间歇化工过程实时优化与控制
|
摘要
间歇生产过程已被广泛应用于生产高附加值产品,如药物、生物材料、聚合物、电化学品等。然而在实际生产过程中，由于间歇过程本身的动态特性以及在同一设备上运行不同的批次所产生的各批次之间的操作条件的变化会导致产品质量不高或重复性差等问题出现，甚至严重的会导致安全事故的发生，实时优化技术及先进控制技术的发展为解决这些问题带来了契机。
针对实际操作过程中进料的变化、过程参数的不确定性以及各种扰动作用，过程操作需要进行实时的调整以保持过程的稳定性及性能，实时优化技术通过在线监测过程参数和产品质量参数的变化而进行实时的寻找过程最优操作点或轨迹，并控制过程在最优目标下运行，从而保证产品质量的最优与稳定，因此研究间歇过程的实时优化技术是产品质量控制研究领域的一个重要研究课题。然而实时优化技术是一个集成概念，其不仅涉及到多个技术的应用，还涉及到不同尺度的信息之间的交互融合，它主要包括动态模型建立、动态优化、在线监测、模型更新和在线控制等技术，其中最基本也是最主要的几个关键技术即是动态建模技术、动态优化算法及在线控制技术等。本研究以基础技术应用为主，研究开发新型实用的间歇过程建模和仿真方法、动态优化技术及非线性模型预测控制技术，为进一步实现间歇过程的实时优化控制技术的理论集成以及工业应用奠定一定的基础。本研究的主要内容有：
（1）提出基于实验设计的方法进行间歇过程动态建模，以及提出基于xPC技术的动态过程实时仿真技术。以Mini-plant为实验对象，构建数据采集及监控平台，建立温度控制模型；
（2）最优解和优化时间是实时优化技术需要考虑的问题，寻找更好的动态优化方法是实时优化中的一个关键问题。提出基于模型降阶的庞特里亚金最大值原理方法、基于改进的有限元正交配置法的实时动态优化技术及基于遗传算法的拟序贯动态优化技术，并以大量案例进行理论分析及验证；
（3）在线控制技术在实时优化技术中主要负责具体输入操作的实施，其表现性能是直接决定实时优化技术能否成功应用的关键。针对传统的非线性模型预测控制技术进行改进，提出基于多模型的预测控制技术以提高在线计算效率，同时增强在各种扰动作用下的控制鲁棒性。
本文提出了一系列的针对间歇过程建模、仿真、优化及控制的理论方法和技术，从理论的角度，这些方法更有效实用，可以作为实时优化集成理论的基本架构，从实际应用的角度，这些方法充分结合了实验测试方法，充分考虑实际过程中会出现的多种因素，相对来说更适合在实际中应用，同时也为将来的间歇过程实时优化技术的集成工业应用提供了一定工作基础。
Batch processes have been widely applied to manufacture a large quantity ofvalue-added products such as pharmaceuticals, biological products, polymers and electronicchemicals. Increased demand for high product quality has motivated the interest in processmodeling, optimization, and advanced process control techniques that can improve theperformance of batch processes. Batch processes are inherently dynamic in nature and veryoften several batches are run in the same equipment. This may cause batch-to-batchvariation in operating conditions resulting in problems in safety, product quality andproductivity, which results in the development of real-time optimization technique andadvanced control technique.
The operating strategy may not be the optimal strategy in presence of parametricuncertainty and disturbances. One approach to address these issues is to develop accuratemathematical models and use real-time optimization techniques to find the optimaloperating policies for each batch operation, and to ensure the optimal and stability ofproduct quality by controlling the process to track the optimal profiles. So integration ofreal-time optimization for batch process is very important for product quality control.However, the real-time optimization technique is a kind of integration strategy whichincludes techniques of dynamic modeling, dynamic optimization, on-line measurement,model update and control. The techniques of dynamic modeling, dynamic optimization andcontrol are the basic and main techniques of them. In this work, we mainly research on thenew practical techniques of batch process modeling and simulation, dynamic optimizationand nonlinear model predictive control (NMPC), which we hope will advance thedevelopment of real-time optimization technique and facilitate the implementation ofreal-time optimization technique in industrial batch processes. The main research contentsinclude:
(1) Dynamic modeling for batch process with experiment design method based onMini-plant. By constructing the platform for data acquisition and process monitoring, atemperature control model has been built for the Mini-plant. Moreover, a dynamicsimulation technique based on xPC technique has been introduced.
(2) Developing better dynamic optimization method for improving the computationalefficiency and the performance of the solution of real-time optimization problem. Threemethods, include dynamic optimization method based on Pontryagin's maximum principlecombined model reduction, dynamic optimization method based on improved finite elementmethod and dynamic optimization method based on natural algorithm, have been developedin this work, and a large number of cases have been studied for validating the methods.
(3) On-line control technique plays an important role in real-time optimization strategy,which in charge of the implementation of optimal trajectory tracking. Based on the standardNMPC technique, a real-time updated model predictive control technique has beenintroduced in this work, which can improve the computational efficiency of the solution ofNMPC problem and improve the robustness of the controller.
In this work, we proposed a series of techniques of dynamic modeling and simulation,optimization and control for batch processes. From the view of theory, these techniques arepractical and efficient, which can become the basis of the real-time optimization strategy.Form the view of real application, since that a large number of experiment tests have beencombined into the methods and many practical problems which will be faced in real processhave been considered in the methods, these methods are more appropriate for real application.All of these works have provided some basic work for the industrial application of real-timeoptimization technique.
针对实际操作过程中进料的变化、过程参数的不确定性以及各种扰动作用，过程操作需要进行实时的调整以保持过程的稳定性及性能，实时优化技术通过在线监测过程参数和产品质量参数的变化而进行实时的寻找过程最优操作点或轨迹，并控制过程在最优目标下运行，从而保证产品质量的最优与稳定，因此研究间歇过程的实时优化技术是产品质量控制研究领域的一个重要研究课题。然而实时优化技术是一个集成概念，其不仅涉及到多个技术的应用，还涉及到不同尺度的信息之间的交互融合，它主要包括动态模型建立、动态优化、在线监测、模型更新和在线控制等技术，其中最基本也是最主要的几个关键技术即是动态建模技术、动态优化算法及在线控制技术等。本研究以基础技术应用为主，研究开发新型实用的间歇过程建模和仿真方法、动态优化技术及非线性模型预测控制技术，为进一步实现间歇过程的实时优化控制技术的理论集成以及工业应用奠定一定的基础。本研究的主要内容有：
（1）提出基于实验设计的方法进行间歇过程动态建模，以及提出基于xPC技术的动态过程实时仿真技术。以Mini-plant为实验对象，构建数据采集及监控平台，建立温度控制模型；
（2）最优解和优化时间是实时优化技术需要考虑的问题，寻找更好的动态优化方法是实时优化中的一个关键问题。提出基于模型降阶的庞特里亚金最大值原理方法、基于改进的有限元正交配置法的实时动态优化技术及基于遗传算法的拟序贯动态优化技术，并以大量案例进行理论分析及验证；
（3）在线控制技术在实时优化技术中主要负责具体输入操作的实施，其表现性能是直接决定实时优化技术能否成功应用的关键。针对传统的非线性模型预测控制技术进行改进，提出基于多模型的预测控制技术以提高在线计算效率，同时增强在各种扰动作用下的控制鲁棒性。
本文提出了一系列的针对间歇过程建模、仿真、优化及控制的理论方法和技术，从理论的角度，这些方法更有效实用，可以作为实时优化集成理论的基本架构，从实际应用的角度，这些方法充分结合了实验测试方法，充分考虑实际过程中会出现的多种因素，相对来说更适合在实际中应用，同时也为将来的间歇过程实时优化技术的集成工业应用提供了一定工作基础。
Batch processes have been widely applied to manufacture a large quantity ofvalue-added products such as pharmaceuticals, biological products, polymers and electronicchemicals. Increased demand for high product quality has motivated the interest in processmodeling, optimization, and advanced process control techniques that can improve theperformance of batch processes. Batch processes are inherently dynamic in nature and veryoften several batches are run in the same equipment. This may cause batch-to-batchvariation in operating conditions resulting in problems in safety, product quality andproductivity, which results in the development of real-time optimization technique andadvanced control technique.
The operating strategy may not be the optimal strategy in presence of parametricuncertainty and disturbances. One approach to address these issues is to develop accuratemathematical models and use real-time optimization techniques to find the optimaloperating policies for each batch operation, and to ensure the optimal and stability ofproduct quality by controlling the process to track the optimal profiles. So integration ofreal-time optimization for batch process is very important for product quality control.However, the real-time optimization technique is a kind of integration strategy whichincludes techniques of dynamic modeling, dynamic optimization, on-line measurement,model update and control. The techniques of dynamic modeling, dynamic optimization andcontrol are the basic and main techniques of them. In this work, we mainly research on thenew practical techniques of batch process modeling and simulation, dynamic optimizationand nonlinear model predictive control (NMPC), which we hope will advance thedevelopment of real-time optimization technique and facilitate the implementation ofreal-time optimization technique in industrial batch processes. The main research contentsinclude:
(1) Dynamic modeling for batch process with experiment design method based onMini-plant. By constructing the platform for data acquisition and process monitoring, atemperature control model has been built for the Mini-plant. Moreover, a dynamicsimulation technique based on xPC technique has been introduced.
(2) Developing better dynamic optimization method for improving the computationalefficiency and the performance of the solution of real-time optimization problem. Threemethods, include dynamic optimization method based on Pontryagin's maximum principlecombined model reduction, dynamic optimization method based on improved finite elementmethod and dynamic optimization method based on natural algorithm, have been developedin this work, and a large number of cases have been studied for validating the methods.
(3) On-line control technique plays an important role in real-time optimization strategy,which in charge of the implementation of optimal trajectory tracking. Based on the standardNMPC technique, a real-time updated model predictive control technique has beenintroduced in this work, which can improve the computational efficiency of the solution ofNMPC problem and improve the robustness of the controller.
In this work, we proposed a series of techniques of dynamic modeling and simulation,optimization and control for batch processes. From the view of theory, these techniques arepractical and efficient, which can become the basis of the real-time optimization strategy.Form the view of real application, since that a large number of experiment tests have beencombined into the methods and many practical problems which will be faced in real processhave been considered in the methods, these methods are more appropriate for real application.All of these works have provided some basic work for the industrial application of real-timeoptimization technique.
