are available. Transit time
effect technologies consist exclusively of
installing one or multiple pairs of probes (a pair
includes one transmitter and one receiver) in a
crossing path within the water stream. These
probes can measure water velocity at different
layers in the conduit to compute flow values
according to water level and pipe section.
Submerged technologies are generally
recognized as being more accurate because they
can measure the different velocities that can co-
exist within a water flow section at the same
time, while non-contacting technologies can
only measure the velocity from the surface of
the water stream.
Practical experiences of wastewater flow
monitoring within sewer pipes ranging from 24
inches to 120 inches in diameter and above
have shown that submerged flow meter
technologies will generally provide
measurements with an accuracy from ±10
percent to 20 percent. Non-submerged flow
meter technologies will provide flow
measurements with an accuracy typically
ranging from ±15 percent to 30 percent. The
cost for procurement, installation, and
maintenance of "non-contacting" devices is
lower than submerged technologies. A
permanent flow meter installation in sewers
typically ranges from $15,000 to $75,000, and
can be even higher if significant work is needed
for the infrastructures and the electrical utilities.
Regular maintenance for cleaning, inspection,
and calibration is recommended at least twice a
year to keep monitoring reliable and accurate.
3.3.2 Alternative Flow Monitoring
Technologies
In some cases, where installing a physical flow
meter becomes too complex or expensive,
indirect means of flow monitoring can be
developed depending on specific hydraulic
conditions.
Implementing Monitoring Technology to Improve
Operations
The San Antonio Water System (SAWS) recently
participated in a study on the use of monitoring to
inform cleaning maintenance programs. SAWS
equipped 10 high frequency cleanout sites with
remote field monitoring units and used analytical
software to monitor day-over-day level trend
changes and receive messages for trend anomalies.
This analysis of the real-time monitoring data
detected small but potentially important changes in
water levels. These data enabled users to consider
actions such as a site inspection or cleaning. Based
on the monitoring data, SAWS reduced cleaning
frequency by 94 percent in the study areas. Other
than a short period in May/June 2016 when nearly
16 inches of rain overwhelmed the SAWS system,
there were zero sanitary sewer overflows at the pilot
locations.
Level to flow relationship: When pipe flows
remain under "free surface flow" conditions,
Manning equations can be used to estimate
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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flow (based on water level sensor data) and
physical attributes (pipe shape and dimensions,
slope, pipe material for the roughness factor) at
the level sensor location. However, the flow
estimation is invalid when the flow experiences
surcharged conditions or backwater effects are
present.
Equations of flow under the gate: When
modulating gates are used for flow control, gate
position and water level data upstream and
downstream from the gate can be used to
efficiently compute the flow regulated through
the gate. The mathematical formula would also
consider the gate's hydraulic conditions and
physical dimensions, the regulation chamber,
and connection pipes. Optimal gate position
(i.e., amount of submergence) can vary
depending on gate size and flow velocity and
must be determined through hydraulic analysis.
Based on several facilities' operations using this
method, the relative error is under 5 percent
during dry flow conditions and around 15
percent in wet weather conditions.
Weir relationship: A common mathematical
means of computing flow values uses level
monitoring data from a static weir upstream.
Specific formulas must be used depending on
the shape of the weir, the physical dimensions
of the weir (length, width), and the angle of the
flow stream according to the weir. This method
can provide fairly accurate flow values for weirs
under 6 feet in length; weir relationship
calculations involve significant uncertainties for
longer weirs.
Bending weir relationship: Bending weirs
consist of mechanical flap gate devices with pre-
determined weights that are designed to
maintain a specified water level on the
upstream side of the equipment. When inflows
cause the upstream level rise, the bending weir
reacts by opening to evacuate excess flow. An
inclinometer can be installed on the bending
weir's flap gate to monitor the angular opening
of the mechanical device. Flow can then be
estimated using the corresponding flow and
weir angle relationship charts provided by the
manufacturer.
Flap gate equations: Similar to bending weir
relationships, mathematical functions can be
developed for computing flows through flap
gates. These relationships will require installing
an inclinometer on the flap gate and a level
meter upstream of the gate. A downstream
level meter will also be required for situations
where the flap gate can become submerged.
Typically, a temporary flow meter calibrates and
validates the equation.
Model-based flow computations: Most utilities
have developed a calibrated hydrological and
hydraulic model (e.g., EPA SWMM 5) to
adequately represent their wastewater system.
These models are typically used to plan, design,
and produce engineering diagnostics. They can
be configured for real-time simulations, based
on real-time rainfall and level data or forecasted
radar rainfall, to provide flow values virtually
everywhere within the wastewater collection or
stormwater system. A well-calibrated hydraulic
model is recognized for providing flow values
within an accuracy range from -15 percent to
+25 percent (WEF 2011).
3.4 Rainfall Monitoring
Atypical rainfall monitoring system deploys a
network of spatially located rain gauges that
allow for representative measurement of
rainfall quantities over a region. As a general
rule for guidance, on average, one rain gauge is
recommended for every 500 hectares (1,235
acres) of coverage (Campisano et al. 2013),
although coverage needs vary depending on
local climate and need for predictive accuracy.
Common rain gauges use tipping bucket
systems—either optical or mechanical—that
count the quantity of rain trapped in a
calibrated cylinder. Each bucket tip will count a
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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specific quantity of rain (e.g., 0.005 inch) over a
specific time increment.
Such rainfall monitoring can be made available
in real time and can be used as inputs to a
hydraulic model to compute flow predictions in
the sewer collection system. The flow
predictions can then be used to determine the
time of concentration of the area tributary to
the monitoring location. In addition, when
combined with radar reflectivity data and
rainfall predictions, flow forecasts can be
provided with a more accurate level over the
entire territory. Generally, rainfall forecasting
windows and grid sizes should be proportional
to the hydrologic element's longest time of
concentration in the tributary collection system
where control is desired—e.g., a large combined
sewer overflow (CSO). Rainfall forecasts should
cover at least two hours ahead.
4. Collection System Optimization
A key benefit of smart data infrastructure is its
application in system optimization to maximize
existing infrastructure investment and reduce
the need for future capital investment. It
provides the framework required to optimize
the design and O&M of wastewater and
stormwater systems by collecting and analyzing
large data sets.
There are two types of system optimization.
One refers to system improvements that are
applied offline (Muleta and Boulos 2007). Some
typical examples include raising weirs to reduce
overflow discharge, developing best efficiency
curves to minimize energy costs and reduce
equipment breakdowns, or optimizing the
placement of localized stormwater management
and green infrastructure control. For example,
the EPA SUSTAIN modeling framework uses an
optimization approach to identify the least cost
and highest benefit solutions to achieve user-
defined objectives (U.S. EPA 2009).
The second type of system optimization is
applied online to actively manage the operation
of wastewater networks and facilities in real
time, a process often referred to as "real-time
control" (RTC). RTC systems are discussed in
greater detail in Section 5 of this document.
Table 1 presents the data used in a smart data
infrastructure approach, regardless of
optimization type.
Optimizing Collection System Capacity and
Performance
The Philadelphia Water Department (PWD) has
committed to reducing 7.9 billion gallons of
overflows in the city by 2036 through better
stormwater runoff management. As part of this
effort, PWD, in collaboration with a private
corporation, implemented smart data
technology to monitor and maximize the
performance of an existing stormwater
retention basin. The existing basin was
retrofitted with technology to monitor basin
water level and precipitation, as well as to
provide real-time active control to selectively
discharge from the basin during optimal times,
effectively increasing the useful capacity of the
asset.
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Smart Data Infrastructure for Wet Weather Control and Decision Support August 2018
Table 1. Data Required to Optimize the Design, Operation, and Maintenance of Wastewater and Stormwater
Systems
Objective
Cause of Problem
Potential Intervention
Data Required
for System Optimization
Eliminate
sanitary sewer
overflows
• Rainfall-derived l/l
• Undersized pipes
• Pipe replacement
• l/l mitigation measures
• Level and flow measurements
• Sewer and land characteristics
• Cost of potential interventions
• Grease, debris, and
sedimentation
buildup
• Improved operating
procedures
• Pipe replacement
• Cleaning (pipes streets)
• Flushing systems
• Level, velocity, and flow
measurements
• Camera inspection
• Cost of potential interventions
• Pipe breaks
• Leaking manholes
• Offset joints
• Repairs
• Pipe replacement
• Flow measurements
• Camera inspections
• Smoke testing
• Cost of potential interventions
Minimize
operating costs
• High electricity
consumption for
pumps and gate
operation
• Pump replacement
• Use of variable frequency
drives
• Improved set points
• Improved controller
parameters
• Time-of-use electricity tariffs
• Level and flow measurements
• Critical elevation for basement
and street flooding
• Gate, pumps, and actuator
characteristics
• Cost of potential interventions
Minimize
maintenance
costs
• High equipment and
sensor failure rate
• Repairs
• Replacement
• Re-localization
• Preventive and predictive
maintenance
• Best efficiency point
• Level and flow measurements
• Equipment and sensor history
• Equipment inventory and cost
• Detailed alarms
• Maintenance and calibration
history
• Cost of potential interventions
• Sedimentation issues
• Improved operating level
• Sewer modification to
increase velocities
• Flushing devices
• Level and velocity
measurements
• Camera inspections
• Cost of potential interventions
Minimize CSOs
• Rainfall-derived
inflow
• Undersized facilities
(conveyance, storage
treatment)
• Upgrade of existing
facilities
• Addition of green and
grey infrastructure
• RTC implementation
• Level and flow measurements
• Sewer and land characteristics
• Operational and physical
constraints
• Cost of potential interventions
Reduce flooding
risks
• Rainfall-derived
inflow
• Undersized facilities
(conveyance, storage)
• Upgrade of existing
facilities
• Addition of green and
grey infrastructure
• RTC implementation
• Level and flow measurements
• Sewer and land characteristics
• Operational and physical
constraints
• Critical elevation for basement
and street flooding
• Cost of potential interventions
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4.1 Capacity Management Operation
and Maintenance and l/l Control
Optimizing the performance of the collection
system is the key component in capacity
management operation and maintenance
(CMOM) programs. CMOM programs combine
standard O&M activities with an increased level
of data gathering and information management
to more effectively operate collection systems.
Smart data infrastructure, equipped with the
data input tools described in Section 3, can help
accomplish this. Successful CMOM programs are
used to identify and mediate capacity-related
issues in a system, reducing the risk of system
failures such as sanitary sewer overflows (SSOs).
CMOM includes l/l control, the process by which
unintended clearwater sources (e.g.,
groundwater and excess stormwater) exceed
the design capacity of a collection system,
typically due to antiquated, deteriorating, or
inadequately maintained infrastructure. Long-
term flow and level metering data can be
analyzed to determine performance trends over
a long period of time. Historical trends of l/l
peak flow rates and volumes can be used to
identify areas with high rates of l/l, prioritize
removal efforts, and evaluate the costs/benefits
of those efforts.
