基于多元统计投影方法的工业过程软测量技术研究
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摘要
在流程工业，尤其在化工生产过程中，软测量技术的发展具有实际意义。这是因为：(1)许多重要变量难于实时测量；(2)对每个生产过程变量设置测量仪表是非常不经济的；(3)在线质量分析仪投资昂贵、难于维护、实时性差。总之，软测量技术将会为生产过程带来极大社会效益和经济效益。
对于高维的复杂生产过程，众多的过程变量之间往往存在严重的多重相关性。为了解决这类病态模型参数估计问题，多元统计投影方法可以通过投影消除过程变量之间的多重相关性并建立起能充分反映关键生产变量的软测量模型。
本文系统和深入地研究了这一方法的若干重要方面。针对现有方法存在的问题，结合工业流化床聚乙烯生产过程的实际应用，提出若干新的算法和解决方法。主要研究内容和成果如下：
1．阐述了软测量技术主要研究内容、常用建模方法、现状和发展情况，着重综述了基于多元统计投影软测量技术的方法和应用发展情况。
2．介绍了基于多元统计投影方法软测量技术的主要数学基础，包括：主元分析、主元回归、偏最小二乘和神经网络，为后续的研究和应用打下理论基础。
3．综合工业流化床聚乙烯生产过程的工艺特点和设计要求，给出了基于上位机的控制系统设计。分析了若干主要过程变量对质量指标(树脂熔融指数和密度)的影响趋势，进行软测量建模变量的挑选和数据准备。
4．提出基于块式递推PLS限定记忆思想的算法，运用这种新方法建立的自适应软测量模型能更好地跟踪过程的变化。完成了对仿真过程和工业流化床聚乙烯生产过程质量指标软测量应用分析。
5．提出将线性PLS模型通过神经网络逼近策略拓展到非线性的PLS-NN方法，构造了基于梯度下降算法的神经网络权值矩阵学习规则。以此算法进行了工业流化床聚乙烯质量指标的软测量建模，以便为生产操作提供指导。
最后，对全文作出总结，归纳了本文解决的问题，指出了基于多元统计投影软测量技术的今后值得关注和深入研究的方向。
In many process control situations, it is often difficult to estimate some important process variables due to the limitation of process techniques or measurement techniques. These variables, which are key indictors of process performance, are normally determined by off-line sample analyses in the laboratory or using an on-line analyzer. Product quality values calculated on-line by using indirect but readily variable measurements (known as soft measurements) have been considered as an efficient method to solve this problem. Soft measurement can be used to obtain a regression model between easily obtained measurements and quality variables using statistical techniques, or neural network, etc.
Soft measurement based on multivariable statistical project method is a kind of data modelling techniques. The paper systematically elaborates this technique from many aspects. Combining the actual application of industrial gas-fluidized bed polyethylene process with the problems which exist in traditional methods, some new algorithms and solutions are brought forward. Contents in this paper are as following:
1. The paper summarizes the definition, content, developing tendency and modeling methods of soft measurement, especially provides complete and historical introduction on multivariate statistical project method.
2. Briefly introduces the academic tools of soft measurement based on multivariate statistical project method, such as principle component analysis, principle component regression, partial least squares and neural network.
3. Analyzing the characters of the gas-fluidized polyethylene process, the paper propose design of control system. In order to build the product quality soft measurement, we discussed the relation of between process variables and product quality variables. Finally, all modeling variables and data are proposed.
4. A new algorithm, finite memory based on recursive PLS, is proposed. The adaptive algorithm is applied to build adaptive soft measurement which have more strong tracking ability and higher precision than traditional model. An application of the method to Slurry-Feed Ceramic Melter and industrial fluidized bed reactor for predicting quality variables is presented to show the effectiveness.
5. By using the universal approximation property of neural networks (NN), a three-layer feedforward neural networks is embedded into the framework of standard PLS (partial least squares) modeling method resulting in a nonlinear PLS-NN model. A gradient descent learning algorithm is employed to train the network.
Finally, the whole thesis is summarized, and some future research areas are highlighted.
