污水处理出水水质软测量预测预报系统开发
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摘要
本论文是围绕如何采用软测量技术解决目前城市污水处理出水水质参数难以用硬仪表在线测量这一现实问题而展开的。研究目标是设计基于软测量技术的计算机软件系统，实现污水处理出水水质的预测预报。
本论文的研究方法是：首先，借助于污水处理过程历史数据，建立出水水质参数的软测量模型；然后将该模型嵌入应用系统中，开发出基于软测量技术的污水处理过程出水水质参数的计算机预测预报系统。
本论文的研究成果主要有以下两方面：
一、获取了污水处理出水水质预测软测量模型
1、建立了昆明市第二污水处理厂运行多年的历史数据库；
2、选取历史数据库中较为完整的数据加以预处理作为软测量建模样本和预测评价样本，并由此设计了大量的多元线性回归、人工神经网络预测算法；
3、结合污水处理工艺特点，创造性地提出了“样本数据插值和多步记忆”的污水处理出水水质预测软测量模型结构，使预测质量大为改善。
二、成功开发了基于软测量技术的污水处理出水水质预测预报软件系统
本系统的前端应用软件采用Visual C++ 6.0开发；软预测器由MATLAB实现；数据由Microsoft SQL Server 2000或Access管理。即：
1、采用MFC ODBC(开放式数据库互联)技术访问数据库，实现应用程序与污水处理数据库的信息集成；
2、采用MATLAB提供的引擎(Engine)技术，结合C++编程技巧，实现了Visual C++和MATLAB的信息集成，即由此将软预测器“捆绑”到应用系统。
总之，本文的研究不仅验证了软测量技术应用于城市污水处理过程出水水质参数预测的可行性，还提供了采用MATLAB、Visual C++、SQL Server(或Access)混合编程的具有创新意义和一定推广价值的具体开发方案。这种开发方案缩短了应用程序的开发周期和开发成本，并在一定程度上提高了可靠性。
另外，本文提出的“样本数据插值和多步记忆”的软测量模型结构对于类似于污水处理的工业过程，即目标参数难以用硬仪表在线测量且可得数据样本间隔时间长、前后相关性强的工业过程之软测量模型构建，也颇具参考价值。
The thesis developed on an existing problem for forecasting the effluent quality parameters of urban sewage treatment factories, which are usually difficult to measure with conventional online apparatus, through applying soft-sensing technique. The goal of this research is to develop a suit of software system for realizing the forecast of effluent quality based on soft-sensing technique.
Research methods applied in the thesis are as follows.
The first method is to construct the soft-sensing models of effluent quality parameters with history data of a sewage treatment factory for years. Another method is to plant the models into the application system to develop the computer forecasting system for effluent quality parameters of sewage treatment factories.
Two main research results of the thesis are as follows. (1) The obtainment of soft-sensing models for effluent quality parameters forecasting.
Firstly, a sewage database is designed with history data of a sewage treatment factory for years. Secondly, many forecasting algorithms for Multiple Linear Regression and BP neural network are designed by using samples exported from the sewage database, At last, a kind of soft-sensing model of sample interpolation and multi-step memory for forecasting effluent quality parameters is presented. The model improved the forecasting of effluent quality parameters mostly.
(2) The accomplishment of the software system for effluent quality parameters forecasting based on soft-sensing technique.
The application of this system developed with Visual C++ while soft-sensing models designed with MATLAB and the sewage database designed with Access or SQL Server 2000. The thesis integrated the above three into one application system by applying MFC ODBC and MATLAB Engine techniques.
In general, there are two innovations in the thesis. One is the hybrid programming technique for Visual C++ and MATLAB with MATLAB Engine, which is an inexpensive and time saving way and can be introduced into other systems. The other is the presentment of the soft-sensing model of sample interpolation and multi-step memory, which can also be introduced into other situations similar to sewage treatment.
