Improving System Efficiency While Reducing Risks and Costs

Consuming up to 35% of the energy used by municipalities, water supply and wastewater treatment systems are among the most energy-intensive facilities...

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by Cari K. Ishida, Elisa Garvey, Shawn Dent, and H. Stephen McDonald

Consuming up to 35% of the energy used by municipalities, water supply and wastewater treatment systems are among the most energy-intensive facilities owned and operated by local governments . Water agencies are increasingly challenged to manage their facilities and resources as efficiently as possible. Confounding this goal is managing the risks of rapidly changing regulatory demands, aging infrastructure needs, and other planning uncertainties. Rising construction costs are also a growing concern to many utilities.

It is essential for planning purposes to understand the sensitivity of systems to changing costs, such as energy costs. Typically, these uncertainties are addressed by incorporating redundancy and conservatism in the planning and design of new facilities, which increases cost. Instead, the best solution is to reduce risks through increased knowledge and forecasting capabilities.


Most municipalities face similar questions when it comes to planning, operations, and engineering, such as:

  • Planning: What new capital facilities are required and when are they needed?
  • Operations: How to optimize existing capacity and minimize operations costs?
  • Engineering: What new technologies are required to meet performance and/or cost requirements, and where in the system are they needed?

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The challenge is addressing these questions in an integrated and comprehensive manner by integrating the data and knowledge held within multiple departments. Often, planning, operations, maintenance, engineering and finance departments each are consumed with their own individual missions such that coordination is difficult to achieve in making integrated capital and operations and maintenance (O&M) cost decisions that require an overall systems perspective. Therefore, an improved approach to facilitate the communication of logistical, physical, and technical information is required.

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Figure 1: OPTIMO tracks flow, pollutants, energy, chemical, and labor costs in each unit process and throughout a treatment plant. "Drag 'n drop" feature allows for rapid model configuration from customized libraries.
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In addition, the difficulty of making integrated decisions regarding planning, operations, and engineering for water and wastewater facilities lies in the inherent complexity of these kinds of systems.


To address these challenges, Carollo Engineers created an integrated, computerized model to support comprehensive decision-making for water and wastewater utilities. It was essential that the model include all of the major components of the water system: facilities for collection, treatment, and distribution. To more accurately simulate complex water operations, a dynamic (versus steady-state or static) model was required. The result was an integrated tool called OPTIMOTM that facilitates the planning process for water utilities.

Model Description

OPTIMO allows configuration of complex water and wastewater systems that may include conveyance systems, treatment processes, flow equalization/storage, and/or water distribution systems from a library of “blocks” (see Fig. 1). The user connects these blocks (much as one would configure a flow chart or schematic) and inputs the desired operational parameters. The benefit of constructing a model of the system in this “drag and drop” fashion is that it is fast and easy to initially configure an existing (or proposed) combination of facilities.

The user may configure a “base case” and numerous scenarios for model test runs. A summary block displays key output data including total O&M and capital costs, processes with insufficient peak and average capacity, and satisfaction of water demands.

The model consists of three modules: 1) Input Data Module, 2) Calculation Module, and 3) Dashboard/Outputs Module. The overall model logic diagram (see Fig. 2) includes a breakdown of the modules and the optimization “do loops.” The main “engine” of the model, also referred to as the calculation module, was custom programmed using an object-oriented software platform. The input and output modules were constructed in Excel.

Temporal scale is critical since it is directly related to the type of questions that need to be answered with the model. Temporal scales used in modeling can be divided into three main categories:

  • Master planning: Typically based on 20-year forecasts, this scale partitions capital projects into yearly intervals.
  • Tactical analyses: Also called permit compliance decisions, these are typically concerned with a monthly time scale down to a daily time scale.
  • Operational analyses: These examine decisions that are typically on less than a 24-hour time scale.

OPTIMO was constructed on an hourly time step to address these three decision types, and can run for a total duration of one year (8,760 hours). Three hourly input data sets include influent flow (diurnal flow patterns), water market demands, and power cost (which can fluctuate by time of day and season).

