基于机器学习的预测城市固体废物的加热值.
Mansour Baziar1, Mahmood Yousefi2, Vahide Oskoei3
1Department of Environmental Health Engineering, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran.
Scientific reports
|April 26, 2025
概括
机器学习准确地预测城市固体废物加热值,使用元素组成和干重. 含量是最重要的预测因素,其表现优于其他模型.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 准确预测城市固体废物 (MSW) 取暖值对于废物转化为能源的过程至关重要.
- 传统的方法往往缺乏优化能量回收所需的精度.
研究的目的:
- 采用和比较机器学习模型来预测MSW加热值.
- 确定最有效的机器学习技术和关键输入参数.
主要方法:
- 使用了XGBoost,额外树,CatBoost和多重线性回归 (MLR).
- 模型使用干样重量和元素组成 (C,H,O,N,S,灰) 进行训练.
- 对额外树木模型进行了超参数调整.
主要成果:
- 调整后的Extra Trees模型实现了0.999 (培训) 和0.979 (测试) 的R2值,其中MSE,MAE和MAPE值较低.
- 在预测准确度方面,额外的树木显著超过了XGBoost,CatBoost和MLR.
- 含量,硫含量,灰含量和干样重量被确定为关键预测因素.
结论:
- 机器学习,特别是Extra Trees算法,提供了一个非常准确和可靠的方法来预测MSW加热值.
- 元素组成,特别是,是确定加热值的关键因素.
- 这种方法可以提高废物能源技术的效率.
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