人工智能在电膜系统中运输过程的数学建模和数值调查中的应用
Ekaterina Kazakovtseva1, Evgenia Kirillova2, Anna Kovalenko1
1Faculty of Computer Technologies and Applied Mathematics, Kuban State University, 350040 Krasnodar, Russia.
Membranes
|January 27, 2026
概括
本研究介绍了一种混合人工智能模型,将神经网络和模拟结合起来,以优化电膜系统 (EMS) 中的电传导. 这种方法提高了淡化效率,并降低了可持续水处理的成本.
科学领域:
- 膜科学 膜科学 膜科学
- 人工智能的人工智能
- 计算机建模 计算建模
背景情况:
- 提高海水淡化效率和降低成本需要用于电膜系统 (EMS) 的先进数学模型.
- 目前的方法往往涉及高的实验成本和大量的计算时间.
研究的目的:
- 开发一种新的混合方法,将神经网络与高精度的电流对流的数值模拟集成在一起.
- 建立基于无维相似性标准的EMS中关键参数之间的功能关系.
主要方法:
- 使用无维相似性标准 (雷诺德数,佩克莱数) 来定义参数之间的关系.
- 通过数字实验与内部数学模型生成训练数据集.
- 用于回归任务的多层前神经网络与反向传播.
主要成果:
- 开发了AI驱动的混合模型,用于快速预测和优化EMS设计和操作参数.
- 显著降低了计算和实验成本.
- 在无尺寸电流 vortex 的直径和电流-电压曲线的平原长度之间建立了功能关系.
结论:
- 由人工智能驱动的混合模型提供了一个可扩展的框架,用于在EMS中集成物理建模和机器学习.
- 这种方法支持下一代智能膜的开发和可持续水处理的自适应控制策略.
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