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
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摘要
针对城市污水处理过程关键出水参数难以实时检测的问题,文中提出了一种基于类脑模块化神经网络(Brain-like modular neural network, BLMNN)的关键出水参数软测量方法.首先,基于互信息和专家知识进行任务分解,分析关键出水参数的相关变量,获取各出水参数的辅助变量.其次,通过模拟大脑皮层模块化分区结构,构建软测量子模型对各水质参数进行同步测量,降低软测量模型复杂度的同时保证了其精度.最后,通过基于实际数据的仿真实验验证了所提出方法的准确性和有效性.
With the goal to realize the real-time measurement of key water quality parameters in wastewater treatment process, this paper constructs a novel soft-measurement model based on the brain-like modular neural network(BLMNN).First, based on the mutation information and expert knowledge, the easy-to-measure variables which have strong correlations to the effluent water quality parameters are chosen as the model inputs. Then, simulating the modular structure of brain cortex, the effluent water parameters are measured by different sub-models, improving both the modeling accuracy and modeling speed. The simulation results based on real data verify the accuracy and effectiveness of the proposed method.
With the goal to realize the real-time measurement of key water quality parameters in wastewater treatment process, this paper constructs a novel soft-measurement model based on the brain-like modular neural network(BLMNN).First, based on the mutation information and expert knowledge, the easy-to-measure variables which have strong correlations to the effluent water quality parameters are chosen as the model inputs. Then, simulating the modular structure of brain cortex, the effluent water parameters are measured by different sub-models, improving both the modeling accuracy and modeling speed. The simulation results based on real data verify the accuracy and effectiveness of the proposed method.
引文
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2 van Loosdrecht M C M, Brdjanovic D. Anticipating the next century of wastewater treatment. Science, 2014, 344(6191):1452-1453
3 Zhao Li-Jie, Yuan De-Cheng, Chai Tian-You. Identification of wastewater operational conditions based on multiclassification probabilistic extreme learning machine. CIESC Journal, 2012, 63(10):3173-3182(赵立杰,袁德成,柴天佑.基于多分类概率极限学习机的污水处理过程操作工况识别.化工学报, 2012, 63(10):3173-3182)
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6 Huang M Z, Ma Y W, Wan J Q, Chen X H. A sensorsoftware based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process. Applied Soft Computing, 2015, 27:1-10
7 Qin X S, Gao F R, Chen G H. Wastewater quality monitoring system using sensor fusion and machine learning techniques. Water Research, 2012, 46(4):1133-1144
8 Haimi H, Mulas M, Corona F, Vahala R. Data-derived soft-sensors for biological wastewater treatment plants:an overview. Environmental Modelling&Software, 2013, 47:88-107
9 Qiu Y, Liu Y Q, Huang D P. Date-driven soft-sensor design for biological wastewater treatment using deep neural networks and genetic algorithms. Journal of Chemical Engineering of Japan, 2016, 49(10):925-936
10 Yu Wei, Luo Fei, Yang Hong, Xu Yu-Ge. Multiple neural network-based model to predict ammonia nitrogen content in wastewater. Journal of South China University of Technology(Natural Science Edition), 2010, 38(12):79-83(余伟,罗飞,杨红,许玉格.基于多神经网络的污水氨氮预测模型.华南理工大学学报(自然科学版), 2010, 38(12):79-83)
11 Jiang Zhao-Hui, Dong Meng-Lin, Gui Wei-Hua, Yang ChunHua, Xie Yong-Fang. Two-dimensional prediction for silicon content of hot metal of blast furnace based on Bootstrap.Acta Automatica Sinica, 2016, 42(5):715-723(蒋朝辉,董梦林,桂卫华,阳春华,谢永芳.基于Bootstrap的高炉铁水硅含量二维预报.自动化学报, 2016, 42(5):715-723)
12 Cecati C, Kolbusz J, Rozycki P, Siano P, Wilamowski B M.A novel RBF training algorithm for short-term electric load forecasting and comparative studies. IEEE Transactions on industrial Electronics, 2015, 62(10):6519-6529
13 He S L, Li J. Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks. Applied Soft Computing, 2009, 9(3):954-961
14 Raduly B, Gernaey K V, Capodaglio A G, Mikkelsen P S,Henze M. Artificial neural networks for rapid WWTP performance evaluation:methodology and case study. Environmental Modelling&Software, 2007, 22(8):1208-1216
15 Bagheri M, Mirbagheri S A, Ehteshami M, Bagheri Z.Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks. Process Safety and Environmental Protection, 2015, 93:111-123
