Process schemes of ethanol coupling to C4 olefins based on a genetic algorithm for back propagation neural network optimization

C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was co...

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Published inHeliyon Vol. 8; no. 12; p. e12301
Main Authors Li, Minghan, Zhao, Lingling, Jin, Shuo, Li, Danlu, Huang, Jingyi, Liu, Jiaxin
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.12.2022
Elsevier
Subjects
BPNN optimizes chemical process
Ethanol to C4 olefins
Improved mixed congruential method
TOPSIS
Improved mixed congruential method
Ethanol to C4 olefins
TOPSIS
BPNN optimizes chemical process
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Abstract C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale. •A scientific evaluation system for evaluating the production of C4 olefins.•Construction of neural network and prediction of C4 olefin yield based on production indicators.•Mixed Congruential method was improved and used to simulate experimental parameters.•The yield of C4 olefin produced by ethanol under different experimental conditions was analyzed. Ethanol to C4 olefins; TOPSIS; BPNN optimizes chemical process; Improved mixed congruential method
AbstractList C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale. •A scientific evaluation system for evaluating the production of C4 olefins.•Construction of neural network and prediction of C4 olefin yield based on production indicators.•Mixed Congruential method was improved and used to simulate experimental parameters.•The yield of C4 olefin produced by ethanol under different experimental conditions was analyzed. Ethanol to C4 olefins; TOPSIS; BPNN optimizes chemical process; Improved mixed congruential method
C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale.
C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale. • A scientific evaluation system for evaluating the production of C4 olefins. • Construction of neural network and prediction of C4 olefin yield based on production indicators. • Mixed Congruential method was improved and used to simulate experimental parameters. • The yield of C4 olefin produced by ethanol under different experimental conditions was analyzed. Ethanol to C4 olefins; TOPSIS; BPNN optimizes chemical process; Improved mixed congruential method
ArticleNumber e12301
Author Zhao, Lingling
Jin, Shuo
Li, Danlu
Liu, Jiaxin
Li, Minghan
Huang, Jingyi
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Issue 12
Keywords Improved mixed congruential method
Ethanol to C4 olefins
TOPSIS
BPNN optimizes chemical process
Language English
License This is an open access article under the CC BY-NC-ND license.
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Snippet C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable...
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SubjectTerms BPNN optimizes chemical process
Ethanol to C4 olefins
Improved mixed congruential method
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Title Process schemes of ethanol coupling to C4 olefins based on a genetic algorithm for back propagation neural network optimization
URI https://dx.doi.org/10.1016/j.heliyon.2022.e12301
https://www.ncbi.nlm.nih.gov/pubmed/36578395
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