Real-time flow rate and level data collection can
be used to identify localized capacity limitations,
blockages, and sediment accumulation. These
data can then inform more proactive
management approaches that can reduce
overflows in both dry and wet weather
conditions. Such approaches help ensure that
the collection system capacity is maximized for
wastewater conveyance, which is a critical
component of all CMOM programs. In addition
to direct monitoring, flow rate and level
metering data can be used along with asset
management data to predict the "unmetered"
portions of a collection system and determine
other areas at risk of capacity-related issues,
such as high l/l.
Facilities can use smart data infrastructure
tools—such as real-time metering and
information analysis—to understand the
different variables that impact collection system
capacity and performance. This knowledge
would allow utilities to better plan for necessary
capital expenditures and optimize system
performance for current and future needs.
Using Smart Data Infrastructure and RTCto
Reduce CSOs
Louisville Metropolitan Sewer District (MSD)
was an early adopter of RTC, applying inline
storage since the 1990s and pioneering the
application of global optimal and predictive RTC
that has been in operation since 2006. The RTC
system is key to maximizing the MSD's
conveyance, storage, and treatment capacity to
reduce CSOs, with consistent operational results
capturing more than 1 billion gallons of CSO
volume annually. Incorporating RTC into MSD's
LTCP has resulted in approximately $200 million
in savings compared to traditional methods.
5. Real-Time Control Systems
RTC can be broadly defined as a system that
dynamically adjusts facility operations in
response to online measurements in the field to
maintain and meet operational objectives
during both dry and wet weather conditions
(U.S. EPA 2006).
Wastewater systems are often purposefully
oversized to provide a factor of safety. This
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Using RTC to Maximize Capacity and
Performance
In 2008, the city of South Bend, Indiana, installed
and commissioned a real-time monitoring system
of more than 120 sensor locations throughout the
city. In 2012, the city and its partners
commissioned and distributed a globally optimal
RTC system to maximize the capacity and
performance of the city's collection system. Since
2012, the city has added additional sensor
locations and rain gauges, bringing the total
number to 152 sites. It also added automated
gates at several stormwater retention basins to
better control when and at what rate stormwater
is released downstream into the combined
system. In the period from 2008 through 2014,
South Bend eliminated illicit dry weather
overflows and reduced its total CSO volume by
roughly 70 percent, or about 1 billion gallons per
year.
extra capacity can provide short-term storage in
the conveyance and treatment system when
rain falls unevenly across the collection system
and varying runoff lag times that introduce
stormwater into the system. RTC presents
opportunities to optimize full system capacity
for both existing and proposed facilities.
Potential benefits include receiving water
quality protection, energy savings (Tan et al.
1988), flow equalization, reduced flooding,
integrated operations, and better facility
planning (Gonwa et al. 1993). Real-time or near
real-time reporting can also help utilities meet
the public notification requirements for CSO and
SSO discharges.
A well-designed RTC system can address a
number of different operational goals at
different times. Examples of operational goals
include (U.S. EPA 2006):
• Reducing or eliminating sewer backups and
street flooding.
• Reducing or eliminating SSOs.
• Reducing or eliminating CSOs.
• Managing/reducing energy consumption.
• Avoiding excessive sediment deposition in
the sewers.
• Managing flows during a planned
(anticipated) system disturbance (e.g.,
major construction).
• Managing flows during an unplanned (not
anticipated) system disturbance, such as
major equipment failure or security-related
incidents.
• Managing the rate of flow arriving at the
wastewater treatment plant.
The application of RTC in a stormwater system is
similar to that of a wastewater system. It
requires continuous monitoring (e.g., water
level, rainfall, weather forecast), control devices
(e.g., valves, gates), and data communication to
actively manage flows and adapt to changing
conditions. If required, temperature, infiltration
rate, and water quality parameters (e.g., total
suspended solids, nitrogen) can be monitored in
real time and integrated into the RTC
management strategy. Associated benefits of
RTC application in stormwater management
include:
• Optimizing the design and sizing of control
measures.
• Reducing the frequency of flooding.
• Improving water quality with extended
residence time.
• Increasing stormwater harvesting and reuse.
• Adapting to evolving conditions through
operation change rather than new
infrastructure.
• Providing auditable performance and
supporting data from the monitoring system
components without additional costs.
• Reducing O&M costs by issuing alerts in real
time.
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5.1 Components of an RTC System
Figure 3 presents a typical layout of the boxes, and the arrows that connect them
components that might be included within an indicate the communications and data that are
RTC system. Some components are essential for passed on between the components.
RTC (e.g., sensors, meters), while others may be
optional depending on the desired level of
control. The components are represented with
leal Time Decision Support
RTC Optimization
Algorithm
Online model
Meteorological
Forecasting
Telemetry
Sensor
Flow Regulator
Pump Station
Remote Site 1
Remote Site 2
Figure 3. Components of an RTC system
An RTC system, at a minimum, includes sensors
that measure the process, control elements that
adjust the process, and data communication
between them (Schilling 1989). Typical control
elements for a wastewater system are
regulators, such as pumps (constant or variable
speed drives), gates (sluice, radial, sliding,
inflatable), and adjustable weirs (bending weir,
weir gates).
At each remote site, sensors are connected to
the inputs of the local RTC device—in most
cases, a programmable logic controller (PLC) or
remote terminal unit. The PLC provides outputs
(control set points and signals) to the control
elements (e.g., gates, pumps) based on the rules
embedded (programmed) into the PLC. These
rules are feedback algorithms, where action is
based on the difference between a set point and
the measured variable. For example, a PLC may
be programmed to maintain a certain level in
the wet well and will reduce the flow through
the pump if the level is too low or increase it if
the level is too high. The PLC programs can
include set points that are defined locally and
receive "remote" set points from a central
server.
5.1.1 Supervisory Control and Data
Acquisition Systems
Supervisory control and data acquisition
(SCADA) systems have become more prevalent
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in the wastewater industry for collecting and
managing monitoring data. SCADA is a control
system architecture that uses computers,
networked data communications, and graphic
user interfaces for high-level process
supervisory management. Large SCADA systems
have evolved to be increasingly similar in
function to distributed control systems, which
are widely used for process control at the
treatment plants. SCADA system designs have
taken full advantage of advances in information
technology (IT) to collect, archive, and process
large amounts of data.
A SCADA system's fundamental purpose is to
communicate data and control commands from
a centrally located operator to geographically
dispersed remote locations in real time. The
communication technology options include
telephone-based transmission (used in early
SCADA systems due to low cost), fiber-optic
cable, radio system, cellular-based
communication, wireless internet access, and
satellite-based systems.
Designing a SCADA system depends on a wide
range of practical considerations, including but
not limited to equipment enclosures,
environmental conditioning, field interface
wiring, system documentation requirements,
system testing requirements, IT requirements,
and cybersecurity.
As utilities invest in continuous monitoring and
SCADA, the generated data must be regarded as
an important investment to extract maximum
values. According to the U.S. Geological Survey,
"poor data quality, redundant data, and lost
data can cost organizations 15 percent to 25
percent of their operating budget" (USGS n.d.).
Information captured in the field needs to be
communicated from the remote stations to the
computers and systems that will process, store,
and archive it. The SCADA system is considered
the backbone of an RTC system. It includes
13
standard graphical user interface (GUI) tools
that operators can access, and it allows them to
manually override any remote site control
actions at any time. As the needs for real-time
or near real-time public notifications rise,
centralized data management can facilitate data
sharing and enable greater transparency.
RTC and CSO Control
The Metropolitan Sewer District of Greater
Cincinnati (MSDGC) has one of the most challenging
collection systems in the country to manage during
wet weather, as it contains more than 200 CSO
points. Together, these overflows discharge over 11
billion gallons of sewage into the Ohio River and its
tributaries annually. In 2014, MSDGC began
installing sensors throughout its largest watershed.
By early 2016, MSDGC had gained both real-time
visibility and control of its wastewater system in this
watershed and transformed the wastewater
collection system into a "smart sewers" network. To
date, MSDGC's smart sewer system covers over 150
square miles (approximately half) of its service area,
incorporating two major treatment plants, six wet
weather storage and treatment facilities, four major
interceptor sewers, 164 overflow points, and 32 rain
gauges and river level sites. Remote monitoring has
improved the maintenance of wet weather facilities
and enabled upstream facilities to account for
downstream interceptor conditions, increasing
overflow capture basin-wide during wet weather.
5.2 Real-Time Decision Support
Systems
A real-time decision support system (RTDSS)
generally overlays the SCADA system. It is
connected to the SCADA database to retrieve
system status information. An RTDSS can use a
SCADA historian and GUI to program and display
system status and trends (e.g., abnormal flow,
critical water level alarm) or provide additional
dashboards involving data analytics to support
O&M decision-making. In an RTC system, an
RTDSS performs complex calculations based on
information inputs to inform operational
decisions and help determine optimal system
set points (e.g., flow to be pumped, water level
to be maintained in a wet well or pipe length).
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Typically, decision support uses advanced
computing algorithms that are interactive and
multi-objective and often involve using an
online model for weather forecasting.
5,3 I I of Control
The RTC system can be automated with a
centralized or distributed control technology.
The main difference is the control and the
input/output subsystems. In distributed control
architectures, the number and quality of central
processing units (CPUs) is determined by the
number of modules. Each module has a
controller, and the system usually features a
central master PLC. The module PLCs automate
their respective areas and usually do not include
visualization features.
A central architecture usually features a
computer, which deals with all tasks such as
input/output connections, PLC, and control.
Computing capacity, therefore, must be
significantly higher than that of a distributed
control technology system. There is only one
CPU, which means that only one such spare part
is needed. RTC system design criteria drive the
selection of a control system platform based on
the physical and logical components of the
system.
Regardless of the control platform, RTC can be
implemented using different levels of control,
including local, regional, and global. The levels
of control are classified according to progressive
increases in complexity, performance, and
benefits (Schiitze et al. 2004).
These set points can be displayed to the
operator for manual control or be sent back to
the SCADA system in real time for automated
control of remote sites. The algorithms used to
determine control logics and set points vary in
complexity from simple operating rules to
complex mathematical optimization techniques
(Garcia-Gutierrez et al. 2014).
Local control, or a local reactive control system,
is the simplest form of automatic control. Local
control is used to solve specific issues that only
require information collected near a regulator
and is usually implemented as single-input,
single-output feedback loop designed to
maintain prescribed set points (e.g., flow or
level set points). It is a good solution only if the
control objectives pursued can be reached
without transferring any information between
other remote sites.
Regional control is similar to local control
except that a telemetry system is required to
exchange data with other remote sites. Regional
control can be implemented as a distributed or
centralized system built on a SCADA system.
