废水处理系统水质特征动态分析的混合智能控制研究
|
摘要
近年来我国水污染问题尤为突出,治理任务相当繁重。为了更高效地治理废水污染,人们已逐步将先进的计算机技术和自动化技术应用于污水处理的过程控制中,构建各种高性能废水处理控制系统,以保证处理过程稳定、可靠、安全,提高出水水质,降低能耗、药耗和人工成本,从而实现污水处理的稳定、经济和良性运行。传统控制理论由于无法逼近复杂的非线性关系等自身的缺陷,控制效果也不尽人意,智能控制是控制理论发展的高级阶段,包括神经网络、模糊控制、专家控制等,可以根据废水水质参数的时变性,准确控制废水处理过程中的各项工艺参数,达到最好的处理效果,是当前污水处理工业控制领域一项备受关注的研究课题。
本课题在较全面地分析了污水处理智能控制研究现状,以及模糊控制(FC)、神经网络(ANN)、遗传算法(GA)基本原理的基础上,以神经网络和模糊逻辑(FL)理论为核心提出了污水处理混合智能控制思想,将模糊控制与PID、神经网络及遗传算法相结合的混合智能控制方法引入本课题组研制开发的高效一体化废水处理工艺自动控制系统中,并用智能方法对废水中的典型有机物在此系统中的降解机理进行建模及对其运行状况进行评估,在污水处理混合智能控制跟建模研究方面进行了一些开拓性和探索性的研究工作,包括如下几个方面:
(1)论文构建了基于Windows CE.NET嵌入式操作系统和MCGS组态软件的嵌入式废水处理自动控制系统,并在此基础上提出了Fuzzy-PID、GA-BP和改进模糊神经网络混合智能控制结构和设计方法。
(2)针对污水处理厂水量、水质对控制系统稳定性的影响和目前水质在线检测仪器(仪表传感器)欠缺这一实际问题,广泛采用智能化建模技术,提出了利用将神经网络建模技术与模糊数学模型结合的水质多目标预测智能计算(软测量)模型与算法,并结合数据统计分析方法(PCA),估计出水水质考核指标,为解决水质测量滞后对污水处理控制系统带来的不平稳进行了创新性研究。
(3)在本课题组自行开发的高效一体化混凝反应器的基础上,通过对废水处理混凝特征及模糊神经网络理论的分析,提出了基于聚类算法的混凝投药智能预测和控制模型。通过模糊C均值聚类方法从样本数据中总结出12条模糊规则,同时结合混合算法完成网络的结构辨识和参数辨识,最后将预测控制模型与MCGS组态挂接实现加药量的自动调节和出水COD跟踪期望COD为目标的反馈控制,能准确的控制加药量。
(4)在对污水机理模型研究的基础上,针对机理建模难以应用于控制研究中的状况,提出活性污泥污水处理系统的一种变参数数学模型,可以反映出曝气量与溶解氧浓度的内在关系,并在对模型性能分析与综合的基础上,结合前馈与反馈控制方法,建立了溶解氧的两级模糊神经网络混合控制器,通过仿真试验,证明控制系统具有较好的快速性、稳定性,稳态性能以及鲁棒性,证实了控制方案的可行性与有效性。
(5)分析确定了不同进水负荷在A/O系统的最佳回流比和反硝化反应对进水负荷利用效率,获得其内循环控制策略,并根据营养物质在系统中的变化规律,建立营养物浓度动态变化的模糊神经网络预测模型,可以跟踪营养物在A/O系统中的动态变化,最后以缺氧池末端的硝酸氮作为建模参数,进行模糊神经网络控制研究,实现反应器的脱氮处理控制。
(6)通过测定邻苯二甲酸二丁酯(DnBP)在A2/O系统的三相有机物含量,确定不同性质有机组分迁移转化规律,研究其在厌氧、缺氧和好氧去除机理,建立起相应的动力学去除模型;同时在此基础上,通过各个参数之间的关系,运用神经网络和遗传算法理论,基于DnBP去除机理,建立了DnBP的GA-BP模型,实现DnBP在A2/O系统中的动态变化的预测,丰富了去除机理。
本文对于智能控制技术在废水处理领域的深入研究和应用具有重要的参考价值。
Recently wastewater pollution is very severe and the treating is still a heavy task. In order to treat wastewater effectively and realize recycling, it is important to develop computer operational decision support systems (computer technology and automation technology) that are able to play a similar role to the expert in wastewater treatment process control, in which it can ensure the operation safety, stable and reliable, and improve operational performance of wastewater treatment processes, so as to realize the wastewater treatment continuous, economic and benign operation. Previously, applications of control theory to wastewater treatment mainly focused on issues of nonlinearity, uncertainty and posterity, where difficulties in establishing accurate mathematical models and designing reliable controllers existed. Intelligent control is recently developed from conventional control theory. It consists of several control theories, such as fuzzy control (FC), artificial neural network control (ANNC), expert control, etc. The intelligent control of wastewater treatment system is a focus in wastewater treatment research field.
Base on the all-around review and analysis of the progress of wastewater treatment study, a hybrid-intelligent control method was presented in line with neural network and fuzzy logic theory, in which FC, PID and GA were all taken into consideration. The intelligent-intelligent control was used in highly effective integration wastewater treatment control system; using intelligent methods, the degradation mechanism of typical organic matter was modeled and the operation status was assessed; and several valuable conclusions were reached.
In this paper, according to the characteristics of wastewater treatment process, the automatic control system based on Windows CE.NET OS and MCGS software of wastewater was constructed , and the design method and the hybrid intelligent structure were proposed with the consideration of Fuzzy -PID, GA- BP and Fuzzy neural network control.
The model construction techniques of ANN and fuzzy mathematics were combined to describe the wastewater treatment process, then a soft-computation model was built for water quality prediction and the influent water indexes were evaluated. Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model’s architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. For this reason, the lag problem of measuring system is solved satisfactorily.
A fuzzy neural network predictive control scheme for studying the coagulation process of wastewater treatment was given based on the characteristic of wastewater treatment and fuzzy neural network’s analysis. An adaptive fuzzy neural network was employed to model the nonlinear relationships between the removal rate of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more ?exible learning ability. The system includes a fuzzy neural network emulator of the reaction process, a fuzzy neural network controller, and an optimization procedure based on a performance function that is used to identify desired control inputs. The gradient descent algorithm method was used to realize the optimization procedure. After fuzzy C-means clustering to identify models’architecture and a hybrid learning rule to identify parameters, the simulation indicated that the predictive model had good ability both in learning and generalization. Also, Fuzzy neural network algorithm with MCGS development package using VB program was developed,and then it was embedded into MCGS according to MCGS interface function criterion to achieve intelligent control system for wastewater treatment,so the control model could change the dosage according to different situation
According to analyze the inherent mechanism of wastewater treatment process, mathematics model was established, which reflects the relations among dissolved oxygen, microorganism and substrate. And an integrated neural-fuzzy process controller was developed for studying the aeration of wastewater treatment. In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. The fuzzy neural network proved to be very effective in modeling the aeration performs better than artificial neural networks (ANN). For comparing between operation with and without the fuzzy neural controller, and comparing with operation with PID controller, a aeration unit in a Wastewater Treatment Process was picked up to support the derivation of a solid fuzzy control rule base. It is shown that, using the fuzzy neural controller, in terms of the cost effectiveness, it enables us to save almost 33% of the operation cost during the time period when the controller can be applied. Thus, the fuzzy neural network proved to be a robust and effective control tool, easy to integrate in a global monitoring system for cost managing.
