基于超亲水性和水下超疏水性陶瓷膜的稳定含油废水分离:综合实验设计和独立机器学习算法

IF 5.5 3区 工程技术 Q1 ENGINEERING, CHEMICAL
Jamilu Usman , Sani I. Abba , Abdullahi G. Usman , Lukka Thuyavan Yogarathinam , Abdullah Bafaqeer , Nadeem Baig , Isam H. Aljundi
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引用次数: 0

摘要

背景评估废水处理中陶瓷膜性能的可靠计算方法标志着在优化分离过程、确保环境可持续性和推进水净化技术方面迈出了变革性的一步。目前的研究利用人工智能(AI)工具探索了超亲水性和水下超疏油性陶瓷膜性能评估中的影响因素,用于含油废水的选择性处理:随机森林(RF)、支持向量回归(SVR)和高斯过程回归(GPR)等先进的人工智能模型来预测这些膜在排斥和通量方面的功效。重要发现从结果来看,在通量预测的训练和测试阶段,高斯过程回归与相关性(WI=99.9)显示出良好的一致性,表明模型非常拟合,误差可忽略不计(在测试阶段,MAPE=0.001,MAE=0.000)。在剔除建模方面,GPR 和 SVR 表现出相似的准确度水平,PCC 和 WI 值适中,而 RF 则显示出明显的局限性,在所有统计指标中得分最低。研究结果凸显了人工智能在优化废水处理工艺方面的潜力,其中 GPR 被认为是最有前途的流量预测模型。这项研究将为含油废水的膜分离过程建模提供深入见解,并将人工智能纳入废水再生的性能评估中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

Background

Reliable computational approaches to evaluate ceramic membrane performance in wastewater treatment mark a transformative step towards optimizing separation processes, ensuring environmental sustainability, and advancing water purification technologies. The current study explores the influential factors using artificial intelligence (AI) tools in the performance evaluation of superhydrophilic and underwater super-oleophobic ceramic membranes for the selective treatment of oily wastewater.

Methods

The chemometrics scenario of the research based on established experimental work employs advanced AI models viz: Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) to predict the efficacy of these membranes in terms of rejection and flux. The model predictions were evaluated using the Pearson Correlation Coefficient (PCC), Willmott Index (WI), mean absolute percentage error (MAPE), and mean absolute error (MAE).

Significant findings

From the results, GPR had shown good agreement with correlations (WI=99.9) during the training and testing phases for flux prediction, indicating an exceptional model fit with negligible error (MAPE=0.001, MAE=0.000 in the testing phase). For rejection modelling, GPR and SVR exhibit similar levels of accuracy, with moderate PCC and WI values, while RF reveals significant limitations with the lowest scores across all statistical metrics. The findings highlight the potential of AI in optimizing wastewater treatment processes, with GPR identified as the most promising model for flux prediction. This study would provide insight into the modelling of the membrane separation process for oily wastewater and integrate AI in the performance evaluation of wastewater reclamation.

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来源期刊
CiteScore
9.10
自引率
14.00%
发文量
362
审稿时长
35 days
期刊介绍: Journal of the Taiwan Institute of Chemical Engineers (formerly known as Journal of the Chinese Institute of Chemical Engineers) publishes original works, from fundamental principles to practical applications, in the broad field of chemical engineering with special focus on three aspects: Chemical and Biomolecular Science and Technology, Energy and Environmental Science and Technology, and Materials Science and Technology. Authors should choose for their manuscript an appropriate aspect section and a few related classifications when submitting to the journal online.
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