Some municipalities design their own decision
support system to control the collection system
based on the specific constraints and
opportunities of each control site. However, the
control remains reactive, not predictive. Based
on a reactive process, there are limitations in
the distances between the control structures
and measurements; as such, the operation must
remain conservative and suboptimal.
Global control is necessary when the control
objectives require strong coordination of the
control actions at numerous remote sites on a
system-wide level. The set points are usually
computed and refreshed periodically (e.g., every
five to 15 minutes). The global strategy used to
determine the set points includes rule-based
and optimization-based techniques (Figure 4).
Rule-based control considers possible scenarios
that can occur during wastewater system
operation and determines appropriate control
actions based on experience. The rules are
generally easy for operators to implement and
understand. However, the quality and the
performance of those rules highly depend on
the available expert knowledge. For large and
complex wastewater systems, the strategy may
demand many rules.
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CONTROLLING
WASTEWATER SYSTEMS
LOCAL /REGIONAL
CONTROL
PUMPING
RULES
LOCAL
FEEDBACK
GLOBALCONTROL
1
HEURISTIC
ALGORITHMS
OPTIMIZATION-BASED
ALGORITHMS
PID Control
Rule-Based
Control
Model Predictive
Control
Population
Dynamics
Evolutionary
Strategies
Agent-Based
Optimization
Figure 4. Control strategies for wastewater utilities
Optimization-based strategies involve an
optimization problem that represents the
desired behavior of the wastewater system.
Various algorithms can be used to solve the
optimization problem (e.g., model predictive
control, agent-based optimization). More
detailed descriptions of optimization strategies
and mathematical models can be found in
Papageorgiou (1988) and Garcia-Gutierrez et al.
(2014).
In the last 20 years, model predictive control has
been the most extensively used optimization-
based strategy. This approach uses a
mathematical model of the wastewater system
to generate a sequence of future actions—
within a finite prediction horizon—that
minimizes a cost function (Gelormino and Ricker
1994). Interest in model predictive control is
justified by its ability to explicitly express
constraints in the system, anticipate future
system behavior, and consider non-ideal
elements such as delays and disturbances.
Optimizing the collection system requires
continuous and strategic adjustment of control
devices, as well as predictions of upcoming
inflows and their spatial distribution (Cartensen
et al. 1998). With proper conditions being
monitored, acknowledged, and controlled, a
global RTC system considers the distribution of
flow in the entire system, both in current
conditions and in the future. By using a global
RTC, a utility has the ability to control flow by
opening and closing gates or pumps allows for
transfer flow and storage capacity between
sites, thus providing the temporary storage and
controlled release of significant volumes of
wastewater.
Table 2 summarizes which components of the
overall system must work properly to support
different control modes/levels (U.S. EPA 2006).
Notably, forecasting may be part of a rule-based
system, but it is not mandatory. A global RTC
system often involves a mixture of lower levels
of RTC and static controls.
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Smart Data Infrastructure for Wet Weather Control and Decision Support August 2018
Table 2. Components Required for Different Control Modes
Control Mode
Instruments
PLCs
SCADA/Communications
Central SCADA server
Active Operator
Input, Monitoring
Central RTC Server
Rainfall Forecasting
Online Model
Local manual control
X
X
Local automatic control
X
X
Regional automatic control
X
X
X
X
Supervisory remote control
X
X
X
X
Global automatic control—rule-based
X
X
X
X
X
Global automatic control—optimization
X
X
X
X
X
X
X
5.4 Guidelines for Applying RTC
In most cases, RTC implementation can offer
benefits and improve the performance of urban
wastewater or stormwater systems. The costs
and extent of these benefits may differ from
one system to the next.
The first step in evaluating if RTC is a suitable
and viable solution for a utility is to develop
criteria for a macroscopic evaluation of RTC
potential using a scoring system (Erbe et al.
2007, Schiitze et al. 2004). Criteria may include
environmental and financial objectives, the
topology of the catchment area, collection
system characteristics and conditions,
operational system behaviors, etc.
The utility may, however, skip the first step if it
has already invested in a hydrological and
hydraulic model that adequately represents its
system and operation and/or has substantial
monitoring coverage (which provides good
system understanding and condition
assessment). The utility can use these existing
tools and data in the second step, which
involves a preliminary analysis of RTC potential
and costs/benefits. The analysis should include a
simulation study of a full range of RTC control
levels to determine which is the most
appropriate; staff interviews with operators,
engineers, and other stakeholders; and
equipment surveys.
If the various scenarios demonstrate the
feasibility and benefits of RTC, the third step
involves detailed planning of the RTC system
and its implementation, including:
• Detailed planning of control infrastructures.
• Detailed design of control algorithms.
• Risk and failure analysis.
• Detailed design of data infrastructure (or
gap analysis if data infrastructure exists).
• Staff training and other organizational
planning (i.e., new roles and
responsibilities).
• Preparations for obtaining consent by the
regulatory authorities.
It is critical to involve operator input from the
beginning of the design process. The operators
are ultimately responsible for the system
operation and performance. Early involvement
will ensure that operators' O&M concerns are
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addressed in the system design and that
operators buy in and accept the RTC system.
5.5 Key Considerations for RTC
Systems
An RTC system should have robust operation,
adequate communication, supervisory manual
override, operational confidence, and
adaptability (Gonwa et al. 1993, Colas et al.
2004). The system must be designed and
configured to ensure a high level of
performance under normal conditions and safe
operation under downgraded conditions. Its
performance should be better than or equal to
the system that existed before RTC
implementation.
Under all conditions, there are critical
constraints, such as operating safely, avoiding
equipment damage, and avoiding flooding. A
well-designed RTC system must effectively
manage different operational objectives and
transition between different operational modes
to operate reliably and efficiently; at a
minimum, it must address externally caused
equipment failures and emergency conditions.
The fail-safe procedures must be configured so
that they are triggered when the requirements
for the system's current operational mode
cannot be met. These procedures should
automatically place the system into the next
(lower) mode/level of operation that can be
fully supported. For example, if the system is
operating in local automatic control mode and
the PLCs malfunction or lose power, it would
need to revert to local manual control.
RTC system risk management procedures must
include the ability to deal with emergency
conditions detected using field measurements.
Special rules can be defined to react to
conditions such as rapidly rising levels within the
system. The emergency response can be either
to adjust the automatic control strategy or
change operational mode by giving the operator
a standard operating procedure.
Using Smart Data Infrastructure to Promote
Resiliency
In response to the historic drought conditions
recently experienced in California, the city of San
Diego has decided to quantify the potential nexus
between stormwater capture and its ongoing effort
to reclaim wastewater as a drinking water resource
(San Diego currently imports more than 80 percent of
its water supply). The city equipped its stormwater
control measures with RTCs and assessed them to
optimize the management of stormwater storage and
release to the reclaimed water system. The
simulations suggested that stormwater harvesting
could substantially augment local water supplies
while complying with stormwater quality regulations.
The reliability of all RTC system components is
key to successful implementation. In addition to
fail-safe and risk management procedures,
system effectiveness can be obtained through
the following:
• Proper selection, location, and number of
sensors to ensure accurate and adequate
measurements.
• Installation of redundant equipment at key
locations using different technologies.
• Real-time validation of monitoring data to
minimize the amount of low quality data
entering the decision-making process.
• Design of safety features, including
emergency isolation gates, power supplies,
generators, and equipment interlocks
specifically designed for safe operation
when a critical alarm is activated.
• Preventive and targeted maintenance to
ensure equipment availability.
• Stock of replacement pieces for critical
infrastructure.
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6. Data Management
Good data management and sharing can allow
operators and control systems to integrate data
faster and more effectively. Organized and
carefully designed data management systems
readily obtain and act on data from various
sources, reducing redundancy and the cost of
collection system operation.
6.1 Big Data Management
More monitoring requires more data
management and storage. To address the
challenges of storing, processing, recovering,
sharing, and updating large data sets,
organizations are finding smarter data
management approaches that enable them to
effectively corral and optimize their data use.
Some of the best practices for big data
management are to reduce the data amount
(because the vast majority of big data is either
duplicated or synthesized), to virtualize the
reuse and storage of the data, and to centralize
management of the data set to transform big
data into small data (Ashutosh and Savitz 2012).
A smarter data management approach not only
allows big data to be backed up far more
effectively, but also makes it more easily
recoverable and accessible at significantly lower
cost. Other benefits include the following:
• Applications require less to process data.
• Data can be better secured because
management is centralized, even though
access is distributed.
• Data analysis results are more accurate
because all copies of data are visible.
6.2 Data Sharing
In addition to the needs of public notification
and regulatory reporting (e.g., post-construction
performance monitoring, permit compliance),
there is a rising need for data sharing among
I Sharing
various departments within an organization to
improve efficiency and interoperability.
Organizations must also be able to securely
exchange data with outside administrative
domains for transparency and for integrated
solutions on city-wide or region-wide scales.
As more data have moved to cloud-based
storage, the protection and encryption of off-
site data has become more important. While
there are still cybersecurity risks, significant
improvements have made it much more difficult
for outside parties to access critical data and
information.
Cybersecurity
The interconnectivity of hardware and data
management has increased the need for utilities to
plan and manage cybersecurity. Although
networking multiple systems provides operational
value, it can also expose systems to new data
security risks. As utilities move to advanced data
storage solutions, addressing cybersecurity will be
an essential aspect of master planning activities.
Cybersecurity provides insurance to protect utility
assets against attacks, outages, and threats, and it
reduces the costs of downtime.
Key considerations for data infrastructure and
data sharing include the following:
• As organizations become more dependent
on cloud-based systems and other internet-
based solutions, the importance of a robust,
maintainable, and secure network
infrastructure becomes critical. Nothing
works when the network goes down.
Secure, redundant, and scalable internet
connections are now required for day-to-
day business as essential processing is
moved off site.
• Network architecture is increasingly
important, and robust, secure solutions
must be designed into systems to manage
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devices potentially numbering in the
thousands, each with multiple data points.
Simply using a "firewall" to secure a
network is no longer feasible.
• Formerly isolated SCADA/industrial control
systems (ICS) are now required to
communicate over the internet. To securely
realize the vast benefits of cloud computing
and the loT, secure data interconnectivity is
essential. Standards have been produced to
ensure a high degree of interoperability and
security for evolving SCADA/ICS solutions.
Emerging Technologies for Big Data
Management
For big data management, all types of data
analytics will be more widespread and
incorporate more artificial intelligence. Already,
machine learning has been applied in predictive
analytics for l/l characterization, based on
analysis of long-term data trends.