The calculations completed in OPTIMO are based upon fundamental engineering principles of conservation of mass, flow, and energy. The calculations are completed based on inputs including projected source water quality and quantity, water market demands, plant performance, and anticipated regulatory requirements. Operations costs are calculated based upon financial input data including labor and supply costs (including chemicals), and power costs (co-generation, natural gas power generation, and power purchased from the grid). Model outputs include total O&M expenditures, capacity exceedance, solids production rates, total power consumption, and market demand constraints.

OPTIMO run options include scenario generation and optimization. For a single run or scenario, the user can compare new scenarios to a base case scenario. An optimization run employs a genetic algorithm optimization engine that automatically runs the model numerous times, varying key parameters to determine the optimal solution (e.g., lowest total cost of operation). Key parameters can include diversions and bypasses within or between treatment plants, and operation of storage basins in the collection system, at the treatment plant, or within the water system. Another important use of the optimizer is testing the sensitivity of key parameters, such as increasing energy costs, to assess how existing facilities can be more efficiently utilized or whether new facilities are needed (e.g., solar or wind turbines). The optimizer can also be configured to minimize energy/chemicals consumption or greenhouse gas emissions.

Application of OPTIMO

Inland Empire Utilities Agency (IEUA) owns and operates multiple wastewater treatment plants with different treatment capacities, process capabilities, and discharge/reuse alternatives. The IEUA system consists of liquid treatment at four reclamation plants and solids treatment at two facilities. Several of the plants are interconnected with the ability to divert wastewater and/or solids to other plants. Given the complexity and interdependent nature of IEUA’s facilities, determining the optimum set of operating conditions was challenging.

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OPTIMO was applied to IEUA’s wastewater system to test scenarios from its short-term business plan for water reuse. The drag and drop capability of the model allowed for rapid construction of reuse projects in the model. IEUA utilized the optimization mode of the model to determine whether hourly reuse demands could be met under different flow scenarios.

The application of this model can help utilities answer some fundamental questions (see Table 1). The model’s greatest strengths include the rapid generation of “what if” scenarios and the ability to incorporate capacity, distribution, and regulatory constraints to determine ideal operating conditions at a minimum cost. Utilities can utilize both the scenario and optimization run modes to address some of the planning issues and challenges that they may encounter. For example, if the existing system cannot convey projected flows, the bottlenecks and additional required capacities for each scenario are identified by the model, raising a red flag to the user that capital improvements are required.

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Graphs (see Fig. 3) are used in the model to quickly alert the user if a process is capacity limiting (if the blue line exceeds the grey bar, the process is out of capacity). Utilities can also test scenarios related to their growing water system. By conducting model runs with different customer demands and timing, they can determine when and where capital projects should be implemented. In addition to optimizing costs, OPTIMO can be configured to minimize energy consumption, a common objective for many utilities.

Integrated Model

Because OPTIMO encompasses several different aspects of water and wastewater systems (collection system, treatment facilities, and distribution system), application of the model provides utilities an “expert” system perspective. Implementation of the model encourages collaboration between many different departments and provides staff members with a greater understanding of their system, and how decisions made by one department affect other departments. Besides providing a means of transferring knowledge across departments, the model may serve as a means to transfer knowledge between different levels within departments.

Whether large or small, many agencies can benefit from an integrated approach such as applied in the OPTIMO model. By viewing their assets as a system, utilities can investigate alternatives that may not have been identified otherwise. For example, utilities can explore storage options not only within treatment plants, but also outside the plant fence line in the collection or distribution systems. The object-oriented construction of the model allows for rapid prototyping and quick testing of “what if” scenarios. The model also provides a means for utilities to examine system-wide consumption of energy and supplies (e.g., chemicals), which is a growing concern for most utilities. Most importantly, the application of this model can assist a utility to fully maximize the use of existing facilities, and when existing facilities become overburdened or inefficient, to test a variety of capital improvements to choose the best options.

About the Authors:

Cari Ishida, Elisa Garvey, and Shawn Dent are members of the model development team led by Steve McDonald, a partner at Carollo Engineers (Walnut Creek, CA;

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