Abstract
In this paper, a new model combining neural networks with genetic algorithm is proposed to solve the problem of waste water discharge optimization. Firstly we apply resilient backpropagation(RPROP) neural networks to water quality daily data prediction based on water quality and waste water discharge history data, then through genetic algorithm process concerning water quality influence and economic costs, get optimal plan of waste water discharge. To demonstrate the accuracy and applicability of model, we conduct experiments on daily data of TaiCang water quality and waste discharge, and it proves to be a good method for waste water discharge optimization problems.
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