本论文的研究方法是：首先，借助于污水处理过程历史数据，建立出水水质参数的软测量模型；然后将该模型嵌入应用系统中，开发出基于软测量技术的污水处理过程出水水质参数的计算机预测预报系统。
本论文的研究成果主要有以下两方面：
一、获取了污水处理出水水质预测软测量模型
1、建立了昆明市第二污水处理厂运行多年的历史数据库；
2、选取历史数据库中较为完整的数据加以预处理作为软测量建模样本和预测评价样本，并由此设计了大量的多元线性回归、人工神经网络预测算法；
3、结合污水处理工艺特点，创造性地提出了“样本数据插值和多步记忆”的污水处理出水水质预测软测量模型结构，使预测质量大为改善。
二、成功开发了基于软测量技术的污水处理出水水质预测预报软件系统
本系统的前端应用软件采用Visual C++ 6.0开发；软预测器由MATLAB实现；数据由Microsoft SQL Server 2000或Access管理。即：
1、采用MFC ODBC(开放式数据库互联)技术访问数据库，实现应用程序与污水处理数据库的信息集成；
2、采用MATLAB提供的引擎(Engine)技术，结合C++编程技巧，实现了Visual C++和MATLAB的信息集成，即由此将软预测器“捆绑”到应用系统。
总之，本文的研究不仅验证了软测量技术应用于城市污水处理过程出水水质参数预测的可行性，还提供了采用MATLAB、Visual C++、SQL Server(或Access)混合编程的具有创新意义和一定推广价值的具体开发方案。这种开发方案缩短了应用程序的开发周期和开发成本，并在一定程度上提高了可靠性。
另外，本文提出的“样本数据插值和多步记忆”的软测量模型结构对于类似于污水处理的工业过程，即目标参数难以用硬仪表在线测量且可得数据样本间隔时间长、前后相关性强的工业过程之软测量模型构建，也颇具参考价值。
The thesis developed on an existing problem for forecasting the effluent quality parameters of urban sewage treatment factories, which are usually difficult to measure with conventional online apparatus, through applying soft-sensing technique. The goal of this research is to develop a suit of software system for realizing the forecast of effluent quality based on soft-sensing technique.
Research methods applied in the thesis are as follows.
The first method is to construct the soft-sensing models of effluent quality parameters with history data of a sewage treatment factory for years. Another method is to plant the models into the application system to develop the computer forecasting system for effluent quality parameters of sewage treatment factories.
Two main research results of the thesis are as follows. (1) The obtainment of soft-sensing models for effluent quality parameters forecasting.
Firstly, a sewage database is designed with history data of a sewage treatment factory for years. Secondly, many forecasting algorithms for Multiple Linear Regression and BP neural network are designed by using samples exported from the sewage database, At last, a kind of soft-sensing model of sample interpolation and multi-step memory for forecasting effluent quality parameters is presented. The model improved the forecasting of effluent quality parameters mostly.
(2) The accomplishment of the software system for effluent quality parameters forecasting based on soft-sensing technique.
The application of this system developed with Visual C++ while soft-sensing models designed with MATLAB and the sewage database designed with Access or SQL Server 2000. The thesis integrated the above three into one application system by applying MFC ODBC and MATLAB Engine techniques.
In general, there are two innovations in the thesis. One is the hybrid programming technique for Visual C++ and MATLAB with MATLAB Engine, which is an inexpensive and time saving way and can be introduced into other systems. The other is the presentment of the soft-sensing model of sample interpolation and multi-step memory, which can also be introduced into other situations similar to sewage treatment.
