MPCE实验装置列管式换热器系统建模与控制算法研究
|
摘要
换热器是一种用来进行热量交换的工艺设备,被广泛应用于流程工业生产企业。在目前尚不具备在实验室中复现真实工业过程的条件下,研究利用具有典型对象模型的实验装置进行模拟仿真无疑是一条探索将控制理论和算法转化为应用技术的捷径。以多功能过程控制实验装置(MPCE)为实验平台,以列管式换热器系统为被控对象,对其进行建模、控制算法以及换热过程模拟仿真的研究,对于改善换热器系统的控制效果,提高换热效率具有重要的意义。
利用机理建模与系统辨识的方法建立了换热器的数学模型,确定了换热器热流出口温度与冷流入口流量之间的关系。通过阶跃响应仿真实验和采用PID控制实验验证了模型的准确性。并利用较准确的换热器模型,在MPCE实验装置上实现了Smith预估控制实验和前馈—反馈控制实验。
针对换热器大时滞、参数时变的特性,研究了全参数自适应Smith预估控制算法(APASPCA).在常规Smith预估器的基础上,基于模型参考自适应原理,利用Lyapunov函数法设计了预估器增益和时间常数的自适应律,并利用信号相关性分析技术中的平均幅度差函数实现了被控对象纯滞后时间的最优估计,使得预估器模型参数能跟踪被控对象参数的变化,从而保证了Smith预估器的控制效果。仿真结果表明,APASPCA算法对于大时滞参数时变的被控对象具有很好的控制效果,是一种性能很好的控制方法,具有实际应用价值。
Heat exchanger is a kind of process equipment for heat exchange which is widely used in process industry manufacturers. Under the condition that cannot recover real industrial processes, researching on typical experiment equipment with typical object model undoubtedly is a shortcut to explore control theory and algorithm into application technology. Using the multifunction process and control experiment (MPCE) device as experiment platform and tube heat exchanger as the controlled object, researches on the modeling and control algorithm of tube heat exchanger and simulation of heat transfer process are significant to improve control effect of heat exchanger system and enhance exchange thermal efficiency.
Mechanism modeling and system identification methods were adopted to develop a mathematical model of heat exchanger, and determined the relationship between outlet temperature of heat fluid and inlet flow of cold fluid of heat exchanger. Step response simulation experiments and PID control experiments were conducted to verify the accuracy of the model.Based on the relatively accurate heat exchanger model,Smith predictive control experiments and feedforward-feedback experiments were realized on MPCE device.
Aiming at the heat exchanger with large time delay and time-varying characteristics, all parameters adaptive Smith predictive control algorithm (APASPCA) was researched. Based on normal Smith predictor and model reference adaptive control principle, the adaptive laws of predictor gain and time constant were designed by adopting Lyapunov function method. Meanwhile the average magnitude difference function in correlation analysis technique was introduced to realize optimal estimation of time delay of controlled object which made real-time regulation of predictor parameters match with controlled object parameters. The algorithm could ensure the control effect of Smith predictor. Simulation results showed that APASPCA could get good control effect for the controlled object with large time delay and time-varying characteristics. The algorithm had good control performance and practical value.
利用机理建模与系统辨识的方法建立了换热器的数学模型,确定了换热器热流出口温度与冷流入口流量之间的关系。通过阶跃响应仿真实验和采用PID控制实验验证了模型的准确性。并利用较准确的换热器模型,在MPCE实验装置上实现了Smith预估控制实验和前馈—反馈控制实验。
针对换热器大时滞、参数时变的特性,研究了全参数自适应Smith预估控制算法(APASPCA).在常规Smith预估器的基础上,基于模型参考自适应原理,利用Lyapunov函数法设计了预估器增益和时间常数的自适应律,并利用信号相关性分析技术中的平均幅度差函数实现了被控对象纯滞后时间的最优估计,使得预估器模型参数能跟踪被控对象参数的变化,从而保证了Smith预估器的控制效果。仿真结果表明,APASPCA算法对于大时滞参数时变的被控对象具有很好的控制效果,是一种性能很好的控制方法,具有实际应用价值。
Heat exchanger is a kind of process equipment for heat exchange which is widely used in process industry manufacturers. Under the condition that cannot recover real industrial processes, researching on typical experiment equipment with typical object model undoubtedly is a shortcut to explore control theory and algorithm into application technology. Using the multifunction process and control experiment (MPCE) device as experiment platform and tube heat exchanger as the controlled object, researches on the modeling and control algorithm of tube heat exchanger and simulation of heat transfer process are significant to improve control effect of heat exchanger system and enhance exchange thermal efficiency.
