聚合反应过程的分布式模型预测控制策略研究
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摘要
聚合反应过程往往表现为强耦合性、不确定性、强非线性和动力学机理复杂等特征,且从生产工艺上一般由若干个子系统构成,子系统之问存在着复杂的关联。模型预测控制由于其对生产中各种约束的有效处理能力,已被广泛地应用在聚合反应过程中。随着计算机网络的迅速发展,预测控制作为一种信息处理手段已不限于集中式的控制,而更多地为分布式控制所取代,这就给传统的控制问题带来了新的挑战和要求。本文以大型聚合反应过程的动态优化控制系统为背景,研究了聚合反应动态特性以及分布式模型预测控制器设计等问题,主要研究成果如下：
以聚苯乙烯反应过程为例分析了聚合过程的反应动力学机理,讨论了聚合反应过程中的重要被控变量,研究了聚苯乙烯本体热聚合过程模型,分析讨论了苯乙烯在连续搅拌釜式反应器中进行热引发本体聚合行为。通过仿真实验分析了聚合反应的多稳态性、可行温度操作区域及聚合反应器温度与聚合产物的数均分子量(NAMW)之间的关系。结果表明,聚合化工反应是一种复杂的化学反应过程,参数的改变可能会导致聚合反应产生多稳态现象及达到不可执行区域。聚合反应器温度决定了代表产物质量的数均分子量及其分布,它是聚合反应过程中的至关重要的操作条件。
研究了大规模系统的典型预测控制方法,针对聚合反应过程的牌号切换问题,给出了一种分布式模型预测控制算法,该算法建立了基于目标跟踪的子系统性能指标具体形式,该子系统性能优化指标充分考虑了其它子系统目标函数产生的关联影响,并采用组合状态空间模型描述子系统特性,详细推导了控制律求解过程,给出了带约束关联优化性能指标二次规划的解析形式。最后将该算法应用到苯乙烯聚合反应过程中,并与多种预测控制策略作比较,充分验证了算法的有效性,为分布式预测控制策略在聚合反应过程牌号切换问题上的应用做出了重要贡献。
针对一类仅使用大批历史数据结构未知的非线性工业过程,基于数据驱动及局部建模的基本思想,提出一种基于局部模型算法的在线多模型辨识策略。给出了一种对初始值的选取不敏感的K-Harmonic Means(KHM)聚类数据库搜索策略,该策略简单可行,可缩短搜索时间,提高搜索效率。从向量相似的角度提出了一种新的选择数据信息(即建模邻域Qk(x)的确定)的方法,有效提高了获得当前时刻系统最佳局部模型的数据精确度。给出了权值选定的适合度标准及带宽h选择的快速方法。最后对算法进行了特性分析及仿真研究,并与其它局部建模算法的计算结果作了比较,验证了本文辨识算法的有效性。
针对一类系统结构未知的聚合反应过程,将本文的局部模型在线辨识算法与过程控制相结合,提出了一种多模型分布式模型预测控制算法。在每一时刻,首先得到当前工况下的参数模型形式,提出一种模型结构变换及结构重组的方法,分离出各子系统的内部信息及与其它子系统间的关联信息,并将子系统状态向量进行扩维处理,得到包含全局信息的子系统状态空间组合模型,建立了多模型分布式模型预测控制器设计的全过程。该算法可在线实时更新过程模型参数,简单可行,不仅解决了一类结构未知的聚合反应过程中的分布式预测控制问题,还弥补了现有分布式模型预测控制中采用单一全局线性模型,不能充分表述非线性系统特性的不足。最后通过在聚合反应过程中的应用,说明本文算法可以使系统动态特性得到明显改善。
对于存在过程干扰和量测噪声的非线性有约束系统,研究了能有效处理约束的滚动时域估计(Moving Horizon Estimation, MHE)方法。根据分离性原则,将滚动时域估计方法与分布式模型预测控制相结合,设计了基于MHE的分布式模型预测控制器,给出了控制器设计的主要部分,预测控制器性能优劣直接依赖于对系统关键状态估计的准确性。最后将基于MHE的分布式模型预测控制应用到聚合反应过程中,充分说明了该算法的有效性。
Polymerization reactions are nonlinear systems which have the characteristics of strong coupling, uncertainty and complicated dynamic mechanism. They are always consisted of many subsystems which have complicated association. Model predictive control strategies can deal with various kinds of constraints effectively, they are used in polymerization reactions widely. With the rapid development of computer network, model predictive control strategies are not limited to the centralized control, they are substituted by distributed control more and more, all this bring new challenge to conventional control problems. Based on large scale polymerization reactions, this thesis has studied dynamic characteristics of polymerization and the design of distributed model predictive control, the main achievements as following:
●The dynamic mechanism of polymerization reaction is analyzed taking styrene polymerization for example. The important controlled variables are discussed. The model of thermal bulk polymerization of styrene is given and reactions in CSTR are analyzed. The analysis of the steady-state multiplicity behavior is presented by simulation. The region of feasible temperature operation, the relationship between reactor temperature and number average molecular weight (NAMW) are discussed. The results show the complexity of polymerization reaction, multiple steady-state and infeasible region may occur with the change of parameters. The temperature of reactor determines NAMW and its distribution of polymer, it is the important operating factor in polymerization.