引文
[1] 李海青，黄志尧，软测量技术原理及应用．北京：化学工业出版，2000
[2] McacvoyT.J.,Contemplative stance for chemical process control..Automatica,1992, 28(2) :441-442
[3] 蒋懋孙，蒋敏伟，过程控制21世纪展望，世界仪表和自动化，1999，3(4)：10—14
[4] 于静江，周春晖，过程控制中的软测量技术，控制理论与应用，1996，13(2．)：137—144
[5] 骆晨钟，邵惠鹤．，软仪表技术及其工业应用，仪表技术与传感器，1999，(1)：32—39
[6] 俞金寿，刘爱伦，软测量技术及其应用，世界仪表和自动化．，1997，1(2)：18—20
[7] 王树青等，先进控制技术及应用．北京：化学工业出版社，2001
[8] 潘承毅，何迎晖，数理统计的原理和方法．上海：同济大学出版社，1993
[9] S.Weisberg著．，王静龙等译，应用线性回归．北京：中国统计出版社，1998
[10] G.A.F.赛伯著，方开泰等译，线性回归分析．北京：科学出版社，1987
[11] 王学仁，温忠舜，应用回归分析．重庆：重庆大学出版社．，1989
[12] 高惠璇，统计计算．北京：北京大学出版社，1995
[13] 南开大学数学系，概率与统计预报及在地震与气象中的应用．北京：科学出版社，1978
[14] 何晓群，回归分析与经济数据建模，北京：中国人民大学出版社，1997
[15] 郭宝林，回归分析法在压缩机故障分析中的应用，石油化工，1989，18(8)：535—538
[16] Russell S. A, et al. Recursive Data_Based Prediction and Control of Batch Product Ouality. A.I. ch.E. Journal, 1998,44(11):2442-2458
[17] Dixon A. G., Gresswell D.L., Theoretical Prediction of Effective Heat Transfer Parameters in Packed Beds. A. I. ch. E. Journal, 1979, 25(4):663-676
[18] Eliana Zamprogna, Massimillano Barolo. Development of a Soft Sensor for a Batch Distillation Column Using Linear and Nonlinear PLS Regression Techniques. In: IFAC. 15th Triennial World Congress. Barcelona, Spain, 2002
[19] 阎平凡，张长水，人工神经网络与模拟进化计算．北京：清华大学出版社，2000
[20] Widrow B, et al. Neural Networks Application in Industry, Business and Science.Communication of the ACM, 1994,37(12):93-105
[21] Hassoun M. Fundamentals of Artificial Neural Networks. The MIT Press, 1995
[22] McCulloch M.S.W. Pitts. A. Logical Calculus of the Ideas Immanent in Nervous Active. Bulletin of Mathematical Btophysixs, 1943 (5):115-133
[23] Hebb D. O. The Organization of Behavior. Wiley Ne4w York, 1949
[24] Pham D. T.,Liu X.,Neural networks for identification. In: Prediction and Control.London, 1995
[25] Rumelhart D. McCelland J. Parallel distributed processing: exploitations in the microstructure of cognition. Cambridge: MIT Press, 1986
[26] Moody J. Darken C.J. Fast learning in networks of locally tuned processing units. Neural Computation, 1989,1():281-294
[27] Volg T.P. et al. Accelerating the convergence of the BP methed.Berlin:Springer-Verlag, 1989
[28] Rumelhart D.E, et al. Learning Representation by BP Errors. Nature(London),1986,7:149-154
[29] Tollenaere T. Super SAB: fast adaptive back-propagation with good scaling properties. Nieural Networks. 1990,3:561-573
[30] Battit R. First and Second Order Methods for Learning: Between Steepest Descent and Newton's Method. Neural Computation, 1992,4:141-166
[31] Fletcher R, et al. Function Minimization by Conjugate Gradients. Computer Journal, 1964, 7:533-536
[32] MathWorks, help files of MATLAB6.0
[33] Patterson Dan W. Neural networks: theory and applications, singapore:Prentice Hall,1996
[34] Armitage A.F. Neural networks in measurement and control. Measurement+Control.1995,28(9):208-215
[35] Massimo C. DI, Saunders A.C.G. et al. Nonlinear estimation and control of mycelial fermentations. Pittsburg, USA:ACC, 1989, 1994-1999
[36] http://www.chinaep.net.废水治理技术专栏