Mechanism modeling and system identification methods were adopted to develop a mathematical model of heat exchanger, and determined the relationship between outlet temperature of heat fluid and inlet flow of cold fluid of heat exchanger. Step response simulation experiments and PID control experiments were conducted to verify the accuracy of the model.Based on the relatively accurate heat exchanger model,Smith predictive control experiments and feedforward-feedback experiments were realized on MPCE device.
Aiming at the heat exchanger with large time delay and time-varying characteristics, all parameters adaptive Smith predictive control algorithm (APASPCA) was researched. Based on normal Smith predictor and model reference adaptive control principle, the adaptive laws of predictor gain and time constant were designed by adopting Lyapunov function method. Meanwhile the average magnitude difference function in correlation analysis technique was introduced to realize optimal estimation of time delay of controlled object which made real-time regulation of predictor parameters match with controlled object parameters. The algorithm could ensure the control effect of Smith predictor. Simulation results showed that APASPCA could get good control effect for the controlled object with large time delay and time-varying characteristics. The algorithm had good control performance and practical value.
引文
[1]王树青.工业过程控制工程[M].北京：化学工业出版社,2003：297-305
[2]朱珍.模糊控制在换热器过程控制中的应用[D].重庆：重庆大学,2003
[3]程林,杨培毅,陆煜.换热器运行导论[M].北京：石油工业出版社,1995：1-46
[4]冯立川.模糊-串级控制在换热器温度控制系统中的应用[D].北京：北京化工大学,2007
[5]张亚萌.智能模糊控制技术在换热器控制系统中的应用[D].天津：天津大学,2005
[6]冷伟,房德山,徐治皋,等.对单相换热器集总参数模型动态初始负偏移的机理分析[J].热能动力工程,2005,16(93)：287-289
[7]周文明,孙文,陈芝久.用于控制的制冷系统稳态建模与仿真[J].上海交通大学学报,1999,33(3)：255-258
[8]王伟,马最良,姚杨.空气源热泵机稳态仿真数学模型的建立与实验验证[J].流体机械,2006,34(5)：52-56
[9]马进,王兵树,马良玉,等.管式换热器动态数学模型的跷跷板效应分析[J].系统仿真学报,2006,18(5)：1105-1107
[10]周少祥,胡三高,宋之平.管式换热器分布参数模型的分段线性化方法研究[J].中国电机工程学报,2002,22(6)：123-125
[11]从松波,袁璞,沈复.换热器动态模型的简化[J].石油炼制与化工,1996,27(10)：5-8
[12]ISLAMONGLU Y. A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger-use of an artificial neural network model[J]. Applied Thermal Engineering,2003,23:243～249
[13]PACHCCO-VEGA A, SEN M, YANG K T, et al.Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data[J].Heat and Mass Tranfer,2001,44:763～770
[14]李强,张月军,林颐清.换热器冷媒出口温度自适应预测控制模拟研究[J].石油化工设计,2007,24(2)：33-37
[15]郭敏,韩新奎,周寒军.基于神经网络的热工过程动态建模[J].节能技术,2005,(4)：321-323
[16]胡饮华,李奎山,苏廷弼.基于ANN的空气处理单元换热器动态性能预测[J]. 计算机工程与应用,2008,44(3)：240-241