引文
[1] Bonvin, D. Control and optimization of batch processes[J]. Control Systems, IEEE,2006,26(6):34-45.
[2] Srinivasan, B., Bonvin, D. Real-time optimization of batch processes by tracking thenecessary conditions of optimality[J]. Industrial&Engineering Chemistry Research,2007,46(2):492-504.
[3] Chachuat, B.,S rinivasan, B., Bonvin, D. Adaptation strategies for real-timeoptimization[J]. Comput. Chem. Eng.,2009,33(10):1557-1567.
[4] Chen, H., He, X., Chen, B., etc. Multiperiod optimization for refinery processing andinventory management using a genetic algorithm[J]. J. Tsinghua Univ., Sci. Technol.,2004,44(3):319-322.
[5] Qian, Y., Wu, Z. H., Jiang, Y. B. Integration of chemical product development, processdesign and operation based on a kilo-plant[J]. Prog. Nat. Sci.,2006,16(6):600-606.
[6] Fang, X. Y., Shao, Z. J., Wang, Z. Q., et al. Mnemonic Enhancement Optimization(MEO) for Real-Time Optimization of Industrial Processes[J]. Industrial&Engineering Chemistry Research,2009,48(1):499-509.
[7] Zhang, R. D., Xue, A. K., Wang, S. Q. Modeling and nonlinear predictive functionalcontrol of liquid level in a coke fractionation tower[J]. Chem. Eng. Sci.,2011,66(23):6002-60136013.
[8] Li, Y. Q., Yuan, X. G., Li, Y. J. An improved PSO algorithm for solving non-convexNLP/MINLP problems with equality constraints[J]. Computers&ChemicalEngineering,2007,31(3):153-162.
[9]贺益君,陈德钊.基于免疫机制的多目标蚁群算法用于间歇反应器的约束动态多目标优化[J].高校化学工程学报,2009,23(02):326-332.
[10]赵振辉,冯霄,刘永忠, et al.氢气网络系统的夹点分析与匹配优化[J].化工进展,2008,27(2):261-264.
[11]岳金彩,杨霞,郑世清, et al.模块环境下的filter-SQP用于过程优化[J].化工学报,2006,57(03):614-619.
[12]王钧炎,黄德先.基于差分进化算法和hysys机理模型的催化重整过程优化[J].化工学报,2008,59(07):1755-1760.
[13]程华农,陈崇春,孔令启, et al. Matlab在乙醇胺反应动力学方程回归和预测中的运用[J].计算机与应用化学,2009,26(02):240-244.
[14]夏琼.基于差分进化算法的加热炉生产过程操作调度[D].沈阳:东北大学,2010.
[15]韩方煜.计算机辅助化工过程设计的理论与实践[J].中国化工,1996,(10):30-34.
[16]李锋,颜学峰,钱锋.常减压装置中基于自适应变异遗传算法的稳态数据协调方法[A].第五届全球智能控制与自动化大会会议论文集[C].杭州:浙江大学,2004.
[17] Peters, N., Guay, M., DeHaan, D. Real-time dynamic optimization of batch systems[J].Journal of Process Control,2007,17(3):261-271.
[18] Adetola, V., Guay, M. Integration of real-time optimization and model predictivecontrol[J]. Journal of Process Control,2010,20(1):125-133.
[19] Alvarez, L. A., Odloak, D. Robust integration of real time optimization with linearmodel predictive control[J]. Computers&Chemical Engineering,2010,34(12):1937-1944.
[20] De Souza, G., Odloak, D., Zanin, A. C. Real time optimization (RTO) with modelpredictive control (MPC)[J]. Computers&Chemical Engineering,2010,34(12):1999-2006.
[21] Ochoa, S., Repke, J. U., Wozny, G. Integrating real-time optimization and control foroptimal operation: Application to the bio-ethanol process[J]. Biochemical EngineeringJournal,2010,53(1):18-25.
[22] Palanki, S. The Application of Pontryagin's Minimum Principle for EndpointOptimization of Batch Processes[M].2012;327-337.
[23] Gattu, G., Zafiriou, E. Nonlinear quadratic dynamic matrix control with stateestimation[J]. Industrial&Engineering Chemistry Research,1992,31(4):1096-1104.
[24] Schlegel, M., Stockmann, K., Binder, T., etc. Dynamic optimization using adaptivecontrol vector parameterization[J]. Comput. Chem. Eng.,2005,29(8):1731-1751.
[25] Teoh, H. K., Turner, M., Titchener-Hooker, N., et al. Experimental verification andoptimisation of a detailed dynamic high performance liquid chromatography columnmodel[A]. In10th European Symposium on Computer Aides Process Engineering[C],Florence, Italy, May07-10,2000;893-903.
[26] Franceschini, G., Macchietto, S. Model-based design of experiments for parameterprecision: State of the art[J]. Chemical Engineering Science,2008,63(19):4846-4872.
[27] Haryanto, A., Hong, K.-S. Modeling and simulation of an oxy-fuel combustion boilersystem with flue gas recirculation[J]. Computers&Chemical Engineering,2011,35(1):25-40.
[28] Hvala, N., Aller, F., Miteva, T., et al. Modelling, simulation and control of anindustrial, semi-batch, emulsion-polymerization reactor[J]. Computers&ChemicalEngineering,2011,35(10):2066-2080.
[29] Guadix, A.,Sorensen, E.,Papageorgiou, L. G., et al. Optimal design and operation ofbatch ultrafiltration systems[A]. In Kraslawski, A.,Turunen, I.,13th EuropeanSymposium on Computer Aided Process Engineering (ESCAPE-13)[C], Lappeenranta,Finland, Jun01-04,2003;149-154.
[30] Sentoni, G. B., Biegler, L. T., Guiver, J. B., et al. State-space nonlinear processmodeling: Identification and universality[J]. Aiche Journal,1998,44(10):2229-2239.
[31] Findeisen, R., Imsland, L., Allgower, F., etc. State and output feedback nonlinearmodel predictive control: An overview[J]. European Journal of Control,2003,2003,9(2-3):190-206.
[32] Liang, J., Qian, J. X. Multivariate statistical process monitoring and control: Recentdevelopments and applications to chemical industry[J]. Chin. J. Chem. Eng., Apr,2003,11(2):191-203.
[33] Martinez, E. C. The statistical simplex method for experimental optimization withprocess data. In European Symposium on Computer-Aided Process Engineering-15,20A and20B, Puigjaner, L.,Espuna, A., Eds.2005; Vol.20a-20b,31-36.
[34] Kvasnica, M., Herceg, M., Cirka, L., et al. Model predictive control of a CSTR: Ahybrid modeling approach[J]. Chemical Papers, Jun,2010,64(3):301-309.
[35] Nascimento Lima, N. M., Manenti, F., Maciel Filho, R., et al. Fuzzy Model-BasedPredictive Hybrid Control of Polymerization Processes[J]. Industrial&EngineeringChemistry Research, Sep16,2009,48(18):8542-8550.
[36]张兵.化工动态优化方法的研究与应用[D].杭州:浙江大学,2005.
[37]陶卉.工业过程动态优化研究[D].杭州:浙江大学,2006.
[38]吴高辉.动态优化研究及其工业应用[D].杭州:浙江大学,2007.
[39] Biegler, L. T., Cervantes, A. M., Wachter, A. Advances in simultaneous strategies fordynamic process optimization[J]. Chemical Engineering Science,2002,57(4):575-593.
[40] Yi-Dong, L., Cervantes, A. M., Biegler, L. T. Dynamic modeling and optimization ofbatch crystallization processes[J]. Proceedings of the14th World Congress.International Federation of Automatic Control,1999,1999.
[41] Hong, W. R., Wang, S. Q., Li, P., etc. A quasi-sequential approach to large-scaledynamic optimization problems[J]. Aiche Journal, Jan,2006,52(1):255-268.
[42] Sch fer, A., Kühl, P., Diehl, M., et al. Fast reduced multiple shooting methods fornonlinear model predictive control[J]. Chemical Engineering and Processing: ProcessIntensification,2007,46(11):1200-1214.
[43] Srinivasan, B., Palanki, S., Bonvin, D. Dynamic optimization of batch processes-I.Characterization of the nominal solution[J]. Comput. Chem. Eng.,2003,27(1):1-26.
[44] Lang, Y. D., Biegler, L. T. A software environment for simultaneous dynamicoptimization[J]. Computers&Chemical Engineering,2007,31(8):931-942.
[45] Kameswaran, S., Biegler, L. T. Simultaneous dynamic optimization strategies: Recentadvances and challenges[J]. Computers& Chemical Engineering,2006,30(10–12):1560-1575.
[46] Hartwich, A., Schlegel, M., Wurth, L., et al. Adaptive control vector parameterizationfor nonlinear model-predictive control[J]. International Journal of Robust andNonlinear Control,2008,18(8):845-861.
[47] Schlegel, M., Stockmann, K., Binder, T., et al. Dynamic optimization using adaptivecontrol vector parameterization[J]. Computers&Chemical Engineering,2005,29(8):1731-1751.
[48] Hertzberg, T., Asbjornsen, O. A. Parameter estimation in nonlinear differentialequations[J]. Computer Applications in the Analysis of Chemical Data and Plants,1978,155-165.
[49] Biegler, L. T. Solution of dynamic optimization problems by successive quadraticprogramming and orthogonal collocation[J]. Computers&Chemical Engineering,1984,8(3-4).
[50] Fabien, B. C. Direct optimization of dynamic systems described bydifferential-algebraic equations[J]. Optim. Control Appl. Methods,2008,29(6):445-466.
[51] Bock, H. G., Diehl, M. M., Leineweber, D. B., et al. A direct multiple shooting methodfor real-time optimization of nonlinear DAE processes[M]. Basel: Birkhauser Verlag2000;245-267.
[52] Leineweber, D. B., Bauer, I., Bock, H. G., et al. An efficient multiple shooting basedreduced SQP strategy for large-scale dynamic process optimization. Part1: theoreticalaspects[J]. Computers&Chemical Engineering,2003,27(2):157-166.
[53] Biegler, L. T., Zavala, V. M. Large-scale nonlinear programming using IPOPT: Anintegrating framework for enterprise-wide dynamic optimization[J]. Computers&Chemical Engineering,2009,33(3):575-582.
[54] Cuthrell, J. E., Biegler, L. T. On the optimization of differential-algebraic processsystems[J]. Aiche Journal,1987,33(8).