16 Han Hong-Gui, Qiao Jun-Fei, Bo Ying-Chun. On structure design for RBF neural network based on information strength. Acta Automatica Sinica, 2012, 38(7):1083-1090(韩红桂,乔俊飞,薄迎春.基于信息强度的RBF神经网络结构设计研究.自动化学报, 2012, 38(7):1083-1090)
17 Wan J Q, Huang M Z, Ma Y W, Gao W J, Wang Y,Zhang H P, et al. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive networkbased fuzzy inference system. Applied Soft Computing,2011, 11(3):3238-3246
18 Li F J, Qiao J F, Han H G, Yang C L. A self-organizing cascade neural network with random weights for nonlinear system modeling. Applied Soft Computing, 2016, 42:184-193
19 Han H G, Lu W, Hou Y, Qiao J F. An Adaptive-PSObased self-organizing RBF neural network. IEEE Transactions on Neural Networks and Learning Systems, 2018,29(1):104-117
20 Zeng Yi, Liu Cheng-Lin, Tan Tie-Niu. Retrospect and outlook of brain-inspired intelligence research. Chinese Journal of Computers, 2016, 39(1):212-222(曾毅,刘成林,谭铁牛.类脑智能研究的回顾与展望.计算机学报,2016, 39(1):212-222)
21 Park H J, Friston K. Structural and functional brain networks:from connections to cognition. Science, 2013,342(6158):1238411
22 Meng X, Rozycki P, Qiao J F, Wilamowski B M. Nonlinear system modeling using RBF networks for industrial application. IEEE Transactions on Industrial Informatics, 2018,14(3):931-940
23 Fernandez de Canete J, del Saz-Orozco P, Baratti R, Mulas M, Ruano A, Garcia-Cerezo A. Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network. Expert Systems with Applications, 2016, 63:8-19
24 Tang Jian, Chai Tian-You, Liu Zhuo, Yu Wen, Zhou XiaoJie. Adaptive ensemble modelling approach based on updating sample intelligent identification. Acta Automatica Sinica, 2016, 42(7):1040-1052(汤健,柴天佑,刘卓,余文,周晓杰.基于更新样本智能识别算法的自适应集成建模.自动化学报, 2016, 42(7):1040-1052)
25 Xie T T, Yu H, Hewlett J, Rozycki P, Wilamowski B. Fast and efficient second-order method for training radial basis function networks. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(4):609-619
26 Wu S Q, Er M J, Gao Y. A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Transactions on Fuzzy Systems, 2001, 9(4):578-594
27 Han H G, Qiao J F. A self-organizing fuzzy neural network based on a growing-and-pruning algorithm. IEEE Transactions on Fuzzy Systems, 2010, 18(6):1129-1143
28 Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3):273-297
29 Huang G B, Chen L, Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 2006, 17(4):879-892
30 Feng H M. Self-generation RBFNs using evolutional PSO learning. Neurocomputing, 2006, 70(1-3):241-251
31 Huang G B, Saratchandran P, Sundararajan N. An efficient sequential learning algorithm for growing and pruning RBF(GAP-RBF)networks. IEEE Transactions on Systems,Man, and Cybernetics, Part B(Cybernetics), 2004, 34(6):2284-2292
2 van Loosdrecht M C M, Brdjanovic D. Anticipating the next century of wastewater treatment. Science, 2014, 344(6191):1452-1453
3 Zhao Li-Jie, Yuan De-Cheng, Chai Tian-You. Identification of wastewater operational conditions based on multiclassification probabilistic extreme learning machine. CIESC Journal, 2012, 63(10):3173-3182(赵立杰,袁德成,柴天佑.基于多分类概率极限学习机的污水处理过程操作工况识别.化工学报, 2012, 63(10):3173-3182)
4 Jin L Y, Zhang G M, Tian H F. Current state of sewage treatment in China. Water Research, 2014, 66:85-98
5 Chon K, Lee Y, Traber J, von Gunten U. Quantification and characterization of dissolved organic nitrogen in wastewater effluents by electrodialysis treatment followed by sizeexclusion chromatography with nitrogen detection. Water research, 2013, 47(14):5381-5391
6 Huang M Z, Ma Y W, Wan J Q, Chen X H. A sensorsoftware based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process. Applied Soft Computing, 2015, 27:1-10
7 Qin X S, Gao F R, Chen G H. Wastewater quality monitoring system using sensor fusion and machine learning techniques. Water Research, 2012, 46(4):1133-1144
8 Haimi H, Mulas M, Corona F, Vahala R. Data-derived soft-sensors for biological wastewater treatment plants:an overview. Environmental Modelling&Software, 2013, 47:88-107
