Improved neural network automation design method for energy saving and carbon emission reduction of petrochemical production processes

Lin Guocong; Ni Qingxu; Liu Xuehai; Geng Zhiqiang; Pan Feng; Tian Jun; Han Yongming

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Improved neural network automation design method for energy saving and carbon emission reduction of petrochemical production processes

Updated：2026-05-14

- Improved neural network automation design method for energy saving and carbon emission reduction of petrochemical production processes
- Chinese Journal of Chemical Engineering Vol. 90, Issue 2, Pages: 348-360(2026)
- Affiliations：
  
  College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing, 100029, China
  Sinopec Sales Co., Ltd., North China Branch, Tianjin 300384, China
  School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
- Author bio：
  
  These authors contributed equally to this work.
  Corresponding authors. E-mail addresses: gengzhiqiang@mail.buct.edu.cn (Z. Geng)
  Corresponding authors. E-mail addresses: 2010500027@buct.edu.cn (J. Tian)
  Corresponding authors. E-mail addresses: hanym@mail.buct.edu.cn (Y. Han).
- Funds：
- DOI：
  CLC：
- Received：04 June 2025，
  
  Revised：2025-08-26，
  
  Accepted：10 September 2025，
  
  Online First：26 November 2025，
  
  Published：2026-02
- Accepted：
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Lin Guocong, Ni Qingxu, Liu Xuehai, et al. Improved neural network automation design method for energy saving and carbon emission reduction of petrochemical production processes[J]. Chinese Journal of Chemical Engineering, 2026, 90(2): 348-360.
DOI：

Lin Guocong, Ni Qingxu, Liu Xuehai, et al. Improved neural network automation design method for energy saving and carbon emission reduction of petrochemical production processes[J]. Chinese Journal of Chemical Engineering, 2026, 90(2): 348-360. DOI：

摘要

Abstract

Ethylene yield serves as a key metric in petrochemical production

where optimizing its energy efficiency remains a critical challenge for sustainable production. Meanwhile

manual hyperparameter tuning of deep learning based yield prediction models often results in suboptimal configurations

which reduces prediction accuracy and reliability due to extreme operating conditions generating outliers in ethylene production processes. Therefore

a novel neural network automatic design method (NNADM) is proposed

which incorporates the neural network parameters automatic optimization and loss function adaptive construction. An innovative adaptive loss formulation is proposed to strategically integrate the complementary strengths of the mean squared error (MSE) and the Log-Cosh functions

featuring dynamic outlier resistance through self-adjusting weight coefficients. Then

the Bayesian optimization search algorithm is utilized to discover optimal hyperparameters of the neural network

including hidden layer unit

epoch

batch size

and the loss function. Finally

the NNADM is integrated with several classical neural networks for ethylene yield prediction. Experimental results show that several classical neural networks realize an average of 16.86% decrease in MSE index after integrating the NNADM. In addition

the proposed model offers direction and development blueprints for ethylene production facilities that have low energy efficiency. By implementing this model

it is possible to cut approximately 8376.4 tons of carbon emissions and simultaneously secure an extra 499 tons of ethylene output.

关键词

Keywords

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