[55] Ganesh, N., Biegler, L. T. A robust technique for process flowsheet optimization usingsimplified model approximations[J]. Computers&Chemical Engineering,1987,11(6):553-565.
[56] Bartlett, R. A., Biegler, L. T. rSQP++: An object-oriented framework for successivequadratic programming. In Large-Scale Pde-Constrained Optimization, Biegler, L. T.,Ed.2003; Vol.30,316-330.
[57] Liu, T., Gao, F. Batch Process Control[M]. Industrial Process Identification andControl Design. In Springer London:2012;433-452.
[58] Wang, Y. Q., Zhang, D. H., Gao, F. Generalized predictive control of linear systemswith actuator arrearage faults[J]. Journal of Process Control,2009,19(5):803-815.
[59] Shukla, P. K., Pushpavanam, S., Khanna, A., et al. Experimental implementation of arecursive algorithm to control the temperature trajectory of an exothermic batchreactor[J]. Industrial&Engineering Chemistry Research,1997,36(1):122-129.
[60] Cott, B. J., Macchietto, S. Temperature control of exothermic batch reactors usinggeneric model control[J]. Industrial&Engineering Chemistry Research,1989,28(8):1177-1184.
[61] Wang, D., Srinivasan, R. Multi-model based real-time final product quality controlstrategy for batch processes[J]. Computers&Chemical Engineering,2009,33(5):992-1003.
[62] Wang, Y. Q., Zhang, D. H., Gao, F. Iterative learning model predictive control formulti-phase batch processes[J]. J. Process Control,2008,18(6):543-557.
[63] Lee, J. H., Lee, K. S. Iterative learning control applied to batch processes: Anoverview[J]. Control Eng. Prac.,2007,15(10):1306-1318.
[64] Dorsey, A. W., Lee, J. H., Russell, S. A. An extended Kalman filter formulation forsystematic transfer of information from batch to batch[J]. Industrial&EngineeringChemistry Research,2003,42(8):1753-1760.
[65] Arpornwichanop, A., Kittisupakorn, A., Mujtaba, I. M. On-line dynamic optimizationand control strategy for improving the performance of batch reactors[J]. ChemicalEngineering and Processing,2005,44(1):101-114.
[66] Pommier, S., Massebeuf, S., Kotai, B., et al. Heterogeneous batch distillationprocesses: Real system optimisation[J]. Chemical Engineering and Processing:Process Intensification,2008,47(3):408-419.
[67] Costa, C. B. B., Maciel, R. Evaluation of optimisation techniques and control variableformulations for a batch cooling crystallization process[J]. Chemical EngineeringScience,2005,60(19):5312-5322.
[68] Shafiee, G., Arefi, M. M., Jahed-Motlagh, M. R., etc. Nonlinear predictive control of apolymerization reactor based on piecewise linear Wiener model[J]. Chem. Eng. J.,2008,143(1-3):282-292.
[69] Morari, M., H. Lee, J. Model predictive control: past, present and future[J]. Computers&Chemical Engineering,1999,23(4-5):667-682.
[70] Henson, M. A. Nonlinear model predictive control: current status and futuredirections[J]. Comput. Chem. Eng.,1998,23(2):187-202.
[71] Cutler, C. R.,Ramaker, B. L. Dynamic matrix control-A computer controlalgorithm[A]. In the Joint Automatic Control Conference[C], San Francisco,1980,1980.
[72] Sentoni, G. B., Biegler, L. T., Guiver, J. B., et al. State-space nonlinear processmodeling: Identification and universality[J]. Aiche Journal,1998,44(10):2229-2239.
[73] Mujtaba, I. M., Aziz, N., Hussain, M. A. Neural Network Based Modelling andControl in Batch Reactor[J]. Chem. Eng. Res. Des.,2006,84(8):635-644.
[74] Mujtaba, I. M., Aziz, N., Hussain, M. A. Neural Network Based Modelling andControl in Batch Reactor[J]. Chemical Engineering Research and Design,2006,84(8):635-644.
[75] Escano, J. M., Bordons, C., Vilas, C., et al. Neurofuzzy model based predictive controlfor thermal batch processes[J]. J. Process Control, Oct,2009,19(9):1566-1575.
[76] Takagi, T., Sugeno, M. Fuzzy identification of systems and its applications tomodeling and control[J]. Systems, Man and Cybernetics, IEEE Transactions on,1985,SMC-15(1):116-132.
[77] Kuure-Kinsey, M., Bequette, B. W. Multiple Model Predictive Control Strategy forDisturbance Rejection[J]. Industrial&Engineering Chemistry Research, Sep,2010,49(17):7983-7989.
[78] Ravi, V. R., Thyagarajan, T., Darshini, M. M. A Multiple Model Adaptive ControlStrategy for Model Predictive Controller for Interacting Non Linear Systems[A]. InProcess Automation, Control and Computing (PACC),2011International Conferenceon[C],20-22July2011,2011;1-8.
[79] Giovanini, L., Ordys, A. W.,Grimble, M. J. Adaptive predictive control using multiplemodels, switching and tuning[J]. International Journal of Control Automation andSystems,2006,4(6):669-681.
[80] Dougherty, D., Cooper, D. A practical multiple model adaptive strategy formultivariable model predictive control[J]. Control Eng. Prac.,2003,11(6):649-664.
[81] Cannon, M., Ng, D.,Kouvaritakis, B. Successive Linearization NMPC for a Class ofStochastic Nonlinear Systems Nonlinear Model Predictive Control. In Magni,L.,Raimondo, D.,Allg wer, F., Eds. Springer Berlin/Heidelberg:2009; Vol.384,249-262.
[82] Qin, S. J., Badgwell, T. A. An overview of nonlinear model predictive controlapplications[M].2000;369-392.
[83] Stefansson, H., Shah, N., Jensson, P. Multiscale planning and scheduling in thesecondary pharmaceutical industry[J]. Aiche Journal,2006,52(12):4133-4149.
[84] Lievo, P., Almark, M., Purola, V. M., etc. Miniplant-Effective tool in processdevelopment and design. In European Symposium on Computer Aided ProcessEngineering-13, Kraslawski, A.,Turunen, I., Eds. Elsevier Science Bv: Amsterdam,2003; Vol.14,761-766.
[85] Heimann, F. Process intensification through the combined use of process simulationand miniplant technology. In European Symposium on Computer Aided ProcessEngineering-13, Kraslawski, A.,Turunen, I., Eds. Elsevier Science Bv: Amsterdam,2003; Vol.14,155-160.
[86]钱宇,李秀喜,程华农, et al.化工过程运行系统的集成[J].化工学报,2003,54(04):557-563.
[87] Paniagua-Quinones, C., Box, G. E. P. Use of strip-strip-block design for multi-stageprocesses to reduce costs of experimentation[J]. Quality Engineering,2008,20(1):46-52.
[88] Draper, N. R., Hunter, W. G. The use of prior distributions in the design ofexperiments for parameter estimation in non-linear situations[J]. Biometrika,1967,54(1):147-53.
[89] Kiefer, J. C. Optimum Experimental Designs. In Breakthroughs in Statistics, Kotz,S.,Johnson, N., Eds. Springer New York:1992;400-436.
[90] Dovi, V. G., Reverberi, A. P., Maga, L. Optimal design of sequential experiments forerror-in-variables models[J]. Computers&Chemical Engineering,1993,17(1):111-15.
[91]张伟,危韧勇,金黎.生产过程实时仿真系统[J].长沙铁道学院学报,2000,18(02):50-52.
[92]吴志辉,钱宇.江燕斌.化学产品过程开发实验平台——公斤实验室的建设和应用[J].现代化工,2005,3(03)::66-68.
[93]刘姜涛,周洪,邓其军.基于opc的远程数据采集与存储[J].微计算机信息,2009,25(01):105-107.
[94]李相育,钱宇.基于OPC的Matlab与WinCC的无缝集成[J].微计算机信息,2007,03(31):297-299.
[95]饶克克,李华.基于opc的多客户端与wincc的数据通信[J].化工自动化及仪表,2012,39(01):133-135.
[96]秦辉,席裕庚.基于Matlab GUI的预测控制仿真平台设计[J].系统仿真学报,2006,18(10):2778-2781.
[97]白金麟,石立宏,周培湘, etc.化工过程实时优化技术现状与未来[J].数字石油和化工,2009,(07):1-6.
[98] Srinivasan, B., Palanki, S., Bonvin, D. Dynamic optimization of batch processes-I.Characterization of the nominal solution[J]. Computers&Chemical Engineering,2003,27(1):1-26.
[99] Sentoni, G. B., Biegler, L. T., Guiver, J. P. Model reduction for nonlinear DABNetmodels[A]. Proceedings of the1999American Control Conference[C],1999.
[100] Hahn, J., Edgar, T. F. An improved method for nonlinear model reduction usingbalancing of empirical gramians[J]. Computers&Chemical Engineering,2002,26(10):1379-1397.
[101] Bonis, I.,Xie, W., Theodoropoulos, C. A linear model predictive control algorithm fornonlinear large-scale distributed parameter systems[J]. AIChE Journal,2012,58(3):801-811.
[102] Yu, W. F.,Hidajat, K.,Ray, A. K. Determination of adsorption and kinetic parametersfor methyl acetate esterification and hydrolysis reaction catalyzed by Amberlyst15[J].Appl. Catal. A-Gen.,2004,260(2):191-205.
[103] Tosukhowong, T., Lee, J. H. Approximate Dynamic Programming Based OptimalControl Applied to an Integrated Plant with a Reactor and a Distillation Column withRecycle[J]. Aiche Journal,2009,55(4):919-930.
[104] Deb, K. An introduction to genetic algorithms[J]. Sadhana-Acad. Proc. Eng. Sci.,1999,24:293-315.
[105] Stender, J. Introduction to genetic algorithms[A]. In Applications of GeneticAlgorithms, IEE Colloquium on[C],15Mar1994,1994:1-4.
[106] Zavala, J. C., Coronado, C. Optimal control problem in batch distillation usingthermodynamic efficiency[J]. Industrial&Engineering Chemistry Research,2008,47(8):2788-2793.
[107] Luyben, M. L., Tyreus, B. D., Luyben, W. L. Plantwide control design procedure[J].AIChE J.,1997,43(12):3161-3174.