Analyzing and identifying the best reflux ratio and the denitrification efficiency of the influent loading under the different influent loading in A/O system, the inner loop control strategy was obtained; and on basis of the change law of nutrients in the system, fuzzy neural network models for forecasting dynamic change of nutrient concentration were designed, which can track the dynamic changes of the nutrients in the A/O system; taking nitrate nitrogen in the end of the anaerobic pond as modeling parameter, the fuzzy neural network control model to control nitrate nitrogen in the reactor is designed.
The lab scale tests for the anaerobic/anoxic/oxic (AAO) process were carried out to investigate the removal and fate of di-n-butyl phthalate (DnBP) and optimum systematic operation parameters. Transfer and transformation of organic substances in the AAO system were especially concerned. The removal characteristics of phthalate esters were studied in anaerobic/anoxic/oxic (AAO) processes. A removal (biological degradation and sorption) model was formulated, and kinetic parameters were evaluated with batch experiments under anaerobic, anoxic, oxic conditions. Furthermore, the intelligent methods (neural networks and genetic algorithm) were used to model the relationships between phthalate esters and the water quality characteristic parameters, so as to realize to forecast the effluent quality of anaerobic/anoxic/oxic reactors of the AAO process.
The research can provide guidance for the further study of intelligent control in the field of wastewater treatment and the popularization of wastewater treatment project with intelligent control.
本课题在较全面地分析了污水处理智能控制研究现状,以及模糊控制(FC)、神经网络(ANN)、遗传算法(GA)基本原理的基础上,以神经网络和模糊逻辑(FL)理论为核心提出了污水处理混合智能控制思想,将模糊控制与PID、神经网络及遗传算法相结合的混合智能控制方法引入本课题组研制开发的高效一体化废水处理工艺自动控制系统中,并用智能方法对废水中的典型有机物在此系统中的降解机理进行建模及对其运行状况进行评估,在污水处理混合智能控制跟建模研究方面进行了一些开拓性和探索性的研究工作,包括如下几个方面:
(1)论文构建了基于Windows CE.NET嵌入式操作系统和MCGS组态软件的嵌入式废水处理自动控制系统,并在此基础上提出了Fuzzy-PID、GA-BP和改进模糊神经网络混合智能控制结构和设计方法。
(2)针对污水处理厂水量、水质对控制系统稳定性的影响和目前水质在线检测仪器(仪表传感器)欠缺这一实际问题,广泛采用智能化建模技术,提出了利用将神经网络建模技术与模糊数学模型结合的水质多目标预测智能计算(软测量)模型与算法,并结合数据统计分析方法(PCA),估计出水水质考核指标,为解决水质测量滞后对污水处理控制系统带来的不平稳进行了创新性研究。
(3)在本课题组自行开发的高效一体化混凝反应器的基础上,通过对废水处理混凝特征及模糊神经网络理论的分析,提出了基于聚类算法的混凝投药智能预测和控制模型。通过模糊C均值聚类方法从样本数据中总结出12条模糊规则,同时结合混合算法完成网络的结构辨识和参数辨识,最后将预测控制模型与MCGS组态挂接实现加药量的自动调节和出水COD跟踪期望COD为目标的反馈控制,能准确的控制加药量。
(4)在对污水机理模型研究的基础上,针对机理建模难以应用于控制研究中的状况,提出活性污泥污水处理系统的一种变参数数学模型,可以反映出曝气量与溶解氧浓度的内在关系,并在对模型性能分析与综合的基础上,结合前馈与反馈控制方法,建立了溶解氧的两级模糊神经网络混合控制器,通过仿真试验,证明控制系统具有较好的快速性、稳定性,稳态性能以及鲁棒性,证实了控制方案的可行性与有效性。
(5)分析确定了不同进水负荷在A/O系统的最佳回流比和反硝化反应对进水负荷利用效率,获得其内循环控制策略,并根据营养物质在系统中的变化规律,建立营养物浓度动态变化的模糊神经网络预测模型,可以跟踪营养物在A/O系统中的动态变化,最后以缺氧池末端的硝酸氮作为建模参数,进行模糊神经网络控制研究,实现反应器的脱氮处理控制。
(6)通过测定邻苯二甲酸二丁酯(DnBP)在A2/O系统的三相有机物含量,确定不同性质有机组分迁移转化规律,研究其在厌氧、缺氧和好氧去除机理,建立起相应的动力学去除模型;同时在此基础上,通过各个参数之间的关系,运用神经网络和遗传算法理论,基于DnBP去除机理,建立了DnBP的GA-BP模型,实现DnBP在A2/O系统中的动态变化的预测,丰富了去除机理。
本文对于智能控制技术在废水处理领域的深入研究和应用具有重要的参考价值。
Recently wastewater pollution is very severe and the treating is still a heavy task. In order to treat wastewater effectively and realize recycling, it is important to develop computer operational decision support systems (computer technology and automation technology) that are able to play a similar role to the expert in wastewater treatment process control, in which it can ensure the operation safety, stable and reliable, and improve operational performance of wastewater treatment processes, so as to realize the wastewater treatment continuous, economic and benign operation. Previously, applications of control theory to wastewater treatment mainly focused on issues of nonlinearity, uncertainty and posterity, where difficulties in establishing accurate mathematical models and designing reliable controllers existed. Intelligent control is recently developed from conventional control theory. It consists of several control theories, such as fuzzy control (FC), artificial neural network control (ANNC), expert control, etc. The intelligent control of wastewater treatment system is a focus in wastewater treatment research field.
Base on the all-around review and analysis of the progress of wastewater treatment study, a hybrid-intelligent control method was presented in line with neural network and fuzzy logic theory, in which FC, PID and GA were all taken into consideration. The intelligent-intelligent control was used in highly effective integration wastewater treatment control system; using intelligent methods, the degradation mechanism of typical organic matter was modeled and the operation status was assessed; and several valuable conclusions were reached.
In this paper, according to the characteristics of wastewater treatment process, the automatic control system based on Windows CE.NET OS and MCGS software of wastewater was constructed , and the design method and the hybrid intelligent structure were proposed with the consideration of Fuzzy -PID, GA- BP and Fuzzy neural network control.
The model construction techniques of ANN and fuzzy mathematics were combined to describe the wastewater treatment process, then a soft-computation model was built for water quality prediction and the influent water indexes were evaluated. Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model’s architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. For this reason, the lag problem of measuring system is solved satisfactorily.
A fuzzy neural network predictive control scheme for studying the coagulation process of wastewater treatment was given based on the characteristic of wastewater treatment and fuzzy neural network’s analysis. An adaptive fuzzy neural network was employed to model the nonlinear relationships between the removal rate of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more ?exible learning ability. The system includes a fuzzy neural network emulator of the reaction process, a fuzzy neural network controller, and an optimization procedure based on a performance function that is used to identify desired control inputs. The gradient descent algorithm method was used to realize the optimization procedure. After fuzzy C-means clustering to identify models’architecture and a hybrid learning rule to identify parameters, the simulation indicated that the predictive model had good ability both in learning and generalization. Also, Fuzzy neural network algorithm with MCGS development package using VB program was developed,and then it was embedded into MCGS according to MCGS interface function criterion to achieve intelligent control system for wastewater treatment,so the control model could change the dosage according to different situation
According to analyze the inherent mechanism of wastewater treatment process, mathematics model was established, which reflects the relations among dissolved oxygen, microorganism and substrate. And an integrated neural-fuzzy process controller was developed for studying the aeration of wastewater treatment. In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. The fuzzy neural network proved to be very effective in modeling the aeration performs better than artificial neural networks (ANN). For comparing between operation with and without the fuzzy neural controller, and comparing with operation with PID controller, a aeration unit in a Wastewater Treatment Process was picked up to support the derivation of a solid fuzzy control rule base. It is shown that, using the fuzzy neural controller, in terms of the cost effectiveness, it enables us to save almost 33% of the operation cost during the time period when the controller can be applied. Thus, the fuzzy neural network proved to be a robust and effective control tool, easy to integrate in a global monitoring system for cost managing.
