Department of Chemical Engineering, Indian Institute of Technology, Kharagpur, West Bengal 721 302, India.
ISA Trans. 2011 Jul;50(3):357-63. doi: 10.1016/j.isatra.2011.01.010. Epub 2011 Feb 22.
In this paper, an artificial neural network (ANN)-based nonlinear control algorithm is proposed for a simulated batch reactive distillation (RD) column. In the homogeneously catalyzed reactive process, an esterification reaction takes place for the production of ethyl acetate. The fundamental model has been derived incorporating the reaction term in the model structure of the nonreactive distillation process. The process operation is simulated at the startup phase under total reflux conditions. The open-loop process dynamics is also addressed running the batch process at the production phase under partial reflux conditions. In this study, a neuro-estimator based generic model controller (GMC), which consists of an ANN-based state predictor and the GMC law, has been synthesized. Finally, this proposed control law has been tested on the representative batch reactive distillation comparing with a gain-scheduled proportional integral (GSPI) controller and with its ideal performance (ideal GMC).
本文提出了一种基于人工神经网络（ANN）的非线性控制算法，用于模拟间歇反应精馏（RD）塔。在均相催化反应过程中，发生酯化反应以生产乙酸乙酯。在模型结构中包含反应项，从而推导出基本模型，以模拟非反应精馏过程。在总回流条件下，在启动阶段对过程操作进行模拟。还在部分回流条件下的生产阶段运行间歇过程来解决开环过程动态。在本研究中，基于神经估计器的通用模型控制器（GMC），由基于 ANN 的状态预测器和 GMC 定律组成，已经被合成。最后，将该提出的控制律与增益调度比例积分（GSPI）控制器及其理想性能（理想 GMC）进行比较，在有代表性的间歇反应精馏塔上进行了测试。