对于高维的复杂生产过程，众多的过程变量之间往往存在严重的多重相关性。为了解决这类病态模型参数估计问题，多元统计投影方法可以通过投影消除过程变量之间的多重相关性并建立起能充分反映关键生产变量的软测量模型。
本文系统和深入地研究了这一方法的若干重要方面。针对现有方法存在的问题，结合工业流化床聚乙烯生产过程的实际应用，提出若干新的算法和解决方法。主要研究内容和成果如下：
1．阐述了软测量技术主要研究内容、常用建模方法、现状和发展情况，着重综述了基于多元统计投影软测量技术的方法和应用发展情况。
2．介绍了基于多元统计投影方法软测量技术的主要数学基础，包括：主元分析、主元回归、偏最小二乘和神经网络，为后续的研究和应用打下理论基础。
3．综合工业流化床聚乙烯生产过程的工艺特点和设计要求，给出了基于上位机的控制系统设计。分析了若干主要过程变量对质量指标(树脂熔融指数和密度)的影响趋势，进行软测量建模变量的挑选和数据准备。
4．提出基于块式递推PLS限定记忆思想的算法，运用这种新方法建立的自适应软测量模型能更好地跟踪过程的变化。完成了对仿真过程和工业流化床聚乙烯生产过程质量指标软测量应用分析。
5．提出将线性PLS模型通过神经网络逼近策略拓展到非线性的PLS-NN方法，构造了基于梯度下降算法的神经网络权值矩阵学习规则。以此算法进行了工业流化床聚乙烯质量指标的软测量建模，以便为生产操作提供指导。
最后，对全文作出总结，归纳了本文解决的问题，指出了基于多元统计投影软测量技术的今后值得关注和深入研究的方向。
In many process control situations, it is often difficult to estimate some important process variables due to the limitation of process techniques or measurement techniques. These variables, which are key indictors of process performance, are normally determined by off-line sample analyses in the laboratory or using an on-line analyzer. Product quality values calculated on-line by using indirect but readily variable measurements (known as soft measurements) have been considered as an efficient method to solve this problem. Soft measurement can be used to obtain a regression model between easily obtained measurements and quality variables using statistical techniques, or neural network, etc.
Soft measurement based on multivariable statistical project method is a kind of data modelling techniques. The paper systematically elaborates this technique from many aspects. Combining the actual application of industrial gas-fluidized bed polyethylene process with the problems which exist in traditional methods, some new algorithms and solutions are brought forward. Contents in this paper are as following:
1. The paper summarizes the definition, content, developing tendency and modeling methods of soft measurement, especially provides complete and historical introduction on multivariate statistical project method.
2. Briefly introduces the academic tools of soft measurement based on multivariate statistical project method, such as principle component analysis, principle component regression, partial least squares and neural network.
3. Analyzing the characters of the gas-fluidized polyethylene process, the paper propose design of control system. In order to build the product quality soft measurement, we discussed the relation of between process variables and product quality variables. Finally, all modeling variables and data are proposed.
4. A new algorithm, finite memory based on recursive PLS, is proposed. The adaptive algorithm is applied to build adaptive soft measurement which have more strong tracking ability and higher precision than traditional model. An application of the method to Slurry-Feed Ceramic Melter and industrial fluidized bed reactor for predicting quality variables is presented to show the effectiveness.
5. By using the universal approximation property of neural networks (NN), a three-layer feedforward neural networks is embedded into the framework of standard PLS (partial least squares) modeling method resulting in a nonlinear PLS-NN model. A gradient descent learning algorithm is employed to train the network.
Finally, the whole thesis is summarized, and some future research areas are highlighted.
引文
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2.Mcacvoy T J. Contemplative stance for chemical process control[J].Automation,1992,28(2):441-442
3.Van den Bos A. Application of statistical parameter estimation methods to physical measurement[J].J. Phys E: Sci Instrum., 1977,10:753-760
4.Rivier J-M, Bayart M, Thiriet J-M. Intelligent instruments: some modeling approaches[J]. Measurement Control, 1996,29:179-186
5.Hartnett M K, Lightbody G, Irwin G .W. Dynamic inferential estimation using principal component regression(PCR)[J].Chemometrics Intel. Lab. Systems, 1998,40:215-224
6.Russelll, S A, Kesavan P, Lee J H, Ogummnaike B A. Recursive data-based prediction and control of batch product quality [J]. AIChE J., 1998,44:2442-2458
7.Kano M, Miyazaki K, Hasebe S, Hashimoto L. Inferential control system of distillation compositions using dynamic PLS regression [J]. J. Process Control, 2000,10:157-166
