环管式丙烯本体聚合反应过程建模、控制与优化研究
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摘要
聚丙烯产品与人们的生产生活息息相关,不少专家学者从聚丙烯的反应机理、工艺流程、催化剂开发等方面对聚丙烯生产过程进行了深入研究,但是到目前为止,对丙烯聚合反应过程,尤其在新工艺基础上的先进控制与优化算法及策略的研究相对较少,国内公开发表和应用的成果有限,而这方面的研究同样可以给国内聚丙烯工业的发展提供较大的突破空间。因此,在前人研究的基础上,本学位论文针对环管工艺丙烯本体聚合反应过程的建模、优化、控制方面展开了较为深入和广泛的研究,取得了相应的研究成果。本文的主要研究工作概括如下：
1.基于Spheripol环管二代工艺,引入环管反应器非理想特性的思想,并参考实际过程操作数据和工艺参数,建立了非理想环管式丙烯本体聚合反应过程的动态机理数学模型和质量物性模型,及对模型进行了特性分析,这为丙烯聚合过程质量指标建模、非线性预测控制方案设计和聚丙烯产品牌号切换优化控制策略的研究提供了可靠的基准(Benchmark)模型和理论支撑。
2.针对环管式聚丙烯生产过程的熔融指数质量预报,通过充分发掘和利用聚丙烯工业过程先验知识,并将各种先验知识有机融合,以非线性约束的形式嵌入到前向神经网络中,提出了一种过程多先验知识神经网络软测量模型。同时基于增广拉格朗日乘子法约束处理机制,提出了自适应PSO-SQP算法优化网络权值。该模型不仅有良好的拟合预测能力,而且能避免出现零增益和增益反转,确保模型在实际应用中的安全性能。此外,将过程多先验知识神经网络模型与聚丙烯熔融指数简化机理模型有机结合为调和平均混合模型,增强了模型外推能力,实现模型外推性和对熔融指数预测精度的有机统一。同时还提出用归一化互信息方法对软测量模型的辅助变量和主导变量进行时延估计,较大程度上提升了模型预测的精度。
3.针对环管聚丙烯生产过程装置多变量、耦合和非线性特性等导致过程不稳定和质量指标波动问题,提出了基于MSSARX-PWL (wiener)模型结构的非线性预测控制算法。利用改进的闭环子空间辨识方法(MSSARX)辨识对象在闭环工况下的线性状态空间模型,同时将线性模型与多变量PWL方法辨识得到的非线性稳态模型结合建立双环管丙烯聚合反应动态过程的非线性预测模型,并将非线性模型反转为线性模型,在线性预测控制算法框架下用二次线性规划方法(QP)优化控制器,而无须用非线性规划方法(NLP)求解。该算法在保证模型和控制精度的同时,提高了计算效率。
4.针对双环管式丙烯本体聚合牌号切换生产过程,提出了一种多牌号切换轨迹优化模型及切换策略。该轨迹优化模型综合考虑了牌号切换过程的经济收益和装置稳定性,在此模型基础上,基于一种变时间尺度控制向量参数化方法(VTS-CVP)和内点优化方法(Interior-Point Optimization, IPOPT)求解动态优化问题,能同时优化控制参数和时间节点,大大减少了牌号切换时间和过渡料的耗费。
5.针对在过程不确定因素和扰动的影响下,聚丙烯牌号切换过程实际对象输出与上层离线优化参考轨迹出现偏差而影响生产的问题,提出了一种双层递阶结构聚丙烯牌号切换优化和控制联立策略。其中,上层的切换轨迹采用变尺度控制向量参数化方法实现动态优化,下层的跟踪控制器基于MSSARX-PWL (wiener-type)模型预测控制器,对上层的切换优化参考轨迹进行快速跟踪及克服过程高频扰动。此外,还引入了轨迹偏差检测和在线更新最优轨迹机制,保证了整个切换过程平稳过渡。
Polypropylene products are closely related to people's production and life, many experts and scholars have already focused on the improvements of propylene polymerization process, such as polypropylene reaction mechanism, process and catalyst development, reaction device and so on. However, on the other hand, published fruits about modeling, control and optimization of advanced control/optimization algorithms and strategies for polypropylene production process are relatively few, especially in China, which can also contribute to the industrial polypropylene production in this respect. Therefore, on the basis of previous studies, this thesis pays more attention on modeling, optimization, and control strategies for double-loop propylene bulk polymerization process. The main research work is summarized as follows:
1. Dynamic mechanism mathematical model and quality properties model of non-ideal loop reactor propylene bulk polymerization process are constructed by the introduction of the non-ideal characteristics, as well as referred to the actual process operating data and process parameters.Then, dynamic and steady state characteristics are analyzed based on this model. The model developed may provide a reliable benchmark model and guiding role for modeling of quality indicators of propylene polymerization process, designing of nonlinear model predictive control scheme and product grade transition optimization.
