一种高效的数据驱动的海水淡化方法,用于元素级向前透 (FO) - 逆透 (RO) 混合系统
Sung-Ju Im1, Nguyen Duc Viet2, Byung-Tae Lee3
1Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, United States.
Environmental research
|July 30, 2023
概括
这项研究优化了海水淡化前透-逆透 (FO-RO) 混合系统. 人工智能准确地预测了系统性能,确定了高效淡水生产的最佳配置.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 水处理技术水处理技术
背景情况:
- 全球人口的增长推动了对淡水的需求,增加了对海水淡化的兴趣.
- 前进透-逆透 (FO-RO) 混合技术为海水淡化提供了低能耗解决方案.
- 扩大FO-RO系统的规模需要了解商业可行性的最佳膜配置.
研究的目的:
- 探索和优化螺旋绕FO反透 (SWFO-RO) 和板与框架FO反透 (PFFO-RO) 混合系统的性能.
- 评估人工智能模型对FO-RO系统性能的预测准确度.
- 为了确定最佳的操作条件和膜元素数量,以有效地淡化海水.
主要方法:
- 对SWFO-RO和PFFO-RO混合系统进行元素级测试.
- 开发和应用人工智能 (AI) 模型用于性能预测 (水流,逆盐流).
- 使用基于马尔科夫的决策树来对水流水位进行分类.
主要成果:
- 在优化条件下,SWFO-RO和PFFO-RO混合系统都实现了高水回收.
- 与传统质量平衡模型相比,人工智能模型显示出更高的预测准确性 (R > 0.99,RMSE < 5%).
- 确定了最佳的操作参数,包括RO中高水透率的料溶液流量,度和操作压力.
- 五个SWFO元件和四个PFFO元件的配置被发现是混合系统的最佳选择.
结论:
- 优化的FO-RO混合系统对海水淡化有效,产生高水回收.
- 人工智能显著提高了FO-RO系统性能的预测和优化.
- 特定的操作条件和膜元素比率对于最大限度地提高效率和确保FO-RO海水淡化技术的长期可行性至关重要.
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