[17]ROPPO M N, GANIC E N. Time-dependent heat exchanger modeling[J].Heat Transfer Engng,1983,4(2):42～46
[18]CORREA D J,MARCHETTI J L. Dynamic simulation of shell-and-tube heat exchangers[J].Heat Transfer Engng,1987,8(1):50～59
[19]ROETZEL W, XUAN Y.Transient behavior of multipass shell-and-tube heat exchangers[J].International Journal of Heat and Mass Transfer,1992,35(3): 703～710
[20]Luo Xing, Guan Xin. Dynamic behavior of one-dimensional flow multistream heat exchangers and their networks[J].International Journal of Heat and Mass Transfer,2003,46:705～715
[21]解增忠,张俊峰,罗雄麟,等.管壳换热器模型库及在换热网络仿真中的应用[J].系统仿真学报,2005,17(12)：2882-2887
[22]SMITH O J M. Close control of loops with dead time[J].Chemical Engineering Progress Transaction,1957,53(5):217～219
[23]IBRAHIM K. A new Smith predictor and controller for control of processes with long dead time[J].ISA Transactions,2003,42,101～110
[24]JULIO E. N, EDUARDO F C.Dead-time compensators:A survey[J].Control Engineering Practice,2008,16:407～428
[25]HANG C C.A performance study of control system with dead time[J]. IEEE, IECI,1980,27:234～241
[26]WATANABE K. A process-model control for linear system with delay[J].IEEE Transactions,1981,(6):1261～1269
[27]GILES R F, BARTLEY T M. Gain-adaptive dead time compensation[J].ISA Transactions,1977,(1):46～49
[28]张美玉,齐家国.动态矩阵控制算法在电热炉温控制中的实现[J].浙江工业大学学报,2002,30(4)：360-365
[29]郑辑光,施仁,王孟效.纸机先进控制算法研究[J].西安交通大学学报,2001,35(12)：1271-1274
[30]王艳经,韩光胜.烟草烘丝过程的水分控制[J].北京工业大学学报,2000,26(4)：90-95
[31]鲁照权,韩江洪.一种新型增益自适应Smith预估器[J].仪器仪表学报,2002,23(2)：195-196
[32]鲁照权,韩江洪.对象参考参数自适应时滞补偿器[J].控制与决策,2001, 16(2)：239-241
[33]刘翠玲,伞冶.一种改进的时滞系统控制器的设计[J].电机与控制学报,2000,4(1)：39-42
[34]Gao Wei, Li Yuan-chun, Liu Guang-jun. An adaptive fuzzy Smith Control of time-varying processes with dominant and variable delay[C].Proceedings of the American Control Conference Denver, Colorado,2003,18(6):4-6
[35]Xia Chan-liang, Gao Geng-ming. Brushless DC motors control based on Smith predictor modified by fuzzy-PI controller[C].Fifth International Conference on Fuzzy Systems and Knowledge Discovery,2008,289～293
[36]李士勇.模糊控制和智能控制理论与应用[M].哈尔滨：哈尔滨工业大学出版社,1990
[37]李丽娟,赵英凯.基于控制规则调整的自适应模糊控制器[J].南京化工大学学报,2001,23(5)：49-52
[38]张平,苑明哲,王宏.大时滞系统自适应模糊Smith控制[J].计算机仿真,2006,23(9)：87-90
[39]赵仕俊,李逊,左光远.灰色预估神经网络在时滞系统控制中的应用[J].微电子学与计算机,2008,25(2)：25-27
[40]黄越洋,石元博,张茜.基于神经网络Smith预估器的预测控制[J].计算机仿真,2009,26(2)：187-189
[41]李界家,邓媛媛,郭宏伟,等.基于神经网络预测的铝电解模糊控制系统应用[J].沈阳建筑大学学报(自然科学版),2008,24(4)：721-723