Real-Time Public Notification with SmartCover™
Systems
The city of Newburgh, New York, replaced its
combined sewer telemetry system with a wireless
SmartCover™ System. The prior telemetry system
used pressure sensors that had to be located
beneath the influent channel, in direct contact with
the flow and in the combined sewer regulator
environment where they would be regularly
impacted and damaged or displaced by debris. The
new SmartCover™ System's sensors hang from the
manhole cover above and do not contact the water,
avoiding damage. The new system's wireless satellite
connectivity is more reliable than land phone lines at
a lower cost. Any computer, tablet, or smartphone
with internet access can communicate with the
telemetry system, allowing for real-time staff and
public notification of CSO events.
6.3 Real-Time Public Notification and
Transparency
Implementation of a smart data infrastructure
allows utilities to disseminate relevant and
current information to ratepayers and
stakeholders. Public notification is becoming the
norm for informing interested parties of current
utility conditions. While some data must be kept
private due to security issues related to
7. Data Analytics
Most utilities already generate a substantial
amount of process and monitoring data for
various purposes. As the amount of data
generated each year increases at an exponential
rate, it is increasingly critical to convert those
data into useful information (Greiner 2011).
Technical advancements in complex
multidimensional data analysis and data mining
can help utilities analyze incredible amounts of
protecting treatment processes, some data can
be shared to better inform the end user. A
common example includes the public
notification for current/recent overflow activity
to local receiving waters. The real-time
notification of overflow activity informs the
public that recreational uses may be temporarily
compromised, potentially reducing public health
issues. Public notification can also include
automated notification to the regulating
agencies as part of permit requirements.
data to detect common patterns or learn new
things. This can lead to significant operational
improvements and dollar savings for
wastewater systems.
Big data analytics, a well-established concept,
involves analyzing the data collected to discover
trends and correlations, uncover hidden
patterns and other insights to understand why
certain behavior or incidents happened, and
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then use that insight to predict what wili
happen. Today's technology and advancements
in big data analytics bring speed and efficiency,
which enable utilities to analyze large quantities
of data and identify insights for immediate
decisions (Figure 5).
Better
customer services
& maximize use
of data
Cost
reduction
Faster,
better
decision
making
Figure 5. Big data analytics support enhanced
decision-making and more effective and less
costly operations
Utilities that have already invested heavily in
continuous monitoring could use data analytics
to get significant value from the data they
collect.
There are many data analysis and data mining
solutions, which also incorporate data
warehousing, database management systems,
and online analytical processing.
7.1 Data Validation and Filtering
Data validation is an important consideration for
wastewater utilities, particularly for monitoring
data within the harsh environment of a
wastewater collection system. Raw monitoring
data can contain erroneous readings, which
could be due to one or a combination of the
following:
• Noise (high frequency fluctuations)
• Missing values
• Values out of range
• Outliers (sudden peaks)
• Constant (or frozen) values
• Drifting values (changes in values over a
longer period of time)
As the quality of the insights gained from data
analytics or the control system's performance
will be directly linked to the quality of the data
used, raw data collected from the sensors needs
to be validated and possibly filtered before
being used for further analysis or control
purposes. This is an important step to improving
the data's reliability.
Emerging Technologies for Data Analytics
The loT industry trend is to provide more
accessibility through cloud computing platforms
and open source technologies. The digital platform
will streamline the integration of data from
various legacy systems and eliminate data
duplication and bad data for more effective and
powerful data analytics and insight. Cloud-based
computing has already been implemented for
SCADA system applications and RTC applications.
Data validation can be carried on a single
variable (single data validation methods) or by
comparing two variables when two or more
measures are correlated (cross-validation) (U.S.
EPA 2006, Sun et ai. 2011).
Single data validation methods include the
following:
• Range validation: The values that are
outside an expected range are flagged as
invalid. The expected range is based on the
working range of the sensor itself and on
the process monitored. For example, a
water level in a collection system cannot be
lower than the bottom of the chamber
where the sensor is located and can seldom
exceed ground level.
• Gap filling: When data are missing (due to
communication failure, sensor automatic
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Smart Data Infrastructure for Wet Weather Control and Decision Support
August 2018
calibration, etc.), it is possible to use an
estimate instead. In a real-time context, the
last valid value can be used. If correlation
exists with other measurements, cross-
validation techniques can also be used to
produce better estimates (see below). In a
post-event analysis, a simple linear
interpolation between the values before
and after the gap can often be used.
• Rate of change validation: If values change
at a greater rate than a probable change in
measured conditions and sensor noise, then
the value is marked as invalid.
• Running variance validation: A value is
flagged as invalid if the variation over a past
value is too small. A frozen value is often
due to a sensor failure.
• Long-term drift: Expected mean check and
acceptable trend check are two methods to
detect long-term drift. Once detected, the
source of the bias or drift then needs to be
identified as it could be caused by sensor
drift, as well as a long-term trend of the
measured value.
Cross-validation methods are used when it is
possible to develop a model or relation between
two or more values. The simplest case is where
some sensors are redundant and measure the
same value or if software can be used to
produce another sensor's estimate. A range or
rate of change validation can then be carried on
the difference between the two values. In more
complex cases, the redundancy can come from
combining sensor data with a model to produce
many estimates of a specific variable (soft
sensors or virtual sensors). The data
reconciliation technique can then be used to
better estimate the variable.
Filtering can be used to reduce the
measurement noise inherent to sensor data.
The result is smoother and easier to analyze and
usually produces better results with control
processes.
All RTC system data should be validated in real
time. Data validation can be implemented at the
local PLC and at the central control station.
Whenever possible, data validation processes
should take advantage of the correlation
between the measurements (i.e., cross-
validation methods). At minimum, the data
validation algorithms should use sensor alarms
and be able to detect missing data, out-of-range
values, outliers, and frozen measurements.
7,2 Key Performan cators
Developing key performance indicators (KPIs)
based on computations of validated data can
provide a quick and general understanding of
the system's performance. Some of the
meaningful KPIs applied for wastewater and
stormwater systems include the following:
• Precipitation frequency: The average
recurrence of rainfall can be assessed using
rain gauge readings (NOAA n.d.). Maximum
rainfall depth over various durations is
calculated and compared to precipitation
frequency estimates for the area and
precipitation data used for hydraulic model
development and calibration.
• Treated flow: Maximum flow conveyed to
the wastewater treatment plant (WWTP) is
compared to the WWTP's treatment
capacity. If CSOs or significant retention
occur while the treatment capacity is not
met, it can signal a suboptimal system or
control.
• Untreated flow: Estimated or measured
overflows from the collection system prior
to treatment is compared to total flow
treated at the WWTP. This is typically
measured as number of overflows and/or
the volume of overflows. These values can
be compared to those projected or allowed
under an approved Long-term Control Plan
or NPDES permit to assess system
performance and compliance.
• Partially treated flow: Estimated or
measured volume of wastewater receiving
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Smart Data Infrastructure for Wet Weather Control and Decision Support
August 2018
only partial treatments prior to discharge.
These values can be used to assess system
performance and compliance.
• Retention volume: Maximum stored
volume can be presented relative to full
capacity. If CSOs occur while the full
retention capacity is not met, it can signal a
suboptimal system or control.
• Retention duration: Exceedingly long
durations can lead to odor problems in
wastewater storage systems.
• CSO/SSO volume and duration: Overflow
discharges can be reported to the public in a
timely manner.
¦' I ' .1 III 'h . .fK'l I a
Systems
Data visualization is the presentation of large
amounts of complex data using charts or
graphs—a quick, easy way to universally convey
concepts. It enables data users and decision-
makers to visually explore analytics, so they can
grasp difficult concepts or identify new patterns.
Interactive visualization allows the user to take
the concept a step further by using technology
to drill down into charts and graphs for more
detail, to interactively change the data displayed
and how it is processed (SAS n.d.).
Data visualization is a key component of the
user interface for any decision support system
(DSS). A DSS (also known as decision-making
software or DMS) is a computer-based
information system that supports business or
organizational decision-making activities. DSS
has three main functions: information
management, data quantification, and model
manipulation.
• Information management refers to the
storage, retrieval, and reporting of
information in a structured format
convenient to the user.
• Data quantification is the process by which
large amounts of information are
condensed and analytically manipulated
into a few core indicators that extract the
essence of data.
Decision Support
• Model manipulation refers to the
construction and resolution of various
scenarios to answer, "what if" questions. It
includes the processes of model
formulation, alternatives generation and
solution of the proposed models, often
through the use of several operations
research/management science approaches
(Inc. n.d.). Its main objective is to convert
data into usable and actionable knowledge.
There are two main types of DSS tools, one for
planning purposes and another for real-time
decision support (Hydrology Project n.d.). For
wastewater and stormwater applications, DSS is
typically structured to allow users to access and
analyze monitoring data, run model simulations,
and assess the impact of potential decisions by
using "what if" scenarios. While the data can be
displayed and analyzed in real time to identify
areas that need attention or improvement, the
appropriate actions can betaken at a later time.
For example, DSS can display real-time level
data correlating to expected flow behavior.
Abnormally high-level data would indicate a
potential debris blockage, and the
corresponding response decision would be to
schedule a maintenance crew to perform a field
investigation. However, this action could be
optimized with other work orders to improve
maintenance efficiency.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
August 2018
An RTDSS allows decision-makers to respond to
short-term variations in wastewater and
stormwater systems where lead times for
decisions vary from a few hours to a few days at
most. Typical RTDSS examples include:
• Hydraulic flow diversions
• Storage basins to manage levels or volumes
• CSO or SSO discharge warnings
• Flood forecasting and warnings
See Section 5.2 for additional details on the
RTDSS.
Before buying the various computer systems
and software needed to create a DSS, utilities
should consider (Inc. n.d., WERF 2005):
• Establishing business needs and value for
DSS, such as providing guidance for complex
operation.
• Evaluating the development of DSS
applications using available software, such
as spreadsheets, SCADA, or asset
management software.
• Integrating information spanning more than
just one functional domain into the DSS, as
well as support decisions from multiple
domains.
• Creating user-friendly DSS for easy viewing
and access, as well as allowing users to
create scenarios and to simulate and
analyze the impacts of different scenarios.
• Ensuring the investment in terms of time
and effort to incorporate DSS into daily
operations.
• Providing necessary training and knowledge
to use DSS effectively.
• Understanding how the DSS is used, such as
the limitations or assumptions of the
mathematical calculations or processing
model used within the DSS.
• Examining other factors, such as future
interest rates and new legislation, in the
decision-making process.
9. The Future of Data Gathering Technology for
Wet Weather Control and Decision-Making
Rapid advancements in data gathering
technologies have already led to substantial
improvements for real-time operational support
and decision-making systems. Future
advancements will continue to be made in the
following areas:
• Monitoring the frequency, volume, and
duration of overflows and discharges within
combined and separate sanitary sewer
systems.
• Water quality of flows within sewer
systems, discharges, and receiving streams;
specifically, real-time measurements of
bacteria, nutrients, suspended solids, and
possibly emerging pollutants.
• Operational data to inform asset
management systems and long-term
planning.