●The typical model predictive control methods of large scale system are studied. A distributed model predictive control strategy is proposed for grade transition in polymerization reaction. Setting up the specific performance index based on target following, the index including effect of other subsystems, the characteristic of subsystems are described by combined model and the detailed derivations of control law are given. The analytical form of constraint quadratic programming is given. Finally, the performance comparison with many other predictive control strategies is given to illustrate the effectiveness of the proposed approach by the application on styrene polymerization reaction. The approach makes important contribution to the application of distributed model predictive control on polymer grade transition.
●The on-line multiple models identification strategy is presented based on data-driven and local-modeling for a class of unknown-structure nonlinear systems just on the basis of numerous historical database. A database searching strategy based on KHM clustering is presented which insensitive to initial value. The approach is easy and feasible, it can shorten searching time and improve searching efficiency. A new approach to determine data information (determine the neighborhoodΩk (x)) is proposed, it can improve precision of the data which used to get optimal local model. The goodness of fit criterion and rapid selection method to determine bandwidth (h) are given. Finally, characteristic analysis and simulation studies were done, the simulation test illustrates the validity of this approach.
●Combining the multiple models thus developed with predictive control, the multi-model distributed cooperative predictive control strategy is proposed, thus solving the control problem of a class of unknown-structure polymerization reaction. At every moment, after getting the parameter model, using a model structure transition and structure recombination method, to get the internal information of subsystems and the interacted information with other subsystems. Then expand the dimension of state vector to get combined model which includes information of other subsystems. The all design process of multi-model cooperative model predictive control strategy is given. This approach can update model parameter on line easily and feasibly, it not only solve the distributed predictive control problem of a class of unknown-structure polymerization reaction, but also make up for the defective of sole overall linear model which can't express nonlinear characteristic well in existing distributed model predictive control. By the application on polymerization, this approach has illustrated obvious improved dynamic performance.
●When process disturbance and measurement noise exist for constraint nonlinear system, moving horizon estimation method is studied, which can deal with constraint effectively. Based on the principle of separation, combining moving horizon estimation with distributed model predictive control, the MHE distributed model predictive controller is designed. The main parts of controller are given. The precision of estimated state is important to controller performance. With the application on polymerization reaction, the simulation test illustrates the validity of this approach.
以聚苯乙烯反应过程为例分析了聚合过程的反应动力学机理,讨论了聚合反应过程中的重要被控变量,研究了聚苯乙烯本体热聚合过程模型,分析讨论了苯乙烯在连续搅拌釜式反应器中进行热引发本体聚合行为。通过仿真实验分析了聚合反应的多稳态性、可行温度操作区域及聚合反应器温度与聚合产物的数均分子量(NAMW)之间的关系。结果表明,聚合化工反应是一种复杂的化学反应过程,参数的改变可能会导致聚合反应产生多稳态现象及达到不可执行区域。聚合反应器温度决定了代表产物质量的数均分子量及其分布,它是聚合反应过程中的至关重要的操作条件。
研究了大规模系统的典型预测控制方法,针对聚合反应过程的牌号切换问题,给出了一种分布式模型预测控制算法,该算法建立了基于目标跟踪的子系统性能指标具体形式,该子系统性能优化指标充分考虑了其它子系统目标函数产生的关联影响,并采用组合状态空间模型描述子系统特性,详细推导了控制律求解过程,给出了带约束关联优化性能指标二次规划的解析形式。最后将该算法应用到苯乙烯聚合反应过程中,并与多种预测控制策略作比较,充分验证了算法的有效性,为分布式预测控制策略在聚合反应过程牌号切换问题上的应用做出了重要贡献。
针对一类仅使用大批历史数据结构未知的非线性工业过程,基于数据驱动及局部建模的基本思想,提出一种基于局部模型算法的在线多模型辨识策略。给出了一种对初始值的选取不敏感的K-Harmonic Means(KHM)聚类数据库搜索策略,该策略简单可行,可缩短搜索时间,提高搜索效率。从向量相似的角度提出了一种新的选择数据信息(即建模邻域Qk(x)的确定)的方法,有效提高了获得当前时刻系统最佳局部模型的数据精确度。给出了权值选定的适合度标准及带宽h选择的快速方法。最后对算法进行了特性分析及仿真研究,并与其它局部建模算法的计算结果作了比较,验证了本文辨识算法的有效性。
针对一类系统结构未知的聚合反应过程,将本文的局部模型在线辨识算法与过程控制相结合,提出了一种多模型分布式模型预测控制算法。在每一时刻,首先得到当前工况下的参数模型形式,提出一种模型结构变换及结构重组的方法,分离出各子系统的内部信息及与其它子系统间的关联信息,并将子系统状态向量进行扩维处理,得到包含全局信息的子系统状态空间组合模型,建立了多模型分布式模型预测控制器设计的全过程。该算法可在线实时更新过程模型参数,简单可行,不仅解决了一类结构未知的聚合反应过程中的分布式预测控制问题,还弥补了现有分布式模型预测控制中采用单一全局线性模型,不能充分表述非线性系统特性的不足。最后通过在聚合反应过程中的应用,说明本文算法可以使系统动态特性得到明显改善。
对于存在过程干扰和量测噪声的非线性有约束系统,研究了能有效处理约束的滚动时域估计(Moving Horizon Estimation, MHE)方法。根据分离性原则,将滚动时域估计方法与分布式模型预测控制相结合,设计了基于MHE的分布式模型预测控制器,给出了控制器设计的主要部分,预测控制器性能优劣直接依赖于对系统关键状态估计的准确性。最后将基于MHE的分布式模型预测控制应用到聚合反应过程中,充分说明了该算法的有效性。
