Neural Computing and Applications | 2019

Changing product specification in extractive distillation process using intelligent control system

 
 
 
 
 
 

Abstract


Obtaining anhydrous ethanol by extractive distillation has already been the object of several studies in the control literature. However, despite the presence of varying degrees of purity of anhydrous ethanol owing to its applications in industrial and commercial sectors, little attention has been given to dynamic and control for changing the operating conditions to provide anhydrous ethanol with different specifications. Using a soft sensor based on artificial neural network, this work aimed to develop an intelligent control system to contemplate the changes in the specification of anhydrous ethanol, considering the whole process (extractive and recovery columns), and keeping the process operating at an optimal point. Using the developed intelligent control system, the only necessary modification is the new specification and all new set-points values for controllers (temperature and solvent to azeotropic feed ratio) are updated automatically, without human interference, while with a conventional control system, all the new set-points values must be modified manually. The results showed that for the studied anhydrous ethanol specification range (99.1–99.9% mole), the new optimum operating conditions (new steady-state) were reached in a short time (between 1 and 2 h), with no evidence of overflow or emptying of sumps and reflux vessels of the columns. In addition to the easy implementation of the intelligent control system, the existing control structure remains unchanged, not requiring the investment for new instrumentation.

Volume 32
Pages 13255 - 13266
DOI 10.1007/s00521-019-04664-1
Language English
Journal Neural Computing and Applications

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