[42]沈洁,刘贺平,许鸣珠.一种基于模糊RBF神经网络的Smith预估器[J].控制工程,2006,14(1)：45-48
[43]MPCE-1000实验单元工艺说明[M].北京：北京化工大学,2007
[44]MPCE-1000实验软件使用说明[M].北京：北京化工大学,2007
[45]武汉大学.化学工程基础[M].北京：高等教育出版社,2001
[46]柯益华.系统辨识[M].四川：四川大学出版社,2003：5-7
[47]杜庆华.基于Deltav实验系统的过程建模与仿真[D].哈尔滨：哈尔滨工业大学,2006
[48]罗雄麟.化工过程动态学[M].北京：化学工业出版社,2005：9-13
[49]庞中华,崔红.系统辨识与自适应控制MATLAB仿真[M].北京：北京航空航天大学出版社,2009：20-30
[50]赵文杰,刘吉臻,金秀章,等.时滞工业对象的一种建模与控制方法[J].华北电力大学学报,2001,28(4)：29-32
[51]吴重光.系统建模与仿真[M].北京：清华大学出版社,2008
[52]Wang Qing-guo, Guo Xin, Zhang Yong. Direct identification of continuous time delay systems from step responses[J].Journal of Process Control,2001,11: 531～542
[53]SALIM A, BIAO H, SIRISH L S.Novel identification method from step response[J]. Control Engineering Practice,2007,15:545～556
[54]GIUSEPPE F. A new method to estimate a first-order plus time delay model from step response[J].Journal of the Franklin Institute,2009,346:1-9
[55]孙滨.优化模糊PID-Smith控制系统仿真研究[D].阜新：辽宁工程技术大学,2006
[56]Zhang Tao, Li Yuan-chun. A fuzzy Smith control of time-varying delay systems based on time delay identification[C].Proceedings of the Second International Conference on Machine Learning and Cybernetics,2003,2(5):614～619
[57]崔玉萍.一种基于时滞辨识的模糊Smith控制[J].电光与控制,2005,12(4)：71-75
[58]王舰,强文义,麻亮,等.一种改进的模糊Smith预估器[J].控制工程,2005,12(4)：331-334
[59]陈宇杰.时滞系统的Smith预估控制研究[D].杭州：浙江大学,2006
[60]潘峰,韩如成.时变大时滞系统的控制方法综述[J].仪器仪表学报,2002,23(3)：789-791
[61]宋云霞.大时滞过程控制策略与方法的研究[D].广州：华南理工大学,2002
[62]冯树臣.模型参考自适应控制研究及在火电厂再热汽温系统中的应用[D].北京：华北电力大学,2000
[63]王华强,李海波,胡平.一种改进的自适应Smith预估器[J].合肥工业大学学报(自然科学版),2007,30(3)：316-318
[64]LINDEMANN M, RAETHJEN J, TIMMER J, et al. Delay estimation for cortico-peripheral Relations[J].Journal of Neuroscience Methods,2001,111: 127～139
[65]DRAKUNOV S V, PERRUQUETTI W, RICHARD J P, et al.Delay identification in time-delay systems using variable structure observers[J].Annual Reviews in Control,2006,30:143～158
[66]BRUNO E, PIERRE C.Estimation of time delays between unknown colored signals[J].Signal Processing,1998,69:93～100
[2]朱珍.模糊控制在换热器过程控制中的应用[D].重庆：重庆大学,2003
[3]程林,杨培毅,陆煜.换热器运行导论[M].北京：石油工业出版社,1995：1-46
[4]冯立川.模糊-串级控制在换热器温度控制系统中的应用[D].北京：北京化工大学,2007
[5]张亚萌.智能模糊控制技术在换热器控制系统中的应用[D].天津：天津大学,2005
[6]冷伟,房德山,徐治皋,等.对单相换热器集总参数模型动态初始负偏移的机理分析[J].热能动力工程,2005,16(93)：287-289
[7]周文明,孙文,陈芝久.用于控制的制冷系统稳态建模与仿真[J].上海交通大学学报,1999,33(3)：255-258
[8]王伟,马最良,姚杨.空气源热泵机稳态仿真数学模型的建立与实验验证[J].流体机械,2006,34(5)：52-56
[9]马进,王兵树,马良玉,等.管式换热器动态数学模型的跷跷板效应分析[J].系统仿真学报,2006,18(5)：1105-1107
[10]周少祥,胡三高,宋之平.管式换热器分布参数模型的分段线性化方法研究[J].中国电机工程学报,2002,22(6)：123-125