As these advancements continue, dischargers
and regulators will need to adapt to new ways
of thinking and embrace the increased role that
smart data infrastructure will play in wet
weather control and decision support.
Dischargers will need to overcome barriers in
educating personnel to operate and interact
with new technology and systems, as well as
adapt to a new culture of enhanced data
operations.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
August 2018
New technologies will only be able to maximize
end-user benefits if they can be implemented
within the framework of regulations.
The advancement and proliferation of new
technologies for gathering and analyzing wet
weather infrastructure data will lead to the
generation of more accurate information and
provide for lower-cost operations. With more
accurate data, operators will be able to make
more informed decisions, increasing efficiency
and reducing risks.
Technology advancements will continue to
improve our ability to quantify wet weather
events and monitor water quality in ways we
have never been able to before. In the future,
better technology will exist for generating data
related to the frequency, volume, and duration
of wet weather events. Operators will have
increasingly better information to determine the
occurrence of wet weather discharges and to
calculate the impact of wet weather events on
collection system capacity. Better understanding
these system characteristics will lead to
improved infrastructure design and
management, and ultimately the prevention of
failures and overflows.
Pollutant sensor technology will also continue to
improve, and operators will be able to monitor
pollutant impacts on water quality more often
and in real time. Operators will also be able to
more closely monitor pollutants (such as
bacteria) of particular concern to public and
environmental health.
Continued improvements in data gathering will
increase the effectiveness and reliability of data-
informed operations, and ultimately change the
pace at which operational decisions can be
made, moving increasingly toward real time.
Increasing the amount and frequency of reliable
data will also enhance asset management
programs and promote more informed capital
planning. Wet weather system O&M was at one
time conducted on a solely reactive basis. As
technology and operational strategies have
advanced, and more precise and accurate data
are more readily available, operators have now
shifted their approaches toward preventive and,
in some cases, predictive O&M practices.
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Smart Data Infrastructure for Wet Weather Control and Decision Support
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10. References
Ashutosh, A., and E. Savitz. 2012. Best practices for managing big data. Forbes Magazine. Accessed Feb.
14, 2017.
Campisano, A., J. Cabot, D. Muschalla, M. Pleau, and P.-A. Vanrolleghem. 2013. Potential and limitations of
modern equipment for real time control of urban wastewater systems. Urban Water Journal,
doi: 10.1080/1573062X.2013.763996.
Cartensen, J., M.K. Nielsen, and H. Strandbaek. 1998. Prediction of hydraulic load for urban storm control
of a municipal WWT plant. Water, Science and Technology 37(12): 363-370.
Colas, H., M. Pleau, J. Lamarre, G. Pelletier, and P. Lavallee. 2004. Practical perspective on real time
control. Water Quality Research Journal of Canada 39(4): 466-478.
Erbe, V., M. Schutze, and U. Haas. 2007. Application of a guideline document for sewer system real time
control. Novatech Conference, Lyon, France: 761-768.
Garcia-Gutierrez, L., E. Escobar, J. Barrero-Gomez, N. Quijano, C. Ocampo-Martinez, and D. Tellez. 2014.
On the modelling and real time control of urban drainage systems: A survey. 11th International
Conference on Hydroinformatics, HIC 2014, New-York City, USA.
Gelormino, M.S., and N.L. Ricker. 1994. Model-predictive control of a combined sewer system/
International Journal of Control 59(3): 793-816.
Gonwa, W., A.G. Capodaglio, and V. Novotny. 1993. New tools for implementing real time control in sewer
systems. Proceedings of 6th International Conference on Urban Storm Drainage, Niagara Falls,
Ontario Canada: 1374-1380. Reference No. 16226.
Greiner, L. 2011. What is data analysis and data mining? Accessed February 15, 2017.
GWI. 2016. Need to know. Global Water Intelligence Magazine 17(12): 4-5.
Hydrology Project, n.d. Decision Support Systems—Decision Support System-Planning (DSS-P). Accessed
February 15, 2017.
Inc. n.d. Decision support systems. Accessed February 14, 2017.
Muleta, M., and P. Boulos. 2007. Multiobjective optimization for optimal design of urban drainage
systems. World Environmental and Water Resources Congress: 1-10.
NOAA. n.d. Precipitation frequency data server (PFDS). National Oceanic and Atmospheric Administration.
Accessed February 15, 2017.
Papageorgiou, M. 1988. Certainty equivalent open-loop feedback control applied to multireservoir
networks. IEEE Transcripts on Automatic Control 33(4): 392-399.
SAS. n.d. Data visualization: What it is and why it matters. Accessed February 10, 2017.
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Schilling, W. 1989. Real time control of urban drainage system. The State-of the-Art IAWPRC Task Group
on Real Time Control of Urban Drainage System, Pergamon Press, London.
Schutze, M., A. Campisano, H. Colas, W. Schilling, and P. Vanrolleghem. 2004. Real time control of urban
wastewater systems—Where do we stand today? Journal of hydrology 299: 335-348.
Sun, S., J.-L. Bertrand-krajewski, A. Lynggaard-Jensen, J. van den Broeke, F. Edthofer, M. do Ceu Almeida,
A. Silva Ribeiro, and J. Menaia. 2011. Literature review of data validation methods. EU PREPARED
2011.019.
Tan, P.C., K.P. Dabke, and R.G. Mein. 1988. Modelling and control of sewer flow for reduced cost
operation of a sewage pumping station. IEE Transcripts on Systems, Man, and Cybernetics 18(5):
807-813.
U.S. EPA. 2006. Real time control of urban drainage networks. U.S. Environmental Protection Agency. EPA-
600-R-06-120.
U.S. EPA. 2009. SUSTAIN—A framework for placement of best management practices in urban watersheds
to protect water quality. U.S. Environmental Protection Agency. EPA-600-R-09-095.
USGS. n.d. Value of data management. U.S. Geological Survey. Accessed February 15, 2017.
WEF. 2011. Prevention and control of sewer system overflows. WEF Manual of Practice No. FD-17, Third
Edition. Water Environment Federation, Prevention and Control of Sewer System Overflows Task
Force.
WERF. 2005. Decision support systems for wastewater facilities management. Water Environment
Research Foundation. WERF Report 00-CTS-7.
26
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Appendix A
Case Studies
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Buffalo, New York:
Real Time Control of Inline Storage
KEY FEATURES
OWNER
Buffalo Sewer Authority
LOCATION
Buffalo, New York
INCEPTION
Commissioned winter 2016; study
period March-May 2016
REFERENCES/LINKS
BSA Awarded EPA Environmental
Quality Award
www.emnet.net/clients/buffalo-new-
vork
www.ghd.com/usa
www.arcadis.com
• Reduced combined sewer overflow (CSO) by 13.3 million gallons at two initial RTC sites between
March 1 and May 31, 2016.
• Sixteen real-time control (RTC) sites to be established by 2020.
• Expected to reduce CSO by 15 to 20 percent at fuil capacity.
• $145 million negotiated out of long-term control plan and consent agreement based on modeled
outcome of inline storage.
PROJECT DESCRIPTION
Once the 8th largest city in the United States, Buffalo has lost half of its population and most of its
industrial base since the 1960s. Before its decline, the city built a massive sewer system to accommodate
as many as 750,000 people, but today Buffalo Sewer Authority (BSA) serves just 250,000. This means the
collection system has substantial inline storage capacity.
Working with its team of engineers and consultants, BSA identified 16 RTC sites for inline storage and
optimal conveyance throughout the city. The sites were chosen for maximum return on investment; from
among them, four representative sites were chosen for initial construction. Two of these four sites are
now live, while the other two are in design. BSA plans to build and commission all 16 sites by 2020.
The first two inline storage sites, the Bird Avenue RTC and the Lang Avenue RTC, were commissioned in
early 2016. Both sites are operated by program logic controllers (PLCs) within BSA's supervisory control
and data acquisition (SCADA) system. These PLCs are driven by remote level sensors upstream and
Bud RTC Chambei
A-l
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
Buffalo, New York
downstream of each site, and are presented digitally in the SCADA interface. From March 1 to May 31,
2016, the two sites were studied and tuned to achieve optimal performance. During this period, Lang
reduced 4 out of 9 (44 percent) of potential activations, resulting in reduced CSO volume of 1.2 million
gallons (64 percent). Bird reduced 14 out of 19 (74 percent) of its potential activations, yielding reduced
CSO volume of 12.1 million gallons (64 percent). Both sites were tuned on an ongoing basis, and
performance improved with each significant storm during the study period.
Citywide, the program is expected to reduce BSA's CSO volume by 15 to 20 percent, or over 500 million
gallons. Based on the modeled outcome of the inline storage program, BSA was able to negotiate $145
million of otherwise needed system improvements out of its long-term control plan and consent
agreement with the New York State Department of Environmental Conservation.
Based on the BSA team's experience, the program could yield further operations and maintenance
benefits, as well as significant potential for further reductions in overflow volume and activations. As the
program develops and all 16 sites are commissioned, the system will benefit substantially from temporal
and spatial distribution of rainfall across the urban watershed.
A-2
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Falcon Heights, Minnesota:
Predictive Flood Control System
OWNER
• Optimized stormwater management using reai-time controls and adaptive logic.
• Doubled flood control capacity of an existing wet pond.
• Reduced risk to nearby residential areas and infrastructure.
PROJECT DESCRIPTION
Curtiss Pond in Falcon Heights, Minnesota, collects runoff from a 38-acre watershed. A playground and
residential area surround the pond. Large storms have caused pond overflows and several feet of
standing water in the surrounding area, threatening infrastructure and private property To eliminate
this flooding, which poses an imminent safety concern, the Capitol Region Watershed District designed a
network of perforated pipes, 10 feet in diameter, to temporarily store and infiltrate the overflow
However, the physical space for the pipe network was limited
To eliminate the flooding, the District installed an intelligent
forecast information to predict the amount of runoff from a
receive the forecasted water. The system autonomously
draws down the pond during dry periods, maximizing
available capacity in advance of wet weather. This active
control allows for a smaller pond design volume while using
its full storage capacity to reduce flood risk.
An 8-inch butterfly valve was installed to allow the system
to control water draining to the infiltration pipe. The
system decreased the storage requirement by 226 feetof
pipe, effectively increasing storage volume by 58 percent
retention system that uses weather
watershed and prepare the pond to
"Did you know that innovative technology
can automatically check the weather and
activate water management structures
that protect your neighborhood from
flooding? The system will reduce flooding
in the park and reduce the risk of damage
tosurrounding properties."
—Capitol Region Watershed District
KEY FEATURES
Capitol Region Watershed District
LOCATION
Falcon Heights, Minnesota
INCEPTION
July 2015
A-3
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
Falcon Heights, Minnesota
without changing the project
footprint. The system also measures
temperature and infiltration rates to
improve stormwater management
during freezing/thawing cycles.
