Abstract
The interception facility is an important and frequently used measure for combined sewer overflow (CSO) control in city-scale drainage systems. The location and capacity of these facilities affects the pollution control efficiency and construction cost. Optimal design of these facilities is always an active research area in environmental engineering, and among candidate optimization methods, the simulation-optimization method is the most attractive method. However, time-consuming simulations of complex drainage system models (e.g., SWMM) make the simulation-optimization approach impractical. This paper proposes a new simulation-optimization method with new features of multithreading individual evaluation and fast data exchange by recoding SWMM with object-oriented programming. These new features extremely accelerate the optimization process. The non-dominated sorting genetic algorithm-III (NSGA-III) is selected as the optimization framework for better performance in dealing with multi-objective optimization. The proposed method is used in the optimal design of a terminal CSO interception facility in Wuhan, China. Compared with empirically designed schemes, the optimized schemes can achieve better pollution control efficiency with less construction cost. Additionally, the time consumption of the optimization process is compressed from days to hours, making the proposed method practical.
Highlights
Nowadays, many urban areas are still drained by combined sewer systems that collect and transport both municipal wastewater and stormwater/snowmelt runoff with the same pipe network [1]
To figure out which method has a better performance, comparisons were made between the optimization results from NSGA-III and NSGA-II
The proposed optimization model was solved with the method based on NSGA-III and a new featured storm water management model (SWMM) module
Summary
Many urban areas are still drained by combined sewer systems that collect and transport both municipal wastewater and stormwater/snowmelt runoff with the same pipe network [1]. SWMM is developed with procedural-oriented programming, so the data structure is organized as global variables This feature means SWMM cannot be called in a multithreading way during the optimization process. Aimed at the outstanding problems of solving efficiency and partial optimization, this study proposes a new optimization model, which considers both the storage tank and pump station as optimal objectives. The design parameters of the tank and pump station should both be considered as decision variables and be solved in the optimization process. For the start-up/shutoff operation control of pumps, only the pump start-up depth needs to be set This decision variable is represented with the ratio of the water depths to the tank’s effective depth
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