Neural network based explicit MPC for chemical reactor control

IF 0.9 Q4 CHEMISTRY, MULTIDISCIPLINARY
Karol Kiš, Martin Klauco
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引用次数: 9

Abstract

Abstract In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical reaction takes place.
基于神经网络的显式MPC化学反应器控制
本文介绍了深度神经网络在过程控制中的应用。在我们的方法中,神经网络的训练是基于模型预测控制的,这是受欢迎的,因为它能够通过加权矩阵进行调整,并且尊重系统约束。引入了一种神经网络,通过模拟控制输入轨迹来近似MPC行为,同时状态约束和控制输入约束不受加权矩阵的影响。该方法在一个涉及多组分化学反应发生的连续搅拌槽式反应器的模拟案例研究中得到了证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Acta Chimica Slovaca
Acta Chimica Slovaca CHEMISTRY, MULTIDISCIPLINARY-
自引率
12.50%
发文量
11
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