AI-powered optimization of reactive red 195 dye decolorization: Evaluating the impact of operational factors

IF 8.3 1区工程技术 Q1 ENGINEERING, CHEMICAL

Desalination Pub Date : 2025-04-27 DOI:10.1016/j.desal.2025.118936

Zubair Khaliq , Anum Javaid , Abdulaziz Bentalib , Muhammad Bilal Qadir , Zubera Naseem , Shumaila Kiran , Fayyaz Ahmad , Nimra Nadeem , Maryam Bibi

{"title":"AI-powered optimization of reactive red 195 dye decolorization: Evaluating the impact of operational factors","authors":"Zubair Khaliq , Anum Javaid , Abdulaziz Bentalib , Muhammad Bilal Qadir , Zubera Naseem , Shumaila Kiran , Fayyaz Ahmad , Nimra Nadeem , Maryam Bibi","doi":"10.1016/j.desal.2025.118936","DOIUrl":null,"url":null,"abstract":"<div><div>Machine learning modelling and optimization of the degradation of synthetic dyes are essential aspects that need the attention of researchers when cleaning wastewater. This study pioneers a synergistic approach combining phyto-assisted green synthesis of magnesium oxide nanoparticles (MgO-NPs) with advanced machine learning techniques to degrade Reactive Red 195 dye from textile effluent effectively. Utilizing the eco-friendly and sustainable properties of <em>Azadirachta indica</em> (Neem) leaf extract for MgO-NPs synthesis, we further integrate a sophisticated gradient-boosting regressor model to analyze and predict the decolorization efficiency under varied conditions. The machine learning model, achieving an accuracy (<em>R</em><sup>2</sup> = 0.7), not only enhances our predictive capabilities regarding dye decolorization from industrial wastewater but also underscores the critical influence of parameters such as pH, temperature, and time on the treatment process. We found the optimal decolorization to be 79.53 % under the following conditions: concentration = 0.0278, time = 56.67, MON = 5, pH = 4, and <em>T</em> = 40 °C. This method avoids using additional chemicals, offering a more eco-conscious solution for dye decolorizing industrial wastewater and incorporating machine learning into environmental nanotechnology research results in a significant step forward, enabling the predictive optimization of treatment methods and facilitating the development of more efficient, data-driven solutions for small and medium enterprises addressing sustainable approaches for optimizing industrial waste treatment.</div></div>","PeriodicalId":299,"journal":{"name":"Desalination","volume":"612 ","pages":"Article 118936"},"PeriodicalIF":8.3000,"publicationDate":"2025-04-27","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/S0011916425004114","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

Machine learning modelling and optimization of the degradation of synthetic dyes are essential aspects that need the attention of researchers when cleaning wastewater. This study pioneers a synergistic approach combining phyto-assisted green synthesis of magnesium oxide nanoparticles (MgO-NPs) with advanced machine learning techniques to degrade Reactive Red 195 dye from textile effluent effectively. Utilizing the eco-friendly and sustainable properties of Azadirachta indica (Neem) leaf extract for MgO-NPs synthesis, we further integrate a sophisticated gradient-boosting regressor model to analyze and predict the decolorization efficiency under varied conditions. The machine learning model, achieving an accuracy (R² = 0.7), not only enhances our predictive capabilities regarding dye decolorization from industrial wastewater but also underscores the critical influence of parameters such as pH, temperature, and time on the treatment process. We found the optimal decolorization to be 79.53 % under the following conditions: concentration = 0.0278, time = 56.67, MON = 5, pH = 4, and T = 40 °C. This method avoids using additional chemicals, offering a more eco-conscious solution for dye decolorizing industrial wastewater and incorporating machine learning into environmental nanotechnology research results in a significant step forward, enabling the predictive optimization of treatment methods and facilitating the development of more efficient, data-driven solutions for small and medium enterprises addressing sustainable approaches for optimizing industrial waste treatment.

Abstract Image

查看原文本刊更多论文

活性红195染料脱色的人工智能优化：评价操作因素的影响

机器学习建模和优化合成染料的降解是研究人员在清洗废水时需要注意的重要方面。本研究首创了一种协同方法，将植物辅助的氧化镁纳米颗粒绿色合成（MgO-NPs）与先进的机器学习技术相结合，有效地降解纺织废水中的活性红195染料。利用印楝叶提取物的环保和可持续特性合成MgO-NPs，我们进一步整合了复杂的梯度增强回归模型来分析和预测不同条件下的脱色效率。该机器学习模型达到了精度（R2 = 0.7），不仅增强了我们对工业废水染料脱色的预测能力，而且强调了pH、温度和时间等参数对处理过程的关键影响。在浓度= 0.0278，时间= 56.67,MON = 5, pH = 4，温度= 40℃的条件下，脱色效果最佳，脱色率为79.53%。这种方法避免了使用额外的化学品，为染料脱色工业废水提供了更环保的解决方案，并将机器学习与环境纳米技术研究成果结合在一起，迈出了重要的一步，使处理方法的预测优化成为可能，并促进了中小型企业开发更有效的、数据驱动的解决方案，以解决优化工业废水处理的可持续方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Desalination 工程技术-工程：化工

CiteScore

14.60

自引率

20.20%

发文量

619

审稿时长

41 days

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.