An inverse design method for determining the optimal tributary flow into a main stream in a rainstorm period

Abstract

To ensure water quality at the control cross-section of main streams (CCMS) in a rainstorm period, an inverse design method was proposed to determine the optimal discharge flow of tributary rivers. The design variables are tributary discharges and the target variables are the required concentrations of chemical oxygen demand (COD), dissolved oxygen (DO) and ammonia nitrogen (NH3-N) at CCMS. The relationship between target variables and design variables was identified using an artificial neural network (ANN). The database was obtained by Environmental Fluid Dynamics Code (EFDC) and the optimal tributary discharges were obtained by a genetic algorithm (GA) coupled with well trained ANN. The results showed the following results: (a) The relative prediction errors of ANN are mostly less than 5%. (b) When the inlet flow rate is 0 m3/s, 30 m3/s, 50 m3/s, 100 m3/s and 200 m3/s, the optimization total discharges of tributaries are 5.7 m3/s, 12.5 m3/s, 18.6 m3/s, 33.4 m3/s and 61.8 m3/s, respectively. (c) Most of optimization plans entirely satisfy the water quality requirements at CCMS except a few plans, in which the relative errors between optimized results and required values of COD and DO are less than 0.4% and 0.1%, respectively. The study showed that the inverse design method is efficient for determining the optimal discharges of multiple tributaries. HIGHLIGHTS An inverse design method of Environmental Fluid Dynamics Code, artificial neural network and genetic algorithm is proposed.; The optimal discharge of multiple tributaries into mainstream is obtained.; The optimal discharge schemes satisfy the water quality requirement of a main stream.; The inverse design method is proved to be highly efficient.;

Keywords

• artificial neural network
• genetic algorithm
• inverse design method
• optimization
• stormwater
• tributary flow