用混合机器学习技术预测真空膜蒸的透流量
Bashar H Ismael1,2, Faidhalrahman Khaleel2, Salah S Ibrahim3
1Construction and Projects Department, University of Fallujah, Fallujah 31002, Iraq.
Membranes
|December 22, 2023
概括
一个新的混合机器学习模型准确地预测真空膜蒸 (VMD) 中的流量压力. 这种方法为膜性能的大规模实验测试提供了具有成本效益的替代方案.
科学领域:
- 膜科学与技术 膜科学与技术
- 化学工程是化学工程的重要组成部分.
- 在工程领域的人工智能.
背景情况:
- 真空膜蒸 (VMD) 在海水淡化之外越来越多地被使用.
- 大规模的VMD实验测试是资源密集的.
- 机器学习 (ML) 为预测膜性能提供了一个有希望的解决方案.
研究的目的:
- 开发一种新的混合ML模型来预测VMD流量压力.
- 提高VMD性能预测的准确性和效率.
- 将拟议的模型与现有的ML技术进行基准测试.
主要方法:
- 开发了一种混合模型,将支持向量回归 (SVR) 与斑点虫优化器 (SHO) 结合起来.
- 该SVR-SHO模型是使用实验VMD数据进行训练和验证的.
- 性能与人工神经网络 (ANN),经典SVR和多线性回归 (MLR) 相比较.
主要成果:
- 该SVR-SHO模型实现了流压的高预测精度,相关系数 (R) 为0.94.4.
- 拟议的混合模型的表现优于ANN,SVR和MLR,其R值在0.801到0.902之间.
- 全球灵敏度分析确定料温度是影响VMD流量的最关键参数.
结论:
- SVR-SHO混合模型提供了一个非常准确和可靠的方法来预测VMD流量压力.
- 这种ML方法可以显著降低与试点规模VMD测试相关的成本和努力.
- 料温度是影响VMD流量的主要因素,其次是料流量,真空压力和料度.
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