Since deployment in July 2015, the
system has successfully collected stormwater runoff from the watershed and prevented the costly
flooding of the surrounding area, which limited park use, damaged infrastructure, and created public
safety concerns. The system also provides real-time and historical data of site performance. At any
time, staff can remotely monitor the system and modify what's happening. This high-efficiency
solution hasenabled the Capital Region Watershed Districtto achieve its stormwater management
objectives within the constraints of a highly developed urban/suburban area. It also holds potential
for expansion to stormwater facilities throughout Falcon Heights to effectively manage storms at
the local watershed scale.
A increase in
© effective storage
gallons managed
A-4
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Hawthorne, California:
Real-Tome Monitoring to Prevent Sewer Overflows
£ *
• •
i
OWNER
City of Hawthorne
LOCATION
Hawthorne, California
INCEPTION
Hawthorne installed 50 SmartCover units in their
collection system - about 2.5% of all of their
manholes - and virtually eliminated overflows.
2006
KEY FEATURES
• Reai-time control technology provides early warning of pre-flow events,
• Sewer overflows reduced by 99 percent.
• Savings estimated at $2 million in fines and mitigation costs since 2006.
PROJECT DESCRIPTION
The City of Hawthorne operates a small gravity-only sewer system southwest of the LAX airport. This
system includes 94 miles of gravity pipeline, no lift stations, no treatment, and just two full-time
collection staff. Before 2006, Hawthorne was
experiencing about 10 sewer overflows per year in their
sanitary sewer collection system. The city estimated that
these spills cost them $400,000 annually in fines, cleanup
and mitigation costs, and legal costs.
In late 2006, the City of Hawthorne positioned 50 reai-
time remote level monitoring sensors covering 66 of the
"hot spots" in the collection system. These systems
provide managers real-time early warning of pre-flow
events through alarms and through the use of a data
analytics tool, used to indicate when pipes were
beginning to accumulate dirt; grit; fats, oils, and grease (FOG); or tree roots, thereby changing the daily
pattern of water flow in the pipes.
Since the installation of the real-time monitoring system, the City of Hawthorne has experienced only
one overflow in its sewer collection system, at a location that was previously unmonitored. This
represents a decrease in sewer overflows of 99 percent. Using its two-man crew and the real-time
control technology, Hawthorne has been able to virtually eliminate sewer overflows in its collection
system, saving more than an estimated $2 million in fines and mitigation costs since 2006.
16 In
16.5 In
2014-
Alarm Setpoint
0-24 13:11 - 2014-10-3113:11 Distance from Sensor
= 5 in Sensor Position = 0.0 in
17 in
17.5 in
18 in
185 in
€
5
"Water level is rising"
10-25-14 00:00
10-25-14 12:00
10-26-14 12:00
10-27-14 00:00
10-27-14 12:00
10-28-14 00:00
10-28-14 12:00
10-29-14 00:00
10-29-14 12:00
10-30-14 00:00
10-30-14 12:00
10-31-14 00:00
10-31-14 12:00
The above graph shows a rise in water level, alerting managers
to a potential issue.
A-5
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Louisville, Kentucky:
Real-Time Control for Integrated Overflow Abatement
OWNER
Louisville and Jefferson County
Metropolitan Sewer District,
Louisville, Kentucky
LOCATION
Louisville, Kentucky
INCEPTION
2006
COST
KEY FEATURES
• Enhanced sustainability of sewer systems and improved
quality of receiving waters from smart use of real-time
control (RTC) technology.
• Maximizes conveyance, storage, and treatment capacity,
with consistent operational results of capturing 1 billion
gallons of combined sewer overflow (CSO) annually.
• Overall cost savings estimated at $117M from the original
CSO long-term control plan (LTCP), a 58% reduction in
capital investment.
PROJECT DESCRIPTION
$21M
REFERENCES/LINKS
Angela Akridge, PE, Chief Engineer
Louisville & Jefferson County
Metropolitan Sewer District
700 West Liberty Street
Louisville, KY 40203-1911
Tel.: 502.540.6136
Louisville Metropolitan Sewer District (MSD) operates and maintains a
complex wastewater and stormwater system, with more than 3,200
miles of wastewater collection sewer lines, 16 small and regional
wastewater treatment plants, over 280 pump stations, and 790 miles of
stream water quality monitoring as well as the Ohio River Flood
Protection System.
Louisville MSD is one of the nation's early adopters of RTC, applying
inline storage since the 1990s and pioneering the application of global
optimal and predictive RTC that has been in operation since 2006. The
RTC system was a key to maximize the conveyance, storage (inline and
office), and treatment capacity to reduce CSO, with consistent
operational results of capturing more than 1 biilion gallons of CSO
annually.
A-6
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
Louisvilie, Kentucky
Louisville MSD is mid-way through implementation of a 19-year initiative known as the Integrated
Overflow Abatement Plan (IOAP). The vision of the IOAP is to provide a long-term plan to eliminate
sanitary sewer overflow (SSO) and other unauthorized discharges and to reduce and mitigate wet
weather CSOs in both the combined and separate sewer systems, in an effort to improve water quality
in both Louisville Metro streams and the Ohio River.
MSD has a progressive vision for total wastewater system optimization, which includes the control of
both inline and offline storage facilities, diversion control within and between the combined and
sanitary sewer systems, and maximizing of wastewater treatment throughout the system. RTC is integral
to the fulfillment of this vision. Smart use of RTC technology has allowed MSD to enhance the
sustainability of their sewer systems while also improving the water quality of receiving waterways.
Technology Description: The global optimal and predictive RTC approach was determined as the most
appropriate level of RTC for the Louisville system based on the control objectives and the system
hydraulic characteristics. The RTC system includes remote control facilities and a central station. Each
remote site includes sensors (flow, level) and a local RTC device (Programmable Logic Controller [PLC] or
Remote Terminal Unit [RTU]). Final control elements (e.g., gates, pumps) at each remote control facility
are connected to the output side of the PLC (or RTU). The PLC controls the final control elements based
on the rules embedded (programmed) into the PLC. These rules are feedback algorithms, where action is
based on the difference between a setpoint and the measured variable. Information collected in the
field is communicated from the remote stations to
the central station via the supervisory control and
data acquisition (SCADA) system. The central station
manages and coordinates the various modules,
including data management and archiving, RTC
control algorithms, hydrologic and hydraulic models,
and weather forecasting.
As conditions are monitored, acknowledged, and
controlled, the RTC system takes into account the
distribution of flow in the entire system, both in
current conditions and in the future, based on rain
forecasts, measurements and sewer simulations in
real time. The RTC system provides continuous and
strategic adjustment of control devices to optimize
flow conveyance, storage, release, and transfer
according to the available capacity in the entire system.
Benefit Cost Analysis: The evaluation of RTC feasibility studies of phase 1 implementation identified a
relatively low unit cost ranging from $0,006 to $0,021 per gallon of CSO reduction per year by
maximizing the existing collection and treatment system. This cost is 4 to 10 times lower than traditional
approaches of building additional storage. The overall cost savings was estimated at $117M from the
original CSO long-term control plan cost of $200M (a 58% reduction in capital investment).
Advantages: The RTC technology is scalable and flexible. The global optimal and predictive RTC system
involves all levels of control—from static to local to global—to provide system-wide optimization. New
control sites can be added and control logics can be modified based on performance monitoring as part
of adaptive management. The use of an online model reduces the number of sites and extent of the
monitoring network required for system-wide optimization.
Disadvantages: The approach relies on online model and weather forecasting to provide predictions of
upcoming inflows and their spatial distribution. This requires the calibration and update of the
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A-7
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
Louisville, Kentucky
hydrologic and hydraulic model to represent the wastewater system adequately. The control strategy
and decisions need to account for inaccuracy and unpredictability in weather forecasting.
Lessons Learned: Lessons learned from this project include the following:
• The adoption of RTC technology requires organizational commitment and staff buy-in.
• The utility needs to consider operation and maintenance (O&M) issues and constraints when
selecting the appropriate level of RTC implementation.
• It is important to involve system operators early in the planning and design, and to identify and
communicate roles and responsibilities at every stage, from design, construction, and
commissioning to post-construction performance monitoring.
• Development, implementation, and monitoring performance of standard operating procedures
and post-event analysis are critical to properly operate, maintain, and improve the RTC system.
RTC Project Cost: RTC program cost is estimated at $21M to date, including retrofit, construction,
monitoring, information technology, etc. The current RTC system
includes the use of two stormwater retention basins (over 30 million
gallons) for CSO control, multiple inline storages, flow diversions,
pump stations, as well as the management of Southwestern outfall,
which is an egg-shaped tunnel with a diameter ranging from 24 to
27 feet.
Future Projects: MSD continues to improve and expand its RTC
system as new storage and treatment facilities are constructed
under the IOAP.
Training Needs: Web-based training modules on the RTC system were developed and used for
continuous training and knowledge transfer. Control site commissioning and start-up provide onsite
training opportunities for instrumentation and control (l&C) and O&M staff.
"Real Time Control is an important
component of MSD's long term plan
to mitigate untreated combined
sewer overflows into Beargrass Creek
and the Ohio River. It is a cost
effective management strategy to
help sustain the resources of our
community."
A-8
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Newburgh, New York:
Real-Time Control to Monitor Discharges for
Reporting/Public Notification
KEY FEATURES:
• Easier, more reliable, more nimble operations.
• Reduced risk of loss or damage to sensors.
• Reduced cost.
PROJECT DESCRIPTION
The City of Newburgh replaced its traditional telemetry system
with smart controls to provide city staff and the public real-time
notification of CSO events and to prepare for increased
regulatory requirements for annual reporting and notification.
The City's prior telemetry system used pressure sensors that
were required to be located at the bottom of the influent
channel, in direct contact with the flow, and in the combined sewer regulator environment. In these
locations, the sensors were regularly impacted and damaged or displaced by debris. On numerous
occasions under high flow conditions, several entire units were swept away down the CSO and lost at
the outfall.
The prior sensors also required expensive calibration equipment and a proprietary consultant to
perform the annual calibration of the telemetry system at each installation location. The old telemetry
system used a dedicated phone line for each telemetry station, with only a single point of access and
control, which was located at the wastewater treatment plant. These hard lines were expensive, had
regular loss of communication, and were very difficult or impossible to locate by the utility company
when service was required.
With the new telemetry system, all of these problems were avoided. The smart control wireless satellite
connectivity proved more reliable than land phone lines, and at a lower cost. Any computer, tablet, or
smartphone with internet access can communicate with the telemetry system. There is little calibration
needed. When calibration or sensor relocation is required, in-house staff can easily perform the
required task with basic tools. The sensors are not in contact with the water, thereby avoiding damage.
