Large and Small Municipal Utilities Benefit as Optimized Decision Support Identifies Least-Cost Improvements for Wastewater and Stormwater Planning

This paper provides an introduction to a structured, decision support approach that is helping municipal wastewater and stormwater utilities identify lower-cost, better performing planning and design solutions compared to current practice. Utility managers are proving on a variety of projects that this formal optimized decision support (ODS) approach can lead to a better understanding of allowable improvement options and ultimately to low-cost, unbiased, defensible solutions embraced by utility managers and staff, elected officials, stakeholders and the public.The ODS approach makes use of advanced computational intelligence to enable evaluation of a vast number of alternatives leading to the development of better solutions. The approach builds on a utility’s existing wastewater collection system or stormwater simulation model making it universally applicable for large, medium and many small utilities. Utility and consultant modelers are learning how they can shift their focus from tedious and time consuming trial and error simulation runs to defining a range of improvement options and then letting the optimization search find the mix of options that yield improved system performance at least cost.A sampling of five recent case studies is presented to illustrate how the ODS approach is being used by a number of different municipal utilities across the U.S. The five case studies cover large and small utilities and each is aimed at reducing combined sewer overflows, sanitary sewer overflows, and/or system surcharge and flooding under design storm events. The specific steps in gathering data, formulating the software model, developing alternative optimized solutions, performing sensitivity analyses, and prioritizing capital improvements are reviewed. Particular attention is given to balancing the cost and performance of a variety of options in order to develop a range of alternative near-optimal solutions that can best meet the utility’s needs and stakeholders’ preferences.