Analyzing and identifying the best reflux ratio and the denitrification efficiency of the influent loading under the different influent loading in A/O system, the inner loop control strategy was obtained; and on basis of the change law of nutrients in the system, fuzzy neural network models for forecasting dynamic change of nutrient concentration were designed, which can track the dynamic changes of the nutrients in the A/O system; taking nitrate nitrogen in the end of the anaerobic pond as modeling parameter, the fuzzy neural network control model to control nitrate nitrogen in the reactor is designed.
The lab scale tests for the anaerobic/anoxic/oxic (AAO) process were carried out to investigate the removal and fate of di-n-butyl phthalate (DnBP) and optimum systematic operation parameters. Transfer and transformation of organic substances in the AAO system were especially concerned. The removal characteristics of phthalate esters were studied in anaerobic/anoxic/oxic (AAO) processes. A removal (biological degradation and sorption) model was formulated, and kinetic parameters were evaluated with batch experiments under anaerobic, anoxic, oxic conditions. Furthermore, the intelligent methods (neural networks and genetic algorithm) were used to model the relationships between phthalate esters and the water quality characteristic parameters, so as to realize to forecast the effluent quality of anaerobic/anoxic/oxic reactors of the AAO process.
The research can provide guidance for the further study of intelligent control in the field of wastewater treatment and the popularization of wastewater treatment project with intelligent control.
引文
[1]建设部,国家环境保护总局.科技部.城市污水处理及污染防治技术政策.环境污染治理技术与设备, 2001, 2(1): 1-2
[2]吴瑞平.污水处理设备国产化的现状与发展探讨[J].中国给水排水, 2006, 22(18): 105-106
[3]张燕聪.废纸造纸废水处理BP神经网络智能控制系统的研制[D].华南理工大学硕士学位论文, 2006
[4]刘华.基于神经网络和模糊PID控制的嵌入式造纸废水处理系统的研究[D].华南理工大学硕士学位论文, 2008
[5]胡志华.基于GA-BP网络的造纸废水处理智能控制研究[D].华南理工大学硕士学位论文, 2007
[6] Hopfield J J, Tank D W. Neural computation of decision in optimization problems [J]. Bio Cybern, 1985, (52): 92-97
[7] Hush D R, Horne B G. Progress in supervised neural networks: what’s new since lippmann [J]. IEEE Signal Progress Magazine, 1993, 10(1): 8-39
[8] Rankovi? V, Radulovi? J, Radojevi? I, Ostoji? A, ?omi? L. Neural network modeling of dissolved oxygen in the Gru?a reservoir, Serbia [J]. Ecological Modelling, 2010, 221(8): 1239-1244
[9] Chen J C, Chang N B, Shieh W K. Assessing wastewater reclamation potential by neural network model[J]. Engineering Applications of Artificial Intelligence, 2003,16: 149–157
[10] Pai TY. Grey and neural network prediction of effluent from the wastewater treatment plant of industrial park using influent quality[J]. Environ Eng Sci, 2008, 25
[11] Sadrzadeh M, Mohammadi T, Ivakpour J, Kasiri N. Neural network modeling of Pb2+ removal from wastewater using electrodialysis [J]. Chemical Engineering and Processing: Process Intensification, 2009, 48( 8): 1371-1381
[12] Pai T Y, Chuang S H, Ho H H, Yu L F, Su H C, Hu H C. Predicting performance of grey and neural network in industrial effluent using online monitoring parameters[J]. Process Biochemistry, 2008, 43: 199–205
[13] Ren Nanqi, Chen Zhaobo, Wang Xiangjing, et al. Optimized operational parameters of a pilot scale membrane bioreactor for high-strength organic wastewater treatment[J]. International Biodeterioration and Biodegradation, 2005, 56(4): 216-223
[14]郭劲松.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
[15]高景峰,彭永臻,王淑莹.人工神经网络和专家系统在污水生物处理系统中的应用[J].环境污染与防治, 2004, 26 (1): 31-33
[16] Faur-Brasquet C, Le Cloirec P. Neural network modeling of organics removal by activated carbon cloths [J]. J Environ Eng, 2001, 10 (1): 889-894
[17] Ruey-Fang Yu. Feed-forward dose control of wastewater chlorination using on- line pH and ORP titration [J]. Chemosphere, 2004, 56: 973-980
[18]王常虹,高晓智,徐立新等.水处理系统的神经网络建模研究[J].控制与决策, 2000, 15 (3):378-380
[19] Martin Cote, Bernard P A, Grandjean. Dynamic modeling of the activated sludge process: improving predation using neural networks. Watater Research,1995, 29(4): 995-1004
[20]李迪,唐辉,万金泉.基于ANN的废纸造纸废水处理过程的动态建模[J].华南理工大学学报, 2005, 33(12): 42-45
[21]吴凡松,彭永臻,王维斌. RBF网络用于活性污泥系统仿真研究[J].中国给水排水, 2003, 19(9): 48-49
[22] Duran A, Monteagudo J M, Mohedano M. Neural networks simulation of photo-Fenton degradation of Reactive Blue 4 [J]. Applied Catalysis B: Environmental, 2006, 65 (1-2): 127–134
[23]白桦,李圭白.混凝投药的神经网络控制方法[J].给水排水, 2001, 27(11):83-86
[24] Syu Mei-jywan. A Study on the Sedimentation Model and Neural Network Online Adaptive Control of a Benzoic Acid Imitated Wastewater Oxidation Process[J]. Ind.Eng.Chem.Res, 2002, 42(46): 6862-6871
[25] Belanche L. Prediction of the bulking phenomenon in wastewater treatment plants [J]. Artificial Intelligence in Engineering, 2000, (14): 307-317
[26] Choi D J, Park H. A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process [J]. Water Research, 2001, 35(16): 3959 -3967
[27] Karama A, Bernard O, Gouze JL. Hybrid neural modeling of an anaerobic digester with respect to biological constraints[J]. Watater Sicience and Technology, 2001, 43 (7): 1-8
[28].Zeng G M, Qin X S, He L, Huang G. H, Liu H L, Lin Y P.A neural network predictive control system for paper mill wastewater treatment. Engineering Application of Artificial Intelligence, 2003, 16: 121-129
[29] Jose S T, Maria L M, Yanez-Sedeno P , Garc?a J. Field determination of phenolic compounds in olive oil mill wastewater by artificial neural network [J]. BiochemicalEngineering Journal, 2008, 38: 171–179
[30] Lijie Zhao, Tianyou Chai. Wastewater BOD Forecasting Model for Optimal Operation Using Robust Time-Delay Neural Network[J]. Advances in Neural Network, 2005, 34(98):1028– 1033
[31] Luccatini L, Porra E, Spagni A. Soft sensors for control of nitrogen and phosphorus removal from wastewaters by neural networks [J]. Water Science and Technology, 2002, 45(4-5): 101-107
[32] Farouq S. Mjalli, S. Al-Asheh, H.E. Alfadala. Use of artificial neural network black-box modeling for the prediction ofwastewater treatment plants performance [J]. Journal of Environmental Management, 2007, 83: 329-338
[33] Traore A, Grieu S, Puig S, Corominas L, Thiery F, Polit M, Colprim J. Control of Sludge Height in a 29 Secondary Settler Using Fuzzy Algorithms[J]. Computers and Chemical Engineering, 2006, 30 (8): 1235–1242
[34] Fiter M, Guell D, Comas J, Colprim J, et al. Energy saving in a wastewater treatment process: an application of Fuzzy Logic Control[J]. Environmental Technology, 2005,26 (11): 1263–1270
