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Modeling and control of a continuous crystallization process using neural networks and model predictive control.

机译：使用神经网络和模型预测控制对连续结晶过程进行建模和控制。

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Continuous crystallizers are distributed dynamical systems. Physical modeling of these systems using basic principles results in partial and integro-differential equations. To exploit the physical models, in the analysis of the system behavior and the design of an appropriate controller, requires complicated measurement techniques especially in the spatial domain (crystal size distribution or crystal population density). Therefore, obtaining a lumped model structure is desirable. The lumped model of a continuous crystallizer can be obtained either from the physical model, using conventional techniques such as the discretization or function separation methods, or from input and output measurements using system identification approaches.; Studies of the crystallization process have indicated that in order to improve the control performance, expressing the process dynamics using single-input, single-output models is insufficient. The aim of this thesis was to investigate the process behavior in a multivariable framework. In this regard, the dynamics of a continuous cooling KCl crystallizer were identified using three-input, three-output linear and nonlinear model structures. The autoregressive exogenous model structures were employed in linear modeling of the process. The nonlinear modeling was performed using several architectures of feedforward and recurrent neural networks. Simulation results demonstrated that the linear modeling, using a single model for the entire dynamics, is not adequate. Either multi-model or nonlinear modeling is recommended. The performance of different neural network structures in the nonlinear modeling of the process was illustrated and, based on the results, some comparisons were made between these networks.; The next step in the study of the crystallization process as a multivariable system was to design and apply a multivariable control scheme. Simulation results from the modeling of the process indicated that strong interactions are present among different loops of the system. The process is nonlinear and some of the outputs exhibit inverse or non-minimum-phase responses. The model predictive control strategy is known to perform well in the control of the systems with the behaviors found in the crystallization process. To ensure a feasible solution, the feasible sequential quadratic optimization algorithm was successfully exploited in a model predictive controller. Computer simulations of the controller were performed in order to demonstrate control of the crystal size distribution, crystal purity, and production rate. The effects of different control parameters were illustrated using the simulation results. A brief discussion on how to select these parameters was also provided. Robustness of the model predictive controller was studied in the presence of mismatch between the model and the process.
机译：连续结晶器是分布式动力学系统。使用基本原理对这些系统进行物理建模会产生偏微分方程和积分微分方程。为了利用物理模型，在系统行为分析和适当控制器的设计中，需要复杂的测量技术，尤其是在空间域（晶体尺寸分布或晶体种群密度）中。因此，期望获得集总模型结构。连续结晶器的集总模型可以使用常规技术（例如离散化或函数分离方法）从物理模型获得，或者使用系统识别方法从输入和输出测量中获得。对结晶过程的研究表明，为了提高控制性能，使用单输入单输出模型来表示过程动力学是不够的。本文的目的是研究多变量框架下的过程行为。在这方面，使用三输入，三输出线性和非线性模型结构确定了连续冷却KCl结晶器的动力学。自回归外生模型结构用于过程的线性建模。非线性建模是使用前馈和递归神经网络的几种架构进行的。仿真结果表明，对整个动力学使用单个模型进行线性建模是不够的。建议使用多模型或非线性模型。说明了在过程的非线性建模中不同神经网络结构的性能，并根据结果对这些神经网络进行了一些比较。作为多变量系统研究结晶过程的下一步是设计和应用多变量控制方案。过程建模的仿真结果表明，系统的不同回路之间存在强大的交互作用。该过程是非线性的，某些输出表现出反相或非最小相位响应。已知模型预测控制策略在具有结晶过程中发现的行为的系统控制中表现良好。为了确保可行的解决方案，在模型预测控制器中成功开发了可行的序列二次优化算法。为了说明晶体尺寸分布，晶体纯度和生产率的控制，进行了控制器的计算机模拟。仿真结果说明了不同控制参数的影响。还提供了有关如何选择这些参数的简短讨论。在模型与过程之间存在不匹配的情况下，研究了模型预测控制器的鲁棒性。

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