2. Melt index inferential model plays an important role in the control and optimization of polypropylene production, a novel multiple-priori-knowledge based neural network (MPKNN) inferential model for melt index prediction is developed. The prior knowledge from the industrial propylene polymerization process is fully exploited and embedded into the construction of multi-layer perceptron neural network in the form of nonlinear constraints. Meanwhile, an adaptive PSO-SQP (Particle Swarm Optimization-Sequential Quadratics Programming) is proposed to optimize the network weights. The proposed MPKNN model has good fitting and prediction ability. Meanwhile, it can avoid unwanted zero value and wrong signal of the model gains. By embedding priori knowledge, the model ensures the safety in the quality control of Melt Index. In addition, a hybrid model combining the MPKNN model with a simplified mechanism model is proposed to enhance the extrapolation capability. A normalized mutual information method is employed to estimate the delay between independent variables and dependent variables.
3. A nonlinear model predictive control algorithm based on MSSARX-PWL (wiener type) model is proposed for loop polypropylene production process with multivariale, coupled and unstable nonlinear characteristics. The MSSARX-PWL model structure, in which linear state space model under the closed-loop conditions is identified by the improved closed-loop subspace identification method (MSSARX), combined with the nonlinear steady-state model identified by the multivariate PWL method, is established for the nonlinear predictive model of double loop propylene polymerization process. Furthermore, the non-linear model can be inversed to linear model that without Non-linear Programming methods (NLP) solver but only the linear Quadratic Programming (QP) optimization controller is needed. The algorithm proposed can not only guarantee the accuracy of model and control, but also improve the computational efficiency.
4. A multi-grade transition trajectory optimization model and transition strategy for the grade transition of double loop polypropylene bulk polymerization production process is developed. The trajectory optimization model took into account the economic benefits and plant stability during the grade transition process. Meanwhile, a variant time scale based control vector parametric methods (VTS-CVP) and (Interior-Point Optimization, IPOPT)algorithm, which can optimize the control parameters and time node together, are both used for solving dynamic optimization problem, that can greatly reduce the grade transition time and material consuming.
5. A two-layer hierarchical structure and strategy of optimization and control for polypropylene grade transition is raised to overcome process uncertainties and disturbances that lead to the deviation between the open-loop reference trajectory and the actual process. In the upper layer, the variant time scale based control vector parametric methods (VTS-CVP) is used for dynamic optimization of transition trajectory, while tracking controller based on MSSARX-PWL (wiener-type) model predictive controller in the lower layer is tracking fast to the reference trajectory from the upper layer and overcome high-frequency disturbances. Besides, a mechanism about trajectory deviation detection and optimal trajectory updating online are introduced to ensure a smooth transition for the entire process.
1.基于Spheripol环管二代工艺,引入环管反应器非理想特性的思想,并参考实际过程操作数据和工艺参数,建立了非理想环管式丙烯本体聚合反应过程的动态机理数学模型和质量物性模型,及对模型进行了特性分析,这为丙烯聚合过程质量指标建模、非线性预测控制方案设计和聚丙烯产品牌号切换优化控制策略的研究提供了可靠的基准(Benchmark)模型和理论支撑。
2.针对环管式聚丙烯生产过程的熔融指数质量预报,通过充分发掘和利用聚丙烯工业过程先验知识,并将各种先验知识有机融合,以非线性约束的形式嵌入到前向神经网络中,提出了一种过程多先验知识神经网络软测量模型。同时基于增广拉格朗日乘子法约束处理机制,提出了自适应PSO-SQP算法优化网络权值。该模型不仅有良好的拟合预测能力,而且能避免出现零增益和增益反转,确保模型在实际应用中的安全性能。此外,将过程多先验知识神经网络模型与聚丙烯熔融指数简化机理模型有机结合为调和平均混合模型,增强了模型外推能力,实现模型外推性和对熔融指数预测精度的有机统一。同时还提出用归一化互信息方法对软测量模型的辅助变量和主导变量进行时延估计,较大程度上提升了模型预测的精度。
3.针对环管聚丙烯生产过程装置多变量、耦合和非线性特性等导致过程不稳定和质量指标波动问题,提出了基于MSSARX-PWL (wiener)模型结构的非线性预测控制算法。利用改进的闭环子空间辨识方法(MSSARX)辨识对象在闭环工况下的线性状态空间模型,同时将线性模型与多变量PWL方法辨识得到的非线性稳态模型结合建立双环管丙烯聚合反应动态过程的非线性预测模型,并将非线性模型反转为线性模型,在线性预测控制算法框架下用二次线性规划方法(QP)优化控制器,而无须用非线性规划方法(NLP)求解。该算法在保证模型和控制精度的同时,提高了计算效率。
4.针对双环管式丙烯本体聚合牌号切换生产过程,提出了一种多牌号切换轨迹优化模型及切换策略。该轨迹优化模型综合考虑了牌号切换过程的经济收益和装置稳定性,在此模型基础上,基于一种变时间尺度控制向量参数化方法(VTS-CVP)和内点优化方法(Interior-Point Optimization, IPOPT)求解动态优化问题,能同时优化控制参数和时间节点,大大减少了牌号切换时间和过渡料的耗费。
5.针对在过程不确定因素和扰动的影响下,聚丙烯牌号切换过程实际对象输出与上层离线优化参考轨迹出现偏差而影响生产的问题,提出了一种双层递阶结构聚丙烯牌号切换优化和控制联立策略。其中,上层的切换轨迹采用变尺度控制向量参数化方法实现动态优化,下层的跟踪控制器基于MSSARX-PWL (wiener-type)模型预测控制器,对上层的切换优化参考轨迹进行快速跟踪及克服过程高频扰动。此外,还引入了轨迹偏差检测和在线更新最优轨迹机制,保证了整个切换过程平稳过渡。
