人工智能技术在提高重要参数的预测性能及其在膜过程中的优化方面的作用:系统性审查
Shuai Yuan1, Hussein Ajam2, Zainab Ali Bu Sinnah3
1Information Engineering College, Yantai Institute of Technology, Yantai, Shandong 264005, China.
Ecotoxicology and environmental safety
|June 1, 2023
概括
将人工智能 (AI) 与计算流体动力学 (CFD) 结合起来,提供了一种强大的方法来优化膜分离过程. 这种混合模型提高了对质量转移和污染的预测准确性,提高了效率和环境效益.
科学领域:
- 膜科学与工程 膜科学与工程
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 化学工程中的人工智能 (AI)
背景情况:
- 与传统方法相比,基于膜的分离工艺因其优越的性能,环保性和易用性而引起了全球的兴趣.
- 计算流体动力学 (CFD) 对于模拟各种应用中的流体行为至关重要,但其在复杂的膜系统中的应用面临着挑战.
- 仅使用CFD优化膜工艺往往是耗时且昂贵的.
研究的目的:
- 为膜分离过程提供混合AI和CFD模型的全面概述.
- 探索如何机器学习 (ML) 技术与CFD相结合可以提高优化准确性.
- 用AI-CFD集成研究膜系统中质量转移和污染事件的预测.
主要方法:
- 审查常用的基于ML的技术,包括监督学习 (SL),无监督学习 (USL),半监督学习 (SSL) 和人工神经网络 (ANN).
- 在膜工艺的背景下讨论这些AI策略的优缺点.
- 对基于膜的分离适用的基于ML的流行算法的分析.
主要成果:
- 人工智能-CFD混合模型显示出对准确预测结果和优化膜过程的巨大潜力.
- 整合人工智能可以提高质量转移预测的准确性,并识别诸如污染等不利事件.
- 特定的人工智能技术显示出各种膜应用的前景,包括污染控制,海水淡化和废水处理.
结论:
- 人工智能和CFD之间的协同作用为推进膜分离技术提供了一个有希望的途径.
- 混合AI-CFD方法可以带来更高效,更准确和更具成本效益的膜流程优化.
- 预计人工智能技术的进一步应用将推动污染减缓,水净化和废物管理方面的创新.
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