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首页> 外文期刊>Brazilian journal of chemical engineering >Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms
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Modeling techniques and processes control application based on Neural Networks with on-line adjustment using Genetic Algorithms

机译：基于遗传算法的神经网络在线调整的建模技术和过程控制应用

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In this work a strategy is presented for the temperature control of the polymerization reaction of styrene in suspension in batch. A three-layer feed forward Artificial Neural Network was trained in an off-line way starting from a removed group of patterns of the experimental system and applied in the recurrent form (RNN) to a Predictive Controller based on a Nonlinear Model (NMPC). This controller presented very superior results to the classic controller PID in the maintenance of the temperature. Still to improve the performance of the model used by NMPC (RNN) that can present differences in relation to the system due to the dead time involved in the control actions, nonlinear characteristic of the system and variable dynamics; an on-line adjustment methodology of the parameters of the exit layer of the Network is implemented, presenting superior results and treating the difficulties satisfactorily in the temperature control. All the presented results are obtained for a real system.
机译：在这项工作中，提出了一种策略，用于控制悬浮液中苯乙烯的聚合反应的温度。从离线的一组实验系统模式开始，以离线方式对三层前馈人工神经网络进行了训练，并将其以递归形式（RNN）应用于基于非线性模型（NMPC）的预测控制器。与传统的PID控制器相比，该控制器在维持温度方面表现出非常优异的效果。仍要改善NMPC（RNN）使用的模型的性能，该模型由于控制动作中涉及的停滞时间，系统的非线性特性和可变动力学而可能表现出与系统有关的差异;实施了网络出口层参数的在线调整方法，呈现出优异的结果，并令人满意地处理了温度控制中的困难。所有给出的结果都是在真实系统中获得的。

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