[108] Floudas, C. A., Akrotirianakis, I. G., Caratzoulas, S., etc. Global optimization in the21st century: Advances and challenges[A]. In14th European Symposium onComputer Aided Process Engineering (ESCAPE-14)[C], Lisbon, PORTUGAL, May16-19,2004:1185-1202.
[109] Flores-Tlacuahuac, A., Biegler, L. T. On The Global Dynamic Optimization of HighlyNonlinear Systems. In16th European Symposium on Computer Aided ProcessEngineering and9th International Symposium on Process Systems Engineering,Marquardt, W. P. C., Ed.2006; Vol.21,1227-1232.
[110] Wassick, J. M. Enterprise-wide optimization in an integrated chemical complex[J].Computers&Chemical Engineering,2009,33(12):1950-1963.
[111] Furlonge, H. I., Pantelides, C. C., Sorensen, E. Optimal operation of multivessel batchdistillation columns[J]. Aiche Journal,1999,45(4):781-801.
[112] Low, K. H., Sorensen, E. Simultaneous optimal configuration, design and operation ofbatch distillation[J]. Aiche Journal,2005,51(6):1700-1713.
[113] Skogestad, S., Wittgens, B., Litto, R., et al. Multivessel batch distillation[J]. AicheJournal,1997,43(4):971-978.
[114] Logsdon, J. S., Biegler, L. T. Accurate determination of optimal reflux policies for themaximum distillate problem in batch distillation[J]. Industrial&EngineeringChemistry Research,1993,32(4):692-700.
[115] Mayur, D. N., Jackson, R. Time-optimal problems in batch distillation formulticomponent mixtures and for columns with holdup[J]. The Chemical EngineeringJournal,1971,2(3):150-163.
[116] Mukherjee, S., Dahule, R. K., Tambe, S. S., etc. Consider genetic algorithms tooptimize batch distillation-Alternative simulation tool calculates best refluxconditions to separate binary mixtures[J]. Hydrocarb. Process., Sep,2001,80(9):59-66.
[117] Tomazi, K. G. Limitations and dynamics imposed on multicomponent batchdistillation by tray hydraulics[J]. Industrial&Engineering Chemistry Research,1997,36(10):4273-4281.
[118] Zavala, J. C., Ceron, R. M., Pali, R. J., etc. Simplified calculation for the liquid holdupeffect in batch distillation[J]. Industrial&Engineering Chemistry Research,2007,46(15):5186-5191.
[119] Chen, Q., Feng, X. Reactor network synthesis for waste reduction using instantaneousvalue of environmental index[J]. Chin. J. Chem. Eng.,2008,16(1):155-158.
[120] Zhou, W., Manousiouthakis, V. I. Automating the AR construction for non-isothermalreactor networks[J]. Computers&Chemical Engineering,2009,33(1):176-180.
[121] Gao, X. D., Chen, B. Z., He, X. R., et al. Multi-objective optimization for the periodicoperation of the naphtha pyrolysis process using a new parallel hybrid algorithmcombining NSGA-II with SQP[J]. Computers&Chemical Engineering,2008,32(11):2801-2811.
[122] Hua, K., Li, Y., Hu, S., et al. Three-distribution-parameter general model for reactornetwork synthesis[J]. Computers&Chemical Engineering,2000,24(2-7):217-223.
[123] Konak, A., Coit, D. W., Smith, A. E. Multi-objective optimization using geneticalgorithms: A tutorial[J]. Reliability Engineering&System Safety,2006,91(9):992-1007.
[124] Rippin, D. W. T. Simulation of single-and multiproduct batch chemical plants foroptimal design and operation[J]. Comput. Chem. Eng.,1983,7(3):137-156.
[125] Youqing, W., Furong, G., Doyle, F. J., III. Survey on iterative learning control,repetitive control, and run-to-run control[J]. Journal of Process Control,2009,19(10):1589-16001600.
[126] Hermanto, M. W., Braatz, R. D., Chiu, M.-S. Integrated batch-to-batch and nonlinearmodel predictive control for polymorphic transformation in pharmaceuticalcrystallization[J]. AIChE J.,2011,57(4):1008-1019.
[127] Nagy, Z. K., Braatz, R. D. Robust nonlinear model predictive control of batchprocesses[J]. AIChE J.,2003,49(7):1776-1786.
[128] Cervantes, A. M., Tonelli, S., Brandolin, A., etc. Large-scale dynamic optimization forgrade transitions in a low density polyethylene plant[J]. Comput. Chem. Eng.,2002,26(2):227-237.
[129] Chatzidoukas, C., Perkins, J. D., Pistikopoulos, E. N., etc. Optimal grade transitionand selection of closed-loop controllers in a gas-phase olefin polymerization fluidizedbed reactor[J]. Chem. Eng. Sci.,2003,58(16):3643-3658.
[130] Manenti, F., Rovaglio, M. Integrated Multilevel Optimization in Large-ScalePoly(Ethylene Terephthalate) Plants[J]. Ind. Eng. Chem. Res.,2007,47(1):92-104.
[131] Garcia, C. E., Prett, D. M., Morari, M. Model predictive control: theory and practice-asurvey[J]. Automatica,1989,25(3):335-348.
[132] Lee, J. H. Model Predictive Control: Review of the Three Decades of Development[J].Int. J. Control Autom.,2011,9(3):415-424.
[133] Bequette, B. W. Nonlinear control of chemical processes: a review[J]. Industrial&Engineering Chemistry Research,1991,30(7):1391-413.
[134] Arellano-Garcia, H., Barz, T., Wendt, M., et al. Real-time feasibility of nonlinearpredictive control for semi-batch reactors. In European Symposium onComputer-Aided Process Engineering-15,20A and20B, Puigjaner, L.,Espuna, A., Eds.2005; Vol.20a-20b,967-972.
[135] Arpornwichanop, A., Kittisupakorn, P., Hussain, M. A. Model-based control strategiesfor a chemical batch reactor with exothermic reactions[J]. Korean J. Chem. Eng., Mar,2002,19(2):221-226.
[136] Bosley, J. R., Jr., Edgar, T. F. Application of nonlinear model predictive control tooptimal batch distillation[A]. In Dynamics and Control of Chemical ReactorsDistillation Columns and Batch Processes (DYCORD+'92). Selected Papers from the3rd IFAC Symposium[C],1993,1993;303-308.
[137] Alhamad, B., Romagnoli, J. A., Gomes, V. G. On-line multi-variable predictive controlof molar mass and particle size distributions in free-radical emulsioncopolymerization[J]. Chemical Engineering Science,2005,60(23):6596-6606.
[138] Peterson, T., Hernández, E., Arkun, Y., etc. A nonlinear DMC algorithm and itsapplication to a semibatch polymerization reactor[J]. Chem. Eng. Sci.,1992,47(4):737-753.
[139] Karaduman, A., Oguz, H., Berber, R. Nonlinear model predictive temperature controlof a batch polymerization reactor[J]. Advances in Process Control4,1995,1995,203-210210.
[140] Ogunnaike, B. A. On-line modelling and predictive control of an industrialterpolymerization reactor[J]. Int. J. Control, March,1994,59(3):711-729.
[141] Su, Q. L., Braatz, R. D., Chiu, M. S. Concentration Control for Semi-Batch Ph-ShiftReactive Crystallization of L-Glutamic Acid[A]. In Kariwala, V., Samavedham,Lakshminarayanan, Braatz, Richard D., Proceedings of the8th IFAC Symposium onAdvanced Control of Chemical Processes[C], Furama Riverfront, Singapore,1996,2012;228-233.
[142] Lopez-Negrete, R., D’Amato, F. J., Biegler, L. T., etc. Fast nonlinear model predictivecontrol: Formulation and industrial process applications[J]. Comput. Chem. Eng.,2013,51(0):55-64
[143] Kansha, Y., Chiu, M. S. Adaptive generalized predictive control based on JITLtechnique[J]. J. Process Control,2009,19(7):1067-1072.
[144] Dones, I., Manenti, F., Preisig, H. A., etc. Nonlinear Model Predictive Control: ASelf-Adaptive Approach[J]. Industrial&Engineering Chemistry Research,2010,49(10):4782-4791.
[145] Biegler, L. T. Efficient solution of dynamic optimization and NMPC problems[M].Basel: Birkhauser Verlag Ag,2000:219-243.
[146] Meadows, E. S. Dynamic programming and model predictive control[A]. InProceedings of the1997American Control Conference [C], Albuquerque, NM, USA,1997,1997;1635-1639
[147] Zavala, V. M., Laird, C. D., Biegler, L. T. Fast implementations and rigorous models:Can both be accommodated in NMPC?[J]. International Journal of Robust andNonlinear Control,2008,18(8):800-815.
[148] Zavala, V. M., Biegler, L. T. Optimization-based strategies for the operation oflow-density polyethylene tubular reactors: nonlinear model predictive control[J].Comput. Chem. Eng.,2009,33(10):1735-1746.
[149] Nagy, Z. K. Model based control of a yeast fermentation bioreactor using optimallydesigned artificial neural networks[J]. Chem. Eng. J.,2007,127(1-3):95-109.
[150] Wu, W., Ding, S. Y. Model predictive control of nonlinear distributed parametersystems using spatial neural-network architectures[J]. Industrial&EngineeringChemistry Research,2008,47(19):7264-7273.
[151] Kuure-Kinsey, M., Bequette, B. W. Multiple Model Predictive Control of NonlinearSystems. In Nonlinear Model Predictive Control, Magni, L.,Raimondo, D.,Allg wer,F., Eds. Springer Berlin Heidelberg:2009; Vol.384,153-165.
[152] Wang, F. Y., Bahri, P.,Lee, P. L., etc. A multiple model, state feedback strategy forrobust control of non-linear processes[J]. Comput. Chem. Eng.,2007,31(5–6):410-418.
[153] Cheng, C., Hashimoto, Y., Min-Sen, C. Adaptive controller design using just-in-timelearning algorithm[A]. In Control Applications,2004. Proceedings of the2004IEEEInternational Conference on[C],2-4Sept.2004,2004;1106-1111Vol.2.
[154] Terwiesch, P., Agarwal, M. A discretized nonlinear state estimator for batchprocesses[J]. Comput. Chem. Eng.,1995,19(2):155-169.
[155] Tatiraju, S., Soroush, M.,Ogunnaike, B. A. Multirate nonlinear state estimation withapplication to a polymerization reactor[J]. AIChE J.,1999,45(4):769-780.
[156] Rui, H., Patwardhan, S. C., Biegler, L. T. Stability of a class of discrete-time nonlinearrecursive observers[J]. J. Process Control,2010,20(10):1150-1160.
