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Updated: Jul 12, 2025

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使用人工神经网络和回归方法,对酸/蒸汽热交换器中污染阻力的估计和灵敏度分析
Rania Jradi1, Christophe Marvillet2, Mohamed Razak Jeday3
1Research Laboratory «Process, Energy, Environment and Electrical Systems», National Engineering School of Gabès, Gabès, Tunisia. raniajradi@yahoo.fr.
Scientific reports
|October 19, 2023
概括
酸工厂的热交换器污染是一个常见的问题. 这项研究使用了部分最小平方 (PLS) 和人工神经网络 (ANN) 来准确估计污染阻力,确定关键操作参数.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程强化 过程强化
- 热传递是一种热传递.
背景情况:
- 换热器污染会显著降低性能,并增加酸度厂的能源损耗.
- 这种积累需要有效的方法来监测和预测污染阻力.
研究的目的:
- 用线性和非线性建模技术估计交叉流热交换器中的污染阻力.
- 确定对污染阻力最有影响的操作参数.
主要方法:
- 部分最小平方 (PLS) 和人工神经网络 (ANN) 模型被开发用于污染阻力估计.
- 主要组件分析 (PCA) 和步骤回归 (SWR) 用于确定参数关系.
- 选择了具有6个隐藏神经元和BFGS训练的最佳ANN配置.
主要成果:
- PLS模型表现出良好的预测能力,其R平方为0.992,预测能力为87%.
- 在361个数据点上训练的ANN模型提供了比PLS模型更好的结果.
- 酸的进出口温度被确定为影响污染抵抗的最重要的参数 (分别为56%和15.4%的重要性).
结论:
- 无论是PLS模型还是ANN模型,都能有效地估计热交换器的污染阻力.
- 人工神经网络为污染现象提供了更高的预测精度.
- 了解像温度这样的操作参数的影响对于减轻酸工厂的污染至关重要.
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