[37] 胡大卫，姚念民等．除磷脱氮新工艺在昆明市第二污水处理厂中的应用．给水排水．1996，22(12)：5-7
[38] 冯生华，胡大卫．昆明市第二污水处理厂设计．中国给水排水．2000，16(4)：39—41
[39] olsson G, Aspegren H, Nielsen M. K. Operation and control of wastewater treatment--a Scandinavian perspective over 20 years. [J]. Water Science and Technology, 1998,37(12):1-13
[40] Ohto T.Control, computers and communications: Fusion in instrumentation, control and automation of water and wastewater system in Japan
[J]. Water Science and Technology. I998, 37(12): 15-19
[41] Garrett M T.Instrumentation, Control and auromation progress in the United States in the last 24 years [J]. Water Science and Technology, 1998, 37 (12):21-25
[42] Huntingdo R. Twenty years developments of ICA in a water utility [J]. Water Science and Technology, 1998, 37 (12):27-34
[43] Kabouris J C.Modeling, instrumentation, automation and optimization of wastewater treatment facilities [J].Water Environment Research, 1999, 71(5): 729-736
[44] 施汉昌，王玉钰，污水处理厂故障诊断专家系统，给水排水，2001，27(8)．
[45] 施汉昌，刁惠芳等，污水处理厂运行模拟、预测软件的应用，中国给水排水，2001，(10)
[46] 姚重华，Nigel Horan，活性污泥过程MISO神经网络模型，华东理工大学学报，2002，28(1)：71-73
[47] 李涛，贺勇军，Matlab工具箱应用指南—应用数学篇．北京：电子工业出版社，2000
[48] 闻新，周露等，Matlab神经网络应用设计．北京：科学出版社，2000
[49] 彭向华．软测量技术在污水处理中的应用研究：[硕士学位论文]．昆明：昆明理工大学自动化系，2002
[50] 刘刀桂，孟繁晶，Visual C++实践与提高—数据库篇．北京：中国铁道出版社，2001
[51] 夏云庆，Visual C++6.0数据库高级编程．北京：北京希望电子出版设，2002
[52] 彭云峰，冯丽辉，郭秀萍等，微软COM技术在工业控制软件开发中的应用，昆明理工大学学报，2002，27(6)：76—79
[53] 彭云峰，冯丽辉，郭秀萍等，利用Matlab引擎实现Matlab和VC的混合编程，昆明理工大学学报，2002，27(Sup)：305—308
[54] 官章全，刘加明，Visual C++6.0类库大全．北京：电子工业出版社，1999
[2] McacvoyT.J.,Contemplative stance for chemical process control..Automatica,1992, 28(2) :441-442
[3] 蒋懋孙，蒋敏伟，过程控制21世纪展望，世界仪表和自动化，1999，3(4)：10—14
[4] 于静江，周春晖，过程控制中的软测量技术，控制理论与应用，1996，13(2．)：137—144
[5] 骆晨钟，邵惠鹤．，软仪表技术及其工业应用，仪表技术与传感器，1999，(1)：32—39
[6] 俞金寿，刘爱伦，软测量技术及其应用，世界仪表和自动化．，1997，1(2)：18—20
[7] 王树青等，先进控制技术及应用．北京：化学工业出版社，2001
[8] 潘承毅，何迎晖，数理统计的原理和方法．上海：同济大学出版社，1993
[9] S.Weisberg著．，王静龙等译，应用线性回归．北京：中国统计出版社，1998
[10] G.A.F.赛伯著，方开泰等译，线性回归分析．北京：科学出版社，1987
[11] 王学仁，温忠舜，应用回归分析．重庆：重庆大学出版社．，1989
[12] 高惠璇，统计计算．北京：北京大学出版社，1995
[13] 南开大学数学系，概率与统计预报及在地震与气象中的应用．北京：科学出版社，1978
[14] 何晓群，回归分析与经济数据建模，北京：中国人民大学出版社，1997
[15] 郭宝林，回归分析法在压缩机故障分析中的应用，石油化工，1989，18(8)：535—538
[16] Russell S. A, et al. Recursive Data_Based Prediction and Control of Batch Product Ouality. A.I. ch.E. Journal, 1998,44(11):2442-2458
[17] Dixon A. G., Gresswell D.L., Theoretical Prediction of Effective Heat Transfer Parameters in Packed Beds. A. I. ch. E. Journal, 1979, 25(4):663-676
[18] Eliana Zamprogna, Massimillano Barolo. Development of a Soft Sensor for a Batch Distillation Column Using Linear and Nonlinear PLS Regression Techniques. In: IFAC. 15th Triennial World Congress. Barcelona, Spain, 2002
[19] 阎平凡，张长水，人工神经网络与模拟进化计算．北京：清华大学出版社，2000
[20] Widrow B, et al. Neural Networks Application in Industry, Business and Science.Communication of the ACM, 1994,37(12):93-105
[21] Hassoun M. Fundamentals of Artificial Neural Networks. The MIT Press, 1995