Polypropylene products are closely related to people's production and life, many experts and scholars have already focused on the improvements of propylene polymerization process, such as polypropylene reaction mechanism, process and catalyst development, reaction device and so on. However, on the other hand, published fruits about modeling, control and optimization of advanced control/optimization algorithms and strategies for polypropylene production process are relatively few, especially in China, which can also contribute to the industrial polypropylene production in this respect. Therefore, on the basis of previous studies, this thesis pays more attention on modeling, optimization, and control strategies for double-loop propylene bulk polymerization process. The main research work is summarized as follows:
1. Dynamic mechanism mathematical model and quality properties model of non-ideal loop reactor propylene bulk polymerization process are constructed by the introduction of the non-ideal characteristics, as well as referred to the actual process operating data and process parameters.Then, dynamic and steady state characteristics are analyzed based on this model. The model developed may provide a reliable benchmark model and guiding role for modeling of quality indicators of propylene polymerization process, designing of nonlinear model predictive control scheme and product grade transition optimization.
2. Melt index inferential model plays an important role in the control and optimization of polypropylene production, a novel multiple-priori-knowledge based neural network (MPKNN) inferential model for melt index prediction is developed. The prior knowledge from the industrial propylene polymerization process is fully exploited and embedded into the construction of multi-layer perceptron neural network in the form of nonlinear constraints. Meanwhile, an adaptive PSO-SQP (Particle Swarm Optimization-Sequential Quadratics Programming) is proposed to optimize the network weights. The proposed MPKNN model has good fitting and prediction ability. Meanwhile, it can avoid unwanted zero value and wrong signal of the model gains. By embedding priori knowledge, the model ensures the safety in the quality control of Melt Index. In addition, a hybrid model combining the MPKNN model with a simplified mechanism model is proposed to enhance the extrapolation capability. A normalized mutual information method is employed to estimate the delay between independent variables and dependent variables.
3. A nonlinear model predictive control algorithm based on MSSARX-PWL (wiener type) model is proposed for loop polypropylene production process with multivariale, coupled and unstable nonlinear characteristics. The MSSARX-PWL model structure, in which linear state space model under the closed-loop conditions is identified by the improved closed-loop subspace identification method (MSSARX), combined with the nonlinear steady-state model identified by the multivariate PWL method, is established for the nonlinear predictive model of double loop propylene polymerization process. Furthermore, the non-linear model can be inversed to linear model that without Non-linear Programming methods (NLP) solver but only the linear Quadratic Programming (QP) optimization controller is needed. The algorithm proposed can not only guarantee the accuracy of model and control, but also improve the computational efficiency.
4. A multi-grade transition trajectory optimization model and transition strategy for the grade transition of double loop polypropylene bulk polymerization production process is developed. The trajectory optimization model took into account the economic benefits and plant stability during the grade transition process. Meanwhile, a variant time scale based control vector parametric methods (VTS-CVP) and (Interior-Point Optimization, IPOPT)algorithm, which can optimize the control parameters and time node together, are both used for solving dynamic optimization problem, that can greatly reduce the grade transition time and material consuming.
5. A two-layer hierarchical structure and strategy of optimization and control for polypropylene grade transition is raised to overcome process uncertainties and disturbances that lead to the deviation between the open-loop reference trajectory and the actual process. In the upper layer, the variant time scale based control vector parametric methods (VTS-CVP) is used for dynamic optimization of transition trajectory, while tracking controller based on MSSARX-PWL (wiener-type) model predictive controller in the lower layer is tracking fast to the reference trajectory from the upper layer and overcome high-frequency disturbances. Besides, a mechanism about trajectory deviation detection and optimal trajectory updating online are introduced to ensure a smooth transition for the entire process.
引文
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