先进控制理论在水厂混凝投药控制中的应用
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摘要
随着我国城市建设的快速发展,对城市供水的质量要求越来越高,水处理过程的自动控制是保证供水质量的关键,而难点主要集中在混凝投药过程的自动控制。目前国内众多水厂采用的混凝投药控制方式主要是基于流动电流的反馈投药控制和基于传统数学模型的前馈反馈投药控制,控制效果都不太理想,存在沉淀池出水浊度波动大,药剂浪费严重等问题,因此本文主要研究基于先进控制理论的混凝投药自动控制问题。主要研究内容如下:
(1)针对流动电流仪检测混凝反应的不准确性,利用多传感器信息融合技术,提出了一种新的混凝反应过程检测方法。该方法通过采集影响混凝反应的2个主要因素(源水浊度、源水流量)修正流动电流值,实现混凝反应的准确检测,为投药系统的优化控制打下基础。
(2)为提高控制效果,提出了一种用于水处理混凝投药控制的模糊控制器。它能根据混凝投药过程出水浊度的状态,自动选取控制规则集,每种控制规则集能根据修正后的流动电流的偏差和偏差变化率来确定具体的控制规则,以此来适应混凝过程环境状态的变化。
(3)为提高控制器的自适应性,提出了一种用于水处理混凝投药控制的模糊神经网络控制模型。它结合模糊控制器对不确定性信息的模糊处理能力和神经网络对样本数据的学习能力,能自适应的处理混凝投药控制的各种复杂情况,为水厂混凝投药控制的自动化提供一条新的途径。
With the rapid development of urban construction in our country, the requirement to the quality of urban water supply is increasing. Automatic control of water treatment process is the key of protection of water quality. The most difficult focuses on the auto control of coagulant process. However, either forward and feedback control or classical feedback control is currently used in coagulation dosing in many water treatment factories in our country. But the results of both controls are not very good. There are some questions of chemicals wastage and sediment output water qualities varieties. Therefore, the paper is mainly to research the automatic control in coagulant dosing based on advanced intelligent control theory .The main research achievements are as follows:
(1) Aiming at Streaming Current Detector's low accuracy of measuring coagulation process, an improved detection method using multi-sensor data fusion technology is developed in this paper. The method adopts two main factors that affecting coagulation reaction (influent turbidity and flow) to correct the value of Streaming Current. So it can detect coagulation process accurately, which lays the foundation for optimizing control in dosing process.
(2) In order to improve control performance, the paper brings forward a piece of new fuzzy controller used in water treatment drug control. Firstly, controller can automatically chooses rules set basing on different state of effluent turbidity in the process of coagulation dosing. Secondly, according to the streaming current's deviation and its rate of change, each rules set can give concrete control rule which is suitable to be used in different state of the environment.
(3) In order to improve adaptability of controller, this paper introduces a control model of the fuzzy neural network. Combining fuzzy controller's fuzzy reasoning ability with the neural network's learning ability, it can adaptively handle all kinds of complex situations in auto control of dosing process. It provides new approaches for the auto control of coagulant dosing in the water factory.
(1)针对流动电流仪检测混凝反应的不准确性,利用多传感器信息融合技术,提出了一种新的混凝反应过程检测方法。该方法通过采集影响混凝反应的2个主要因素(源水浊度、源水流量)修正流动电流值,实现混凝反应的准确检测,为投药系统的优化控制打下基础。
(2)为提高控制效果,提出了一种用于水处理混凝投药控制的模糊控制器。它能根据混凝投药过程出水浊度的状态,自动选取控制规则集,每种控制规则集能根据修正后的流动电流的偏差和偏差变化率来确定具体的控制规则,以此来适应混凝过程环境状态的变化。
(3)为提高控制器的自适应性,提出了一种用于水处理混凝投药控制的模糊神经网络控制模型。它结合模糊控制器对不确定性信息的模糊处理能力和神经网络对样本数据的学习能力,能自适应的处理混凝投药控制的各种复杂情况,为水厂混凝投药控制的自动化提供一条新的途径。
With the rapid development of urban construction in our country, the requirement to the quality of urban water supply is increasing. Automatic control of water treatment process is the key of protection of water quality. The most difficult focuses on the auto control of coagulant process. However, either forward and feedback control or classical feedback control is currently used in coagulation dosing in many water treatment factories in our country. But the results of both controls are not very good. There are some questions of chemicals wastage and sediment output water qualities varieties. Therefore, the paper is mainly to research the automatic control in coagulant dosing based on advanced intelligent control theory .The main research achievements are as follows:
(1) Aiming at Streaming Current Detector's low accuracy of measuring coagulation process, an improved detection method using multi-sensor data fusion technology is developed in this paper. The method adopts two main factors that affecting coagulation reaction (influent turbidity and flow) to correct the value of Streaming Current. So it can detect coagulation process accurately, which lays the foundation for optimizing control in dosing process.
(2) In order to improve control performance, the paper brings forward a piece of new fuzzy controller used in water treatment drug control. Firstly, controller can automatically chooses rules set basing on different state of effluent turbidity in the process of coagulation dosing. Secondly, according to the streaming current's deviation and its rate of change, each rules set can give concrete control rule which is suitable to be used in different state of the environment.
