在电化学淡化过程中自主管理水质
Zahid Ullah1, Nakyeong Yun2, Ruggero Rossi3
1Center for Water Cycle Research, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea; Division of Energy and Environment Technology, KIST-School, University of Science and Technology, Seoul 02792, Republic of Korea.
Water research
|March 28, 2025
概括
先进的AI控制器显著改善了膜电容脱离离子 (MCDI) 系统中的水质. 多重并行ANN-Integral (MPAI) 控制器实现了卓越的精度,超过了用于高效处理水的强化学习方法.
科学领域:
- 环境科学与工程环境科学与工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 膜电容脱离离子 (MCDI) 是一个有前途的水淡化和净化技术.
- 有效的控制策略对于优化MCDI系统性能和确保水质至关重要.
- 现有的控制方法可能会与料条件的动态变化和系统污染作斗争.
研究的目的:
- 开发和比较MCDI系统的先进人工智能 (AI) 控制策略.
- 评估不同人工神经网络 (ANN) 控制器和强化学习算法的性能.
- 优化控制参数以提高水质和系统可靠性.
主要方法:
- 三个ANN控制器的开发和评估:ANN-比例-积分-导数,ANN-整数和多个并行ANN-整数 (MPAI).
- 将MPAI控制器与使用保守的Q学习 (CQL) 算法的离线强化学习控制器进行比较.
- 使用各种奖励函数优化CQL控制器,并根据其有效性选择高斯奖励函数.
主要成果:
- 与其他ANN控制器和CQL控制器相比,MPAI控制器表现出卓越的性能.
- 无论MPAI和CQL控制器都有效地保持了废水度在17mM左右,尽管入口变化和污染.
- 该MPAI控制器实现了更高的精度与接近零的误差幅度,超过了CQL控制器.
结论:
- 人工智能驱动的控制器,特别是MPAI,为提高MCDI系统效率和可靠性提供了巨大的潜力.
- 先进的控制策略可以在具有挑战性的操作条件下稳定地管理水质.
- 这项研究有助于推进智能水处理技术的发展.
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