SBR法污水处理过程建模与控制技术研究
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摘要
随着经济的发展和城市化进程的加快,水资源的污染程度也越来越严重。为加大对水资源的保护。近几年,我国建立了大量的污水处理厂。序列间歇式活性污泥(SBR)法作为一种利用自然界微生物生命活动来去除污水中的有机物以及脱氮除磷的有效方法,在新建污水处理厂中得到广泛应用。然而由于污水处理过程具有非线性、多变量、和大时滞的特点,因此运用传统的数学建模方法很难为污水处理过程建立精确的模型,从而在一定程度上制约了污水处理行业自动控制水平的提高。因此,加强污水处理过程建模及控制算法的研究,将有利于水处理行业的快速发展。
本文从解析ASM1模型入手,较为全面的了解了污水处理过程的内部机理。在污水处理行业建模经验的基础上,分别用BP和RBF两种神经网络建立了两个可以预测出水水质中化学需氧量(COD)含量的预测模型。通过Matlab软件进行了仿真,并分析了选择不同隐含层神经元个数和不同隐含层激励函数对模型预测精度的影响。仿真结果显示:两种模型都能较好的完成对出水COD的预测,其中BP网络模型的相对误差在3%以内, RBF网络模型的最大相对误差达到了9%,但从训练时间上来看RBF网络要远小于BP网络。
另外,从污水处理过程来看,溶解氧(DO)作为影响污水处理效果的重要参数,对它的控制效果直接决定了出水水质的好坏,因此对DO的控制将是污水处理过程中的控制重点。但是对于城市生活污水来讲,在不同的时间段,污水中各种成分的含量波动比较大,所以对DO的控制又是一个控制难点。针对传统PID控制算法在污水处理过程中控制效果不尽理想的现状,结合污水处理过程本身的特点。本文引入了带自修正因子的模糊控制算法,并建立了相应的模糊控制器,实现了对DO含量的控制。通过Matlab软件对普通模糊控制器和带自调整因子的模糊控制器进行了仿真,结果显示:带自修正因子的模糊控制器可以很好的实现控制要求。带自修正因子的模糊控制系统比普通的模糊控制系统有着更好稳定性、实时性和鲁棒性。
With economic development and accelerated urbanization,the pollution of water resources are becoming increasingly serious. To increase the water resources protection, a large number of sewage treatment plants under construction. Activated sludge process is a kind of effective method, which consumes the substrates and phosphate release and denitrification in wastewater by microorganism’s metabolism. In the new sewage treatment plant, it also has been widely used. However, due to the sewage treatment process with the characteristics of nonlinear, multivariable, and large time delay, it is difficult to establish a precisely model for the sewage treatment process. To a certain extent, it restricts the control level of the sewage treatment process. Therefore, to enhance wastewater treatment process modeling and control algorithm, will be conducive to the rapid development of the water treatment industry
In this paper, from the analytical model of the ASM1 to understand the internal mechanism of the sewage treatment. On the basis of modeling experience in sewage treatment industry, has established two prediction models to predict COD. It was simulated by Matlab software, and analyzed the effects of the number of hidden layer neurons and different hidden layer activation function to the model prediction accuracy. Simulation results show that: both models can better predict the content of the effluent COD, and the relative error of BP network model is less than 3%, while the maximum relative error of the RBF network model achieves 9%, however, the RBF network training time is far less than the BP network.
In addition, the dissolved oxygen (DO) is an important parameter of sewage treatment, it’s control result determines the water quality. Therefore, the control of the DO is the control focus of the sewage treatment process. But the components of urban sewage is Volatile at different time, so,it is difficult to control the DO very well. as the traditional PID control method does not work very well in sewage treatment process, the author introduces the fuzzy controller based on self- adjustment factor and established a control system for the DO concentration. The ordinary fuzzy control and with self-adjusting factor fuzzy control is simulated by Matlab software.The simulation results show that: with self-modifying factor fuzzy controller can achieve good control requirements. It has better stability, real-time and robustness than ordinary fuzzy control system.
