Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions

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

Chemosphere Pub Date : 2024-12-01 DOI:10.1016/j.chemosphere.2024.143812

Yizhan Deng , Bing Pu , Xiang Tang , Xuran Liu , Xiaofei Tan , Qi Yang , Dongbo Wang , Changzheng Fan , Xiaoming Li

{"title":"Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions","authors":"Yizhan Deng , Bing Pu , Xiang Tang , Xuran Liu , Xiaofei Tan , Qi Yang , Dongbo Wang , Changzheng Fan , Xiaoming Li","doi":"10.1016/j.chemosphere.2024.143812","DOIUrl":null,"url":null,"abstract":"<div><div>Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC. The models achieved robust predictive accuracy (test R<sup>2</sup> = 0.82–0.95), except for surface area. Notably, analysis of the optimal models revealed SS characteristics had a significant impact on SSBC properties, particularly total and fixed carbon content (combined importance exceeding 80%). This underscores the needs of source analysis and preparation optimization in targeted SS recovery or SSBC applications. To facilitate this, a graphical user interface was developed for strategic analyzation of sludge sources and pyrolysis settings.</div></div>","PeriodicalId":276,"journal":{"name":"Chemosphere","volume":"369 ","pages":"Article 143812"},"PeriodicalIF":8.1000,"publicationDate":"2024-12-01","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/S0045653524027139","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

Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC. The models achieved robust predictive accuracy (test R² = 0.82–0.95), except for surface area. Notably, analysis of the optimal models revealed SS characteristics had a significant impact on SSBC properties, particularly total and fixed carbon content (combined importance exceeding 80%). This underscores the needs of source analysis and preparation optimization in targeted SS recovery or SSBC applications. To facilitate this, a graphical user interface was developed for strategic analyzation of sludge sources and pyrolysis settings.

Abstract Image

查看原文本刊更多论文

基于污泥特性和热解条件的污水污泥生物炭基本特性机器学习预测。

污水污泥生物炭（SSBC）具有从污水污泥（SS）中回收资源的巨大潜力，已在各个领域得到广泛研究和应用。然而，由于污水污泥的性质多种多样，且与热解条件的相互作用错综复杂，导致 SSBC 的性质千差万别，这给其实际应用带来了显著的挑战。本研究采用机器学习技术来预测 SSBC 的基本特性，包括元素含量、近似成分、表面积和产量，这些对于评估 SSBC 的适用性至关重要。除表面积外，其他模型都达到了很高的预测精度（测试 R2 = 0.82-0.95）。值得注意的是，对最佳模型的分析表明，SS 特征对 SSBC 的特性有显著影响，特别是总碳含量和固定碳含量（总和重要性超过 80%）。这强调了在有针对性的 SS 回收或 SSBC 应用中进行源分析和制备优化的必要性。为此，我们开发了一个图形用户界面，用于对污泥来源和热解设置进行战略性分析。

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.