基于机器学习的透气透的预测,使用透膜的物理化学特性和工艺条件
Muhammad Mujiburohman1, Marwen Elkamel2, Farzad Hourfar3,4
1Chemical Engineering Department, Universitas Muhammadiyah Surakarta (UMS), 57102, Indonesia.
Heliyon
|March 5, 2025
概括
本研究引入了一种人工神经网络 (ANN) 模型,利用材料特性和工艺条件来预测蒸发 (PV) 流量. 该模型准确地预测了光伏透率,确定了影响流量的关键因素.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 蒸发 (PV) 是一个关键的分离过程,受材料特性和操作条件的影响.
- 开发光伏流量的预测模型对于过程优化和设计至关重要.
- 现有的模型往往缺乏全面整合各种物理化学和工艺参数的能力.
研究的目的:
- 开发和评估一个人工神经网络 (ANN) 模型,用于预测蒸发 (PV) 流量.
- 确定影响光伏流量的透膜材料和工艺条件的关键物理化学性质.
- 为了比较使用常规变量与无维群作为输入的模型的预测性能.
主要方法:
- 从现有文献中汇集了FV透的综合数据集.
- 利用不同架构的人工神经网络 (ANN) 来预测光伏流量.
- 评估了两种输入类型:常规的物理化学和过程变量 (I型) 和衍生的无维群 (II型).
- 使用加森方程确定变量重要性.
主要成果:
- 对I型输入的最佳ANN模型是深度网络 (2层,7个神经元/层),R=0.93674.
- 对于II型输入的最佳ANN是一个浅层网络 (1层,6个神经元),R=0.88332.
- 影响光伏流量的关键因素是玻璃过渡温度,可溶性差异,体积和透压力.
结论:
- 根据材料和工艺参数,ANN模型提供了基于光伏流量的准确预测.
- 该模型有效地识别了控制蒸发性能的关键变量.
- 这种方法为优化光伏工艺和材料选择提供了有价值的工具.
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