Integrated Simulation and Optimization Models for Treatment Plant Placement in Drinking Water Systems

Abstract

Drinking water systems are critical to human health and economic development. System design considers treatment infrastructure to remove contaminants from water supplies and distribution system infrastructure to transport and maintain the quality of treated water to households and businesses. Therefore, treatment and distribution components should be evaluated together when considering system designs and the associated effects on water quality. Planning models are often used to aid decision makers in evaluating drinking water system changes over time. This work proposes a planning modeling approach to determine the optimal treatment and alternative supply infrastructure location and capacity within an existing distribution system. The model consists of four stages: (1) simulating system hydraulic and water quality behavior to set optimization model parameters; (2) selecting potential system configurations through optimization; (3) validating the system hydraulic and water quality behavior through simulation; and (4) selecting network treatment configuration. Results indicate an optimal number of treatment locations that provide the best delivered water quality. For the demonstration network, more than three plants did not provide any improvement in water quality.

Integrated Multiobjective Optimization and Simulation Model Applied to Drinking Water Treatment Placement in the Context of Existing Infrastructure

“…The present work expands the model framework designed by Schwetschenau et al (2019) to integrate multiple objectives into the mixed-integer linear program (MILP) facility-assignment optimization model component. The model framework has five steps (Fig.…”

“…Fig. 1 was modified from Schwetschenau et al (2019) to reflect changes made to Step 2. In Step 2 the singleobjective optimization from Schwetschenau et al ( 2019) is replaced with a multiobjective mixed-integer linear program optimization model (MOMILP).…”

“…In Step 2 the singleobjective optimization from Schwetschenau et al ( 2019) is replaced with a multiobjective mixed-integer linear program optimization model (MOMILP). Similar to Schwetschenau et al (2019), the MOMILP identifies the location and capacity of treatment that best meets the defined objectives. The Appendix describes this model framework in detail.…”

“…Depending on the system, the decision makers, and how well the decision space is understood, the uncertainty in these assumptions may be discouraging use of these models. Schwetschenau et al (2019) described a deterministic sequential optimization-simulation model framework that can be applied to the engineering problem of identifying the best location for a treatment plant or new supply tie-in within an existing distribution system based on a simple water quality objective. In that framework, a physicochemical simulation model predicts water quality and flow behavior within an existing distribution system for selection of feasible treatment solutions.…”

“…Multiobjective models that have demonstrated the use of mixedinteger or linear optimization formulations, or the use of deterministic optimization approaches integrated with simulation models, are limited. This work extends the single-objective formulation presented by Schwetschenau et al (2019) into a multiobjective framework.…”