基于混合模型优化C4烯生产条件
Yancong Zhou1, Chenheng Xu2, Yongqiang Chen1
1School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
人工智能优化了乙醇反应条件,以获得更高的C4烯产量. 通过SHAP值增强的混合GXGB-SSA模型,与手工实验相比,C4烯产量增加了25.46%.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 催化剂是一种催化剂.
- 人工智能的人工智能
背景情况:
- 由于复杂的产品混合物和资源密集型的乙醇反应条件的手动优化,低C4烯酸产量是一个挑战.
- 开发有效的方法来确定最佳的反应参数对于最大限度地提高C4烯产量至关重要.
研究的目的:
- 引入基于人工智能的方法来优化乙醇反应条件,以最大限度地提高C4烯产量.
- 为复杂的优化问题开发结合GXGB和SSA的混合模型.
- 使用SHAP值来解释反应条件对C4烯产量的影响.
主要方法:
- 基于高斯噪声的极端梯度增强树 (GXGB) 已开发出来,以建立优化目标函数.
- 搜索算法 (SSA) 与GXGB集成,以创建GXGB-SSA混合模型,以提高优化效率.
- 使用SHAP (夏普利添加剂扩展) 值来分析单个反应参数对C4烯产量的影响.
主要成果:
- GXGB-SSA模型成功地确定了最佳的乙醇反应条件,产生最大的C4烯酸输出量为5611.46%.
- 优化条件包括特定的Co负载,Co/SiO2和HAP质量比,乙醇度,催化剂质量和反应温度.
- 在人工智能驱动的方法导致25.46%的高收益率比手工实验的最佳结果 (4472.81%).
结论:
- 拟议的GXGB-SSA混合模型有效地优化了C4烯生产的复杂反应条件.
- 整合SHAP值为参数影响提供了有价值的见解,使得可以为收益率最大化做出明智的调整.
- 这种人工智能驱动的方法比化学过程优化的传统实验方法有了显著的进步.
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