A Soft Measurement Model of Wastewater Treatment Process Based on BP Neural Network with Improved Particle Swarm Optimization Algorithms

HE Dan; LIN Laipeng; LI Xiaoyong; NIU Guoqiang; YI Xiaohui; HUANG Mingzhi

doi:10.6054/j.jscnun.2021034

Journal of South China Normal University (Natural Science Edition) > 2021 > 53(2): 114-120. > DOI: 10.6054/j.jscnun.2021034

HE Dan, LIN Laipeng, LI Xiaoyong, NIU Guoqiang, YI Xiaohui, HUANG Mingzhi. A Soft Measurement Model of Wastewater Treatment Process Based on BP Neural Network with Improved Particle Swarm Optimization Algorithms[J]. Journal of South China Normal University (Natural Science Edition), 2021, 53(2): 114-120. DOI: 10.6054/j.jscnun.2021034

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A Soft Measurement Model of Wastewater Treatment Process Based on BP Neural Network with Improved Particle Swarm Optimization Algorithms

1.
Guangdong Guangye Environmental Protection Industry Group Co., Ltd, Guangzhou 510030, China
2.
School of Environment// Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety//MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China
3.
Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization//Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China

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Received Date: November 08, 2020
Available Online: April 28, 2021

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Abstract

Abstract

Considering that the BP neural network-based soft sensor model of wastewater treatment process is affected by the nonlinear characteristics of the system and has such problems as low convergence speed and local mini-mum, a hybrid soft measurement PSO-BP model for predicting the effluent chemical oxygen demand (COD_eff) and the effluent solid suspended matter (SS_eff) in the wastewater treatment process is developed based on BP neural network with improved particle swarm optimization algorithms. This model is compared with models based on genetic algorithm-BP neural network (GA-BP model) and BP neural network. The research results show that when using the PSO-BP model to predict COD_eff, the root mean square error (RMSE) and the coefficient of determination (R²) are 3.995 5 and 0.640 1, respectively. When it is used to predict SS_eff, RMSE and R² are 0.650 3 and 0.681 1, respectively. Compared with the BP model and the GA-BP model, the prediction performance of the PSO-BP model on COD_eff is improved by 4.49% and 0.44% respectively, the prediction performance of SS_eff is improved by 40.11% and 24.77% respectively.
- wastewater treatment,
- BP neural network,
- improved particle swarm optimization,
- soft measurement

