基于机器学习的分析,分析了微塑料诱导的无氧消化参数的变化,影响了甲产量
Zhenghui Gao1, Zongqiang Ren1, Tianyi Cui1
1School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK.
Journal of environmental management
|February 24, 2025
概括
机器学习准确地预测了无氧消化产生的甲产量,即使有微塑料污染. 梯度增强模型确定了消化时间和pH等关键因素,以优化废物能量回收.
科学领域:
- 环境科学与工程环境科学与工程
- 生物技术和生物工程 生物技术和生物工程
- 废物管理和可再生能源
背景情况:
- 微塑料 (MP) 对无氧消化 (AD) 从有机废物中回收能源的过程构成重大挑战.
- 现有的研究表明,在国会议员的存在下,可变的最佳AD条件是可变的,需要预测建模.
- 准确评估MP对AD的影响对于高效的废物转化能源系统至关重要.
研究的目的:
- 开发和验证机器学习模型,用于预测无氧消化中含有或不含微塑料污染的甲产量.
- 为了确定影响不同MP污染水平下的甲生产的关键过程参数.
- 优化AD过程,以提高微塑料污染环境中的能量回收.
主要方法:
- 应用了四种机器学习算法 (包括梯度增强回归) 来使用两个不同的数据集 (带有和没有MP) 预测甲产量.
- 通过删除多余和不重要的功能,优化了表现最佳的模型.
- 利用特征重要性分析和部分依赖图表来理解参数的影响.
主要成果:
- 梯度增强回归实现了高预测准确性 (R2=0.996没有MP,R2=0.998有MP).
- 消化时间和基质有机物含量是甲产量的关键积极预测因素.
- 在受MP污染的系统中,基质pH和注射液总固体成为影响甲生产的关键因素.
结论:
- 机器学习,特别是梯度增强,为预测受微塑料影响的AD系统中的甲产量提供了强大的工具.
- 确定了优化AD性能的关键参数 (消化时间,有机物质,pH,总固体).
- 结果为改善微塑料污染有机废物能量回收提供了见解.
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