本文从解析ASM1模型入手,较为全面的了解了污水处理过程的内部机理。在污水处理行业建模经验的基础上,分别用BP和RBF两种神经网络建立了两个可以预测出水水质中化学需氧量(COD)含量的预测模型。通过Matlab软件进行了仿真,并分析了选择不同隐含层神经元个数和不同隐含层激励函数对模型预测精度的影响。仿真结果显示:两种模型都能较好的完成对出水COD的预测,其中BP网络模型的相对误差在3%以内, RBF网络模型的最大相对误差达到了9%,但从训练时间上来看RBF网络要远小于BP网络。
另外,从污水处理过程来看,溶解氧(DO)作为影响污水处理效果的重要参数,对它的控制效果直接决定了出水水质的好坏,因此对DO的控制将是污水处理过程中的控制重点。但是对于城市生活污水来讲,在不同的时间段,污水中各种成分的含量波动比较大,所以对DO的控制又是一个控制难点。针对传统PID控制算法在污水处理过程中控制效果不尽理想的现状,结合污水处理过程本身的特点。本文引入了带自修正因子的模糊控制算法,并建立了相应的模糊控制器,实现了对DO含量的控制。通过Matlab软件对普通模糊控制器和带自调整因子的模糊控制器进行了仿真,结果显示:带自修正因子的模糊控制器可以很好的实现控制要求。带自修正因子的模糊控制系统比普通的模糊控制系统有着更好稳定性、实时性和鲁棒性。
With economic development and accelerated urbanization,the pollution of water resources are becoming increasingly serious. To increase the water resources protection, a large number of sewage treatment plants under construction. Activated sludge process is a kind of effective method, which consumes the substrates and phosphate release and denitrification in wastewater by microorganism’s metabolism. In the new sewage treatment plant, it also has been widely used. However, due to the sewage treatment process with the characteristics of nonlinear, multivariable, and large time delay, it is difficult to establish a precisely model for the sewage treatment process. To a certain extent, it restricts the control level of the sewage treatment process. Therefore, to enhance wastewater treatment process modeling and control algorithm, will be conducive to the rapid development of the water treatment industry
In this paper, from the analytical model of the ASM1 to understand the internal mechanism of the sewage treatment. On the basis of modeling experience in sewage treatment industry, has established two prediction models to predict COD. It was simulated by Matlab software, and analyzed the effects of the number of hidden layer neurons and different hidden layer activation function to the model prediction accuracy. Simulation results show that: both models can better predict the content of the effluent COD, and the relative error of BP network model is less than 3%, while the maximum relative error of the RBF network model achieves 9%, however, the RBF network training time is far less than the BP network.
In addition, the dissolved oxygen (DO) is an important parameter of sewage treatment, it’s control result determines the water quality. Therefore, the control of the DO is the control focus of the sewage treatment process. But the components of urban sewage is Volatile at different time, so,it is difficult to control the DO very well. as the traditional PID control method does not work very well in sewage treatment process, the author introduces the fuzzy controller based on self- adjustment factor and established a control system for the DO concentration. The ordinary fuzzy control and with self-adjusting factor fuzzy control is simulated by Matlab software.The simulation results show that: with self-modifying factor fuzzy controller can achieve good control requirements. It has better stability, real-time and robustness than ordinary fuzzy control system.
引文
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[6]刘欢.SBR工艺脱氮除磷的应用研究[M].西安建筑科技大学,2006.6
[7]叶玲平.污水处理过程建模与控制[M].浙江工业大学,2005
[8]石婷.活性污泥数学模型(ASM1)水质特性参数研究[M].西安建筑科技大学,2007.6
[9]李士勇.模糊控制、神经控制和智能控制论[M].哈尔滨工业大学出版社,2004
[10]葛哲学,孙志强.神经网络理论与MATLABR2007实现[M].电子工业出版社,2007
[11] Cote M, Grand jean B.P, Lizard P. Dynamic modeling of the activated sludge process: improving prediction using neural network [J]. Water Research.1995, 29(4):995~1004
[12] Tay JH, Zhang X Y. A fast predicting neural fuzzy model for high rate anaerobic wastewater treatment systems[J]. Water Research.2000, 34(11):2849~2860
[13]彭永臻等.基于BP神经网络的A/O脱氮系统外加碳源的仿真研究[J].化工学报,2004,第2期
[14]董长虹. MATLAB神经网络与应用[M].国防工业出版社
[15]郭劲松,龙腾锐.间歇曝气活性污泥系统神经网络水质模型[J].中国给水排水.2000,16(11):15~18
[16]田禹,王宝贞,周定.基于BP人工神经网络的臭氧生物活性污泥系统建模研究[J].中国给水排水.1998,14(3):24~27
[17]张乃尧,阎平凡.神经网络与模糊控制[M].清华大学出版社,1998
[18]胡洁.BP和RBF神经网络短时交通流预测结果比较[J].长江大学学报(自然版)理工卷.2007,9第4卷第3期
[19]诸静.模糊控制原理与应用[M].机械工业出版社,2005
[20]曾光奇,胡均安,王东等.模糊控制理论与工程应用[M].华中科技大学出版社,2006
[21] Lee C C. Fuzzy Logic Control System: Fuzzy Logic Controller-Part I[J]. IEEE Trans, SMC,1990,20(2)404~418.