Polymerization reactions are nonlinear systems which have the characteristics of strong coupling, uncertainty and complicated dynamic mechanism. They are always consisted of many subsystems which have complicated association. Model predictive control strategies can deal with various kinds of constraints effectively, they are used in polymerization reactions widely. With the rapid development of computer network, model predictive control strategies are not limited to the centralized control, they are substituted by distributed control more and more, all this bring new challenge to conventional control problems. Based on large scale polymerization reactions, this thesis has studied dynamic characteristics of polymerization and the design of distributed model predictive control, the main achievements as following:
●The dynamic mechanism of polymerization reaction is analyzed taking styrene polymerization for example. The important controlled variables are discussed. The model of thermal bulk polymerization of styrene is given and reactions in CSTR are analyzed. The analysis of the steady-state multiplicity behavior is presented by simulation. The region of feasible temperature operation, the relationship between reactor temperature and number average molecular weight (NAMW) are discussed. The results show the complexity of polymerization reaction, multiple steady-state and infeasible region may occur with the change of parameters. The temperature of reactor determines NAMW and its distribution of polymer, it is the important operating factor in polymerization.
●The typical model predictive control methods of large scale system are studied. A distributed model predictive control strategy is proposed for grade transition in polymerization reaction. Setting up the specific performance index based on target following, the index including effect of other subsystems, the characteristic of subsystems are described by combined model and the detailed derivations of control law are given. The analytical form of constraint quadratic programming is given. Finally, the performance comparison with many other predictive control strategies is given to illustrate the effectiveness of the proposed approach by the application on styrene polymerization reaction. The approach makes important contribution to the application of distributed model predictive control on polymer grade transition.
●The on-line multiple models identification strategy is presented based on data-driven and local-modeling for a class of unknown-structure nonlinear systems just on the basis of numerous historical database. A database searching strategy based on KHM clustering is presented which insensitive to initial value. The approach is easy and feasible, it can shorten searching time and improve searching efficiency. A new approach to determine data information (determine the neighborhoodΩk (x)) is proposed, it can improve precision of the data which used to get optimal local model. The goodness of fit criterion and rapid selection method to determine bandwidth (h) are given. Finally, characteristic analysis and simulation studies were done, the simulation test illustrates the validity of this approach.
●Combining the multiple models thus developed with predictive control, the multi-model distributed cooperative predictive control strategy is proposed, thus solving the control problem of a class of unknown-structure polymerization reaction. At every moment, after getting the parameter model, using a model structure transition and structure recombination method, to get the internal information of subsystems and the interacted information with other subsystems. Then expand the dimension of state vector to get combined model which includes information of other subsystems. The all design process of multi-model cooperative model predictive control strategy is given. This approach can update model parameter on line easily and feasibly, it not only solve the distributed predictive control problem of a class of unknown-structure polymerization reaction, but also make up for the defective of sole overall linear model which can't express nonlinear characteristic well in existing distributed model predictive control. By the application on polymerization, this approach has illustrated obvious improved dynamic performance.
●When process disturbance and measurement noise exist for constraint nonlinear system, moving horizon estimation method is studied, which can deal with constraint effectively. Based on the principle of separation, combining moving horizon estimation with distributed model predictive control, the MHE distributed model predictive controller is designed. The main parts of controller are given. The precision of estimated state is important to controller performance. With the application on polymerization reaction, the simulation test illustrates the validity of this approach.
引文
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