[157] Peters, N., Guay, M., DeHaan, D. Real-time dynamic optimization of batch systems[J].J. Process Control,2007,17(3):261-271.
[158] Skogestad, S. Control structure design for complete chemical plants[J]. Comput.Chem. Eng.,2004,28(1-2):219-234.
[159] Jie, X., Yinlun, H., Zheng, L. Proactive product quality control: An integrated productand process control approach to MIMO systems[J]. Chem. Eng. J.,2009,149(1-3):435-446.
[160] Yuan, Z., Chen, B., Zhao, J. Effect of Manipulated Variables Selection on theControllability of Chemical Processes[J]. Industrial&Engineering ChemistryResearch,2011,50(12):7403-7413.
[161] McGahey, S. L., Cameron, I. T. A multi-model repository with manipulation andanalysis tools[J]. Comput. Chem. Eng.,2007,31(8):919-930.
[162] Dougherty, D., Arbogast, J., Cooper, D. J. A multiple model adaptive control strategyfor DMC[A]. In American Control Conference,2003. Proceedings of the2003[C],4-6June2003,2003;2497-2502vol.3.
[163] Sistu, P. B., Bequette, B. W. Nonlinear model-predictive control: Closed-loop stabilityanalysis[J]. AIChE J.,1996,42(12):3388-3402.
[164] Meadows, E. S., Rawlings, J. B. Model predictive control. In Nonlinear ProcessControl, Henson, M. A.,Seborg, D. E., Eds. Prentice Hall:1997;233-310.
[165] Mayne, D. Q., Rawlings, J. B., Rao, C. V., et al. Constrained model predictive control:Stability and optimality[J]. Automatica,2000,36(6):789-814.
[166] Qin, S. J., Badgwell, T. A. A survey of industrial model predictive controltechnology[J]. Control Engineering Practice,2003,11(7):733-764.
[167] de Nicolao, G., Magni, L., Scattolini, R. On the robustness of receding-horizon controlwith terminal constraints[J]. Automatic Control, IEEE Transactions on,1996,41(3):451-453.
[168] Scokaert, P. O. M., Rawlings, J. B., Meadows, E. S. Discrete-time stability withperturbations: Application to model predictive control[J]. Automatica,1997,33(3):463-470.
[169] Valappil, J., Georgakis, C. Systematic estimation of state noise statistics for extendedKalman filters[J]. Aiche J.,2000,46(2):292-308.
[170] Yildiz, U., Gurkan, U. A.,Ozgen, C., etc. State estimator design for multicomponentbatch distillation columns[J]. Chem. Eng. Res. Des.,2005,83(A5):433-444.
[171] Sirohi, A., Choi, K. Y. On-Line Parameter Estimation in a Continuous PolymerizationProcess[J]. Industrial&Engineering Chemistry Research,1996,35(4):1332-1343.
[172] Zavala, V. M., Biegler, L. T. Nonlinear Programming Strategies for State Estimationand Model Predictive Control. In Nonlinear Model Predictive Control: Towards NewChallenging Applications, Magni, L. R. D. M. A. F., Ed.2009; Vol.384,419-432.
[173] Rui, H., Patwardhan, S. C., Biegler, L. T. Stability of a class of discrete-time nonlinearrecursive observers[J]. J. Process Control,2010,20(10):1150-11601160.
[174] Rui, H., Patwardhan, S. C., Biegler, L. T. Robust stability of nonlinear modelpredictive control based on extended Kalman filter[J]. J. Process Control,2012,22(1):82-8989.
[175] Huang, S. D., Gamini, D. Convergence and Consistency Analysis for ExtendedKalman Filter Based SLAM[J]. Robotics, IEEE Transactions on,2007,23(5):1036-1049.
[176] Tamjidi, A., Taghirad, H. D., Aghamohammadi, A. On the consistency of EKF-SLAM:Focusing on the observation models[A]. In Intelligent Robots and Systems,2009.IROS2009. IEEE/RSJ International Conference on[C],10-15Oct.2009,2009;2083-2088.
[177] Zavala, V. M., Laird, C. D., Biegler, L. T. A fast moving horizon estimation algorithmbased on nonlinear programming sensitivity[J]. Journal of Process Control, Oct,2008,18(9):876-884.
[178] Boutayeb, M., Rafaralahy, H., Darouach, M. Convergence analysis of the extendedKalman filter used as an observer for nonlinear deterministic discrete-time systems[J].Automatic Control, IEEE Transactions on,1997,42(4):581-586.
[179] Tatiraju, S., Soroush, M., Ogunnaike, B. A. Multirate nonlinear state estimation withapplication to a polymerization reactor[J]. AIChE Journal,1999,45(4):769-780.
[180] Sheikhzadeh, M.,Trifkovic, M., Rohani, S. Real-time optimal control of ananti-solvent isothermal semi-batch crystallization process[J]. Chemical EngineeringScience,2008,63(3):829-839.
[181] Oisiovici, R. M., Cruz, S. L. State estimation of batch distillation columns using anextended Kalman filter[J]. Chemical Engineering Science,2000,55(20):4667-4680.
[182] Kittisupakorn, P. The use of nonlinear model predictive control techniques for thecontrol of a reactor with exothermic reactions[D]. Ph.D. thesis, University of London1995.
[2] Srinivasan, B., Bonvin, D. Real-time optimization of batch processes by tracking thenecessary conditions of optimality[J]. Industrial&Engineering Chemistry Research,2007,46(2):492-504.
[3] Chachuat, B.,S rinivasan, B., Bonvin, D. Adaptation strategies for real-timeoptimization[J]. Comput. Chem. Eng.,2009,33(10):1557-1567.
[4] Chen, H., He, X., Chen, B., etc. Multiperiod optimization for refinery processing andinventory management using a genetic algorithm[J]. J. Tsinghua Univ., Sci. Technol.,2004,44(3):319-322.
[5] Qian, Y., Wu, Z. H., Jiang, Y. B. Integration of chemical product development, processdesign and operation based on a kilo-plant[J]. Prog. Nat. Sci.,2006,16(6):600-606.
[6] Fang, X. Y., Shao, Z. J., Wang, Z. Q., et al. Mnemonic Enhancement Optimization(MEO) for Real-Time Optimization of Industrial Processes[J]. Industrial&Engineering Chemistry Research,2009,48(1):499-509.
[7] Zhang, R. D., Xue, A. K., Wang, S. Q. Modeling and nonlinear predictive functionalcontrol of liquid level in a coke fractionation tower[J]. Chem. Eng. Sci.,2011,66(23):6002-60136013.
[8] Li, Y. Q., Yuan, X. G., Li, Y. J. An improved PSO algorithm for solving non-convexNLP/MINLP problems with equality constraints[J]. Computers&ChemicalEngineering,2007,31(3):153-162.
[9]贺益君,陈德钊.基于免疫机制的多目标蚁群算法用于间歇反应器的约束动态多目标优化[J].高校化学工程学报,2009,23(02):326-332.
[10]赵振辉,冯霄,刘永忠, et al.氢气网络系统的夹点分析与匹配优化[J].化工进展,2008,27(2):261-264.
[11]岳金彩,杨霞,郑世清, et al.模块环境下的filter-SQP用于过程优化[J].化工学报,2006,57(03):614-619.
[12]王钧炎,黄德先.基于差分进化算法和hysys机理模型的催化重整过程优化[J].化工学报,2008,59(07):1755-1760.
[13]程华农,陈崇春,孔令启, et al. Matlab在乙醇胺反应动力学方程回归和预测中的运用[J].计算机与应用化学,2009,26(02):240-244.
[14]夏琼.基于差分进化算法的加热炉生产过程操作调度[D].沈阳:东北大学,2010.
[15]韩方煜.计算机辅助化工过程设计的理论与实践[J].中国化工,1996,(10):30-34.
[16]李锋,颜学峰,钱锋.常减压装置中基于自适应变异遗传算法的稳态数据协调方法[A].第五届全球智能控制与自动化大会会议论文集[C].杭州:浙江大学,2004.
[17] Peters, N., Guay, M., DeHaan, D. Real-time dynamic optimization of batch systems[J].Journal of Process Control,2007,17(3):261-271.
[18] Adetola, V., Guay, M. Integration of real-time optimization and model predictivecontrol[J]. Journal of Process Control,2010,20(1):125-133.
[19] Alvarez, L. A., Odloak, D. Robust integration of real time optimization with linearmodel predictive control[J]. Computers&Chemical Engineering,2010,34(12):1937-1944.
[20] De Souza, G., Odloak, D., Zanin, A. C. Real time optimization (RTO) with modelpredictive control (MPC)[J]. Computers&Chemical Engineering,2010,34(12):1999-2006.
[21] Ochoa, S., Repke, J. U., Wozny, G. Integrating real-time optimization and control foroptimal operation: Application to the bio-ethanol process[J]. Biochemical EngineeringJournal,2010,53(1):18-25.
[22] Palanki, S. The Application of Pontryagin's Minimum Principle for EndpointOptimization of Batch Processes[M].2012;327-337.
[23] Gattu, G., Zafiriou, E. Nonlinear quadratic dynamic matrix control with stateestimation[J]. Industrial&Engineering Chemistry Research,1992,31(4):1096-1104.
[24] Schlegel, M., Stockmann, K., Binder, T., etc. Dynamic optimization using adaptivecontrol vector parameterization[J]. Comput. Chem. Eng.,2005,29(8):1731-1751.
[25] Teoh, H. K., Turner, M., Titchener-Hooker, N., et al. Experimental verification andoptimisation of a detailed dynamic high performance liquid chromatography columnmodel[A]. In10th European Symposium on Computer Aides Process Engineering[C],Florence, Italy, May07-10,2000;893-903.
[26] Franceschini, G., Macchietto, S. Model-based design of experiments for parameterprecision: State of the art[J]. Chemical Engineering Science,2008,63(19):4846-4872.
[27] Haryanto, A., Hong, K.-S. Modeling and simulation of an oxy-fuel combustion boilersystem with flue gas recirculation[J]. Computers&Chemical Engineering,2011,35(1):25-40.
[28] Hvala, N., Aller, F., Miteva, T., et al. Modelling, simulation and control of anindustrial, semi-batch, emulsion-polymerization reactor[J]. Computers&ChemicalEngineering,2011,35(10):2066-2080.