[35] Zhu G B, Peng Y Z, Ma B, Wang Y, Yin C Q. Optimization of anoxic/oxic step feeding activated sludge process with fuzzy control model for improving nitrogen removal [J]. Chemical Engineering Journal, 2009, 151( 1-3) :195-201
[36] Marsili-Libelli S. Control of SBR Switching by Fuzzy Pattern Recognition[J]. Water research, 2006, 40(5): 1095-1107
[37] Chen J C, Chang N B. Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process[J]. Engineering Applications of Artificial Intelligence, 2007,20(7): 959-969
[38] Tong R M, Beck M B, Latten A. Fuzzy Control of the Activated Sludge Waste water Treatment Process[ J]. Automatic, 1980, 16(6): 21-24
[39] Aoi T, Okaniwa Y, Hagiwara K, et al. A direct ammonium control system using fuzzy inference in a high-load biological denitrification process treating collected human excreta [J]. Water Science and Technology, 1992, 26(5-6): 1325-1334
[40] Galluzzo M, Cosenza B. Control of the biodegradation of mixed wastes in a continuous bioreactor by a type-2 fuzzy logic controller [J]. Computers and Chemical Engineering, 2009 33: 1475–1483
[41]吴建辉,章兢,侯庆平.仿人智能模糊控制在污水处理中的应用[J].电脑与信息技术, 2000, (2): 8-10
[42]曾薇,彭永臻,王淑莹等.以溶解氧浓度作为SBR法模糊控制参数[J].中国给水排水, 2000, 16(4): 5-11
[43] Galluzzo M, Ducato R, Bartolozzi V, et al. Expert control of DO in the aerobic reactor of an activated sludge process[J]. Computers and Chemical Engineering, 2001, 25(4-6): 619-625
[44]欧林林,王先路,陈长琦等.污水处理实验室系统中溶解氧模糊控制器的设计[J].自动化与仪器仪表, 2003, 105(1): 17-21
[45] Meyer U, Popel H J. Fuzzy control for nitrogen removal and energy saving in wwt-plants with pre-denitrification [J]. W at Sci & Tech, 2003, 47(11): 69-76
[46]高景峰,彭永臻,王淑莹.以DO、ORP、pH控制SBR法的脱氮过程[J].中国给水排水, 2001, 17(4): 6-11
[47] Zeybek Z, Alpbaz M. Fuzzy-dynamic matrix pH control for treatment of dye wastewater plant[J]. Proceedings of Sixth International Conference on Computational Intelligence and Multimedia Applications[C]. 2005: 118-223
[48]彭永臻,王淑莹,周利.生物电极脱氮工艺的在线模糊控制研究一[J].中国给水排水, 1999, 15(2): 5-9
[49]叶柳,彭永臻,唐冰,侯红勋.含盐污水SBR法生物脱氮模糊控制参数[J].化学学报, 2008, 59(4): 995-1000
[50] Bernard O, Polit M, Hadj-Sadok Z. et al. Advanced Monitoring and Control of Anaerobic Wastewater Treatment Plants: Software Sensors and Controllers for An Anaerobic Digester [J]. Water Science and Technology, 2001, 43(7): 175-182
[51] Traore A , Grieu S, Puig S, Corominas L, Thiery F, Polit M, Colprim J. Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant [J]. Chemical Engineering Journal, 2005,111:13-19
[52]陆杰.模糊控制在污水处理系统中的应用[J].盐湖研究, 2000, 8(2): 29-32
[53] Murnleitner E, Matthias T, Delgado A. State detection and control of overloads in the anaerobic wastewater treatment using fuzzy logic[J]. Water Research, 2002, 36(1): 201-211
[54] Ohtsuki T, Kawazoe T, Masui T. Intelligent control system based on blackboard concept for wastewater treatment processes[J]. Water Science and Technology, 1998, 37(12): 77-85
[55] Park K S, Kim B J, Moon Y H. Application of fuzzy expert system to estimate dimensional errors of forging products having complicated shape [J]. Journal of Materials Processing Technology, 2007, 187-188: 720-724
[56] Roda I R. Development of a case-based system for the supervision of an activated sludge process [J]. Environmental Technology, 2001, 22: 477-486
[57] Wei Tang, Qian Feng, Mengxiao Wang, Qian Hou, Leilei Wang . Expert system based dissolved oxygen control in APMP wastewater aerobic treatment process[C]. Automation and Logistics, 2008.ICAL 2008. IEEE International Conference, Qingdao, China, 2008
[58] Fortes P R, Feres M A, Zagatto E A G. An expert flow system involving in-line prior assay for turbidimetric determination of chloride and sulphate in natural waters [J]. Talanta, 2008, 77(2): 571-575
[59] Nam S.W, Myung N J, Lee K S. On-line intefrated control system for an industrial activated sludge process[J]. Water Environment Research, 1996, 68(1):70-75
[60] Baeza J A, Gabriel D, Lafuente J. Improving the nitrogen removal efficiency of A2/O based WWTP by using an on-line knowledge expert system [J]. Water Research, 2002, 36: 2109-2123
[61] Roda I R. Development of a case-based system for the supervision of an activated sludge process [J]. Environmental Techonlogy, 2001, 22: 477-486
[62] Punal A, Roca E, Lema J M. An expert system for monitoring and diagnosis of anaerobic wastewater treatment plants [J]. Water Research, 2002, 36: 2656-2666
[63]施汉昌,王玉环.污水处理厂故障诊断专家系统[J].给水排水, 2001, 27(8): 88-90
[64] Riesco J, Calvo J, Martin–Sanchez J M. Adaptive predictive expert (ADEX) control: Application to waste water treatment plants [C]. IEEE Conference on Control Applications Proceedings, Glasgow, United Kingdom, 2002
[65] Flores J, Arcay B, Arias J. An intelligent system for distributed control of an anaerobic wastewater treatment process[J]. Engineering Applications of Articial Intelligence, 2000, 13(4): 485-494
[66] Galluzzo M, Ducato R, Bartolozzi V, et al. Expert control of DO in the aerobic reactor of an activated sludge process[J]. Computer and Chemical Engineering, 2001, 25: 619-625
[67]薛薇,齐国元. pH过程的FNNC-PI控制研究[J].仪器仪表学报, 2004, 25(6): 721-724
[68] Tay Joo–Hwa, Zhang Xiyue. Fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems [J]. Water Research, 2000, 34(11): 2849-2860
[69] Carrasco E F, Rodriguez J, Punal A, etal. Expert system for the online diagnosis of anaerobic wastewater treatment plants [J]. Control Eng. Pract., 2004, 12(1): 59-64
[70] Bongards M. Improving the efficiency of a wastewater treatment plant by fuzzy control and neural networks [J]. Water Science and Technology, 2001, 3(11): 189-196
[71]苏敏,涂源钊.城市废水生物处理过程的模糊神经网络控制[J].四川联合大学学报(工程科学版), 1998, 2(1): 40-45
[72] Du Y G, Tyagi R D, Bhamidimarri R. Use of fuzzy neural-net model for rule generation of activated sludge process [J]. Process Biochemistry, 1999, 35: 77-83
[73]曹刚,徐向阳,冯孝善. GA-ANN模拟毒物冲击下UASB性能变化[J].中国给水排水, 2003, 19(13): 55-57
[74]赵诚,王中琪,王成端等. MSBR法污水处理模糊神经网络控制的研究[J].现代电子技术, 2006, 18: 54-56
[75]余颖,乔俊飞.基于ANFIS的污水处理过程建模的研究[J].计算机工程, 2006, 32(8): 266-268
[76]欧长劲,吴海列,李军等.基于模糊神经网络的SBR污水处理控制系统研究[J].计算机测量与控制, 2006, 14(12): 1643-1645
[77] Huang M Z., Ma Y W, J.Q. Wan, and Y. Wang, Simulation of a paper mill wastewater treatment using a fuzzy neural network [J]. Expert Systems with Applications, 2009, 36(3): 5064-5070
[78] Echanobe J, I.del Campo, Bosque G.. An adaptive neuro-fuzzy system for efficient implementations[J]. Information Sciences, 2008, 178: 2150–2162
[79] Jimenez L, Angulo V, Caparros ., Perez A, Ferrer J L. Neural fuzzy modeling of ethanolamine pulping of vine shoots [J]. Biochemical Engineering Journal, 2007, 34: 62–68
[80] Guan-De Wu, Shang-Lien Lo. Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system. Engineering Applications of Artificial Intelligence, 2008, 21: 1189– 1195.