[11]从松波,袁璞,沈复.换热器动态模型的简化[J].石油炼制与化工,1996,27(10)：5-8
[12]ISLAMONGLU Y. A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger-use of an artificial neural network model[J]. Applied Thermal Engineering,2003,23:243～249
[13]PACHCCO-VEGA A, SEN M, YANG K T, et al.Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data[J].Heat and Mass Tranfer,2001,44:763～770
[14]李强,张月军,林颐清.换热器冷媒出口温度自适应预测控制模拟研究[J].石油化工设计,2007,24(2)：33-37
[15]郭敏,韩新奎,周寒军.基于神经网络的热工过程动态建模[J].节能技术,2005,(4)：321-323
[16]胡饮华,李奎山,苏廷弼.基于ANN的空气处理单元换热器动态性能预测[J]. 计算机工程与应用,2008,44(3)：240-241
[17]ROPPO M N, GANIC E N. Time-dependent heat exchanger modeling[J].Heat Transfer Engng,1983,4(2):42～46
[18]CORREA D J,MARCHETTI J L. Dynamic simulation of shell-and-tube heat exchangers[J].Heat Transfer Engng,1987,8(1):50～59
[19]ROETZEL W, XUAN Y.Transient behavior of multipass shell-and-tube heat exchangers[J].International Journal of Heat and Mass Transfer,1992,35(3): 703～710
[20]Luo Xing, Guan Xin. Dynamic behavior of one-dimensional flow multistream heat exchangers and their networks[J].International Journal of Heat and Mass Transfer,2003,46:705～715
[21]解增忠,张俊峰,罗雄麟,等.管壳换热器模型库及在换热网络仿真中的应用[J].系统仿真学报,2005,17(12)：2882-2887
[22]SMITH O J M. Close control of loops with dead time[J].Chemical Engineering Progress Transaction,1957,53(5):217～219
[23]IBRAHIM K. A new Smith predictor and controller for control of processes with long dead time[J].ISA Transactions,2003,42,101～110
[24]JULIO E. N, EDUARDO F C.Dead-time compensators:A survey[J].Control Engineering Practice,2008,16:407～428
[25]HANG C C.A performance study of control system with dead time[J]. IEEE, IECI,1980,27:234～241
[26]WATANABE K. A process-model control for linear system with delay[J].IEEE Transactions,1981,(6):1261～1269
[27]GILES R F, BARTLEY T M. Gain-adaptive dead time compensation[J].ISA Transactions,1977,(1):46～49
[28]张美玉,齐家国.动态矩阵控制算法在电热炉温控制中的实现[J].浙江工业大学学报,2002,30(4)：360-365
[29]郑辑光,施仁,王孟效.纸机先进控制算法研究[J].西安交通大学学报,2001,35(12)：1271-1274
[30]王艳经,韩光胜.烟草烘丝过程的水分控制[J].北京工业大学学报,2000,26(4)：90-95
[31]鲁照权,韩江洪.一种新型增益自适应Smith预估器[J].仪器仪表学报,2002,23(2)：195-196
[32]鲁照权,韩江洪.对象参考参数自适应时滞补偿器[J].控制与决策,2001, 16(2)：239-241
[33]刘翠玲,伞冶.一种改进的时滞系统控制器的设计[J].电机与控制学报,2000,4(1)：39-42
[34]Gao Wei, Li Yuan-chun, Liu Guang-jun. An adaptive fuzzy Smith Control of time-varying processes with dominant and variable delay[C].Proceedings of the American Control Conference Denver, Colorado,2003,18(6):4-6