[29] Guadix, A.,Sorensen, E.,Papageorgiou, L. G., et al. Optimal design and operation ofbatch ultrafiltration systems[A]. In Kraslawski, A.,Turunen, I.,13th EuropeanSymposium on Computer Aided Process Engineering (ESCAPE-13)[C], Lappeenranta,Finland, Jun01-04,2003;149-154.
[30] Sentoni, G. B., Biegler, L. T., Guiver, J. B., et al. State-space nonlinear processmodeling: Identification and universality[J]. Aiche Journal,1998,44(10):2229-2239.
[31] Findeisen, R., Imsland, L., Allgower, F., etc. State and output feedback nonlinearmodel predictive control: An overview[J]. European Journal of Control,2003,2003,9(2-3):190-206.
[32] Liang, J., Qian, J. X. Multivariate statistical process monitoring and control: Recentdevelopments and applications to chemical industry[J]. Chin. J. Chem. Eng., Apr,2003,11(2):191-203.
[33] Martinez, E. C. The statistical simplex method for experimental optimization withprocess data. In European Symposium on Computer-Aided Process Engineering-15,20A and20B, Puigjaner, L.,Espuna, A., Eds.2005; Vol.20a-20b,31-36.
[34] Kvasnica, M., Herceg, M., Cirka, L., et al. Model predictive control of a CSTR: Ahybrid modeling approach[J]. Chemical Papers, Jun,2010,64(3):301-309.
[35] Nascimento Lima, N. M., Manenti, F., Maciel Filho, R., et al. Fuzzy Model-BasedPredictive Hybrid Control of Polymerization Processes[J]. Industrial&EngineeringChemistry Research, Sep16,2009,48(18):8542-8550.
[36]张兵.化工动态优化方法的研究与应用[D].杭州:浙江大学,2005.
[37]陶卉.工业过程动态优化研究[D].杭州:浙江大学,2006.
[38]吴高辉.动态优化研究及其工业应用[D].杭州:浙江大学,2007.
[39] Biegler, L. T., Cervantes, A. M., Wachter, A. Advances in simultaneous strategies fordynamic process optimization[J]. Chemical Engineering Science,2002,57(4):575-593.
[40] Yi-Dong, L., Cervantes, A. M., Biegler, L. T. Dynamic modeling and optimization ofbatch crystallization processes[J]. Proceedings of the14th World Congress.International Federation of Automatic Control,1999,1999.
[41] Hong, W. R., Wang, S. Q., Li, P., etc. A quasi-sequential approach to large-scaledynamic optimization problems[J]. Aiche Journal, Jan,2006,52(1):255-268.
[42] Sch fer, A., Kühl, P., Diehl, M., et al. Fast reduced multiple shooting methods fornonlinear model predictive control[J]. Chemical Engineering and Processing: ProcessIntensification,2007,46(11):1200-1214.
[43] Srinivasan, B., Palanki, S., Bonvin, D. Dynamic optimization of batch processes-I.Characterization of the nominal solution[J]. Comput. Chem. Eng.,2003,27(1):1-26.
[44] Lang, Y. D., Biegler, L. T. A software environment for simultaneous dynamicoptimization[J]. Computers&Chemical Engineering,2007,31(8):931-942.
[45] Kameswaran, S., Biegler, L. T. Simultaneous dynamic optimization strategies: Recentadvances and challenges[J]. Computers& Chemical Engineering,2006,30(10–12):1560-1575.
[46] Hartwich, A., Schlegel, M., Wurth, L., et al. Adaptive control vector parameterizationfor nonlinear model-predictive control[J]. International Journal of Robust andNonlinear Control,2008,18(8):845-861.
[47] Schlegel, M., Stockmann, K., Binder, T., et al. Dynamic optimization using adaptivecontrol vector parameterization[J]. Computers&Chemical Engineering,2005,29(8):1731-1751.
[48] Hertzberg, T., Asbjornsen, O. A. Parameter estimation in nonlinear differentialequations[J]. Computer Applications in the Analysis of Chemical Data and Plants,1978,155-165.
[49] Biegler, L. T. Solution of dynamic optimization problems by successive quadraticprogramming and orthogonal collocation[J]. Computers&Chemical Engineering,1984,8(3-4).
[50] Fabien, B. C. Direct optimization of dynamic systems described bydifferential-algebraic equations[J]. Optim. Control Appl. Methods,2008,29(6):445-466.
[51] Bock, H. G., Diehl, M. M., Leineweber, D. B., et al. A direct multiple shooting methodfor real-time optimization of nonlinear DAE processes[M]. Basel: Birkhauser Verlag2000;245-267.
[52] Leineweber, D. B., Bauer, I., Bock, H. G., et al. An efficient multiple shooting basedreduced SQP strategy for large-scale dynamic process optimization. Part1: theoreticalaspects[J]. Computers&Chemical Engineering,2003,27(2):157-166.
[53] Biegler, L. T., Zavala, V. M. Large-scale nonlinear programming using IPOPT: Anintegrating framework for enterprise-wide dynamic optimization[J]. Computers&Chemical Engineering,2009,33(3):575-582.
[54] Cuthrell, J. E., Biegler, L. T. On the optimization of differential-algebraic processsystems[J]. Aiche Journal,1987,33(8).
[55] Ganesh, N., Biegler, L. T. A robust technique for process flowsheet optimization usingsimplified model approximations[J]. Computers&Chemical Engineering,1987,11(6):553-565.
[56] Bartlett, R. A., Biegler, L. T. rSQP++: An object-oriented framework for successivequadratic programming. In Large-Scale Pde-Constrained Optimization, Biegler, L. T.,Ed.2003; Vol.30,316-330.
[57] Liu, T., Gao, F. Batch Process Control[M]. Industrial Process Identification andControl Design. In Springer London:2012;433-452.
[58] Wang, Y. Q., Zhang, D. H., Gao, F. Generalized predictive control of linear systemswith actuator arrearage faults[J]. Journal of Process Control,2009,19(5):803-815.
[59] Shukla, P. K., Pushpavanam, S., Khanna, A., et al. Experimental implementation of arecursive algorithm to control the temperature trajectory of an exothermic batchreactor[J]. Industrial&Engineering Chemistry Research,1997,36(1):122-129.
[60] Cott, B. J., Macchietto, S. Temperature control of exothermic batch reactors usinggeneric model control[J]. Industrial&Engineering Chemistry Research,1989,28(8):1177-1184.
[61] Wang, D., Srinivasan, R. Multi-model based real-time final product quality controlstrategy for batch processes[J]. Computers&Chemical Engineering,2009,33(5):992-1003.
[62] Wang, Y. Q., Zhang, D. H., Gao, F. Iterative learning model predictive control formulti-phase batch processes[J]. J. Process Control,2008,18(6):543-557.
[63] Lee, J. H., Lee, K. S. Iterative learning control applied to batch processes: Anoverview[J]. Control Eng. Prac.,2007,15(10):1306-1318.
[64] Dorsey, A. W., Lee, J. H., Russell, S. A. An extended Kalman filter formulation forsystematic transfer of information from batch to batch[J]. Industrial&EngineeringChemistry Research,2003,42(8):1753-1760.
[65] Arpornwichanop, A., Kittisupakorn, A., Mujtaba, I. M. On-line dynamic optimizationand control strategy for improving the performance of batch reactors[J]. ChemicalEngineering and Processing,2005,44(1):101-114.
[66] Pommier, S., Massebeuf, S., Kotai, B., et al. Heterogeneous batch distillationprocesses: Real system optimisation[J]. Chemical Engineering and Processing:Process Intensification,2008,47(3):408-419.
[67] Costa, C. B. B., Maciel, R. Evaluation of optimisation techniques and control variableformulations for a batch cooling crystallization process[J]. Chemical EngineeringScience,2005,60(19):5312-5322.
[68] Shafiee, G., Arefi, M. M., Jahed-Motlagh, M. R., etc. Nonlinear predictive control of apolymerization reactor based on piecewise linear Wiener model[J]. Chem. Eng. J.,2008,143(1-3):282-292.
[69] Morari, M., H. Lee, J. Model predictive control: past, present and future[J]. Computers&Chemical Engineering,1999,23(4-5):667-682.
[70] Henson, M. A. Nonlinear model predictive control: current status and futuredirections[J]. Comput. Chem. Eng.,1998,23(2):187-202.
[71] Cutler, C. R.,Ramaker, B. L. Dynamic matrix control-A computer controlalgorithm[A]. In the Joint Automatic Control Conference[C], San Francisco,1980,1980.
[72] Sentoni, G. B., Biegler, L. T., Guiver, J. B., et al. State-space nonlinear processmodeling: Identification and universality[J]. Aiche Journal,1998,44(10):2229-2239.
[73] Mujtaba, I. M., Aziz, N., Hussain, M. A. Neural Network Based Modelling andControl in Batch Reactor[J]. Chem. Eng. Res. Des.,2006,84(8):635-644.
[74] Mujtaba, I. M., Aziz, N., Hussain, M. A. Neural Network Based Modelling andControl in Batch Reactor[J]. Chemical Engineering Research and Design,2006,84(8):635-644.
[75] Escano, J. M., Bordons, C., Vilas, C., et al. Neurofuzzy model based predictive controlfor thermal batch processes[J]. J. Process Control, Oct,2009,19(9):1566-1575.
[76] Takagi, T., Sugeno, M. Fuzzy identification of systems and its applications tomodeling and control[J]. Systems, Man and Cybernetics, IEEE Transactions on,1985,SMC-15(1):116-132.
[77] Kuure-Kinsey, M., Bequette, B. W. Multiple Model Predictive Control Strategy forDisturbance Rejection[J]. Industrial&Engineering Chemistry Research, Sep,2010,49(17):7983-7989.
[78] Ravi, V. R., Thyagarajan, T., Darshini, M. M. A Multiple Model Adaptive ControlStrategy for Model Predictive Controller for Interacting Non Linear Systems[A]. InProcess Automation, Control and Computing (PACC),2011International Conferenceon[C],20-22July2011,2011;1-8.
[79] Giovanini, L., Ordys, A. W.,Grimble, M. J. Adaptive predictive control using multiplemodels, switching and tuning[J]. International Journal of Control Automation andSystems,2006,4(6):669-681.
[80] Dougherty, D., Cooper, D. A practical multiple model adaptive strategy formultivariable model predictive control[J]. Control Eng. Prac.,2003,11(6):649-664.