[81]马邕文,吴娇,万金泉等.废纸造纸废水的水解酸化处理[J].中国造纸, 2006, 25 (10) : 22-24
[82]万金泉,马邕文,王艳等.废纸造纸废水特点及其处理技术[J].造纸科学与技术, 2005, 24 (5) : 58-60
[83]万金泉,马邕文.废纸造纸及其污染控制[M].中国轻工业出版社, 2004
[84]王岩.污水生物曝气系统的模糊PID控制技术研究[D].天津大学硕士学位论文, 2004
[85]邹敏,陆继来,尹协东.回流比对缺氧/好氧生活污水处理装置处理效果的影响[J].污染防治技术, 2008, 6(21): 47-50
[86]美国哈希公司.哈希公司COD在线检测仪说明书
[87]西门子公司. STEP7-MicroWIN32编程软件说明书
[88]北京昆仑通态自动化软件科技公司. MCGS工控组态软件参考手册, 2006
[89]冉维丽.基于神经计算学的污水水质软测量研究[D].北京工业大学硕士学位论文, 2004
[90] Luccarini L, Bragadin G L, Colombini G, Mancini M, Mello P, Montali M, Sottara D. Formal verification of wastewater treatment processes using events detected from continuous signals by means of artificial neural networks. Case study: SBR plant[J]. Environmental Modelling & Software, 2010, 25( 5): 648-660
[91]杨斌,田永青,朱仲英.智能建模方法中的数据预处理.信息与控制[J]. 2002, 31(4): 380-384
[92]彭向华.软测量技术在废水处理中的应用[D].昆明理工大学硕士学位论文, 1999
[93]管秋.基于人工神经网络的污水水质指标软测量方法研究[D].浙江大学硕士学位论文, 2004
[94]王宁会,刘敏.神经元网络软测量技术的研究进展[J].控制工程, 2003, 10(1):15-17
[95]孙曾圻,张再兴,邓兴东.智能控制理论与技术[M].第2版,清华大学出版社, 1997
[96]黄明护.基于模糊神经网络模型的废纸造纸废水处理智能控制研究[D].华南理工大学硕士学位论文, 2007
[97] Wang Y M, Taha M S. Elhag An adaptive neuro-fuzzy inference system for bridge risk assessment [J]. Expert Systems with Applications, 2008, 34: 3099–3106
[98]冉维丽,乔俊飞.基于PCA-GABP神经网络的BOD软测量方法[J].控制工程, 2004,11(3): 99-102
[99] Abbaspour A, Baramakeh L. Application of principle component analysis–artificial neural network for simultaneous determination of zirconium and hafnium in real samples [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2006, 64(2): 477-482
[100]杨尔辅,周强,胡益锋,徐用懋.基于PCA-RBF神经网络的工业裂解炉收率在线预测软测量方法[J].系统仿真学报, 2001, (13):194-197
[101]苏金明,阮沈勇,王永利. MATLAB工程数学[M].电子工业出版社, 2005
[102]高新波.模糊聚类分析及其应用[M].西安电子科技大学出版社, 2004
[103]张震,张德聪.实验设计与数据评价[M].华南理工大学出版社, 2004
[104]张卫,陈文会.污水处理自动控制系统的应用[J].科技情报开发与经济, 2007, 17(5): 238-240
[105]何海波.活性污泥系统中曝气过程控制策略研究[D].重庆大学硕士学位论文, 2006
[106]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
[107] Valnyr Vasconcelos Lira, Jose Sergio da Rocha Neto, Pericles Rezende Barros, Adrianuus Cornelius van Haandel. Automation of an anaerobic-aerobic wastewater treatment process[J]. IEEE Transactions on Instrumentation and Measurement, 2003, 52(3): 909-915
[108]姚加飞,林德全,王晓静.活性污泥系统中曝气机的速度控制[J].中国给水排水, 2000, 16(2): 53-54
[109]诸静.模糊控制原理与应用[M].机械工业出版社, 1995
[110]Yuan Zhi-guo, Oehmen A, Inglldsen P. Control of nitrate recirculation flow in predenitrification systems[J]. Water Science Technology, 2002, 45(4-5):29-36
[111]彭永臻,马勇,王洪臣,甘一萍,杨向平. A/O脱氮工艺中内循环控制策略的建立与研究[J].北京工业大学学报, 2004, 30(2): 201-206
[112]陈勇.基于多元统计分析的生产过程故障诊断研究[D].浙江大学硕士学位论文, 2003
[113] Jang S R. ANFIS: Adaptive- network- based Fuzzy Inference Systems, IEEE Transaction on Systems. Man and Cybernetics,1993, 23: 665-685.
[114] Spérandio M, Queinnec I. Online Estimation of Wastewater Nitrifiable Nitrogen, Nitrification Dynamics and Denitrification Rates, Using ORP and DO Dynamics [J]. Water Science and Technology, 2004, 49: 31–38
[115] Lee Y, Oleszkiewicz J A. Effects of Predation and ORP Conditions on the Performance of Nitrifiers in Activated Sludge Systems [J]. Water Research, 2003, 37: 4202–4210
[116] Wu C Y, Chen Z Q, Liu X H, Peng Y Z. Nitrification–denitrification via nitrite in SBR using real-time control strategy when treating domestic wastewater [J]. Biochemical Engineering Journal, 2007, 36, 87-92.
[117]徐峥勇.序批式生物膜反应器高效生物脱氮途径优化控制策略研究[D].湖南大学硕士学历论文, 2007
[118]费学宁,吕岩,张天永.几种环境激素酞酸酯的光催化降解研究[J].环境化学, 2006, 25(5): 567-571
[119]鲁翌,徐轶鸣,王颖,唐非,曾欣,王琳,罗启芳.高效酞酸酯降解菌的驯化、筛选及其降解的初步研究[J].卫生研究, 2007, 36(6): 671-673
[120]李文兰,杨玉楠,季宇彬,等.驯化活性污泥对邻苯二甲酸丁基苄酯的降解[J].环境科, 2005, 26(4): 156-159
[121] Parkerton T F, Konkel W J. Application of quantitative structure activity relationships for assessing the aquatic toxicity phthalate esters[J]. Ecotoxico Environ Safety, 2000, 45(1): 61-78.
