使用人工智能模型在电容电极和电池电极去化中将离子度与废水导电性概况脱,使用人工智能模型
Hoo Hugo Kim1, Byeongwook Choi1, Zahid Ullah2
1Center for Water Cycle Research, Korea Institute of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea.
Water research
|July 20, 2024
概括
淡化过程中的废水导电性测量可能不准确. 一个人工智能 (AI) 随机森林模型准确地预测了电容脱离离子 (CDI) 和电池电极脱离离子 (BDI) 系统中的离子度.
科学领域:
- 水处理技术水处理技术.
- 环境科学环境科学
- 材料科学 是一种材料科学.
背景情况:
- 废水导电性是一种常见的,但往往不准确的指标,用于评估像电容脱离离 (CDI) 和电池电极脱离离 (BDI) 等过程中的海水淡化性能.
- 准确的实时测量离子度对于可靠的海水淡化性能评估至关重要.
研究的目的:
- 开发和验证一种人工智能 (AI) 模型,用于准确的实时预测CDI和BDI系统中的废水导电性离子度.
- 为了克服使用直接废水导电性来估计离子度的局限性.
主要方法:
- 基于随机森林 (RF) 的AI模型的开发.
- 用于预测CDI和BDI中的废水导电性的RF模型的验证.
- 应用射频模型来从导电数据中预测单个离子度 (Na+,K+,Ca2+,Cl−).
- 评估抽样间隔对预测准确度的影响.
主要成果:
- 射频模型在预测CDI (R2 = 0.86) 和BDI (R2 = 0.95) 的废水导电性方面表现出很高的准确性.
- 该模型成功地预测了单个离子度,其准确性可能超过导电性预测.
- 预测准确度在采样间隔高达80秒时保持稳定.
结论:
- 基于射频的AI模型提供了一种可靠的方法来预测CDI/BDI系统中的离子度.
- 这种人工智能方法为实时海水淡化性能评估提供了一个比单纯的废水导电性更一致的方法.
- 这些发现支持使用预测的离子度作为评估海水淡化效率的关键指标.
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