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- In relation to this article, we declare that there is no conflict of interest.
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Received July 13, 2022
Revised October 17, 2022
Accepted December 20, 2022
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Investigation and optimization of olefin purification in methanol-to-olefin process based on machine learning approach coupled with genetic algorithm
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Abstract
In this study, the goal was to develop a robust model for prediction of the performance of a purification section of methanol to olefin (MTO) process based on machine learning approach. The optimum operating conditions
were determined by performing a genetic algorithm as an optimization technique. Finding the optimum conditions
caused a considerable decrease both in fixed capital investment (FCI) and working capital investment (WCI). To do this,
the separation section of MTO process was investigated and modelled through artificial intelligence (AI). This separation section of MTO process is comprised of three columns: C2-stripper, deethanizer, and C3-stripprer. For each column, three operative parameters (number of stages, reflux ratio, and pressure) were selected and investigated. The
performance of columns was assessed through monitoring the purity of products in top stream and energy consumption. The experimental layout was designed using response surface methodology (RSM). And the obtained data were
used to develop artificial neural network (ANN) models for each column. Several structures of ANNs were investigated
to select the optimum model parameters. The observations show good agreement between the real and predicted data.
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