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使用RSM和ANN方法学探索吸附脱硫效率

Mahyar Mansouri¹, Mohsen Shayanmehr¹, Ahad Ghaemi²

¹School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, Narmak, Tehran, 16846, Iran.

Scientific reports

|July 2, 2025

概括

这项研究优化了使用响应表面方法 (RSM) 和人工神经网络 (ANN) 进行吸附脱硫的热性能. 在预测硫吸附方面,ANN模型,特别是RBF,取得了卓越的准确性,并将微孔体积确定为关键.

科学领域:

材料科学材料科学材料科学
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:

石是非常有效的吸附剂,可以去除硫化合物,因为它们的表面积很大,并且具有可调节的特性.
吸附脱硫对于生产超低硫燃料和减轻环境污染至关重要.

研究的目的:

使用先进的计算技术,模拟和优化修改过的化石的硫吸附性能.
为了比较响应表面方法 (RSM) 和人工神经网络 (ANN) 在预测焦化脱硫效率方面的有效性.

主要方法:

响应表面方法 (RSM) 与中央复合设计 (CCD) 被用于初始建模.
人工神经网络 (ANN),包括辐射基函数 (RBF) 和多层感知器 (MLP),被开发用于增强预测.
全球灵敏度分析 (GSA) 和蒙特卡洛模拟用于参数影响和不确定性评估.

主要成果:

RSM实现了0.9502的高调整R平方,但ANN模型显示出更高的精度.
该RBF网络产生了0.9951的R平方和0.0015的低MSE,超过了MLP.
微孔体积被确定为影响硫吸附的最重要因素.

结论:

人工神经网络 (ANN) 为吸附性脱硫的传统建模提供了强大的,高度准确的替代方案.
优化的ANN模型为高效,可扩展的超低硫燃料生产提供了途径,并减少了实验力度.
这项研究增强了对工艺的理解,并促进了先进的脱硫技术的开发.

关键词:

一个年龄,一个年龄.脱硫吸附是一种脱硫的方法.在 RSM RSM 中.泽奥莱特石是一种热带石.

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