Lessons Learned: The new sensors are generally installed hanging from the manhole cover above. At
some installation locations, some initial erroneous readings resulted in the discovery that, in some
locations within the sewer, plugs of air can cause the sensors to swing. At these locations, a restrained
installation of the sensor is required. This has been accomplished in-house with stainless steel angle
brackets and associated hardware.
In some sites, initial erroneous readings were caused by low flows with a large distance from the
influent channel to the sensor above. This challenge was overcome with the installation of replacement
long-range sensors.
OWNER
City of Newburgh
LOCATION
Newburgh, New York
COST
$78K for 18 units
A-9
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Philadelphia, Pennsylvania:
Real-Time Control to Manage Retention Pond Discharge
KEY FEATURES
• Retrofit of an existing stormwater management pond
with active control technology to increase treatment
and reduce wet weather flows.
• Minimization of wet weather discharge for storms up
to 2 inches in rainfall depth.
• Integrated system monitoring and reporting
capabilities.
PROJECT DESCRIPTION
An existing stormwater management pond (SMP) collecting
runoff from 8 acres on private property in the combined
sewer area was not meeting Philadelphia Water
Department's (PWD's) stormwater management standards.
For all areas served by a combined sewer and for which
infiltration is infeasible, 100 percent of the runoff from 1.5
inches of rainfall must be routed through an acceptable
pollutant-reducing practice and detained in each SMP for no
more than 72 hours. Any runoff detained must also be released from the site at a maximum rate of 0.05
cfs per impervious acre. The existing pond was originally designed as an infiltration basin, but does not
achieve sufficient infiltration because of errors in the construction process.
A PWD Stormwater Management Incentives Program (SMIP) grant was awarded to fund a facility retrofit
to increase treatment and further reduce wet weather flows. The SMP enhancement was achieved
through the installation of a continuous monitoring and adaptive control (CMAC) on the existing outlet
control structure of the basin. The system includes a level sensor, actuated valve, and integrated
software that will provide dynamic control of stormwater storage and discharge above the permanent
pool of water in the existing basin.
The stormwater pond contains a permanent pool of 22,500 cubic feet maintained by an outlet structure
with a 6-inch orifice. A second, 8-inch orifice is positioned approximately 2 feet above the invert of the
lower 6-inch orifice and an overflow weir is positioned approximately 2 feet above the 8-inch orifice.
The retrofit involved installing a 6-inch actuated valve on the existing 6-inch orifice, a water level sensor,
and the associated communications hardware to connect these to cloud-based control software. The
software uses the water level data along with NOAA storm forecasts to determine an optimal valve open
percentage based on water quality, storm retention, and flood protection objectives. For this basin, the
software was configured to achieve the following logic:
• When a forecasted storm can be fully captured within the basin storage between the permanent
pool and the 8-inch orifice, close the 6-inch valve to eliminate wet weather flow.
• After the event, open the valve to release the captured runoff within the 72-hour retention
period without exceeding a discharge rate of 0.26 cfs (0.05 cfs per impervious acre).
• When the forecast indicates that an upcoming storm cannot be fully captured, release water at
the lowest possible rate to avoid overflowing the riser structure. This logic ensures that the
OWNER
Philadelphia Water Department
LOCATION
Philadelphia, Pennsylvania
INCEPTION
2016
COST
Estimated retrofit cost of $53,000 per
greened acre
A-10
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile Philadelphia, Pennsylvania
0.260 cfs target is only exceeded during large events to mitigate high water levels and discharge
rates. Post-event, release any captured storm runoff within the 72-hour retention period
without exceeding 0.26 cfs target.
The storage volume available above the current permanent pool of water and below the invert of the 8-
inch orifice is 38,000 cubic feet. This volume is larger than the runoff generated by the 2-inch storm
event (34,000 cubic feet). Therefore, for all rainfall events up to 2 inches, the CMAC basin is able to fully
capture the runoff with no discharge to the combined sewer during the wet weather event. After the
event, the valve will slowly but continuously adjust (i.e., open further as the driving head drops) to
match the target 0.26 cfs rate until the basin returns to its permanent pool elevation.
In addition to meeting the requirements for stormwater retention credits, the retrofit facility still
provides safe passage for larger events. The pond depth and outlet structure configuration were not
changed from the existing conditions. When the system is fully functioning, the software logic will open
the valve as far as is needed to avoid overtopping the outlet structure, up to fully open for very large
events. When the valve is fully open, the retrofit and existing conditions peak flow and maximum water
surface elevations are identical. If the CMAC system fails to function properly and the 6-inch valve is
closed during a large event, modeling shows that the 100-year event is still safely contained within the
basin and will not contribute to local flooding. The CMAC system includes fail-safe features that protect
the infrastructure in the event of connectivity or physical hardware failures. The retrofit was installed in
November 2016 and has been collecting hydraulic data while adaptively managing the pond discharge.
A-ll
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
San Antonio, Texas:
Real-Time Control for Cleaning Optimization
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KEY FEATURES:
• Decreased cleaning frequency by 94 percent at 10
pilot sites with no increase in spill risk.
• Identified potential savings of $4,000 per location
per year.
PROJECT DESCRIPTION
OWNER
San Antonio Water System
LOCATION
San Antonio, Texas
INCEPTION
Summer 2015
REFERENCES/LINKS
Jeff Haby, Director, Sewer System
Improvements
Tamsen McNarie, Director, Operations
Support
Blockages or flow restrictions in collection systems are a common cause of sewer overflows. Cleaning
the collection system pipes can prevent these overflows. High frequency cleanings (HFC) may be
necessary where a utility has repeated overflow problems, typically caused by fats, oils, and grease, root
intrusion, or debris collection from stormwater runoffs or other sources.
HFC can reduce near-term risk, and the more frequent site visits can yield
timely and valuable information about the site. However, HFC can be
costly and capital intensive, adds traffic and operational risk to field staff,
and increases wear and tear on pipes.
To help reduce overflows and mitigate the disadvantages of HFC, the San
Antonio Water System (SAWS) implemented a pilot project at 10 monthly
cleaning locations beginning in the summer of 2015. The pilot used a
smart control analytic tool, which automatically scans water flow patterns
in a location and detects changes that may signify changing pipe
conditions upstream or downstream from the monitored location. The
system effectively provides real-time continuous pipe condition
assessment, allowing SAWS to use data to determine when to clean a
sewer pipe segment rather than using a predetermined cleaning
schedule.
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A-12
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
San Antonio, Texas
At the 10 sites monitored, cleaning
frequency was decreased by 94 percent,
while spill risk decreased due to
continuous remote monitoring. With the
exception of a period in late May/early
June of 2016, when San Antonio
experienced 16 inches of rain in a week,
overwhelming the SAWS system, there
were no spills at monitored sites during
the pilot period.
SAWS estimated that, net the costs of the
monitoring, use of the system for
maintenance optimization can save about
$4,000 per monitored location per year
for sites currently designated for monthly
cleaning.
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Cleaning frequency was reduced by 94 percent at 10 pilot locations.
A-13
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
San Diego, California:
Stormwater Harvesting Augmentation Analysis
OWNER
undiry
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KEY FEATURES
• Optimized stormwater/wastewater management using
real-time controls and adaptive logic.
• Cost savings from program coordination.
• Magnitude of water supply augmentation.
• Water quality benefits.
City of San Diego, Stormwater Division
LOCATION
City of San Diego
various locations
INCEPTION
2016
COST
$168,900
REFERENCES/LINKS
Andrea Demich
(858) 541-4348
PROJECT DESCRIPTION
California experienced a historic drought with much of the state reaching D4 "exceptional" conditions
on the U.S. Drought Monitor. In response, Governor Jerry Brown declared a state of emergency in
January 2014 and established the first statewide mandatory water restrictions in March 2015.
Concurrently, significant investments in green infrastructure are needed to address water quality
impairments throughout Southern California. Despite the apparent synergy, urban stormwater is still
underutilized as a water resource in coastal areas and is often conveyed directly to the ocean without
beneficial uses. Synergy between drought resiliency planning and water quality protection could be
realized if green infrastructure could be optimized to collect, treat, and distribute urban runoff as a
supplemental, local water source.
This work explored and quantified the potential nexus between an emerging stormwater capture
program and ongoing efforts to reclaim wastewater as a drinking water resource in San Diego, which
currently imports over 80 percent of its water supply. The project considered both (1) the need to
pursue water independence in response to prolonged droughts, rising imported water costs, and the
city's growing population and (2) the need to plan, construct, and maintain extensive green
infrastructure to comply with water quality regulations and flooding issues. As such, it provided valuable
data on technological approaches to bolster San Diego's water resiliency while reducing pollution,
flooding, spending, and redundancy.
A-14
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
Sari Diego, California
The analysis first defined treatment plant boundary conditions to determine what additional hydraulic
and mass loading (from stormwater) the expanding water reclamation program could accommodate.
The team leveraged a calibrated watershed model to predict the loading to the plant from raw
stormwater and from effluent from the green infrastructure that will be constructed to address water
quality regulations. The team then assessed the cost-effectiveness of methods to convey stormwater to
the plant, including using the existing sanitary collection infrastructure and implementing a separate
storm drain conveyance. Finally, they assessed upstream stormwater control measures—equipped with
real-time controls (RTCs)—to optimize the management of stormwater storage and release to the
reclaimed water system. The model included various scales of green infrastructure within the two major
sewershed areas served by two existing pump plants. The resulting integrated water management
analysis synthesized the benefits, costs, and energy demands of various alternatives to inform data-
driven decision-making for municipalities with simultaneous water, wastewater, and stormwater
stressors.
Analysis of the coordinated approach to water management hinged on simulating the capabilities of
RTCs operated by cloud-based adaptive logic for intelligently managing storage and conveyance of water
throughout the collection network (i.e., to reduce stormwater overflow to receiving waters while
regulating diverted flow not to exceed the capacity of the treatment plant). This was accomplished using
a software package to simulate optimization of control setpoints throughout the sewer network. The
software identifies when valves, gates, and pumps should be operated to manage overall system
performance in response to forecasted runoff and treatment plant capacity. It is well suited to an
application where flows and storage must be actively controlled to enforce certain constraints and
multiple objectives must be optimized over a long-term simulation. The analysis demonstrated potential
cost savings and co-funding opportunities, as well as solutions to create resilient, low-impact
communities. The simulations suggested that stormwater harvesting (enabled by RTCs) could
substantially augment local water supplies while complying with stormwater quality regulations.
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A-15
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
South Bend, Indiana:
Real-Time Control and Real-Time Decision Support
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KEY FEATURES
• Uses a real-time decision support system (RT-DSS) to
maximize conveyance capacity.
• Eliminated illicit dry weather overflows and reduced total
OWNER
South Bend Department of Public
Works
LOCATION
South Bend, Indiana
INCEPTION
2008-present
REFERENCES/LINKS
https://www.southbendin.gov/govern
ment/department/public-works
https://www.emnet.net
http://www.greelev-hansen.com
http://pubs.acs.org/doi/abs/10.1021/ac
s.est,5b05870
combined sewer overflow (CSO) volume by about 70
percent.
