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首页|期刊导航|石油化工|基于ANN和NSGA-Ⅱ的磷钨酸/SiO2催化酯交换合成4-丙烯酸羟丁酯工艺优化

基于ANN和NSGA-Ⅱ的磷钨酸/SiO2催化酯交换合成4-丙烯酸羟丁酯工艺优化

王俊 蒋阳琦 杨双兵 孙玉玉 汤吉海 乔旭

石油化工2026,Vol.55Issue(3):341-348,8.
石油化工2026,Vol.55Issue(3):341-348,8.DOI:10.3969/j.issn.1000-8144.2026.03.004

基于ANN和NSGA-Ⅱ的磷钨酸/SiO2催化酯交换合成4-丙烯酸羟丁酯工艺优化

Optimization of 4-hydroxybutyl acrylate synthesis via phosphotungstic acid/SiO2 catalyzed transesterification based on ANN and NSGA-Ⅱ

王俊 1蒋阳琦 1杨双兵 2孙玉玉 3汤吉海 1乔旭1

作者信息

  • 1. 南京工业大学 化工学院 材料化学工程全国重点实验室,江苏 南京 211816
  • 2. 菏泽昌盛源科技股份有限公司,山东 菏泽 274500
  • 3. 中建安装集团有限公司,江苏 南京 210023
  • 折叠

摘要

Abstract

Using an impregnation method,phosphotungstic acid(PTA)/SiO2 catalysts with varying PTA loadings were prepared with SiO2 carriers synthesized via the Stöber method.The structural and acidic properties of the catalysts were characterized using methods including XRD,FTIR,NH3-TPD,and TG.A single factor experiment was conducted with the process parameters for the transesterification of 1,4-butanediol(BDO)and methyl acrylate to produce 4-hydroxybutyl acrylate(4-HBA).A Bayesian optimized BP neural network(ANN)model was constructed using Matlab.BDO conversion and 4-HBA selectivity under various reaction parameters were predicted,including reaction temperature,reaction time,catalyst amount,PTA loading and ester/alcohol molar ratio.The second-generation non-dominated sorting genetic(NSGA-Ⅱ)algorithm was used to optimize the process parameters.According to the predicted results,the prediction values of the optimized process parameters under experimental conditions exhibit good agreement with the actual experimental values,which demonstrates that the constructed ANN model with NSGA-Ⅱ algorithm can accurately describe and optimize the transesterification reaction system.

关键词

4-丙烯酸羟丁酯/酯交换/磷钨酸/二氧化硅/人工神经网络

Key words

4-hydroxybutyl acrylate/transesterification/phosphotungstic acid/silicon dioxide/artificial neural network

分类

化学化工

引用本文复制引用

王俊,蒋阳琦,杨双兵,孙玉玉,汤吉海,乔旭..基于ANN和NSGA-Ⅱ的磷钨酸/SiO2催化酯交换合成4-丙烯酸羟丁酯工艺优化[J].石油化工,2026,55(3):341-348,8.

基金项目

江苏省产学研合作项目(BY20221239) (BY20221239)

山东省重点研发计划项目(2024TSGC0989). (2024TSGC0989)

石油化工

1000-8144

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