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[1]	蒙西, 乔俊飞, 韩红桂. 基于类脑模块化神经网络的污水处理过程关键出水参数软测量[J]. 自动化学报, 2019, 45(5): 906-919. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201905008.htm MENG X, QIAO J F, HAN H G. Soft measurement of key effluent parameters in wastewater treatment process using brain-like modular neural networks[J]. ACTA Automatica Sinica, 2019, 45(5): 906-919. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201905008.htm
[2]	HUANG M Z, MA Y W, WAN J Q, et al. A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process[J]. Applied Soft Computing, 2015, 27: 1-10. doi: 10.1016/j.asoc.2014.10.034
[3]	严玉洁. AMBR工艺畜禽养殖污水处理及BP神经网络水质预测研究[D]. 银川: 宁夏大学, 2017. YAN Y J. Study on dairy wastewater treatment by AMBR and effluent quality forecast by BP neural network[D]. Yinchuan: Ningxia University, 2017.
[4]	CANETE J F D, SAZ-OROZCO P D, BARATTI R, et al. Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network[J]. Expert Systems with Applications, 2016, 63: 8-19. doi: 10.1016/j.eswa.2016.06.028
[5]	韩广, 乔俊飞, 韩红桂, 等. 基于Hopfield神经网络的污水处理过程优化控制[J]. 控制与决策, 2014, 29(11): 2085-2088. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201411029.htm HAN G, QIAO J F, HAN H G, et al. Optimal control for wastewater treatment process based on Hopfied neural network[J]. Control and Decision, 2014, 29(11): 2085-2088. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201411029.htm
[6]	WANG J, WEN Y, YE Z, et al. Convergence analysis of BP neural networks via sparse response regularization[J]. Applied Soft Computing, 2017, 61: 354-363. doi: 10.1016/j.asoc.2017.07.059
[7]	SRIDEVI K, SIVARAMAN E, MULLAI P. Back propagation neural network modelling of biodegradation and fermentative biohydrogen production using distillery waste-water in a hybrid upflow anaerobic sludge blanket reactor[J]. Bioresource Technology, 2014, 165: 233-240. doi: 10.1016/j.biortech.2014.03.074
[8]	ZAGHLOUL M S, HAMZA R A, IORHEMEN O T, et al. Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks[J]. Science of the Total Environment, 2018, 645: 449-459. doi: 10.1016/j.scitotenv.2018.07.140
[9]	刘亚丽, 李英娜, 李川. 基于遗传算法优化BP神经网络的线损计算研究[J]. 计算机应用与软件, 2019, 36(3): 72-75. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903015.htm LIU Y L, LI Y N, LI C. Line loss calculation of optimized BP neural network based on genetic algorithm[J]. Computer Applications and Software, 2019, 36(3): 72-75. https://www.cnki.com.cn/Article/CJFDTOTAL-JYRJ201903015.htm
[10]	谢武明, 李俊, 周峰平, 等. 基于BP神经网络和遗传算法的污水处理厂电耗预测[J]. 水电能源科学, 2018, 36(8): 202-204. https://www.cnki.com.cn/Article/CJFDTOTAL-SDNY201808050.htm XIE W M, LI J, ZHOU F P, et al. Prediction of power consumption of wastewater treatment plant using BP neural network and genetic algorithm[J]. Water Resources and Power, 2018, 36(8): 202-204. https://www.cnki.com.cn/Article/CJFDTOTAL-SDNY201808050.htm
[11]	ABUALIGAH L M, KHADER A T, HANANDEH E S. A new feature selection method to improve the document clustering using particle swarm optimization algorithm[J]. Journal of Computational Science, 2017, 25: 456-466. http://www.sciencedirect.com/science/article/pii/S1877750316305002
[12]	叶永伟, 葛沈浩, 任设东, 等. 基于PSO-DE算法的污水处理优化控制研究[J]. 计算机测量与控制, 2016, 24(2): 68-70;76. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201602020.htm YE Y W, GE S H, REN S D, et al. Optimal control for wastewater treatment process based on mixed POS-DE algorithm[J]. Computer Measurement and Control, 2016, 24(2): 68-70;76. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201602020.htm
[13]	乔俊飞, 逄泽芳, 韩红桂. 基于改进粒子群算法的污水处理过程神经网络优化控制[J]. 智能系统学报, 2012, 7(5): 429-436. doi: 10.3969/j.issn.1673-4785.201205034 QIAO J F, PANG Z F, HAN H G. Neural network optimal control for wastewater treatment process based on APSO[J]. CAAI Transactions on Intelligent Systems, 2012, 7(5): 429-436. doi: 10.3969/j.issn.1673-4785.201205034
[14]	徐建栋. 遗传算法和神经网络在污水处理控制及监测系统中的应用研究[D]. 重庆: 重庆大学, 2007. XU J D. Application research of genetic algorithms and artificial neural network in control and monitoring system of waste water treatment[D]. Chongqing: Chongqing University, 2007.
[15]	JAMALIPOUR M, SAVAREH R, GHARIB M, et al. Quantum behaved particle swarm optimization with differential mutation operator applied to WWER-1000 in-core fuel management optimization[J]. Annals of Nuclear Energy, 2013, 54: 134-140. doi: 10.1016/j.anucene.2012.11.008
[16]	CHEN Y, LI L, HAIPENG P, et al. Particle swarm optimizer with two differential mutation[J]. Applied Soft Computing, 2017, 61: 314-330. doi: 10.1016/j.asoc.2017.07.020
[17]	吕振肃, 侯志荣. 自适应变异的粒子群优化算法[J]. 电子学报, 2004(3): 416-420. doi: 10.3321/j.issn:0372-2112.2004.03.016 LV Z S, HOU Z R. Particle swarm optimization with adaptive mutation[J]. Acta Electronica Sinica, 2004(3): 416-420. doi: 10.3321/j.issn:0372-2112.2004.03.016
[18]	孙湘, 周大为, 张希望. 惯性权重粒子群算法模型收敛性分析及参数选择[J]. 计算机工程与设计, 2010, 31(18): 4068-4071. https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201018037.htm SUN X, ZHOU D W, ZHANF X W. Convergence analysis and parameter selection of PSO model with inertia weight[J]. Computer Engineering and Design, 2010, 31(18): 4068-4071. https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201018037.htm
[19]	RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323: 533-536. doi: 10.1038/323533a0
[20]	PARK J B, LEE K S, SHIN J R, et al. A particle swarm optimization for economic dispatch with nonsmooth cost functions[J]. IEEE Transactions on Power Systems, 2005, 20(1): 34-42. doi: 10.1109/TPWRS.2004.831275
[21]	NEYESTANI M, FARSANGI M M, NEZAMABADI-POUR H, et al. A modified particle swarm optimization for economic dispatch with nonsmooth cost functions[J]. IFAC Proceedings Volumes, 2009, 42: 267-272. http://www.emeraldinsight.com/servlet/linkout?suffix=frd3&dbid=16&doi=10.1108%2F03321640710823046&key=10.1109%2FTPWRS.2004.831275
[22]	NIU G Q, YI X H, CHEN C, et al. A novel effluent quality predicting model based on genetic-deep belief network algorithm for cleaner production in a full-scale paper-ma-king wastewater treatment[J]. Journal of Cleaner Production, 2020, 265: 433-441. http://www.sciencedirect.com/science/article/pii/S0959652620318345

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