[81] Cannon, M., Ng, D.,Kouvaritakis, B. Successive Linearization NMPC for a Class ofStochastic Nonlinear Systems Nonlinear Model Predictive Control. In Magni,L.,Raimondo, D.,Allg wer, F., Eds. Springer Berlin/Heidelberg:2009; Vol.384,249-262.
[82] Qin, S. J., Badgwell, T. A. An overview of nonlinear model predictive controlapplications[M].2000;369-392.
[83] Stefansson, H., Shah, N., Jensson, P. Multiscale planning and scheduling in thesecondary pharmaceutical industry[J]. Aiche Journal,2006,52(12):4133-4149.
[84] Lievo, P., Almark, M., Purola, V. M., etc. Miniplant-Effective tool in processdevelopment and design. In European Symposium on Computer Aided ProcessEngineering-13, Kraslawski, A.,Turunen, I., Eds. Elsevier Science Bv: Amsterdam,2003; Vol.14,761-766.
[85] Heimann, F. Process intensification through the combined use of process simulationand miniplant technology. In European Symposium on Computer Aided ProcessEngineering-13, Kraslawski, A.,Turunen, I., Eds. Elsevier Science Bv: Amsterdam,2003; Vol.14,155-160.
[86]钱宇,李秀喜,程华农, et al.化工过程运行系统的集成[J].化工学报,2003,54(04):557-563.
[87] Paniagua-Quinones, C., Box, G. E. P. Use of strip-strip-block design for multi-stageprocesses to reduce costs of experimentation[J]. Quality Engineering,2008,20(1):46-52.
[88] Draper, N. R., Hunter, W. G. The use of prior distributions in the design ofexperiments for parameter estimation in non-linear situations[J]. Biometrika,1967,54(1):147-53.
[89] Kiefer, J. C. Optimum Experimental Designs. In Breakthroughs in Statistics, Kotz,S.,Johnson, N., Eds. Springer New York:1992;400-436.
[90] Dovi, V. G., Reverberi, A. P., Maga, L. Optimal design of sequential experiments forerror-in-variables models[J]. Computers&Chemical Engineering,1993,17(1):111-15.
[91]张伟,危韧勇,金黎.生产过程实时仿真系统[J].长沙铁道学院学报,2000,18(02):50-52.
[92]吴志辉,钱宇.江燕斌.化学产品过程开发实验平台——公斤实验室的建设和应用[J].现代化工,2005,3(03)::66-68.
[93]刘姜涛,周洪,邓其军.基于opc的远程数据采集与存储[J].微计算机信息,2009,25(01):105-107.
[94]李相育,钱宇.基于OPC的Matlab与WinCC的无缝集成[J].微计算机信息,2007,03(31):297-299.
[95]饶克克,李华.基于opc的多客户端与wincc的数据通信[J].化工自动化及仪表,2012,39(01):133-135.
[96]秦辉,席裕庚.基于Matlab GUI的预测控制仿真平台设计[J].系统仿真学报,2006,18(10):2778-2781.
[97]白金麟,石立宏,周培湘, etc.化工过程实时优化技术现状与未来[J].数字石油和化工,2009,(07):1-6.
[98] Srinivasan, B., Palanki, S., Bonvin, D. Dynamic optimization of batch processes-I.Characterization of the nominal solution[J]. Computers&Chemical Engineering,2003,27(1):1-26.
[99] Sentoni, G. B., Biegler, L. T., Guiver, J. P. Model reduction for nonlinear DABNetmodels[A]. Proceedings of the1999American Control Conference[C],1999.
[100] Hahn, J., Edgar, T. F. An improved method for nonlinear model reduction usingbalancing of empirical gramians[J]. Computers&Chemical Engineering,2002,26(10):1379-1397.
[101] Bonis, I.,Xie, W., Theodoropoulos, C. A linear model predictive control algorithm fornonlinear large-scale distributed parameter systems[J]. AIChE Journal,2012,58(3):801-811.
[102] Yu, W. F.,Hidajat, K.,Ray, A. K. Determination of adsorption and kinetic parametersfor methyl acetate esterification and hydrolysis reaction catalyzed by Amberlyst15[J].Appl. Catal. A-Gen.,2004,260(2):191-205.
[103] Tosukhowong, T., Lee, J. H. Approximate Dynamic Programming Based OptimalControl Applied to an Integrated Plant with a Reactor and a Distillation Column withRecycle[J]. Aiche Journal,2009,55(4):919-930.
[104] Deb, K. An introduction to genetic algorithms[J]. Sadhana-Acad. Proc. Eng. Sci.,1999,24:293-315.
[105] Stender, J. Introduction to genetic algorithms[A]. In Applications of GeneticAlgorithms, IEE Colloquium on[C],15Mar1994,1994:1-4.
[106] Zavala, J. C., Coronado, C. Optimal control problem in batch distillation usingthermodynamic efficiency[J]. Industrial&Engineering Chemistry Research,2008,47(8):2788-2793.
[107] Luyben, M. L., Tyreus, B. D., Luyben, W. L. Plantwide control design procedure[J].AIChE J.,1997,43(12):3161-3174.
[108] Floudas, C. A., Akrotirianakis, I. G., Caratzoulas, S., etc. Global optimization in the21st century: Advances and challenges[A]. In14th European Symposium onComputer Aided Process Engineering (ESCAPE-14)[C], Lisbon, PORTUGAL, May16-19,2004:1185-1202.
[109] Flores-Tlacuahuac, A., Biegler, L. T. On The Global Dynamic Optimization of HighlyNonlinear Systems. In16th European Symposium on Computer Aided ProcessEngineering and9th International Symposium on Process Systems Engineering,Marquardt, W. P. C., Ed.2006; Vol.21,1227-1232.
[110] Wassick, J. M. Enterprise-wide optimization in an integrated chemical complex[J].Computers&Chemical Engineering,2009,33(12):1950-1963.
[111] Furlonge, H. I., Pantelides, C. C., Sorensen, E. Optimal operation of multivessel batchdistillation columns[J]. Aiche Journal,1999,45(4):781-801.
[112] Low, K. H., Sorensen, E. Simultaneous optimal configuration, design and operation ofbatch distillation[J]. Aiche Journal,2005,51(6):1700-1713.
[113] Skogestad, S., Wittgens, B., Litto, R., et al. Multivessel batch distillation[J]. AicheJournal,1997,43(4):971-978.
[114] Logsdon, J. S., Biegler, L. T. Accurate determination of optimal reflux policies for themaximum distillate problem in batch distillation[J]. Industrial&EngineeringChemistry Research,1993,32(4):692-700.
[115] Mayur, D. N., Jackson, R. Time-optimal problems in batch distillation formulticomponent mixtures and for columns with holdup[J]. The Chemical EngineeringJournal,1971,2(3):150-163.
[116] Mukherjee, S., Dahule, R. K., Tambe, S. S., etc. Consider genetic algorithms tooptimize batch distillation-Alternative simulation tool calculates best refluxconditions to separate binary mixtures[J]. Hydrocarb. Process., Sep,2001,80(9):59-66.
[117] Tomazi, K. G. Limitations and dynamics imposed on multicomponent batchdistillation by tray hydraulics[J]. Industrial&Engineering Chemistry Research,1997,36(10):4273-4281.
[118] Zavala, J. C., Ceron, R. M., Pali, R. J., etc. Simplified calculation for the liquid holdupeffect in batch distillation[J]. Industrial&Engineering Chemistry Research,2007,46(15):5186-5191.
[119] Chen, Q., Feng, X. Reactor network synthesis for waste reduction using instantaneousvalue of environmental index[J]. Chin. J. Chem. Eng.,2008,16(1):155-158.
[120] Zhou, W., Manousiouthakis, V. I. Automating the AR construction for non-isothermalreactor networks[J]. Computers&Chemical Engineering,2009,33(1):176-180.
[121] Gao, X. D., Chen, B. Z., He, X. R., et al. Multi-objective optimization for the periodicoperation of the naphtha pyrolysis process using a new parallel hybrid algorithmcombining NSGA-II with SQP[J]. Computers&Chemical Engineering,2008,32(11):2801-2811.
[122] Hua, K., Li, Y., Hu, S., et al. Three-distribution-parameter general model for reactornetwork synthesis[J]. Computers&Chemical Engineering,2000,24(2-7):217-223.
[123] Konak, A., Coit, D. W., Smith, A. E. Multi-objective optimization using geneticalgorithms: A tutorial[J]. Reliability Engineering&System Safety,2006,91(9):992-1007.
[124] Rippin, D. W. T. Simulation of single-and multiproduct batch chemical plants foroptimal design and operation[J]. Comput. Chem. Eng.,1983,7(3):137-156.
[125] Youqing, W., Furong, G., Doyle, F. J., III. Survey on iterative learning control,repetitive control, and run-to-run control[J]. Journal of Process Control,2009,19(10):1589-16001600.
[126] Hermanto, M. W., Braatz, R. D., Chiu, M.-S. Integrated batch-to-batch and nonlinearmodel predictive control for polymorphic transformation in pharmaceuticalcrystallization[J]. AIChE J.,2011,57(4):1008-1019.
[127] Nagy, Z. K., Braatz, R. D. Robust nonlinear model predictive control of batchprocesses[J]. AIChE J.,2003,49(7):1776-1786.
[128] Cervantes, A. M., Tonelli, S., Brandolin, A., etc. Large-scale dynamic optimization forgrade transitions in a low density polyethylene plant[J]. Comput. Chem. Eng.,2002,26(2):227-237.
[129] Chatzidoukas, C., Perkins, J. D., Pistikopoulos, E. N., etc. Optimal grade transitionand selection of closed-loop controllers in a gas-phase olefin polymerization fluidizedbed reactor[J]. Chem. Eng. Sci.,2003,58(16):3643-3658.
[130] Manenti, F., Rovaglio, M. Integrated Multilevel Optimization in Large-ScalePoly(Ethylene Terephthalate) Plants[J]. Ind. Eng. Chem. Res.,2007,47(1):92-104.
[131] Garcia, C. E., Prett, D. M., Morari, M. Model predictive control: theory and practice-asurvey[J]. Automatica,1989,25(3):335-348.
[132] Lee, J. H. Model Predictive Control: Review of the Three Decades of Development[J].Int. J. Control Autom.,2011,9(3):415-424.
[133] Bequette, B. W. Nonlinear control of chemical processes: a review[J]. Industrial&Engineering Chemistry Research,1991,30(7):1391-413.