[122]张锡辉.高等环境化学与微生物学原理及应用[M].北京:化学工业出版社, 2001
[123] US EPA. National Primary Drinking Water Regulations, Federal Register, 40 CFR Chapter 1, Part 141 [M]. Washington, DC: US Environmental Protection Agency, 1991
[124]黄满红,李咏梅,顾国维.直链烷基苯磺酸钠在厌氧-缺氧-好氧污水处理系统中的迁移转化规律[J].环境科学, 2007, 28(7): 1502-1506
[125]黄满红.厌氧-缺氧-好氧污活性污泥系统中典型有机物迁移转化研究[D].同济大学博士学位论文,2006
[126] Holland J H Adaptation in Natural and Artificial Systems .The University of Michigan Press,1975, MIT Press,1992
[127]周家莉. GA-BP神经网络在拮抗药化合物活性模式识别中的应用[J].中国现代药物应用, 2008, 2(11): 11-13
[128]张明远,钟璐.基于GA-BP神经网络的单桩承载力预测[J].武汉理工大学学报, 2008, 30(3): 86-89
[2]吴瑞平.污水处理设备国产化的现状与发展探讨[J].中国给水排水, 2006, 22(18): 105-106
[3]张燕聪.废纸造纸废水处理BP神经网络智能控制系统的研制[D].华南理工大学硕士学位论文, 2006
[4]刘华.基于神经网络和模糊PID控制的嵌入式造纸废水处理系统的研究[D].华南理工大学硕士学位论文, 2008
[5]胡志华.基于GA-BP网络的造纸废水处理智能控制研究[D].华南理工大学硕士学位论文, 2007
[6] Hopfield J J, Tank D W. Neural computation of decision in optimization problems [J]. Bio Cybern, 1985, (52): 92-97
[7] Hush D R, Horne B G. Progress in supervised neural networks: what’s new since lippmann [J]. IEEE Signal Progress Magazine, 1993, 10(1): 8-39
[8] Rankovi? V, Radulovi? J, Radojevi? I, Ostoji? A, ?omi? L. Neural network modeling of dissolved oxygen in the Gru?a reservoir, Serbia [J]. Ecological Modelling, 2010, 221(8): 1239-1244
[9] Chen J C, Chang N B, Shieh W K. Assessing wastewater reclamation potential by neural network model[J]. Engineering Applications of Artificial Intelligence, 2003,16: 149–157
[10] Pai TY. Grey and neural network prediction of effluent from the wastewater treatment plant of industrial park using influent quality[J]. Environ Eng Sci, 2008, 25
[11] Sadrzadeh M, Mohammadi T, Ivakpour J, Kasiri N. Neural network modeling of Pb2+ removal from wastewater using electrodialysis [J]. Chemical Engineering and Processing: Process Intensification, 2009, 48( 8): 1371-1381
[12] Pai T Y, Chuang S H, Ho H H, Yu L F, Su H C, Hu H C. Predicting performance of grey and neural network in industrial effluent using online monitoring parameters[J]. Process Biochemistry, 2008, 43: 199–205
[13] Ren Nanqi, Chen Zhaobo, Wang Xiangjing, et al. Optimized operational parameters of a pilot scale membrane bioreactor for high-strength organic wastewater treatment[J]. International Biodeterioration and Biodegradation, 2005, 56(4): 216-223
[14]郭劲松.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
[15]高景峰,彭永臻,王淑莹.人工神经网络和专家系统在污水生物处理系统中的应用[J].环境污染与防治, 2004, 26 (1): 31-33
[16] Faur-Brasquet C, Le Cloirec P. Neural network modeling of organics removal by activated carbon cloths [J]. J Environ Eng, 2001, 10 (1): 889-894
[17] Ruey-Fang Yu. Feed-forward dose control of wastewater chlorination using on- line pH and ORP titration [J]. Chemosphere, 2004, 56: 973-980
[18]王常虹,高晓智,徐立新等.水处理系统的神经网络建模研究[J].控制与决策, 2000, 15 (3):378-380
[19] Martin Cote, Bernard P A, Grandjean. Dynamic modeling of the activated sludge process: improving predation using neural networks. Watater Research,1995, 29(4): 995-1004
[20]李迪,唐辉,万金泉.基于ANN的废纸造纸废水处理过程的动态建模[J].华南理工大学学报, 2005, 33(12): 42-45
[21]吴凡松,彭永臻,王维斌. RBF网络用于活性污泥系统仿真研究[J].中国给水排水, 2003, 19(9): 48-49
[22] Duran A, Monteagudo J M, Mohedano M. Neural networks simulation of photo-Fenton degradation of Reactive Blue 4 [J]. Applied Catalysis B: Environmental, 2006, 65 (1-2): 127–134
[23]白桦,李圭白.混凝投药的神经网络控制方法[J].给水排水, 2001, 27(11):83-86
[24] Syu Mei-jywan. A Study on the Sedimentation Model and Neural Network Online Adaptive Control of a Benzoic Acid Imitated Wastewater Oxidation Process[J]. Ind.Eng.Chem.Res, 2002, 42(46): 6862-6871
[25] Belanche L. Prediction of the bulking phenomenon in wastewater treatment plants [J]. Artificial Intelligence in Engineering, 2000, (14): 307-317
[26] Choi D J, Park H. A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process [J]. Water Research, 2001, 35(16): 3959 -3967
[27] Karama A, Bernard O, Gouze JL. Hybrid neural modeling of an anaerobic digester with respect to biological constraints[J]. Watater Sicience and Technology, 2001, 43 (7): 1-8
[28].Zeng G M, Qin X S, He L, Huang G. H, Liu H L, Lin Y P.A neural network predictive control system for paper mill wastewater treatment. Engineering Application of Artificial Intelligence, 2003, 16: 121-129
[29] Jose S T, Maria L M, Yanez-Sedeno P , Garc?a J. Field determination of phenolic compounds in olive oil mill wastewater by artificial neural network [J]. BiochemicalEngineering Journal, 2008, 38: 171–179
[30] Lijie Zhao, Tianyou Chai. Wastewater BOD Forecasting Model for Optimal Operation Using Robust Time-Delay Neural Network[J]. Advances in Neural Network, 2005, 34(98):1028– 1033
[31] Luccatini L, Porra E, Spagni A. Soft sensors for control of nitrogen and phosphorus removal from wastewaters by neural networks [J]. Water Science and Technology, 2002, 45(4-5): 101-107
[32] Farouq S. Mjalli, S. Al-Asheh, H.E. Alfadala. Use of artificial neural network black-box modeling for the prediction ofwastewater treatment plants performance [J]. Journal of Environmental Management, 2007, 83: 329-338
[33] Traore A, Grieu S, Puig S, Corominas L, Thiery F, Polit M, Colprim J. Control of Sludge Height in a 29 Secondary Settler Using Fuzzy Algorithms[J]. Computers and Chemical Engineering, 2006, 30 (8): 1235–1242
[34] Fiter M, Guell D, Comas J, Colprim J, et al. Energy saving in a wastewater treatment process: an application of Fuzzy Logic Control[J]. Environmental Technology, 2005,26 (11): 1263–1270
[35] Zhu G B, Peng Y Z, Ma B, Wang Y, Yin C Q. Optimization of anoxic/oxic step feeding activated sludge process with fuzzy control model for improving nitrogen removal [J]. Chemical Engineering Journal, 2009, 151( 1-3) :195-201
[36] Marsili-Libelli S. Control of SBR Switching by Fuzzy Pattern Recognition[J]. Water research, 2006, 40(5): 1095-1107
[37] Chen J C, Chang N B. Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process[J]. Engineering Applications of Artificial Intelligence, 2007,20(7): 959-969
[38] Tong R M, Beck M B, Latten A. Fuzzy Control of the Activated Sludge Waste water Treatment Process[ J]. Automatic, 1980, 16(6): 21-24
