[1]ZHANG Wei,QIAO Junfei.Multi-objective optimization control for wastewatertreatment processing based on neural network[J].CAAI Transactions on Intelligent Systems,2016,11(5):594-599.[doi:10.11992/tis.201512022]
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Multi-objective optimization control for wastewatertreatment processing based on neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 5 Page number: 594-599 Column: 学术论文—机器学习 Public date: 2016-11-01
Title:
Multi-objective optimization control for wastewatertreatment processing based on neural network
Author(s):
ZHANG Wei1; 2; 3;  QIAO Junfei1; 3
1. College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo 454000, China;
3. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing 100124, China
Keywords:
multi-objective optimization; neural network; energy consumption; wastewater treatment; benchmark simulation model
CLC:
TP18
DOI:
10.11992/tis.201512022
Abstract:
To solve the energy-extensive consumption problem of the wastewater treatment process (WWTP), a dynamic multi-objective optimization control strategy is proposed in this paper.The proposed method simultaneously optimizes the aerate energy and pumped energy consumption of WWTP, and the set-points of dissolved oxygen concentration and nitrate level can be optimized dynamically using the NSGA-Ⅱ evolutionary algorithm. The proportion-integral-derivative(PID) is chosen to realize the tracking control task for the low layer. To overcome the difficulty of establishing an optimal model for WWTP, an online neural network modeling method was proposed for constructing the multi-objective optimization model, which solves the problem that there is no accurate mathematical description with the optimization variables and performance indexes. The simulation results, based on the international benchmark simulation model No. 1, demonstrate that compared with the PID and the single-objective optimization methods, energy consumption can be significantly reduced by using the proposed method while still assuring water quality.
References:
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