关于向前透系统的评估膜流量:数据评估和先进的智能建模
Hesamedin Hekmatmehr1, Ali Esmaeili1, Saeid Atashrouz2
1Renewable Energies Engineering Department, Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran.
概括
使用人工智能模型准确地预测了前方透 (FO) 膜流量. 基于树的模型,特别是XGBoost,表现优于其他模型,为这个新兴的淡水技术提供了精确的优化.
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.
- 数据科学数据科学数据科学
背景情况:
- 向前透 (FO) 是一种新兴的海水淡化技术,对于解决全球水资源短缺至关重要.
- 优化FO系统需要准确和简单的预测模型.
- 膜流量是FO系统的关键性能指标.
研究的目的:
- 用各种人工智能 (AI) 模型预测和评估FO系统中的膜流量.
- 为了确定最准确的AI模型用于FO膜流量预测.
- 分析操作参数对FO膜流量的影响.
主要方法:
- 广泛的数据收集和预处理,包括重复删除,异常值检测和特征选择.
- 实施和比较六个基于人工智能的模型:两个基于树的模型 (XGBoost,CatBoost),两个深度学习和两个各种模型.
- 使用确定系数 (R2) 评估模型性能.
主要成果:
- XGBoost模型实现了最高的预测准确性,R2为0.992.
- 与深度学习和其他模型相比,基于树的模型 (XGBoost和CatBoost) 显示出更高的性能.
- 灵敏度分析显示,料溶液 (FS) 和吸液 (DS) 度与膜流量有很强的相关性.
结论:
- 基于人工智能的建模,特别是基于树的算法,如XGBoost,为预测FO膜流量提供了非常准确的方法.
- 严格的数据预处理对于在FO应用中可靠的AI模型开发至关重要.
- 了解FS和DS度的影响对于优化FO系统性能至关重要.
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