[22]汤兵勇,路林吉等.模糊控制理论与应用技术[M].清华大学出版社,2002
[23]毕雪芹,倪原,王丽娟.活性污泥水处理模糊控制系统设计及仿真[J].西安工业大学学报.2007.12第27卷第6期
[24]曾薇,彭永臻.以溶解氧浓度作为SBR法模糊控制参数[J].中国给水排水,1999,15(4):5~10
[25]曾薇,彭永臻.pH值与温度对SBR法反应时间控制的影响[J].中国环境科学,2002,22(5):456~459.
[26]曾薇,王淑莹,彭永臻等.供氧方式对SBR法硝化过程控制的影响[J].环境科学,2002,21(6):571~575
[27]张自杰.排水工程(第四版)[M].北京:中国建筑工业出版社,1999
[28]李探微,王亚宜.SBR法中DO的变化及其作为污水处理控制参数的研究[J].浙江工业大学学报,2001,29(2):95~97
[29] Edgar N. Sanchez, Jose M, Gonzalez Esperanza Ramirez. Minmal PD Fuzzy Control of A Wastewater Treatment Plant. Proceedings of the 15th IEEE International Symposium on Intelligent Control, Rio, Patras, GREECE.2000, 7:17~19
[30]胡玉玲.活性污泥污水处理系统模糊神经网络控制研究[M].北京工业大学,2004.5
[31]钟权龙,姜长洪等.改善模糊控制器动态特性的方法研究[J].沈阳化工学院学报,2000,14(2):95-99
[32]高宏岩,王建辉.在线自调整修正因子模糊控制方法和应用[J].控制系统,2006,第22卷第5期
[33]胡玉玲.变参数活性污泥系统溶解氧的模糊神经网络控制[J].电工技术学报,2004,19(3):36~40
[34]李志强.SBR污水处理工艺模糊控制器设计及仿真[M].重庆大学,2008.4
[35]程金,张承慧,夏东伟.一种控制规则自调整的模糊控制器[J].中国工程科学2003,9第5卷第9期
[36] Sun Julu, etal. Application of self-adjusting fuzzy controller in a vector controlled induction motor drive [J]. IPEMC’2000, Beijing China, 2000: 1197~1201
[37]马长华,朱伟兴.比例因子对模糊控制器动态性能的影响[J].江苏理工大学学报(自然科学版),2000,21(5):87~90
[38]王丽娟,张建锋,王斌.活性污泥水处理模糊控制系统设计[J].计算机工程与设计,2009,30(18)
[39] Aoi T, Okaniwa Y, Hagiwara K, etal.A direct ammonium control system using fuzzy inference in a high-load biological denitrification process treating collected human create [J].Wat.SciTech,1992,26(5-6):1325~1334
[40] Mamdani,E.H,Application of fuzzy algoriehms for control of simple dynamic plant[J]. IEEE Proc control and science, 1974,121 (12):1585~1588
[41]孙建民.基于待修正因子的模糊控制汽车主动悬架系统的研究[J].振动工程学报.2005.3第18卷第1期
[42] Gao X D etal. Application of fuzzy logic controller in the seam tracking of arc-welding robot [J]. IECON’97, The 23rd Annual International Conference of the IEEE Industrial Electronics Society, New Orlean, Louisiana, 1997: 1367~1372
[43]龙升照,汪培庄.FUZZY控制规则的自调整问题[J].模糊数学,1982.3,105~112
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