8.Kresta J, Marlin T, MacGregor J. Choosing inferential variables for multicomponent distillation using PLS [J]. In: AIChE Annual Meeting, Chicago, 1990
9.MacGregor J F, Skagerberg B, Kiparissides C. Multivariate SPC and property inference applied to low density polyethylene reactor. In: Proceedings of lFAC Symp. ADCHEM'91, 1991,Toulouse,France:663-670
10.Zhang W, Yu J. MIMO soft sensors for estimating product quality with on-line correction [J]. Trans. IchemE, 2000,78A:612-620
11.Qin S J, Yue H, Dunia R. Self-validating inferential sensors with application to air emission monitoring [J].Indus. Eng. Chem. Res., 1997,36:1675-1685
12.Wise B M, Gallagher N B. The process chemometrics approach to process monitoring and fault detection [J]. J. Process Control, 1996,6:329-348
13.Santen A, Koot G L M, Zullo L C. Statistical data analysis of a chemical plant [J]. Computers Chem. Eng. J., 1997,21(suppl.):1123-1129
14.Piovoso M J, Kosanovich K A. Applications of multivariate statistical methods to process monitoring and controller design [J]. International J. Control, 1994,59:743-765
15.Wadi I. Neural network model predicts naphtha cut point[J]. Oil& Gas J., 1996,11:67-70
16.Chen G, McAvoy T J. Process control utilizing data based multivariate statistical models[J]. The Canadian J. Chem. Eng., 1996,74:1010-1024
17.Chen G, McAvoy T J, Pivoso M J. A multivariate statistical controller for on-line quality improvement[J]. J. Process Control, 1998,8:139-149
18.Mejdell T, Skogestad S. Estimation of distillation compositions from multiple temperature measurements using PLS [J]. Indus. Eng. Chem. Res., 1991,30:2543-2555
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21.Baffi G, Martin E B, Morari A J. Nonlinear PLS revisited: The quadratic PLS algorithm [J]. Computers Chem. Eng., 1999,23:395-411
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Oxford: Pergamon Press, 1993:37-46
23.Embirucu M, Lima E L and Pinto J C. A survey of advanced control of polymerization reactors [J]. Polymer Engineering & Science, 1996, 36(2):433-447
24.MacGregor J F, Penlidis A, Hamielec A E. Control of polymerization reactors: a review [J]. Polymer Process Engineering, 1984, 2(2&3):179-206
25.Lines B, Hartlen D, Paquin F D. Polyethylene reactor modeling and control design[J]. Hydrocarbon Processing(International Ed.), 1993,72(6):119-120
26.McAuley K B, MacGregor J F. Nonlinear product property control in industrial gas-phase polyethylene reactors[J]. AIChE J, 1993, 39:855-866
27.Ellis M F, Taylor T W, Jensen K F. On-line molecular weight distribution estimation and control in batch polymerization[J]. AIChE J.,1994,40:445-462
28.Wise B M, A non-linear dynamic model identification challenge problem, Chemom. Intell. Lab. Syst., 1995, 30, 91-96
29.MacGregor J F, Kourti T, Nomikos R Analysis, monitoring and fault diagnosis of industrial processes using multivariate statistical projection methods. Proceedings of 13~(th) IFAC World Congress, San Francisco, USA, 1996
30.McAuley K B, MacGregor J F. A kinetic model for industrial gas-phase ethylene copolymerization[J]. AIChEJ, 1990, 36(6):837-849
31.Geladi P, Kowalski B R. Partial least squares: a tutorial[J]. Analytical Chimica Acta, 1986, 185:1-17
32.Wold S. et al. Nonlinear PLS modeling[J]. Chemometrics and Intelligent Lab. Systems, 1989, 7:53-65
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34.Helland, et al. Recursive algorithm for partial least squares regression[J]. Chemometrics Intell. Lab. Syst., 1992,14:129-137
35.Qin S J. Recursive PLS algorithms for adaptive data modeling[J]. Computers &
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2.Mcacvoy T J. Contemplative stance for chemical process control[J].Automation,1992,28(2):441-442
3.Van den Bos A. Application of statistical parameter estimation methods to physical measurement[J].J. Phys E: Sci Instrum., 1977,10:753-760
4.Rivier J-M, Bayart M, Thiriet J-M. Intelligent instruments: some modeling approaches[J]. Measurement Control, 1996,29:179-186
5.Hartnett M K, Lightbody G, Irwin G .W. Dynamic inferential estimation using principal component regression(PCR)[J].Chemometrics Intel. Lab. Systems, 1998,40:215-224
6.Russelll, S A, Kesavan P, Lee J H, Ogummnaike B A. Recursive data-based prediction and control of batch product quality [J]. AIChE J., 1998,44:2442-2458