[22] McCulloch M.S.W. Pitts. A. Logical Calculus of the Ideas Immanent in Nervous Active. Bulletin of Mathematical Btophysixs, 1943 (5):115-133
[23] Hebb D. O. The Organization of Behavior. Wiley Ne4w York, 1949
[24] Pham D. T.,Liu X.,Neural networks for identification. In: Prediction and Control.London, 1995
[25] Rumelhart D. McCelland J. Parallel distributed processing: exploitations in the microstructure of cognition. Cambridge: MIT Press, 1986
[26] Moody J. Darken C.J. Fast learning in networks of locally tuned processing units. Neural Computation, 1989,1():281-294
[27] Volg T.P. et al. Accelerating the convergence of the BP methed.Berlin:Springer-Verlag, 1989
[28] Rumelhart D.E, et al. Learning Representation by BP Errors. Nature(London),1986,7:149-154
[29] Tollenaere T. Super SAB: fast adaptive back-propagation with good scaling properties. Nieural Networks. 1990,3:561-573
[30] Battit R. First and Second Order Methods for Learning: Between Steepest Descent and Newton's Method. Neural Computation, 1992,4:141-166
[31] Fletcher R, et al. Function Minimization by Conjugate Gradients. Computer Journal, 1964, 7:533-536
[32] MathWorks, help files of MATLAB6.0
[33] Patterson Dan W. Neural networks: theory and applications, singapore:Prentice Hall,1996
[34] Armitage A.F. Neural networks in measurement and control. Measurement+Control.1995,28(9):208-215
[35] Massimo C. DI, Saunders A.C.G. et al. Nonlinear estimation and control of mycelial fermentations. Pittsburg, USA:ACC, 1989, 1994-1999
[36] http://www.chinaep.net.废水治理技术专栏
[37] 胡大卫，姚念民等．除磷脱氮新工艺在昆明市第二污水处理厂中的应用．给水排水．1996，22(12)：5-7
[38] 冯生华，胡大卫．昆明市第二污水处理厂设计．中国给水排水．2000，16(4)：39—41
[39] olsson G, Aspegren H, Nielsen M. K. Operation and control of wastewater treatment--a Scandinavian perspective over 20 years. [J]. Water Science and Technology, 1998,37(12):1-13
[40] Ohto T.Control, computers and communications: Fusion in instrumentation, control and automation of water and wastewater system in Japan
[J]. Water Science and Technology. I998, 37(12): 15-19
[41] Garrett M T.Instrumentation, Control and auromation progress in the United States in the last 24 years [J]. Water Science and Technology, 1998, 37 (12):21-25
[42] Huntingdo R. Twenty years developments of ICA in a water utility [J]. Water Science and Technology, 1998, 37 (12):27-34
[43] Kabouris J C.Modeling, instrumentation, automation and optimization of wastewater treatment facilities [J].Water Environment Research, 1999, 71(5): 729-736
[44] 施汉昌，王玉钰，污水处理厂故障诊断专家系统，给水排水，2001，27(8)．
[45] 施汉昌，刁惠芳等，污水处理厂运行模拟、预测软件的应用，中国给水排水，2001，(10)
[46] 姚重华，Nigel Horan，活性污泥过程MISO神经网络模型，华东理工大学学报，2002，28(1)：71-73
[47] 李涛，贺勇军，Matlab工具箱应用指南—应用数学篇．北京：电子工业出版社，2000
[48] 闻新，周露等，Matlab神经网络应用设计．北京：科学出版社，2000
[49] 彭向华．软测量技术在污水处理中的应用研究：[硕士学位论文]．昆明：昆明理工大学自动化系，2002
[50] 刘刀桂，孟繁晶，Visual C++实践与提高—数据库篇．北京：中国铁道出版社，2001
[51] 夏云庆，Visual C++6.0数据库高级编程．北京：北京希望电子出版设，2002
[52] 彭云峰，冯丽辉，郭秀萍等，微软COM技术在工业控制软件开发中的应用，昆明理工大学学报，2002，27(6)：76—79
[53] 彭云峰，冯丽辉，郭秀萍等，利用Matlab引擎实现Matlab和VC的混合编程，昆明理工大学学报，2002，27(Sup)：305—308
[54] 官章全，刘加明，Visual C++6.0类库大全．北京：电子工业出版社，1999