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基于污泥特性和热解条件的机器学习预测基本的污水污泥生物炭特性

Yizhan Deng¹, Bing Pu², Xiang Tang³

¹College of Environmental Science and Engineering and Key Laboratory of Environmental Biology and Pollution Control (Ministry of Education), Hunan University, Changsha, 410082, PR China.

|November 27, 2024

概括

污水污泥生物炭 (SSBC) 的特性可以通过机器学习来预测. 污泥来源显著影响SSBC碳含量,指导有针对性的应用和资源回收.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.

背景情况:

污水污泥生物炭 (SSBC) 提供了资源回收潜力,但由于可变性质而面临挑战.
不同的污水污泥 (SS) 来源和热解条件造成了不可预测的SSBC特征.
稳定的SSBC质量对于其广泛的实际应用至关重要.

研究的目的:

开发用于预测关键SSBC属性的机器学习模型.
确定影响SSBC特征的关键因素.
创建一个工具来优化SSBC生产和应用.

主要方法:

机器学习技术被用来预测SSBC属性.
对元素含量,近距离组成,表面积和产量开发了预测模型.
对模型重要性进行了分析,以确定关键影响因素.

主要成果:

机器学习模型对大多数SSBC属性 (不包括表面积) 显示出高预测精度 (测试R2=0.820.95).
污水污泥的特性被确定为SSBC特性的主要决定因素,特别是总和固定碳 (超过80%的重要性).
开发了一个图形用户界面,以帮助分析污泥来源和热解参数.

结论:

机器学习提供了一个可靠的方法来预测SSBC属性,促进其实际使用.
了解SS源材料的影响对于为特定应用量身定制SSBC至关重要.
开发的接口支持污泥管理和生物炭生产的战略决策.

关键词:

生物炭是一种生物炭.图形化用户界面图形化用户界面机器学习机器学习热解是一种热解过程.污水污泥的污水污泥是污水中的污泥.

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