Neural-network approach to dynamic optimization of batch distillation: Application to a middle-vessel column
Document Type
Article
Publication Date
1-1-2003
Abstract
A framework is proposed to optimize the operation of batch columns with substantial reduction of the computational power needed to carry out the optimization calculations. The proposed framework relies on the use of an artificial neural network (ANN) based process model to be employed by the optimizer. To test the viability of the framework, the optimization of a pilot-plant middle-vessel batch column (MVBC) is considered. The maximum-product problem is formulated and solved by optimizing the column operating parameters, such as the reflux and reboil ratios and the batch time. It is shown that the ANN based model is capable of reproducing the actual plant dynamics with good accuracy, and that the proposed framework allows a large number of optimization studies to be carried out with little computational effort.
Keywords
neural network, batch distillation, middle vessel column, dynamic optimization, optimal operation, design.
Divisions
fac_eng
Publication Title
Chemical Engineering Research and Design
Volume
81
Issue
A3
Publisher
Elsevier
Additional Information
667WH Times Cited:3 Cited References Count:18