(3) In order to improve adaptability of controller, this paper introduces a control model of the fuzzy neural network. Combining fuzzy controller's fuzzy reasoning ability with the neural network's learning ability, it can adaptively handle all kinds of complex situations in auto control of dosing process. It provides new approaches for the auto control of coagulant dosing in the water factory.
引文
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[2]Bui-Suck Nahm,Su-Bum Lee,Kwang-Bang Woo,et al.Development of an optimum control software package for coagulant dosing process in water purification system.SICE' 96.Proceedings of the 35th SICE Annual Conference.International Session Papers,1996.1157-1161
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[4]杨万东,石颖,吕宏德.水厂自动控制中串级调节的应用研究.哈尔滨建筑大学学报,1998,31(5):13-15
[5]费峻涛,鲍远律,朱民,等.神经网络控制的应用和发展.自动化与仪表,1998,13(2):27-29
[6]P J Jackson,E J Tomlinson.Automatic coagulation control-Evaluation of strategies and techniques.War Supply,1986,4:55-67
[6]白桦,李圭白.混凝投药的神经网络控制方法.中国给水排水,2001,27(11):21-23
[7]张启海.城市给水工程.中国建筑工业出版社,2003
[8]严煦世.给水工程(第四版).中国建筑工业出版社,1999
[9]崔福义,彭永臻.给水排水工程仪表与控制.中国建筑工业出版社,1999
[10]崔福义.混凝剂投加的优化自动控制-检测器法的试验研究.给水排水,1989,7(2):4-8
[11]崔福义,曲久辉,李虹,李圭白.国产流动电流投药控制系统的基本性能与应用评价.给水排水,1994,8(3):5-7
[12]崔福义,李圭白著.流动电流及其在混凝控制中的应用.黑龙江科技出版社,1995
[13]汪光焘主编.城市供水行业2000年技术进步发展规划.中国建筑工业出版社,1993
[14]周晓东,张阿卜.单因子自动投药控制系统在水净化站中的应用.电力建设,2004,25(9):34-36
[15]边朝辉.混凝投药工艺的控制策略及其实现http://co.163.com/index_gps.htm
[16]杨凯人.显示式絮凝控制系统(FCD)在水厂的应用.中国给水排水,2000,12(9):31-33
[17]于水利,李圭白,孙景浩.高浊度水絮凝投药自控系统生产试验.中国给水排水,1996,12(3):10-14
[18]Ohtsuki T.Intelligent control system based on blackboard concept for wastewater treatment process.Wat Sci Tech,1998,37(12):77-85
[19]高大文彭永臻王淑莹.污水处理智能控制的研究、应用与发展.http://co.163.COm/index_gps.htm
[20]王丽萍,方厚辉,基于多传感器信息融合技术的火灾探测系统研究:[硕士学位论文].长沙:湖南大学,2006
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[22]Costantini M,Farina A,Zirilli F.Ihe Fusion of Different Resolution Sar Images.Proceedings of the IEEE,1997,85(1):139-146
[23]Ng K C,Trivedi M M.A Neuro-Fuzzy Controller for Mobile Robot Navigation and Multirobot Convoying.IEEE Transaction on Systems,Man,and Cybernetic-Part B:Cybernetics,1998,28(6):829"-'840
[24]Nejatali A,Ciric I R.Novel image fusion methodology using fi~Tzy set theory.Optical Engineering,1998,37(2):485"--491
[25]Herrera F.Hn'rera-Viedma E Aggregation Operators for Linguistic Weighted Information.IEEE Transaction on Systems,Man,and Cybernetic-Part A:Systems and Humans,1997,27(5):646"--656
[26]Kosko B.Fuzzy associative memories.In:Kondel A(ed).Fuzzy Expert System Reading.IdA:Addison ~/eley,1987
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[28]W.Pedrycz.Fuzzy neural network and neuro-computations.Fuzzy Sets and System,1993,56:1-'--28
[29]J.M.Keller etc.Neural network implementation of fuzzy logic.Fuzzy Sets and Systems,1992,45(1):1"--12
[30]J.J.Buckley.Sugeno type controllers are universal controllers.Fuzzy Sets and Systems,1993,53(3):299"-~303
[31]Special Issue on Fuzzy logic and Neural Networks.IEEE Transcations on Neural networks,1992,3(5)
[32]Jang.J.R.Transactions ANFIS:Adaptive-Network-Based Fuzzy Inference System[J].IEEE on Systems,Man,and Cybernetics,1993,23:665-684
[33]田民乐等.一种新型的模糊神经网络及其应用.模糊系统与数学,1998,4:45-47
[34]达飞鹏等.基于输入输出模型的模糊神经网络滑模控制.自动化学报,2000,12(1):23-270
[35]L.Tran.Haoi,S.Osowski.Neuro-fuzzy TSK network for approximation of staticand dynamic functions[J].Control and Cybernetics,2002,31(2):309-326
[36]D.Nauck,R.Kruse.Neuro-fuzzy systems for function approximation[J].Fuzzv Sets and Svstems.1999.10:261-271
[37]仲崇权张立勇杨素英.基于最小二乘原理的多传感器加权融合算法.仪器仪表学报.2003,24(4):427-430
[38]蒋志凯,数字滤波与卡尔曼滤波,中国科学技术出版社,1993,42-43
[39]杨海波,陈鸿玺,基于贝叶斯估计法的传感器目标定位研究,电脑知识与技术,2006,12(2),2-3
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