Artificial neural network and mathematical modeling for Congo red dye remediation using acid-activated mixed waste biomass

IF 8.1 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Chemosphere Pub Date : 2025-08-30 DOI:10.1016/j.chemosphere.2025.144670

R. Kamalesh, S. Karishma, Alan Shaji, Y.P. Ragini, V.C. Deivayanai, A. Saravanan, A.S. Vickram

{"title":"Artificial neural network and mathematical modeling for Congo red dye remediation using acid-activated mixed waste biomass","authors":"R. Kamalesh, S. Karishma, Alan Shaji, Y.P. Ragini, V.C. Deivayanai, A. Saravanan, A.S. Vickram","doi":"10.1016/j.chemosphere.2025.144670","DOIUrl":null,"url":null,"abstract":"<div><div>The study explores the potential of novel acid-activated algal – pineapple peel biomass (AAPPB) for the removal of Congo red dye with artificial intelligence-based predictive modeling. The characterization analysis confirmed the better surface and functional nature of AAPPB. Batch parameter studies revealed an optimal dose of 1 g/L with a contact time of 40 min. Isotherm and kinetic modeling analysis inferred Redlich-Peterson and Pseudo-second order model to be the best fit, indicating the monolayer, heterogeneous, and chemisorption nature. Maximal adsorption removal ability of 152.3 mg/g was observed from isotherm analysis for AAPPB. Thermodynamic analysis inferred the interaction between AAPPB and Congo red dye molecules to be spontaneous, favourable, and exothermic. A predictive model using Artificial Neural Network (ANN) achieved a correlation coefficient of 0.9943 with ANN testing demonstrating strong agreement with experimental results, confirming the ANN model's reliability in estimating Congo red dye removal by AAPPB. The study provides a sustainable adsorbent with strong potential for dye remediation applications.</div></div>","PeriodicalId":276,"journal":{"name":"Chemosphere","volume":"387 ","pages":"Article 144670"},"PeriodicalIF":8.1000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemosphere","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045653525006186","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

The study explores the potential of novel acid-activated algal – pineapple peel biomass (AAPPB) for the removal of Congo red dye with artificial intelligence-based predictive modeling. The characterization analysis confirmed the better surface and functional nature of AAPPB. Batch parameter studies revealed an optimal dose of 1 g/L with a contact time of 40 min. Isotherm and kinetic modeling analysis inferred Redlich-Peterson and Pseudo-second order model to be the best fit, indicating the monolayer, heterogeneous, and chemisorption nature. Maximal adsorption removal ability of 152.3 mg/g was observed from isotherm analysis for AAPPB. Thermodynamic analysis inferred the interaction between AAPPB and Congo red dye molecules to be spontaneous, favourable, and exothermic. A predictive model using Artificial Neural Network (ANN) achieved a correlation coefficient of 0.9943 with ANN testing demonstrating strong agreement with experimental results, confirming the ANN model's reliability in estimating Congo red dye removal by AAPPB. The study provides a sustainable adsorbent with strong potential for dye remediation applications.

Abstract Image

查看原文本刊更多论文

酸活化混合废生物质修复刚果红的人工神经网络及数学建模

该研究利用基于人工智能的预测模型，探索了新型酸激活藻-菠萝皮生物质（AAPPB）去除刚果红染料的潜力。表征分析证实了AAPPB具有较好的表面和功能特性。批量参数研究表明，最佳剂量为1 g/L，接触时间为40 min。等温线和动力学模型分析推断Redlich-Peterson和伪二阶模型最适合，表明其为单层、非均相和化学吸附性质。等温线分析显示，AAPPB的最大吸附去除率为152.3 mg/g。热力学分析表明，AAPPB与刚果红染料分子之间的相互作用是自发的、有利的、放热的。人工神经网络（ANN）预测模型的相关系数为0.9943，与实验结果吻合较好，证实了人工神经网络模型预测AAPPB去除刚果红染料的可靠性。该研究为染料修复提供了一种具有强大潜力的可持续吸附剂。

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

Chemosphere 环境科学-环境科学

CiteScore

15.80

自引率

8.00%

发文量

4975

审稿时长

3.4 months

期刊介绍： Chemosphere, being an international multidisciplinary journal, is dedicated to publishing original communications and review articles on chemicals in the environment. The scope covers a wide range of topics, including the identification, quantification, behavior, fate, toxicology, treatment, and remediation of chemicals in the bio-, hydro-, litho-, and atmosphere, ensuring the broad dissemination of research in this field.