• Reduced the potential cost of the city's long-term control plan (LTCP) by an estimated $300-
$400 million.
PROJECT DESCRIPTION
Before 2008, South Bend, Indiana had one of the largest CSO discharge volumes per capita in the Great
Lakes Watershed. With a population of little over 100,000, South Bend generated annual CSO discharge
volumes of 1-2 billion gallons and 25-30 dry weather overflows per year. Had the city simply
implemented the prescribed projects in its LTCP, the total cost of mitigating its CSO problem would have
totaled roughly $800 million.
In 2008, the City of South Bend commissioned a real-time monitoring system of more than 120 sensor
locations throughout the city. In 2012, after reviewing data from the system and selecting sites
accordingly, the City launched a distributed, globally optimal real-time control (RTC) system. The RTC
system consists of nine auxiliary throttle lines with valves governed by an agent-based optimization
strategy. Distributed computing agents trade available conveyance capacity in real time, similar to a
commodities market.
The system provides information to staff throughout the organization through supervisory control and
data acquisition (SCADA) screens for the operators, smart phones and tablets for field staff, and
customized websites jointly developed with the city's engineering staff. Operations staff can override
automated controls and take over valve and gate operation at any time.
A-16
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile South Bend, Indiana
Since 2012, the City has added additional sensor locations and rain gauges, bringing the total number to
152 sites. It also added automated gates at several stormwater retention basins to better control the
timing and rate of stormwater releases into the combined system.
Maximizing conveyance capacity utilization throughout the Saint Joseph interceptor line was the original
objective of the RT-DSS. From 2008 through 2014, South Bend eliminated illicit dry weather overflows in
the first 12 months and subsequently reduced its total CSO volume by about 1 billion gallons per year,
about 70 percent. This program is estimated to reduce the cost of the city's LTCP by $300-$400 million,
nearly 50 percent less than the original $800 million estimate and has already surpassed its original
target of a 25 percent reduction in CSOs.
A-17
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Washington, DC:
Real Time Controls for Rainwater Harvesting and Combined
Sewer Overflow Control
INCEPTION
• Real-time controls to retain water for on-site irrigation and to reduce wet weather discharge to
the combined sewer.
• Captures 100 percent of all 1-inch and less rain events, preventing approximately 100,000
gallons of wet weather flow from entering the combined sewer each year.
PROJECT DESCRIPTION
KEY FEATURES
OWNER
U.S. Environmental Protection Agency
LOCATION
Washington, DC
2014
EPA and General Services Administration sought to upgrade an existing 6,000-gallon rainwater
harvesting system at EPA headquarters in Washington, D.C. Two competing priorities needed to be
addressed: minimizing wet weather discharge while also maintaining water availability for irrigation on
site. Uncaptured wet weather flows contributed to the local combined sewer system, increasing the
potential for CSOs and poor water quality in the Chesapeake Bay.
To monitor storage volumes and expected storage
needs based on weather, the rainwater harvesting
system was retrofitted with a continuous monitoring
and adaptive control (CMAC) technology. The cloud-
based platform automatically monitors the weather
forecast and calculates expected runoff volume
from future storms. The system then automatically
opens the discharge valve in advance of the storm
and releases a predicted volume equal to the
potential runoff. As the forecast changes, the
system adjusts intelligently. Before the storm begins
the system closes the valve, capturing rain to refill the cistern. The valve remains closed until another
rain event is in the forecast, ensuring that water is available for reuse.
square-feet
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prevented per year
A-18
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile Washington, DC
A 1-inch solenoid valve was installed to allow the CMAC technology to control water draining to the
combined sewer system. The CMAC technology also monitors discharge flow, irrigation flow, and air
temperature and activates a freeze protection system during cold weather. The addition of CMAC
technology to the existing rainwater harvesting system eliminated the need to install additional storage
volume to meet otherwise competing objectives.
Since deployment in 2014, the advanced rainwater harvesting system at EPA headquarters has proven
be a low-cost, high-performance solution for meeting stormwater management goals. The increased
data transparency and opportunities for adaptive management can achieve a range of stormwater
management objectives.
A-19
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Wilmington, Delaware:
Real-Time Control to Reduce Combined Sewer Overflow
Discharges
LOCATION
OWNER
City of Wilmington
Wilmington, Delaware
COST
$12M
KEY FEATURES
• Anticipated increase of Wilmington's average annual wet weather capture from 50 percent to
morethan85 percent.
• Overall cost savings estimated at $87 million from the original CSO long-term control plan
(LTCP).
• Fully automated operation, with remote supervision and manual override capacity at all times
by treatment plant operators.
PROJECT DESCRIPTION
Since the early 1990s, the City of Wilmington has initiated a series of improvement projects to reduce
CSO events and increase the annual average flow intercepted at the wastewater treatment plant. These
projects included the upgrade of treatment plant
capacity, the construction of the 2.7 million-gallon Canby
Park CSO Storage Basin, the elimination of certain CSOs,
other specific collection system improvements, and public
outreach.
As part of its enhanced long-term control plan (ELTCP),
Wilmington implemented a coordinated system-wide real-
time control (RTC) solution. The RTC system provides
efficient flow management to reduce CSOs along the
Brandywine Creek and the Christina River and optimizes
the capacity available in the interceptor and pump
stations. Overall, the ELTCP will increase the average annual percent capture from 50 percent to more
than 85 percent, meeting the CSO control objective via a presumptive approach. Wilmington's green
A-20
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
Wilmington, Delaware
infrastructure program is expected to meet the total maximum daily load (TMDL) objectives by
increasing the wet weather capture rate to over 90 percent.
The city has adopted an adaptive management
approach whereby site-specific system improvement,
such as localized separation and additional green
infrastructure, will be determined based on post-
construction performance of implemented projects.
The RTC project encompasses the design, retrofitting,
and implementation of four flow control stations, the
control of Canby Park CSO Storage Basin, the control
of the three existing siphons, and the design and
implementation of a network of data collection and
measuring sites (equipped with flowmeters and rain gauges) for monitoring purposes. All of the local
stations are linked to the central station via a telemetry system and automatically managed under a
global optimal and predictive RTC approach from the central station, under the supervision of operators.
Smart use of RTC technology has allowed the City of Wilmington to significantly reduce overall costs of
the LTCP.
Technology description: The RTC system is fully automated, with remote supervision and manual
override capacity at all times by treatment plant operators.
The system consists of four major components:
• A monitoring system including level, flow, and rainfall
• Local control facilities equipped with control elements (gate and pumps), programmable logic
controllers (PLC), and remote telemetry units (RTUs) with backup power.
• A supervisory control and data acquisition (SCADA) system for data acquisition of sensor
information and control facility status, as well as for communication of control set points.
• A central station which manages andcoordinates the various components, including data
management and archiving, RTC control algorithms and optimization, hydrologic and hydraulic
models, and weather forecasting.
As conditions are monitored, acknowledged, and controlled, Wilmington's RTC system accounts for
current and future flow distribution throughout the system based on rain forecasts, measurements, and
sewer simulations in real time. It provides continuous and strategic adjustment of control devices to
optimize flow conveyance, storage, release, and transfer according to the available capacity in the entire
system.
Cost-benefit analysis: The evaluation of RTC feasibility studies identified a relatively low unit cost of $0.07
per gallon of CSO reduction per year by maximizing the existing collection and treatment system, a cost
four times lower than traditional approaches of building additional storage. The overall cost savings is
estimated at $87 million from the original CSO LTCP cost of $114 million, for a final LTCP cost of $27
million.
Advantages: The RTC technology is scalable and flexible, and involves all levels of control—from static to
local to global—to provide system-wide optimization. Additionally, new control sites can be added and
control logics modified based on performance monitoring as part of adaptive management.
The RTC system design and operation takes into account equipment and sensor failures and provides
fail-safe control for a robust performance system in real time.
A-21
Any mention of trade names or commercial products does not constitute an endorsement or
recommendation for use. EPA and its employees do not endorse any products, services or enterprises.
-------
Project Profile
Wilmington, Delaware
The RTC approach enables the system to meet multiple objectives in a predefined priority order: 1) flood
protection, 2) CSO minimization with local priorities, 3) minimal retention time with local priority order,
and 4) minimal gate movements.
The use of an online model reduces the number of sites and the extent of the monitoring network
required for system-wide optimization. The RTC system provides the city with greatly enhanced
capability to monitor, analyze, assess, and report on CSO discharges and collection system performance
(capture rate) on an annual basis. This has been useful for reporting to the regulating agency and for
integrating adaptive management into LTCP planning.
Disadvantages: The RTC approach relies on an online model and real-time rain gauges to provide
predictions of upcoming inflows and their spatial distribution. This requires the calibration and update
of the hydrologic and hydraulic model to represent the wastewater system adequately. The control
strategy and decisions need to account for inaccuracy in rainfall distributions and real-time monitoring
data.
Lessons Learned: The lessons learned from this project include the following:
• The adoption of RTC technology requires organizational commitmentand staff buy-in.
• The utility needs to consider O&M issues and constraints when selecting the appropriate level of
RTC at the outset.
• It is important to involve system operators early in the planning and design, and to identify and
communicate roles and responsibilities at every stage, from design, construction, and
commissioning, to post-construction performance monitoring.
• Documentation such as standard operation procedures and post-event analysis is critical to
properly operate, maintain, and improve the RTC system.
• Achievement of the anticipated performance was delayed until initially unidentified system
collection anomalies were resolved. These included pipes obstructed with up to 50 percent
sedimentation or root blockages, and pump
station control logic that deviated from the
reported operational condition.
• Key to the project has been the City of
Wilmington and its designated operator taking
ownership of the instrumentation and control
(l&C) and SCADA system to maintain equipment
and instrumentation in a proactive manner.
RTC Project Cost: The project cost is $12 million,
including retrofit, construction, monitoring, information
technology, etc. The current RTC system includes the use
of one retention basins (2.7 million gallons) for CSO
control, an additional 2.0 million gallons of inline
storage, the management of three siphons, and the
operation of a 135 MGD pumping station.
"We'd have to tear up several parks in the city
to build more tanks, I'm not a scientist, but we
knew there had to be ways to divert the way
water flows in pipes. We are among the
selected communities that have utilized Real
Time Control that makes optimum use of our
sewer capacity to manage and minimize
overflows. This plan is cheaper, quicker and
actually increases the amount of overflow
we're trying to catch. The Enhanced LTCP
would increase the CSO capture and
treatment rate to 87% or higher, reduce CSO
control costs by more than $87 million and
accelerate implementation by ten years."
- Mayor James M. Baker,
City of Wilmington, Delaware
A-22
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