Design of wastewater treatment plant (WWTP) networks can be complicated by the existence of various uncertainties and multiscale nature of the planning process. This article presented a multiscale two-stage mixed integer stochastic (MSTMIS) model for optimal design of WWTP networks under uncertainty. The model was first formulated by a general two-stage stochastic nonlinear programming problem and solved by genetic algorithm to obtain the deterministic single nominal scenario and fix the first-stage long-term decisions. A sensitivity analysis was then used to select the most influential parameters, from which second-stage short-term decisions were finalized by generating stochastic scenarios. A real-world case study on development of a WWTP network in the metropolitan area of St. John's, Canada was conducted to examine the efficacy of the proposed model. Optimization results indicated that the total cost over a 20-year span was optimized at $8.28 × 107 by the MSTMIS model, which is lower than that optimized by the traditional one-stage solution algorithm. The proposed MSTMIS model can simultaneously address the challenges posed by uncertainty and multiscale nature and, thus, provide the decision makers more confidence in making economic decisions related to WWTP network design.
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