Neural network - based estimation of reaction rates with partly unknown states and completely known kinetics coefficients

P. Georgieva, S. de Azevedo
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Abstract

This work is focused on developing a more efficient computational scheme for estimation of process reaction rates based on NN models. In contrast to the traditional way of process reaction rates estimation by exhaustive and expensive search for the most appropriate parameterized structure, a neural network (NN) based procedure is proposed here to identify the reaction rates in the framework of an analytical process model. The reaction rates are not measured, therefore a special hybrid NN training structure and adaptation algorithm are proposed to make possible the supervised NN learning. The present contribution is focused on the general modelling of a class of nonlinear systems representing several industrial processes including crystallization and precipitation, polymerization reactors, distillation columns, biochemical fermentation and biological systems. The proposed algorithm is further applied for estimation of the sugar crystallization growth rate and compared with alternative solution.
基于神经网络的部分未知状态和完全已知动力学系数的反应速率估计
这项工作的重点是开发一种更有效的计算方案,用于估计基于神经网络模型的过程反应速率。传统的过程反应速率估计方法是通过穷举和昂贵的搜索最合适的参数化结构来实现的,与此相反,本文提出了一种基于神经网络(NN)的方法来识别分析过程模型框架中的反应速率。由于不测量反应速率,因此提出了一种特殊的混合神经网络训练结构和自适应算法,使监督式神经网络学习成为可能。目前的贡献是集中在一类非线性系统的一般建模代表几个工业过程,包括结晶和沉淀,聚合反应器,蒸馏塔,生化发酵和生物系统。将该算法进一步应用于糖结晶生长速率的估计,并与备选方案进行了比较。
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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