[134] Arellano-Garcia, H., Barz, T., Wendt, M., et al. Real-time feasibility of nonlinearpredictive control for semi-batch reactors. In European Symposium onComputer-Aided Process Engineering-15,20A and20B, Puigjaner, L.,Espuna, A., Eds.2005; Vol.20a-20b,967-972.
[135] Arpornwichanop, A., Kittisupakorn, P., Hussain, M. A. Model-based control strategiesfor a chemical batch reactor with exothermic reactions[J]. Korean J. Chem. Eng., Mar,2002,19(2):221-226.
[136] Bosley, J. R., Jr., Edgar, T. F. Application of nonlinear model predictive control tooptimal batch distillation[A]. In Dynamics and Control of Chemical ReactorsDistillation Columns and Batch Processes (DYCORD+'92). Selected Papers from the3rd IFAC Symposium[C],1993,1993;303-308.
[137] Alhamad, B., Romagnoli, J. A., Gomes, V. G. On-line multi-variable predictive controlof molar mass and particle size distributions in free-radical emulsioncopolymerization[J]. Chemical Engineering Science,2005,60(23):6596-6606.
[138] Peterson, T., Hernández, E., Arkun, Y., etc. A nonlinear DMC algorithm and itsapplication to a semibatch polymerization reactor[J]. Chem. Eng. Sci.,1992,47(4):737-753.
[139] Karaduman, A., Oguz, H., Berber, R. Nonlinear model predictive temperature controlof a batch polymerization reactor[J]. Advances in Process Control4,1995,1995,203-210210.
[140] Ogunnaike, B. A. On-line modelling and predictive control of an industrialterpolymerization reactor[J]. Int. J. Control, March,1994,59(3):711-729.
[141] Su, Q. L., Braatz, R. D., Chiu, M. S. Concentration Control for Semi-Batch Ph-ShiftReactive Crystallization of L-Glutamic Acid[A]. In Kariwala, V., Samavedham,Lakshminarayanan, Braatz, Richard D., Proceedings of the8th IFAC Symposium onAdvanced Control of Chemical Processes[C], Furama Riverfront, Singapore,1996,2012;228-233.
[142] Lopez-Negrete, R., D’Amato, F. J., Biegler, L. T., etc. Fast nonlinear model predictivecontrol: Formulation and industrial process applications[J]. Comput. Chem. Eng.,2013,51(0):55-64
[143] Kansha, Y., Chiu, M. S. Adaptive generalized predictive control based on JITLtechnique[J]. J. Process Control,2009,19(7):1067-1072.
[144] Dones, I., Manenti, F., Preisig, H. A., etc. Nonlinear Model Predictive Control: ASelf-Adaptive Approach[J]. Industrial&Engineering Chemistry Research,2010,49(10):4782-4791.
[145] Biegler, L. T. Efficient solution of dynamic optimization and NMPC problems[M].Basel: Birkhauser Verlag Ag,2000:219-243.
[146] Meadows, E. S. Dynamic programming and model predictive control[A]. InProceedings of the1997American Control Conference [C], Albuquerque, NM, USA,1997,1997;1635-1639
[147] Zavala, V. M., Laird, C. D., Biegler, L. T. Fast implementations and rigorous models:Can both be accommodated in NMPC?[J]. International Journal of Robust andNonlinear Control,2008,18(8):800-815.
[148] Zavala, V. M., Biegler, L. T. Optimization-based strategies for the operation oflow-density polyethylene tubular reactors: nonlinear model predictive control[J].Comput. Chem. Eng.,2009,33(10):1735-1746.
[149] Nagy, Z. K. Model based control of a yeast fermentation bioreactor using optimallydesigned artificial neural networks[J]. Chem. Eng. J.,2007,127(1-3):95-109.
[150] Wu, W., Ding, S. Y. Model predictive control of nonlinear distributed parametersystems using spatial neural-network architectures[J]. Industrial&EngineeringChemistry Research,2008,47(19):7264-7273.
[151] Kuure-Kinsey, M., Bequette, B. W. Multiple Model Predictive Control of NonlinearSystems. In Nonlinear Model Predictive Control, Magni, L.,Raimondo, D.,Allg wer,F., Eds. Springer Berlin Heidelberg:2009; Vol.384,153-165.
[152] Wang, F. Y., Bahri, P.,Lee, P. L., etc. A multiple model, state feedback strategy forrobust control of non-linear processes[J]. Comput. Chem. Eng.,2007,31(5–6):410-418.
[153] Cheng, C., Hashimoto, Y., Min-Sen, C. Adaptive controller design using just-in-timelearning algorithm[A]. In Control Applications,2004. Proceedings of the2004IEEEInternational Conference on[C],2-4Sept.2004,2004;1106-1111Vol.2.
[154] Terwiesch, P., Agarwal, M. A discretized nonlinear state estimator for batchprocesses[J]. Comput. Chem. Eng.,1995,19(2):155-169.
[155] Tatiraju, S., Soroush, M.,Ogunnaike, B. A. Multirate nonlinear state estimation withapplication to a polymerization reactor[J]. AIChE J.,1999,45(4):769-780.
[156] Rui, H., Patwardhan, S. C., Biegler, L. T. Stability of a class of discrete-time nonlinearrecursive observers[J]. J. Process Control,2010,20(10):1150-1160.
[157] Peters, N., Guay, M., DeHaan, D. Real-time dynamic optimization of batch systems[J].J. Process Control,2007,17(3):261-271.
[158] Skogestad, S. Control structure design for complete chemical plants[J]. Comput.Chem. Eng.,2004,28(1-2):219-234.
[159] Jie, X., Yinlun, H., Zheng, L. Proactive product quality control: An integrated productand process control approach to MIMO systems[J]. Chem. Eng. J.,2009,149(1-3):435-446.
[160] Yuan, Z., Chen, B., Zhao, J. Effect of Manipulated Variables Selection on theControllability of Chemical Processes[J]. Industrial&Engineering ChemistryResearch,2011,50(12):7403-7413.
[161] McGahey, S. L., Cameron, I. T. A multi-model repository with manipulation andanalysis tools[J]. Comput. Chem. Eng.,2007,31(8):919-930.
[162] Dougherty, D., Arbogast, J., Cooper, D. J. A multiple model adaptive control strategyfor DMC[A]. In American Control Conference,2003. Proceedings of the2003[C],4-6June2003,2003;2497-2502vol.3.
[163] Sistu, P. B., Bequette, B. W. Nonlinear model-predictive control: Closed-loop stabilityanalysis[J]. AIChE J.,1996,42(12):3388-3402.
[164] Meadows, E. S., Rawlings, J. B. Model predictive control. In Nonlinear ProcessControl, Henson, M. A.,Seborg, D. E., Eds. Prentice Hall:1997;233-310.
[165] Mayne, D. Q., Rawlings, J. B., Rao, C. V., et al. Constrained model predictive control:Stability and optimality[J]. Automatica,2000,36(6):789-814.
[166] Qin, S. J., Badgwell, T. A. A survey of industrial model predictive controltechnology[J]. Control Engineering Practice,2003,11(7):733-764.
[167] de Nicolao, G., Magni, L., Scattolini, R. On the robustness of receding-horizon controlwith terminal constraints[J]. Automatic Control, IEEE Transactions on,1996,41(3):451-453.
[168] Scokaert, P. O. M., Rawlings, J. B., Meadows, E. S. Discrete-time stability withperturbations: Application to model predictive control[J]. Automatica,1997,33(3):463-470.
[169] Valappil, J., Georgakis, C. Systematic estimation of state noise statistics for extendedKalman filters[J]. Aiche J.,2000,46(2):292-308.
[170] Yildiz, U., Gurkan, U. A.,Ozgen, C., etc. State estimator design for multicomponentbatch distillation columns[J]. Chem. Eng. Res. Des.,2005,83(A5):433-444.
[171] Sirohi, A., Choi, K. Y. On-Line Parameter Estimation in a Continuous PolymerizationProcess[J]. Industrial&Engineering Chemistry Research,1996,35(4):1332-1343.
[172] Zavala, V. M., Biegler, L. T. Nonlinear Programming Strategies for State Estimationand Model Predictive Control. In Nonlinear Model Predictive Control: Towards NewChallenging Applications, Magni, L. R. D. M. A. F., Ed.2009; Vol.384,419-432.
[173] Rui, H., Patwardhan, S. C., Biegler, L. T. Stability of a class of discrete-time nonlinearrecursive observers[J]. J. Process Control,2010,20(10):1150-11601160.
[174] Rui, H., Patwardhan, S. C., Biegler, L. T. Robust stability of nonlinear modelpredictive control based on extended Kalman filter[J]. J. Process Control,2012,22(1):82-8989.
[175] Huang, S. D., Gamini, D. Convergence and Consistency Analysis for ExtendedKalman Filter Based SLAM[J]. Robotics, IEEE Transactions on,2007,23(5):1036-1049.
[176] Tamjidi, A., Taghirad, H. D., Aghamohammadi, A. On the consistency of EKF-SLAM:Focusing on the observation models[A]. In Intelligent Robots and Systems,2009.IROS2009. IEEE/RSJ International Conference on[C],10-15Oct.2009,2009;2083-2088.
[177] Zavala, V. M., Laird, C. D., Biegler, L. T. A fast moving horizon estimation algorithmbased on nonlinear programming sensitivity[J]. Journal of Process Control, Oct,2008,18(9):876-884.
[178] Boutayeb, M., Rafaralahy, H., Darouach, M. Convergence analysis of the extendedKalman filter used as an observer for nonlinear deterministic discrete-time systems[J].Automatic Control, IEEE Transactions on,1997,42(4):581-586.
[179] Tatiraju, S., Soroush, M., Ogunnaike, B. A. Multirate nonlinear state estimation withapplication to a polymerization reactor[J]. AIChE Journal,1999,45(4):769-780.
[180] Sheikhzadeh, M.,Trifkovic, M., Rohani, S. Real-time optimal control of ananti-solvent isothermal semi-batch crystallization process[J]. Chemical EngineeringScience,2008,63(3):829-839.
[181] Oisiovici, R. M., Cruz, S. L. State estimation of batch distillation columns using anextended Kalman filter[J]. Chemical Engineering Science,2000,55(20):4667-4680.
[182] Kittisupakorn, P. The use of nonlinear model predictive control techniques for thecontrol of a reactor with exothermic reactions[D]. Ph.D. thesis, University of London1995.