Synthesis of activated carbon using pyrolytic degradation of multi plastic waste and its removal efficiency of dye

Sayan Mukherjee , Shashank Pal , Subhasis Ghosh , Sandipan Bhattacharya , Surajit Mondal , Papita Das
{"title":"Synthesis of activated carbon using pyrolytic degradation of multi plastic waste and its removal efficiency of dye","authors":"Sayan Mukherjee ,&nbsp;Shashank Pal ,&nbsp;Subhasis Ghosh ,&nbsp;Sandipan Bhattacharya ,&nbsp;Surajit Mondal ,&nbsp;Papita Das","doi":"10.1016/j.wmb.2025.100214","DOIUrl":null,"url":null,"abstract":"<div><div>Malachite Green is a persistent pollutant, and it has been reported to cause many harmful health hazards for both humans and aquatic organisms. Growing use of plastic is posing a lot of problems and the most pertinent of which is perhaps the treatment of plastic waste. In the present work, plastic waste has been pyrolyzed, annealed and treated with sodium hydroxide to synthesize a char. Then this char has been used to remove the cationic dye Malachite Green from water and was observed highest removal of Malachite Green by the char was 99.3 %. From the thermodynamic study, it was observed that the value of Gibbs free energy was negative across all the temperature thereby denoting that the process was spontaneous. The process was optimized with an Artificial Neural Network system and from there it was observed that the Levenberg-Marquardt backpropagation model best optimized the experimental data. The char also exhibited high efficiency for the purpose of removing other cationic dyes and polyaromatic hydrocarbons, whereas showing a lower affinity for anionic dyes and pharmaceutical compounds.</div></div>","PeriodicalId":101276,"journal":{"name":"Waste Management Bulletin","volume":"3 3","pages":"Article 100214"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Waste Management Bulletin","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949750725000434","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Malachite Green is a persistent pollutant, and it has been reported to cause many harmful health hazards for both humans and aquatic organisms. Growing use of plastic is posing a lot of problems and the most pertinent of which is perhaps the treatment of plastic waste. In the present work, plastic waste has been pyrolyzed, annealed and treated with sodium hydroxide to synthesize a char. Then this char has been used to remove the cationic dye Malachite Green from water and was observed highest removal of Malachite Green by the char was 99.3 %. From the thermodynamic study, it was observed that the value of Gibbs free energy was negative across all the temperature thereby denoting that the process was spontaneous. The process was optimized with an Artificial Neural Network system and from there it was observed that the Levenberg-Marquardt backpropagation model best optimized the experimental data. The char also exhibited high efficiency for the purpose of removing other cationic dyes and polyaromatic hydrocarbons, whereas showing a lower affinity for anionic dyes and pharmaceutical compounds.
热解降解多种塑料垃圾合成活性炭及其对染料的去除率
孔雀石绿是一种持久性污染物,据报道对人类和水生生物造成许多有害的健康危害。塑料的日益使用带来了许多问题,其中最相关的问题可能是塑料废物的处理。在本工作中,对塑料垃圾进行了热解、退火和氢氧化钠处理,合成了一种炭。然后用该焦炭对水中阳离子染料孔雀石绿进行脱除,观察到该焦炭对孔雀石绿的去除率最高达99.3%。热力学研究发现,在整个温度范围内,吉布斯自由能均为负,表明反应是自发的。利用人工神经网络系统对该过程进行优化,并观察到Levenberg-Marquardt反向传播模型对实验数据的优化效果最好。该炭对其他阳离子染料和多芳烃的去除率较高,而对阴离子染料和药物化合物的去除率较低。
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信