[39] Aoi T, Okaniwa Y, Hagiwara K, et al. A direct ammonium control system using fuzzy inference in a high-load biological denitrification process treating collected human excreta [J]. Water Science and Technology, 1992, 26(5-6): 1325-1334
[40] Galluzzo M, Cosenza B. Control of the biodegradation of mixed wastes in a continuous bioreactor by a type-2 fuzzy logic controller [J]. Computers and Chemical Engineering, 2009 33: 1475–1483
[41]吴建辉,章兢,侯庆平.仿人智能模糊控制在污水处理中的应用[J].电脑与信息技术, 2000, (2): 8-10
[42]曾薇,彭永臻,王淑莹等.以溶解氧浓度作为SBR法模糊控制参数[J].中国给水排水, 2000, 16(4): 5-11
[43] Galluzzo M, Ducato R, Bartolozzi V, et al. Expert control of DO in the aerobic reactor of an activated sludge process[J]. Computers and Chemical Engineering, 2001, 25(4-6): 619-625
[44]欧林林,王先路,陈长琦等.污水处理实验室系统中溶解氧模糊控制器的设计[J].自动化与仪器仪表, 2003, 105(1): 17-21
[45] Meyer U, Popel H J. Fuzzy control for nitrogen removal and energy saving in wwt-plants with pre-denitrification [J]. W at Sci & Tech, 2003, 47(11): 69-76
[46]高景峰,彭永臻,王淑莹.以DO、ORP、pH控制SBR法的脱氮过程[J].中国给水排水, 2001, 17(4): 6-11
[47] Zeybek Z, Alpbaz M. Fuzzy-dynamic matrix pH control for treatment of dye wastewater plant[J]. Proceedings of Sixth International Conference on Computational Intelligence and Multimedia Applications[C]. 2005: 118-223
[48]彭永臻,王淑莹,周利.生物电极脱氮工艺的在线模糊控制研究一[J].中国给水排水, 1999, 15(2): 5-9
[49]叶柳,彭永臻,唐冰,侯红勋.含盐污水SBR法生物脱氮模糊控制参数[J].化学学报, 2008, 59(4): 995-1000
[50] Bernard O, Polit M, Hadj-Sadok Z. et al. Advanced Monitoring and Control of Anaerobic Wastewater Treatment Plants: Software Sensors and Controllers for An Anaerobic Digester [J]. Water Science and Technology, 2001, 43(7): 175-182
[51] Traore A , Grieu S, Puig S, Corominas L, Thiery F, Polit M, Colprim J. Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant [J]. Chemical Engineering Journal, 2005,111:13-19
[52]陆杰.模糊控制在污水处理系统中的应用[J].盐湖研究, 2000, 8(2): 29-32
[53] Murnleitner E, Matthias T, Delgado A. State detection and control of overloads in the anaerobic wastewater treatment using fuzzy logic[J]. Water Research, 2002, 36(1): 201-211
[54] Ohtsuki T, Kawazoe T, Masui T. Intelligent control system based on blackboard concept for wastewater treatment processes[J]. Water Science and Technology, 1998, 37(12): 77-85
[55] Park K S, Kim B J, Moon Y H. Application of fuzzy expert system to estimate dimensional errors of forging products having complicated shape [J]. Journal of Materials Processing Technology, 2007, 187-188: 720-724
[56] Roda I R. Development of a case-based system for the supervision of an activated sludge process [J]. Environmental Technology, 2001, 22: 477-486
[57] Wei Tang, Qian Feng, Mengxiao Wang, Qian Hou, Leilei Wang . Expert system based dissolved oxygen control in APMP wastewater aerobic treatment process[C]. Automation and Logistics, 2008.ICAL 2008. IEEE International Conference, Qingdao, China, 2008
[58] Fortes P R, Feres M A, Zagatto E A G. An expert flow system involving in-line prior assay for turbidimetric determination of chloride and sulphate in natural waters [J]. Talanta, 2008, 77(2): 571-575
[59] Nam S.W, Myung N J, Lee K S. On-line intefrated control system for an industrial activated sludge process[J]. Water Environment Research, 1996, 68(1):70-75
[60] Baeza J A, Gabriel D, Lafuente J. Improving the nitrogen removal efficiency of A2/O based WWTP by using an on-line knowledge expert system [J]. Water Research, 2002, 36: 2109-2123
[61] Roda I R. Development of a case-based system for the supervision of an activated sludge process [J]. Environmental Techonlogy, 2001, 22: 477-486
[62] Punal A, Roca E, Lema J M. An expert system for monitoring and diagnosis of anaerobic wastewater treatment plants [J]. Water Research, 2002, 36: 2656-2666
[63]施汉昌,王玉环.污水处理厂故障诊断专家系统[J].给水排水, 2001, 27(8): 88-90
[64] Riesco J, Calvo J, Martin–Sanchez J M. Adaptive predictive expert (ADEX) control: Application to waste water treatment plants [C]. IEEE Conference on Control Applications Proceedings, Glasgow, United Kingdom, 2002
[65] Flores J, Arcay B, Arias J. An intelligent system for distributed control of an anaerobic wastewater treatment process[J]. Engineering Applications of Articial Intelligence, 2000, 13(4): 485-494
[66] Galluzzo M, Ducato R, Bartolozzi V, et al. Expert control of DO in the aerobic reactor of an activated sludge process[J]. Computer and Chemical Engineering, 2001, 25: 619-625
[67]薛薇,齐国元. pH过程的FNNC-PI控制研究[J].仪器仪表学报, 2004, 25(6): 721-724
[68] Tay Joo–Hwa, Zhang Xiyue. Fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems [J]. Water Research, 2000, 34(11): 2849-2860
[69] Carrasco E F, Rodriguez J, Punal A, etal. Expert system for the online diagnosis of anaerobic wastewater treatment plants [J]. Control Eng. Pract., 2004, 12(1): 59-64
[70] Bongards M. Improving the efficiency of a wastewater treatment plant by fuzzy control and neural networks [J]. Water Science and Technology, 2001, 3(11): 189-196
[71]苏敏,涂源钊.城市废水生物处理过程的模糊神经网络控制[J].四川联合大学学报(工程科学版), 1998, 2(1): 40-45
[72] Du Y G, Tyagi R D, Bhamidimarri R. Use of fuzzy neural-net model for rule generation of activated sludge process [J]. Process Biochemistry, 1999, 35: 77-83
[73]曹刚,徐向阳,冯孝善. GA-ANN模拟毒物冲击下UASB性能变化[J].中国给水排水, 2003, 19(13): 55-57
[74]赵诚,王中琪,王成端等. MSBR法污水处理模糊神经网络控制的研究[J].现代电子技术, 2006, 18: 54-56
[75]余颖,乔俊飞.基于ANFIS的污水处理过程建模的研究[J].计算机工程, 2006, 32(8): 266-268
[76]欧长劲,吴海列,李军等.基于模糊神经网络的SBR污水处理控制系统研究[J].计算机测量与控制, 2006, 14(12): 1643-1645
[77] Huang M Z., Ma Y W, J.Q. Wan, and Y. Wang, Simulation of a paper mill wastewater treatment using a fuzzy neural network [J]. Expert Systems with Applications, 2009, 36(3): 5064-5070
[78] Echanobe J, I.del Campo, Bosque G.. An adaptive neuro-fuzzy system for efficient implementations[J]. Information Sciences, 2008, 178: 2150–2162
[79] Jimenez L, Angulo V, Caparros ., Perez A, Ferrer J L. Neural fuzzy modeling of ethanolamine pulping of vine shoots [J]. Biochemical Engineering Journal, 2007, 34: 62–68
[80] Guan-De Wu, Shang-Lien Lo. Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system. Engineering Applications of Artificial Intelligence, 2008, 21: 1189– 1195.