[35]Xia Chan-liang, Gao Geng-ming. Brushless DC motors control based on Smith predictor modified by fuzzy-PI controller[C].Fifth International Conference on Fuzzy Systems and Knowledge Discovery,2008,289～293
[36]李士勇.模糊控制和智能控制理论与应用[M].哈尔滨：哈尔滨工业大学出版社,1990
[37]李丽娟,赵英凯.基于控制规则调整的自适应模糊控制器[J].南京化工大学学报,2001,23(5)：49-52
[38]张平,苑明哲,王宏.大时滞系统自适应模糊Smith控制[J].计算机仿真,2006,23(9)：87-90
[39]赵仕俊,李逊,左光远.灰色预估神经网络在时滞系统控制中的应用[J].微电子学与计算机,2008,25(2)：25-27
[40]黄越洋,石元博,张茜.基于神经网络Smith预估器的预测控制[J].计算机仿真,2009,26(2)：187-189
[41]李界家,邓媛媛,郭宏伟,等.基于神经网络预测的铝电解模糊控制系统应用[J].沈阳建筑大学学报(自然科学版),2008,24(4)：721-723
[42]沈洁,刘贺平,许鸣珠.一种基于模糊RBF神经网络的Smith预估器[J].控制工程,2006,14(1)：45-48
[43]MPCE-1000实验单元工艺说明[M].北京：北京化工大学,2007
[44]MPCE-1000实验软件使用说明[M].北京：北京化工大学,2007
[45]武汉大学.化学工程基础[M].北京：高等教育出版社,2001
[46]柯益华.系统辨识[M].四川：四川大学出版社,2003：5-7
[47]杜庆华.基于Deltav实验系统的过程建模与仿真[D].哈尔滨：哈尔滨工业大学,2006
[48]罗雄麟.化工过程动态学[M].北京：化学工业出版社,2005：9-13
[49]庞中华,崔红.系统辨识与自适应控制MATLAB仿真[M].北京：北京航空航天大学出版社,2009：20-30
[50]赵文杰,刘吉臻,金秀章,等.时滞工业对象的一种建模与控制方法[J].华北电力大学学报,2001,28(4)：29-32
[51]吴重光.系统建模与仿真[M].北京：清华大学出版社,2008
[52]Wang Qing-guo, Guo Xin, Zhang Yong. Direct identification of continuous time delay systems from step responses[J].Journal of Process Control,2001,11: 531～542
[53]SALIM A, BIAO H, SIRISH L S.Novel identification method from step response[J]. Control Engineering Practice,2007,15:545～556
[54]GIUSEPPE F. A new method to estimate a first-order plus time delay model from step response[J].Journal of the Franklin Institute,2009,346:1-9
[55]孙滨.优化模糊PID-Smith控制系统仿真研究[D].阜新：辽宁工程技术大学,2006
[56]Zhang Tao, Li Yuan-chun. A fuzzy Smith control of time-varying delay systems based on time delay identification[C].Proceedings of the Second International Conference on Machine Learning and Cybernetics,2003,2(5):614～619
[57]崔玉萍.一种基于时滞辨识的模糊Smith控制[J].电光与控制,2005,12(4)：71-75
[58]王舰,强文义,麻亮,等.一种改进的模糊Smith预估器[J].控制工程,2005,12(4)：331-334
[59]陈宇杰.时滞系统的Smith预估控制研究[D].杭州：浙江大学,2006
[60]潘峰,韩如成.时变大时滞系统的控制方法综述[J].仪器仪表学报,2002,23(3)：789-791
[61]宋云霞.大时滞过程控制策略与方法的研究[D].广州：华南理工大学,2002
[62]冯树臣.模型参考自适应控制研究及在火电厂再热汽温系统中的应用[D].北京：华北电力大学,2000
[63]王华强,李海波,胡平.一种改进的自适应Smith预估器[J].合肥工业大学学报(自然科学版),2007,30(3)：316-318
[64]LINDEMANN M, RAETHJEN J, TIMMER J, et al. Delay estimation for cortico-peripheral Relations[J].Journal of Neuroscience Methods,2001,111: 127～139
[65]DRAKUNOV S V, PERRUQUETTI W, RICHARD J P, et al.Delay identification in time-delay systems using variable structure observers[J].Annual Reviews in Control,2006,30:143～158
[66]BRUNO E, PIERRE C.Estimation of time delays between unknown colored signals[J].Signal Processing,1998,69:93～100