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RETRACTED ARTICLE: Combinatorial optimization analysis of the production process of C4 olefins from ethanol based on the PSO–BP algorithm

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This article was retracted on 02 April 2024

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Abstract

The main objective of this study was to optimize the design of a production process for the preparation of C4 olefins from ethanol. Firstly, the data were preprocessed to investigate the association between temperature, ethanol conversion, and C4 olefin selectivity for various catalyst combinations using polynomial fitting methods based on data distribution patterns. Secondly, SVM regression, Gaussian process regression, and BP neural network regression models were used to investigate and select the best models for ethanol conversion and C4 olefin yield for different catalyst combinations and temperatures. Finally, neural network and particle swarm optimization algorithms were used to derive the optimal catalyst combination and temperature to maximize C4 olefin yield. The use of neural networks and particle swarm optimization algorithms proved to be effective in optimizing the reaction conditions for the production of C4 olefins from ethanol.

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Acknowledgements

I would like to express my sincere gratitude to the editors and reviewers for their helpful comments in improving the quality of the original manuscript.

Funding

This study was funded by Anhui Quality Engineering Project Teaching Demonstration Course “mathematical modeling” (2020SJJXSFK0018).

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Authors and Affiliations

  1. School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, 233041, China

    Ze-Hua He

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  1. Ze-Hua He
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Correspondence to Ze-Hua He.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s10878-024-01146-6

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He, ZH. RETRACTED ARTICLE: Combinatorial optimization analysis of the production process of C4 olefins from ethanol based on the PSO–BP algorithm. J Comb Optim 45, 136 (2023). https://doi.org/10.1007/s10878-023-01062-1

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  • DOI: https://doi.org/10.1007/s10878-023-01062-1

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