9 Qiu Y, Liu Y Q, Huang D P. Date-driven soft-sensor design for biological wastewater treatment using deep neural networks and genetic algorithms. Journal of Chemical Engineering of Japan, 2016, 49(10):925-936
10 Yu Wei, Luo Fei, Yang Hong, Xu Yu-Ge. Multiple neural network-based model to predict ammonia nitrogen content in wastewater. Journal of South China University of Technology(Natural Science Edition), 2010, 38(12):79-83(余伟,罗飞,杨红,许玉格.基于多神经网络的污水氨氮预测模型.华南理工大学学报(自然科学版), 2010, 38(12):79-83)
11 Jiang Zhao-Hui, Dong Meng-Lin, Gui Wei-Hua, Yang ChunHua, Xie Yong-Fang. Two-dimensional prediction for silicon content of hot metal of blast furnace based on Bootstrap.Acta Automatica Sinica, 2016, 42(5):715-723(蒋朝辉,董梦林,桂卫华,阳春华,谢永芳.基于Bootstrap的高炉铁水硅含量二维预报.自动化学报, 2016, 42(5):715-723)
12 Cecati C, Kolbusz J, Rozycki P, Siano P, Wilamowski B M.A novel RBF training algorithm for short-term electric load forecasting and comparative studies. IEEE Transactions on industrial Electronics, 2015, 62(10):6519-6529
13 He S L, Li J. Modeling nonlinear elastic behavior of reinforced soil using artificial neural networks. Applied Soft Computing, 2009, 9(3):954-961
14 Raduly B, Gernaey K V, Capodaglio A G, Mikkelsen P S,Henze M. Artificial neural networks for rapid WWTP performance evaluation:methodology and case study. Environmental Modelling&Software, 2007, 22(8):1208-1216
15 Bagheri M, Mirbagheri S A, Ehteshami M, Bagheri Z.Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks. Process Safety and Environmental Protection, 2015, 93:111-123
16 Han Hong-Gui, Qiao Jun-Fei, Bo Ying-Chun. On structure design for RBF neural network based on information strength. Acta Automatica Sinica, 2012, 38(7):1083-1090(韩红桂,乔俊飞,薄迎春.基于信息强度的RBF神经网络结构设计研究.自动化学报, 2012, 38(7):1083-1090)
17 Wan J Q, Huang M Z, Ma Y W, Gao W J, Wang Y,Zhang H P, et al. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive networkbased fuzzy inference system. Applied Soft Computing,2011, 11(3):3238-3246
18 Li F J, Qiao J F, Han H G, Yang C L. A self-organizing cascade neural network with random weights for nonlinear system modeling. Applied Soft Computing, 2016, 42:184-193
19 Han H G, Lu W, Hou Y, Qiao J F. An Adaptive-PSObased self-organizing RBF neural network. IEEE Transactions on Neural Networks and Learning Systems, 2018,29(1):104-117
20 Zeng Yi, Liu Cheng-Lin, Tan Tie-Niu. Retrospect and outlook of brain-inspired intelligence research. Chinese Journal of Computers, 2016, 39(1):212-222(曾毅,刘成林,谭铁牛.类脑智能研究的回顾与展望.计算机学报,2016, 39(1):212-222)
21 Park H J, Friston K. Structural and functional brain networks:from connections to cognition. Science, 2013,342(6158):1238411
22 Meng X, Rozycki P, Qiao J F, Wilamowski B M. Nonlinear system modeling using RBF networks for industrial application. IEEE Transactions on Industrial Informatics, 2018,14(3):931-940
23 Fernandez de Canete J, del Saz-Orozco P, Baratti R, Mulas M, Ruano A, Garcia-Cerezo A. Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network. Expert Systems with Applications, 2016, 63:8-19
24 Tang Jian, Chai Tian-You, Liu Zhuo, Yu Wen, Zhou XiaoJie. Adaptive ensemble modelling approach based on updating sample intelligent identification. Acta Automatica Sinica, 2016, 42(7):1040-1052(汤健,柴天佑,刘卓,余文,周晓杰.基于更新样本智能识别算法的自适应集成建模.自动化学报, 2016, 42(7):1040-1052)
25 Xie T T, Yu H, Hewlett J, Rozycki P, Wilamowski B. Fast and efficient second-order method for training radial basis function networks. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(4):609-619
26 Wu S Q, Er M J, Gao Y. A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Transactions on Fuzzy Systems, 2001, 9(4):578-594
27 Han H G, Qiao J F. A self-organizing fuzzy neural network based on a growing-and-pruning algorithm. IEEE Transactions on Fuzzy Systems, 2010, 18(6):1129-1143
28 Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3):273-297
29 Huang G B, Chen L, Siew C K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 2006, 17(4):879-892
30 Feng H M. Self-generation RBFNs using evolutional PSO learning. Neurocomputing, 2006, 70(1-3):241-251
31 Huang G B, Saratchandran P, Sundararajan N. An efficient sequential learning algorithm for growing and pruning RBF(GAP-RBF)networks. IEEE Transactions on Systems,Man, and Cybernetics, Part B(Cybernetics), 2004, 34(6):2284-2292