[1]QIAO Junfei,WANG Lili,HAN Honggui.Optimal control for wastewater treatment process based on ESN neural network[J].CAAI Transactions on Intelligent Systems,2015,10(6):831-837.[doi:10.11992/tis.201401009]
Copy

Optimal control for wastewater treatment process based on ESN neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 6 Page number: 831-837 Column: 学术论文—智能系统 Public date: 2015-12-25
Title:
Optimal control for wastewater treatment process based on ESN neural network
Author(s):
QIAO Junfei;  WANG Lili;  HAN Honggui
College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China
Keywords:
wastewater treatment process; optimal control; echo state network; performance prediction model; benchmark simulation model
CLC:
TP18
DOI:
10.11992/tis.201401009
Abstract:
To address the problem of the high-energy consumption in wastewater treatment processes, we propose an online optimal control method based on an echo state network(ESN). This method has three main steps. First, we develop a performance prediction model of the wastewater treatment process. Second, based on the system state and the predicted performance index, we optimize the set point of the control variable using the ESN in real time. Then we transfer the optimized set point to the underlying controller for tracking control. This ESN-based online optimal control method is carried out using the benchmark simulation model 1(BSM1). The simulation results show that the proposed method can not only meet the effluent quality requirements, but also efficiently reduce the operation costs of the wastewater treatment process.
References:
[1] HAIMIA H, MULAS M, CORONA F, et al. Data-derived soft-sensors for biological wastewater treatment plants:an overview[J]. Environmental Modelling and Software, 2013, 47:88-107.
[2] ZHANG Ping, YUAN Mingzhe, WANG Hong. Improvement of nitrogen removal and reduction of operating costs in an activated sludge process with feedforward-cascade control strategy[J]. Biochemical Engineering Journal, 2008, 41(1):53-58.
[3] OSTACE G S, CRISTEA V M, AGACHI P S. Cost reduction of the wastewater treatment plant operation by MPC based on modified ASM1 with two-step nitrification/denitrification model[J]. Computers and Chemical Engineering, 2011, 35(11):2469-2479.
[4] PIOTROWSKI R, BRDYS M A, KONARCZAK K, et al. Hierarchical dissolved oxygen control for activated sludge processes[J]. Control Engineering Practice, 2008, 16(1):114-131.
[5] BRDYS M A, GROCHOWSKI M, GMINSKI T, et al. Hierarchical predictive control of integrated wastewater treatment systems[J]. Control Engineering Practice, 2008, 16(6):751-767.
[6] HOLENDA B, DOMOKOS E, RéDEY á, et al. Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control[J]. Computers and Chemical Engineering, 2008, 32(6):1270-1278.
[7] 薄迎春, 乔俊飞. 启发式动态规划在污水处理过程控制中的应用[J]. 控制理论与应用, 2013, 30(7):828-833. BO Yingchun, QIAO Junfei. Application of heuristic dynamic programming to wastewater treatment process control[J]. Control Theory and Applications, 2013, 30(7):828-833.
[8] 丛秋梅, 柴天佑, 余文, 等. 污水处理过程的递阶神经网络建模[J]. 控制理论与应用, 2009, 26(1):8-14. CONG Qiumei, CHAI Tianyou, YU Wen, et al. Modeling wastewater treatment plant via hierarchical neural networks[J]. Control Theory and Applications, 2009, 26(1):8-14.
[9] JOUANNEAU S, RECOULES L, DURAND M J, et al. Methods for assessing biochemical oxygen demand(BOD):a review[J]. Water Research, 2014, 49:62-82.
[10] IQBAL J, GURIA C. Optimization of an operating domestic wastewater treatment plant using elitist non-dominated sorting genetic algorithm[J]. Chemical Engineering Research and Design, 2009, 87(11):1481-1496.
[11] 史雄伟, 乔俊飞, 苑明哲. 基于改进粒子群优化算法的污水处理过程优化控制[J]. 信息与控制, 2011, 40(5):698-703. SHI Xiongwei, QIAO Junfei, YUAN Mingzhe. Optimal control for wastewater treatment process based on improved particle swarm optimization algorithm[J]. Information and Control, 2011, 40(5):698-703.
[12] VRABIE D, LEWIS F. Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems[J]. Neural Networks, 2009, 22(3):237-246.
[13] COPP J B. The COST simulation benchmark:description and simulator manual[R]. Luxembourg:Office for Publications of the European Community, 2001.
[14] QIAO Junfei, BO Yingchun, CHAI Wei, et al. Adaptive optimal control for a wastewater treatment plant based on a data-driven method[J]. Water Science and Technology, 2013, 67(10):2314-2320.
[15] CHACHUAT B, ROCHE N, LATIFI M. Long-term optimal aeration strategies for small-size alternating activated sludge treatment plants[J]. Chemical Engineering and Processing:Process Intensification, 2005, 44(5):591-604.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems