用户名: 密码: 验证码:
城市供水水处理系统的建模、控制与运行优化研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
城市供水水处理系统作为一个城市的生活基础设施,其供水质量关系到居民的身体健康,混凝沉淀和过滤作为水处理系统的关键工序,其中水的混凝效果及滤池工况既决定出水质量还影响制水成本。因此,对水处理系统建模,控制与运行优化进行深入研究,在保证供水质量安全的同时尽可能降低能耗是十分有意义的研究课题。
     在相关科研项目的支持和广东某水厂的协助配合下,对水处理系统的建模、控制与运行优化进行了四方面的研究,主要研究内容和取得成果如下:
     1.对混凝投药进行了建模研究,建立了三种不同结构的数学模型。
     针对目前国内绝大部分水厂投药量由人工经验确定的不足,建立了一种简化适用的投药指数模型,提出了一种改进的差分进化算法,并用该算法来辨识指数模型中的参数;结合现有水厂的实际投药方法,建立了一种投药分段线性模型,该模型以源水浊度变化分区间,以源水浊度、源水流量为前馈量实现投药粗调,以待滤水浊度为反馈量实现投药细调;最后建立了一种性能较优的神经网络模型。各自的模型检验结果表明得到的三种模型较为准确,可利用其计算结果指导现场操作人员投药,减少生产过程投药的盲目性,确保了待滤水浊度的稳定。
     2.对混凝沉淀过程的控制算法进行了分析,研究了几种控制算法并对其做了仿真分析。
     针对对象模型不易获取,提出了两种改进的PID算法:基于改进差分辨识的PID(MDE-PID)和基于迭代反馈的二自由度PID(IFT-PID)。MDE-PID算法中采用了一种根据个体适应值优劣来变异的改进策略,仿真结果表明该算法可以提高收敛速度和收敛精度。IFT-PID算法对迭代步长更新作了适当改进以确保获得解的准确性,仿真结果表明该算法辨识得到解比其它传统方法更优。针对对象受扰因素多和大滞后特性,提出了两种新的控制算法:基于Smith模型预估的双控制器控制和基于迭代反馈的内模控制(IFT-IMC)。Smith模型预估的双控制器方案将Smith的模型补偿优点和双回路的独立性控制优点结合起来,并对跟踪PI控制器进行非线性补偿。仿真结果表明此算法具有较好的自适应能力和稳定性,可实现对大滞后、干扰多的复杂系统的良好控制。IFT-IMC算法集合了内模控制鲁棒性强和IFT自适应强的优点,具有较好的自适应性和鲁棒性,控制性能优于IMC-PID和Smith-PID,可用于实现对此类难控系统的有效控制。
     3.对滤池水头损失和比沉积变化进行了实验研究,建立了滤池水头损失和比沉积两种数学模型。
     针对过滤过程中水头损失不易求取和比沉积无法获得的不足,建立了水头损失数学模型和比沉积数学模型。首先根据实验数据求取了不同滤层深度水头损失值,给出了水头损失随滤层深度变化的指数关系式以及一种新的水头损失与比沉积的定量关系式,求取了不同滤层深度比沉积。然后借助获得的关系和现有文献结果,给出了水头损失和比沉积随各种影响因素之间的关系式。最后借助获取的水头损失数据和比沉积数据,采用改进差分方法辨识各自模型参数,建立了水头损失模型和比沉积模型,各自的模型检验结果表明,求得的两种模型是准确的。借助两种模型可以从水头损失宏观角度和比沉积微观角度了解滤层堵塞情况,从而更好的设置过滤周期。
     4.建立了最优待滤水浊度数学模型,并对滤池过滤和反冲洗进行了运行优化实验研究。
     从总能耗角度出发,给出了能耗最低时的最优待滤水浊度具体表达式,采用回归辨识法获得了表达式中的未知参数,从而建立了最优待滤水浊度数学模型,证明了现有研究成果中定性提出的最优经济浊度概念的存在性。分析了课题水厂滤池的目前运行工况,提出了两种优化实验方案,并对滤池优化前后的水头损失做了对比分析,实验结果表明,本文提出的优化结论是合理的,实施该优化方案,可以比较明显地降低能耗,具有较好的经济效益和社会效益。
     最后在总结本文研究的基础上,对水处理系统中建模与控制技术的今后发展及联合优化运行方面进行了展望。
City water supply as a city infrastructure,the water quality of water treatment system isclosely related to the people’s health.As the primary part of the water treatment process, thecoagulation result of water will influence the subsequent processing units,filtration as afollow-up treatment process,it determines the quality and the cost of water.Therefore, toensure water quality and safety while minimizing energy consumption,modeling, control andoperation optimization of the water treatment system in-depth study is very meaningfulresearch.
     with the support of the relevant research projects and the cooperation of the relevantwaterworks,the study on water treatment system includes4aspects, and the main contentsand research achievements are as follows:
     1. Study the modeling of the dosage in the coagulation process, and establish three differentstructural mathematical models.
     Aiming at the arbitrariness of dosage usage in current water treatment process, thereduced exponential model of coagulation dosage has been established based on existingresearch achievement and the realtime data, the parameters of the exponential model havebeen identified by using an improved differential evolution algorithm. Depending on thescope of raw water turbidity, the piecewise linear model has been established, which includesthree input parameters:the raw water turbidity, raw water flow and the pending filter turbidity,the former two as feed-forward variable adjustment and the latter one as feedback variableadjustment.the parameters of the piecewise linear model have been identified by using linearregression analysis. Finally using neural network technology to model, a neural networkblack model with the best performance has been given by comparing and analysingperformance of model,which is composed of different intermediate nodes and mappingfunctions. The model test results show that the models are accurate and instructive, accordingto the model to achieve accurate control of dosage, reduce blindness in the production processand ensure the stability of water quality.
     2. Analysis the control algorithms of the coagulation process in-depth and study severalcontrol algorithms and analysis their simulation results.
     Aiming at the difficulty to obtain the model of coagulation process, two improved PIDalgorithm have been introuuced:the PID controller based on improved differentialevolution(MDE-PID) and the two freedom degrees PID based on iterative feedback(IFT-PID).MDE-PID algorithm uses an improved mutation strategy that the target vector depends on thebest vector according to their fitness value, the simulation results show that the algorithm canimprove the convergence speed and accuracy. for the IFT-PID algorithm, the effects of theiterative step and the parameters’ initial value on the iterative process have been discussedand the iterative step has been improved,the simulation results show that the method is betterthan other traditional methods.Taking into consideration more disturbance and large delay,two new algorithms have been proposed: dual controllers scheme based on Smith modelcontrol and internal model control based on iterative feedback tuning(IFT-IMC). The dualcontrollers scheme combines the advantage of the traditional Smith predictor and theindependence of dual controllers,and compensate the tracking PI controller. the simulationresults show that the adaptation to object parameters change and the stability are better.IFT-IMC algorithm combines the strong robustness of internal model control and the strongadaptivity of IFT, it has good adaptability and robustness and the control performance is bestamong IMC-PID and Smith-PID, so it can be used to control this system to achieve effectivecontrol.
     3. Study on the change of the headloss and sludge content per unit volume during filtrationprocess from the experimental point of view and establish two kind of mathematical modelsfor filter.
     Aiming at the difficulty to obtain the headloss and sludge content per unit volume duringfiltration process, the mathematical models of head loss and sludge content per unit volumehave been established. Firstly, the headloss values of different filter depth have been obtainedby means of experiment data, the exponential relationship of headloss with the filterlayer depth change and the quantitative relation between the headloss and sludge content aregiven, so the sludge content of different layer depth has been calculated. Then with the helpof the relations and the existing research results,the relationship of the headloss with thevarious influencing factors and the relationship of the sludge content with thevarious influencing factors have been obtained. Finally, based on the data of headloss and sludge content, adopted modified differential method to identify the parameters,the model ofheadloss and sludge content are established and the model test results shows that the twomodels are accurate respectively. With the help of two kind of models, from themacro perspective of headloss and the micro perspective of sludge content, the cloggedsituation of the filter layer is clear and the filter cycle has been set more reasonabley.
     4. Establish the turbidity mathematical model of water pending treatment and study onoptimization experiment of filter filtration and backwash.
     From the perspective of the total energy consumption, the energy consumption equationof coagulation and filtration has been made,so the optimal turbidity expression has beenobtained when the total energy consumption is minimal. Based on the filter parameters andreal-time data, the expression parameters has been identified by using recursive identificationmethod.this research result proves the existence of optimal economic turbidity concept thathas been proposed qualitatively.Then the actual work situation of water plant has beenanalyzed and the optimization experimental study on the filtration cycle and backwashingtime has been implemented,lastly comparison of energy consumption between before andafter optimization has been discussed, The experimental results show that optimizing theexisting filter conditions appropriatly can reduce energy consumption significantly andsave the cost of water, which has good economic benefit and social benefit.
     Finally, the research achievements of this paper have been concluded and the future ofdevelopment of modeling,control method and joint optimal operation has been introduced.
引文
[1]邓晓燕,城市供水水处理系统建模与滤池优化运行研究[D],华南理工大学硕士学位论文,2011,5
    [2]刘倩,王良元,程恩,袁飞,基于图像处理的水厂加矾量自动决策系统[J],合肥工业大学学报(自然科学版),2013,36(6):671-673
    [3]Palani Sundarambal,Liong Shie-Yui,Tkalich Pavel. An ANN Application for WaterQuality Forecasting[J]. Marine Pollution Bulletin,2008,56(9):1586-1597
    [4]Omer Faruk, Durdu. A Hybrid Neural Network and ARIMA Model for Water QualityTime Series Prediction[J]. Engineering Applications of ArtificialIntelligence,2010,23(4):586-594
    [5]Zhao Ying, Nan Jun, Cui Fu-Yi, Guo Liang. Water Quality Forecast Through Applicationof BP Neural Network at Yuqiao Reservoir[J]. Journal of Zhejiang University: ScienceA,2007,8(9):1482-1487
    [6]邹振裕,罗永恒,李展峰等,沙口水厂混凝剂投加的研究与实践[J],中国给水排水,2009,25(17):
    [7]郭钰锋,马军,于达仁等.给水处理投药控制的非线性数学模型及仿真[J]哈尔滨工业大学学报2009,41(5):64-68
    [8]赵英,南军,崔福义,郭亮,神经网络技术在水处理工艺建模中的应用[J],给水排水,2007,33(10):110-113
    [9]杨开明,张建强,杨小林.混凝沉淀过程中最佳混凝剂投量的研究[J],工业水处理,2005,25(9):49-51
    [10]王艳,吴学伟,龙志宏.西洲水厂混凝剂投量数学模型的建立[J],山西建筑,2007,33(3)167-168
    [11]王大志,柳秉洁,混凝剂最优投加量数学模型[J],中国给水排水,1998,4(4)16-20
    [12]张宏伟,镡新,储诚山,净水厂最优投药量数学模型[J],工业水处理,1999,14(4):1-3
    [13]张宏伟,傅玉芬,牛志广,水处理系统优化运行数学模型及其求解方法的研究[J],天津工业大学学报,2004,23(3)1-5
    [14]余亚冰,罗磊,于波等,大型净水厂自动投药系统的建模与研究[J],甘肃科学学报,2012,24(2):120-123
    [15]于东江,水厂最佳投矾量的确定[J],给水排水,1992,5:41-42
    [16]Kazmi A.A.,Agrawal L.K.,Jensen,J.K.,Water Quality Modelling for Yamuna Action PlanPhase II (YAP II)[J]. Journal of the Institution of Engineers (India):Environmental Engineering Division,2007,88(SEPT.):33-42
    [17]吴雨翔,魏星,王平尧,北仑水厂实时自动投药系统的建模与研究[J],宁波大学学报〔理工版),2010,23(2):90-93
    [18]徐孝全.运用数学模型方法建立水厂投药的自动化控制[D].重庆大学工程硕士学位论文,2004.7
    [19]崔福义,李圭白.流动电流混凝控制技术在我国的应用[J].中国给水排水,1999,15(7):24-26
    [20]孙连鹏,南军,杨艳玲.原水浊度对透光率脉动混凝投药控制技术的影响分析[J].给水排水,2002,28(7):19-22
    [21]张俊,罗大庸,广义预测控制和PID控制在混凝投药中的应用[J],信息与控制,2012,41(1):89-94
    [22]张俊,孙辰昊,薛廷民,混凝投药的组态监控系统和复合控制方案的设计[J],工业控制计算机,2012,25(9):80-84
    [23]白桦,李圭白,净水厂最佳投药量的神经网络控制系统[J],工业仪表与自动化装置,2002,4:37-39
    [24]王军栋,混凝投药过程非线性预测控制研究[D],哈尔滨工业大学博士学位论文,2011,6
    [25]哀薇,朱学峰,水厂混凝投药大滞后过程的数据驱动直接控制方法[J],控制理论与应用,2011,28(3):335-342
    [26]阎有运,常波,刘建国,常万仓,ANFIS在混凝投药前馈控制器中应用的仿真研究[J],环境工程学报,2010,4(6):1357-1362
    [27]胡茗,陈征,曾明如,前馈神经网络在水厂混凝投药中的应用[J],南昌大学学报(理科版),2010,34(1):94-97
    [28]张俊,薛廷民,孙辰昊,预测控制和前馈控制在混凝投药中的应用[J],计算技术与自动化,2012,31(2):21-24
    [29]刘前军,白桦,李圭白.透光率脉动混凝投药系统的智能控制[J].中国给水排水,2003,19(8):51-53
    [30]李孟,许国仁,南军,李圭白.透光率脉动絮凝投药自控系统配置的工程实践研究[J].给水排水,1999,25(9):66-68
    [31]白桦,李圭白,透光率脉动混凝投药模糊控制系统的试验研究[J],哈尔滨工业大学学报,2003,35(7):792-794
    [32]江智军,童立君,何小斌.基于专家系统的水厂自动投药系统的研究[J].南昌大学学报,2004,26(3):51-54
    [33]孙达智,江智军.专家系统控制自来水生产的自动加药工艺[J].中国给水排水,2003,19(3):63-65
    [34]曾明如,孙达智,江智军.给水厂混凝投药实时控制专家系统研究[J].给水排水,2004,30(5):100-102
    [35]熊红艳,郭华芳,章云.模糊控制和PID控制结合的净水厂投药控制系统[J].自动化技术与应用,2005,24(2):55-57
    [36]南军,李圭白.新型混凝投药智能复合环控制系统[J].中国给水排水,2001,17(9):49-51
    [37]杨振海,陈霞.混凝投药的前-反馈控制系统设计[J].中国给水排水,1999,15(11):42-44
    [38]朱学峰,刘桂香,陈菊,李展峰等,自来水厂出水浊度的前馈反馈智能控制[J].控制工程,2010,17(3):290-292
    [39]肖术骏.时滞过程的闭环辨识与先进控制策略的仿真与应用研究[D].广州:华南理工大学,2010,6
    [40]肖术骏,陶睿等.IMC-PID在水厂出水浊度控制中的仿真研究[J],自动化与仪表,2009,1:36-38
    [41]王伟,水处理混凝投药过程的智能控制研究[D],华南理工大学硕士论文,2008,6
    [42]张中炜,丁永生.净水厂混凝投药的无模型自适应控制系统设计[J],计算机仿真,2007,24(4):176-179
    [43]王旭芳,王环武,范跃华, V型滤池工艺设计的标准化和智能化探讨[J],安全与环境工程,2005.12(3):22-25
    [44]曹勇锋,张朝升, V型滤池滤砂优选和反冲洗参数确定[J],广州大学学报(白然科学版),2008.7(4):64-67
    [45]傅舟跃,徐亚青, V型滤池气水反冲参数对冲洗效果的影响[J],中国高新技术企业,2009.13:92-93
    [46]景有海,金同轨,范瑾初,均质滤料过滤过程的毛细管去除浊质模型[J],中国给水排水,2000.16(6):1-4
    [47]景有海,金同轨,范瑾初,均质滤料过滤过程的水头损失计算模型[J],中国给水排水,2000.16(2):9-12
    [48]Pendse H, Tien C, Rajaopalan R,et al.Dispersion measurement in clogged filter beds:adiagnostic study on the morphology of particle deposits[J] J AIChE,1978:24(3):473-484
    [49]Coad M A,Ives K J. Investigation of deep bed of filtration in using tracers[C]. In: FiltechConference. London.1981:131-136
    [50]Matsui Y.,et al. Mathematical description of deep filter performance[J], J WaterSRT-Aqua,1995.44(5):166-179
    [51]张建锋,王晓昌,金同轨,均质滤料过滤阻力的数学模型[J],环境科学学报,2003.23(2):246-251
    [52]徐勇鹏,王在刚,崔福义.滤池反冲洗时间控制模式的分析与优化[J],中国给水排水,2006.22(1):66-69
    [53]安明,滤池气、水反冲洗技术的节水、节能效益研究[J],工业水处理,2006.26(7):91-93
    [54]张俊贞,邓彩玲,安鼎年.滤池气水反冲洗的数学模型[J],中国给水排水,1997.13(3):10-13
    [55]王利平,金同轨,金伟如,殷震育.石英砂均质滤料气水反冲洗强度数学模型的建立[J],给水排水,2002.28(8):26-28
    [56]张建锋,王磊波.滤池气水反冲洗强度控制指标的建立与分析[J],给水排水,2008.34(2):15-18
    [57]刘俊新,李圭白.滤池气水反冲洗时排水浊度变化的数学模式[J],哈尔冰建筑工程学院学报,2009.22(4):60-67
    [58]唐友尧.滤池最大过滤水头损失值的确定[J],武汉城市建设学院学报,1990.7(2):53-59
    [59]陈冬毅,施志强.气水反冲洗滤池的优化运行[J],中国给水排水,2001.17(4):55-58
    [60]王利平,金同轨,陈保平,王逢慧.石英砂均质滤料气水反冲洗试脸研究[J],给水排水,1996.22(1):16-18
    [61]周锡文.给水厂运行探讨[J].给水排水,1998(10):32-33
    [62]杜玉柱,郑海军.给水厂水处理系统优化运行初探[J].给水排水,1999.25(8):23-25
    [63]田一梅,张宏伟,齐庚中,罗津悦.水处理系统运行状态数学模拟的研究[J].中国给水排水,1998,14(4):10-12
    [64]田一梅,单金林,陈浙良,阎萍.水处理系统优化运行[J].中国给水排水,1999,15(5):5-9
    [65]崔福义,石明岩.宾县水厂处理系统的优化运行研究[J].中国给水排水,2000.16(7):8-10
    [66]石明岩,崔福义,张海龙,给水处理系统优化运行的中试研究[J],东南大学学报(白然科学版),2002,32(1):105-108
    [67]赵志伟,高晗,崔福义,低浊度出水条件下给水处理系统优化运行的中试研究[J],沈阳大学学报,2006,18(2):73-76
    [68]张宏伟,牛志广.城市供水系统优化运行模型的研究[J].天津大学学报,2003,36(4):434-438
    [69]张宏伟,傅玉芬,牛志广.水处理系统优化运行数学模型及其求解方法的研究[J].天津工业大学学报,2004,23(3):1-5
    [70]孙博雅,颗粒物计数法用于水厂运行优化的研究[D].同济大学硕士学位论文,2008.7
    [71]张土乔,吕谋,赵洪宾,供水系统优化运行的建模方法研究[J],浙江大学学报(工学版),2000,34(4):428-431
    [72] ZHANG Hongwei,TAN Xin,CHEN Chunfang, Research on optimal operation of waterproducing[J], Transaction of Tianjin University,1999.5(2):215:218
    [73]邹振裕,罗永恒,李展峰,水厂滤池自动反冲洗控制的优化研究[J],工业水处理,2011.(1)
    [74]邓泽喜,曹敦虔,刘晓冀等.一种新的差分进化算法[J].计算机工程与应用,2008,44(24):40-42.
