{"title":"Predicting and optimizing forward osmosis membrane operation using machine learning","authors":"","doi":"10.1016/j.desal.2024.118154","DOIUrl":null,"url":null,"abstract":"<div><div>Forward osmosis (FO) utilizes a draw solution to transport water across a semipermeable membrane, offering energy-efficient water treatment and resource recovery. This study explores machine learning models to predict FO performance at pilot scale, overcoming the limitations of traditional mathematical models in terms of computational load and time. By analyzing data from FO pilot experiments, we compare various algorithms, including multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree, and artificial neural networks (ANNs). Among these, ANN was evaluated as most suitable and further optimized with input features of the permeate flux, membrane area, feed and draw solution flow rates, and feed and draw solution concentrations. The optimized ANN model demonstrated high accuracy for water flux prediction, with R<sup>2</sup> values of 0.9886 and RMSE values of 0.3498 Lm<sup>−2</sup> h<sup>−1</sup>. Additionally, an ANN model is developed to predict operating pressures under various FO operation conditions. By integrating FO water flux and operating pressure predictions, our model identifies optimal operating conditions that balance specific energy consumption and water recovery. Our findings offer insights and practical guidance for process engineers to efficiently design and operate FO systems to minimize energy consumption and maximize recovery.</div></div>","PeriodicalId":299,"journal":{"name":"Desalination","volume":null,"pages":null},"PeriodicalIF":8.3000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Desalination","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0011916424008658","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Forward osmosis (FO) utilizes a draw solution to transport water across a semipermeable membrane, offering energy-efficient water treatment and resource recovery. This study explores machine learning models to predict FO performance at pilot scale, overcoming the limitations of traditional mathematical models in terms of computational load and time. By analyzing data from FO pilot experiments, we compare various algorithms, including multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree, and artificial neural networks (ANNs). Among these, ANN was evaluated as most suitable and further optimized with input features of the permeate flux, membrane area, feed and draw solution flow rates, and feed and draw solution concentrations. The optimized ANN model demonstrated high accuracy for water flux prediction, with R2 values of 0.9886 and RMSE values of 0.3498 Lm−2 h−1. Additionally, an ANN model is developed to predict operating pressures under various FO operation conditions. By integrating FO water flux and operating pressure predictions, our model identifies optimal operating conditions that balance specific energy consumption and water recovery. Our findings offer insights and practical guidance for process engineers to efficiently design and operate FO systems to minimize energy consumption and maximize recovery.
期刊介绍:
Desalination is a scholarly journal that focuses on the field of desalination materials, processes, and associated technologies. It encompasses a wide range of disciplines and aims to publish exceptional papers in this area.
The journal invites submissions that explicitly revolve around water desalting and its applications to various sources such as seawater, groundwater, and wastewater. It particularly encourages research on diverse desalination methods including thermal, membrane, sorption, and hybrid processes.
By providing a platform for innovative studies, Desalination aims to advance the understanding and development of desalination technologies, promoting sustainable solutions for water scarcity challenges.