7.Kano M, Miyazaki K, Hasebe S, Hashimoto L. Inferential control system of distillation compositions using dynamic PLS regression [J]. J. Process Control, 2000,10:157-166
8.Kresta J, Marlin T, MacGregor J. Choosing inferential variables for multicomponent distillation using PLS [J]. In: AIChE Annual Meeting, Chicago, 1990
9.MacGregor J F, Skagerberg B, Kiparissides C. Multivariate SPC and property inference applied to low density polyethylene reactor. In: Proceedings of lFAC Symp. ADCHEM'91, 1991,Toulouse,France:663-670
10.Zhang W, Yu J. MIMO soft sensors for estimating product quality with on-line correction [J]. Trans. IchemE, 2000,78A:612-620
11.Qin S J, Yue H, Dunia R. Self-validating inferential sensors with application to air emission monitoring [J].Indus. Eng. Chem. Res., 1997,36:1675-1685
12.Wise B M, Gallagher N B. The process chemometrics approach to process monitoring and fault detection [J]. J. Process Control, 1996,6:329-348
13.Santen A, Koot G L M, Zullo L C. Statistical data analysis of a chemical plant [J]. Computers Chem. Eng. J., 1997,21(suppl.):1123-1129
14.Piovoso M J, Kosanovich K A. Applications of multivariate statistical methods to process monitoring and controller design [J]. International J. Control, 1994,59:743-765
15.Wadi I. Neural network model predicts naphtha cut point[J]. Oil& Gas J., 1996,11:67-70
16.Chen G, McAvoy T J. Process control utilizing data based multivariate statistical models[J]. The Canadian J. Chem. Eng., 1996,74:1010-1024
17.Chen G, McAvoy T J, Pivoso M J. A multivariate statistical controller for on-line quality improvement[J]. J. Process Control, 1998,8:139-149
18.Mejdell T, Skogestad S. Estimation of distillation compositions from multiple temperature measurements using PLS [J]. Indus. Eng. Chem. Res., 1991,30:2543-2555
19.Mejdell T, Skogestad S. Composition estimator in a pilot-plant distillation column using multiple temperatures [J]. Indus. Eng. Chem. Res., 1991,30:2555-2564
20.Yabuki Y, MacGregor J F. Product quality control in semibatch reactors using midcourse corrections policies [J]. In: proceedings of IFAC Symposium on Advanced Control of Chemical Process, Elsevier Science Ltd., 1997,Banff:189-194
21.Baffi G, Martin E B, Morari A J. Nonlinear PLS revisited: The quadratic PLS algorithm [J]. Computers Chem. Eng., 1999,23:395-411
22.Ray W H. Modeling and control of polymerization reactors [J]. In "Dynamics and Control of Chemical Reactors, Distillation Columns and Batch Process",
Oxford: Pergamon Press, 1993:37-46
23.Embirucu M, Lima E L and Pinto J C. A survey of advanced control of polymerization reactors [J]. Polymer Engineering & Science, 1996, 36(2):433-447
24.MacGregor J F, Penlidis A, Hamielec A E. Control of polymerization reactors: a review [J]. Polymer Process Engineering, 1984, 2(2&3):179-206
25.Lines B, Hartlen D, Paquin F D. Polyethylene reactor modeling and control design[J]. Hydrocarbon Processing(International Ed.), 1993,72(6):119-120
26.McAuley K B, MacGregor J F. Nonlinear product property control in industrial gas-phase polyethylene reactors[J]. AIChE J, 1993, 39:855-866
27.Ellis M F, Taylor T W, Jensen K F. On-line molecular weight distribution estimation and control in batch polymerization[J]. AIChE J.,1994,40:445-462
28.Wise B M, A non-linear dynamic model identification challenge problem, Chemom. Intell. Lab. Syst., 1995, 30, 91-96
29.MacGregor J F, Kourti T, Nomikos R Analysis, monitoring and fault diagnosis of industrial processes using multivariate statistical projection methods. Proceedings of 13~(th) IFAC World Congress, San Francisco, USA, 1996
30.McAuley K B, MacGregor J F. A kinetic model for industrial gas-phase ethylene copolymerization[J]. AIChEJ, 1990, 36(6):837-849
31.Geladi P, Kowalski B R. Partial least squares: a tutorial[J]. Analytical Chimica Acta, 1986, 185:1-17
32.Wold S. et al. Nonlinear PLS modeling[J]. Chemometrics and Intelligent Lab. Systems, 1989, 7:53-65
33.Kano M, et al. Inferential Control System of Distillation Compositions Using Dynamic PLS Regression[J]. Journal of Process Control, 2000,10:157-166
34.Helland, et al. Recursive algorithm for partial least squares regression[J]. Chemometrics Intell. Lab. Syst., 1992,14:129-137
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