    [75]栾丽君,谭立静,牛奔.一种基于粒子群优化算法和差分进化算法的新型混合全局优化算法[J].信息与控制,2007.36(6):708-714
    [76]吴亮红.差分进化算法及其应用研究[D].湖南大学硕士学位论文,2007
    [77]WU Yanling,LU Jiangang,SUN Youxian,An Improved Differential Evolution forOptimization of Chemical Process[J],Chinese Journal of ChemicalEngineering,2008,16(2):228-234
    [78] Brest,S Greiner, B Boskovic, M Mernik, V umer.Self-Adapting Control Parameters inDifferential Evolution: A Comparative Study on Numerical Benchmark Problems[C].IEEETrans.Evol.Comput.vol.10,no.6,pp.646-657,Dec.2006
    [79] Hesheng Tang, Songtao Xue, Cunxin Fan, Differential evolution strategy for structuralsystem identification, Computers and Structures,2008.86:2004-2012
    [80]牛大鹏,王福利,何大阔等.改进差分进化算法及其在发酵优化中的应用[J].东北大学学报自然科学版.2008.29(4):469-472
    [81]黄骅,俞立,张贵军等.改进的差分进化算法及在聚丙烯牌号切换优化中的应用[J].化工学报.2008.59(7):1711-1714
    [82]颜学峰,余娟,钱锋自适应变异差分进化算法估计软测量参数[J].控制理论与应用.2006.23(5):744-748
    [83]林彬.多元线性回归分析及其应用[J].中国科技信息,2010,9:60-61
    [84]沈花玉,王兆霞,高成耀,秦娟等. BP神经网络隐含层单元数的确定[J].天津理工大学学报,2008,24(5):13-15
    [85]王红双,张欣蕾.BP神经网络在防城港货物吞吐量预测中的应用[J].河北交通科技,2009,6(3):49-51
    [86]何宏等,基于免疫进化算法的PID参数整定[J].计算机应用,2007,27(5):1174-1176
    [87]刘国联等,基于改进人工免疫算法的PID参数优化研究[J].计算机工程与应用,2008.44(19):84-86
    [88]P. Wang, D. P. Kwok. Auto-tuning of classical PID controllers using an advanced geneticalgorithm [A]. Proc IEEE Int. Conf. on Power Electronics and Motion Control [C]. SanDiego,1992:1224-1229.
    [89]王凌,李文峰,郑大中.非最小相位系统控制器优化设计[[J].自动化学报,2003,29(1):135-141.
    [90]Z. L. Gaing. A particle swarm optimization approach for optimum design of PIDcontroller in AVR system [J]. IEEE Transactions on Energy Conversion,2004,19(2):384-391.
    [91]王介生等.基于粒子群优化算法的PID控制器参数自整定[[J].控制与决策,2005,20(1):73-76.
    [92]文孟超,吴杰长,常广晖等,基于改进PSO算法的柴油机PID控制器参数优化[J],武汉理工大学学报,2013,35(6):81-85
    [93]邓丽,蒋婧,费敏锐,基于免疫粒子群算法的PID参数整定与自适应[J],自动化仪表,2013,34(2):65-67
    [94]吴姗姗,黄友锐,基于改进人工鱼群算法的PID控制器参数优化[J],安徽理工大学学报(自然科学版),2013,33(2):23-26
    [95]李丽香等.基于混沌蚂蚁算法的PID控制器的参数整定[[J].仪器仪表学报,2006,27(9):1104-1106.
    [96] G. Z. Tan, Q. D. Zeng, W. B. Li. Design of PID controller with incomplete derivationbased on ant system algorithm [J]. Journal of Control Theory and Applications,2004,2(3):246-252.
