使用碳基吸附剂从液体燃料中去除硫的优化和建模,通过RSM和机器学习的协同应用
Karim Maghfour Sarkarabad1, Mohsen Shayanmehr1, Ahad Ghaemi2
1School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Tehran, Iran.
Scientific reports
|February 5, 2025
概括
人工神经网络 (ANN) 准确预测液体燃料脱硫,超过响应表面方法 (RSM). 温度和度是使用这些先进的建模技术有效去除硫的关键因素.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 环境科学 环境科学
背景情况:
- 液体燃料脱硫对于减少环境污染至关重要.
- 传统方法需要优化,以提高效率和准确性.
研究的目的:
- 为了比较人工神经网络 (ANN) 和响应表面方法 (RSM) 用于预测液体燃料脱硫.
- 确定影响除硫效率的关键因素.
主要方法:
- 用于ANN建模的辐射基函数 (RBF) 和多层感知子 (MLP).
- 使用响应表面方法 (RSM) 采用双因素交互设计.
- 分析的因素:温度,度,表面积,燃料/吸附剂比率和时间.
主要成果:
- 与RSM (R2=0.91) 相比,ANN模型 (MLP和RBF) 实现了高精度 (R2=0.98) 的高精度.
- RBF网络的表现略有改善,MSE=0.0026.
- 温度和度被确定为影响硫去除的最重要因素.
结论:
- ANN模型,特别是RBF,提供了比RSM更准确的脱硫预测.
- 安恩为优化液体燃料脱硫过程提供了一种可靠的工具.
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