[81]马邕文,吴娇,万金泉等.废纸造纸废水的水解酸化处理[J].中国造纸, 2006, 25 (10) : 22-24
[82]万金泉,马邕文,王艳等.废纸造纸废水特点及其处理技术[J].造纸科学与技术, 2005, 24 (5) : 58-60
[83]万金泉,马邕文.废纸造纸及其污染控制[M].中国轻工业出版社, 2004
[84]王岩.污水生物曝气系统的模糊PID控制技术研究[D].天津大学硕士学位论文, 2004
[85]邹敏,陆继来,尹协东.回流比对缺氧/好氧生活污水处理装置处理效果的影响[J].污染防治技术, 2008, 6(21): 47-50
[86]美国哈希公司.哈希公司COD在线检测仪说明书
[87]西门子公司. STEP7-MicroWIN32编程软件说明书
[88]北京昆仑通态自动化软件科技公司. MCGS工控组态软件参考手册, 2006
[89]冉维丽.基于神经计算学的污水水质软测量研究[D].北京工业大学硕士学位论文, 2004
[90] Luccarini L, Bragadin G L, Colombini G, Mancini M, Mello P, Montali M, Sottara D. Formal verification of wastewater treatment processes using events detected from continuous signals by means of artificial neural networks. Case study: SBR plant[J]. Environmental Modelling & Software, 2010, 25( 5): 648-660
[91]杨斌,田永青,朱仲英.智能建模方法中的数据预处理.信息与控制[J]. 2002, 31(4): 380-384
[92]彭向华.软测量技术在废水处理中的应用[D].昆明理工大学硕士学位论文, 1999
[93]管秋.基于人工神经网络的污水水质指标软测量方法研究[D].浙江大学硕士学位论文, 2004
[94]王宁会,刘敏.神经元网络软测量技术的研究进展[J].控制工程, 2003, 10(1):15-17
[95]孙曾圻,张再兴,邓兴东.智能控制理论与技术[M].第2版,清华大学出版社, 1997
[96]黄明护.基于模糊神经网络模型的废纸造纸废水处理智能控制研究[D].华南理工大学硕士学位论文, 2007
[97] Wang Y M, Taha M S. Elhag An adaptive neuro-fuzzy inference system for bridge risk assessment [J]. Expert Systems with Applications, 2008, 34: 3099–3106
[98]冉维丽,乔俊飞.基于PCA-GABP神经网络的BOD软测量方法[J].控制工程, 2004,11(3): 99-102
[99] Abbaspour A, Baramakeh L. Application of principle component analysis–artificial neural network for simultaneous determination of zirconium and hafnium in real samples [J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2006, 64(2): 477-482
[100]杨尔辅,周强,胡益锋,徐用懋.基于PCA-RBF神经网络的工业裂解炉收率在线预测软测量方法[J].系统仿真学报, 2001, (13):194-197
[101]苏金明,阮沈勇,王永利. MATLAB工程数学[M].电子工业出版社, 2005
[102]高新波.模糊聚类分析及其应用[M].西安电子科技大学出版社, 2004
[103]张震,张德聪.实验设计与数据评价[M].华南理工大学出版社, 2004
[104]张卫,陈文会.污水处理自动控制系统的应用[J].科技情报开发与经济, 2007, 17(5): 238-240
[105]何海波.活性污泥系统中曝气过程控制策略研究[D].重庆大学硕士学位论文, 2006
[106]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
[107] Valnyr Vasconcelos Lira, Jose Sergio da Rocha Neto, Pericles Rezende Barros, Adrianuus Cornelius van Haandel. Automation of an anaerobic-aerobic wastewater treatment process[J]. IEEE Transactions on Instrumentation and Measurement, 2003, 52(3): 909-915
[108]姚加飞,林德全,王晓静.活性污泥系统中曝气机的速度控制[J].中国给水排水, 2000, 16(2): 53-54
[109]诸静.模糊控制原理与应用[M].机械工业出版社, 1995
[110]Yuan Zhi-guo, Oehmen A, Inglldsen P. Control of nitrate recirculation flow in predenitrification systems[J]. Water Science Technology, 2002, 45(4-5):29-36
[111]彭永臻,马勇,王洪臣,甘一萍,杨向平. A/O脱氮工艺中内循环控制策略的建立与研究[J].北京工业大学学报, 2004, 30(2): 201-206
[112]陈勇.基于多元统计分析的生产过程故障诊断研究[D].浙江大学硕士学位论文, 2003
[113] Jang S R. ANFIS: Adaptive- network- based Fuzzy Inference Systems, IEEE Transaction on Systems. Man and Cybernetics,1993, 23: 665-685.
[114] Spérandio M, Queinnec I. Online Estimation of Wastewater Nitrifiable Nitrogen, Nitrification Dynamics and Denitrification Rates, Using ORP and DO Dynamics [J]. Water Science and Technology, 2004, 49: 31–38
[115] Lee Y, Oleszkiewicz J A. Effects of Predation and ORP Conditions on the Performance of Nitrifiers in Activated Sludge Systems [J]. Water Research, 2003, 37: 4202–4210
[116] Wu C Y, Chen Z Q, Liu X H, Peng Y Z. Nitrification–denitrification via nitrite in SBR using real-time control strategy when treating domestic wastewater [J]. Biochemical Engineering Journal, 2007, 36, 87-92.
[117]徐峥勇.序批式生物膜反应器高效生物脱氮途径优化控制策略研究[D].湖南大学硕士学历论文, 2007
[118]费学宁,吕岩,张天永.几种环境激素酞酸酯的光催化降解研究[J].环境化学, 2006, 25(5): 567-571
[119]鲁翌,徐轶鸣,王颖,唐非,曾欣,王琳,罗启芳.高效酞酸酯降解菌的驯化、筛选及其降解的初步研究[J].卫生研究, 2007, 36(6): 671-673
[120]李文兰,杨玉楠,季宇彬,等.驯化活性污泥对邻苯二甲酸丁基苄酯的降解[J].环境科, 2005, 26(4): 156-159
[121] Parkerton T F, Konkel W J. Application of quantitative structure activity relationships for assessing the aquatic toxicity phthalate esters[J]. Ecotoxico Environ Safety, 2000, 45(1): 61-78.
[122]张锡辉.高等环境化学与微生物学原理及应用[M].北京:化学工业出版社, 2001
[123] US EPA. National Primary Drinking Water Regulations, Federal Register, 40 CFR Chapter 1, Part 141 [M]. Washington, DC: US Environmental Protection Agency, 1991
[124]黄满红,李咏梅,顾国维.直链烷基苯磺酸钠在厌氧-缺氧-好氧污水处理系统中的迁移转化规律[J].环境科学, 2007, 28(7): 1502-1506
[125]黄满红.厌氧-缺氧-好氧污活性污泥系统中典型有机物迁移转化研究[D].同济大学博士学位论文,2006
[126] Holland J H Adaptation in Natural and Artificial Systems .The University of Michigan Press,1975, MIT Press,1992
[127]周家莉. GA-BP神经网络在拮抗药化合物活性模式识别中的应用[J].中国现代药物应用, 2008, 2(11): 11-13
[128]张明远,钟璐.基于GA-BP神经网络的单桩承载力预测[J].武汉理工大学学报, 2008, 30(3): 86-89