    [97]周刘喜等,基于差分进化算法的PID优化设计[J].机械与电子,2007,12:54-56
    [98]吴亮红,王耀南,袁小芳等.自适应二次变异差分进化算法[[J].控制与决策,2006,21(8):898-902.
    [99]郭佩佩,甘艳珍,朱雪峰,基于BP神经网络的水厂加药凝絮过程辨识研究[J].
    [100] Donna M. Schneider,Control of Processes with Time Delays[J], IEEE Transactions oninduty applications,1988,Vol.24, No.2:186-191
    [101]田玉楚,大时滞工业过程的双控制器结构[J],自动化学报,1999.6:824-827
    [102]李春生,王耀南,鄂加强,一阶纯滞后智能非线性PI控制器优化设计[J],控制与决策,2007.3:341-344
    [103]闻博,李宏光,含分段线性隶属函数的模糊规划建模方法[J],化工学报,2010.8:541-544.
    [104]刘桂香,自来水厂混凝加药的智能控制[D],华南理工大学硕士学位论文,2009
    [105]侯忠生,许建新,数据驱动控制理论及方法的回顾和展望[J],自动化学报,2009.35(6):650-665
    [106]H kan Hjalmarsson,Michel Gevers,Iterative feedback Tuning:theory andapplication[C],IEEE control system,1998.8:26-41
    [107] Itthisek Nilkhamhang, AkiraSano, Iterative Tuning Algorithm for Feedforward andFeedback Control of Two-Mass Motor System with Physical Parameter Identication[C],IEEE Conference on Control Applications,2005.8:28-31
    [108] Daniel Rupp,Lino Guzzella,Iterative tuning of internal model controllers withapplication to air/fuel ration control[C],IEEE transactions on control systemstechnology,2010.18(1):177-184
    [109]Jakob Kj bsted Huusom, H kan Hjalmarsson, Niels Kj lestad Poulsen,and Sten BayJ rgensen. Improving Convergence of Iterative Feedback Tuning using Optimal ExternalPerturbations[J]. Proceedings of the47thIEEE Conference on Decision andControl,Cancun,Mexico,2008,9:2618-2623
    [110]Proch’azka H, Gevers M. Iterative Feedback Tuning for Robust Controller Design andOptimization[C]. Proceedings of the44th IEEE Conference on Decision and Control, and theEuropean Control Conference2005,Sevill,Spain,2005:3602-3607.
    [111]Olivier L,Michel G, Magnus M.Iterative Feedback Tuning of PIDParameters:Comparision with Classical Tuning Rules[J]. Control Engineering Practice,2003,11(9):1023一1033.
    [112]SungEun Jo, Sang Woo Kim,Normalized Iterative Feedback Tuning with ModifiedFeedback Experiment[J],Proceedings of the American Control Conference2001,Arling,2001.6:612-617
    [113]Nakamoto M. Parameter Tuning of Multiple PID controllers by Using IterativeFeedback Tuning[C]. SICE Annual Conference in Fukui,2003:183-186
    [114]Graham A E, Young A J, Xie S Q. Rapid tuning of controllers by IFT for profile cuttingmachines[J]. Mechatronics,2007,17(2):121-128
    [115]陈大伟,肖为周,李旭宏,何流,迭代反馈约束下的城市轨道交通客流预测分析[J],华南理土大学学报(自然科学版),2011,39(8):99-103
    [116]唐友尧.滤池最大过滤水头损失值的确定[J],武汉城市建设学院学报,1990.7(2):53-59
    [117]陈冬毅,施志强.气水反冲洗滤池的优化运行[J],中国给水排水,2001.17(4):55-58
    [118]王圃,龙腾锐,陆柯,李肖.城市给水处理厂能耗研究进展[J].给水排水,2005,31(1):93-97
    [119]许保玖.给水处理理论[M].北京:中国建筑工业出版社,2000
    [120]王圃.城市给水处理厂及泵站能耗分析与应用研究[D],重庆大学博士学位论文,2004.7
    [121]李露,陶瓷滤料在给水处理中的应用研究[D],武汉理工大学硕士论文,2007.5

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700