优化猪堆肥参数与集成的CatBoost和XGBoost模型:减轻损失和机制
Xuan Wu1, Ying Ren1, Weilong Wu1
1College of Natural Resources and Environment, Northwest A&F University (NWAFU), Yangling, 712100, China.
Journal of environmental management
|May 31, 2025
概括
机器学习优化了猪肥堆肥,提高了固和堆肥质量. 这种可持续农业方法通过改善资源利用来减少对化学肥料的依赖.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 可持续农业需要高质量的有机肥料.
- 猪肥堆肥对于废物管理和营养物质回收至关重要.
- 优化有氧堆肥是最大限度地保持和堆肥成熟度的关键.
研究的目的:
- 使用机器学习优化猪的有氧堆肥过程.
- 提高固和堆肥成熟度,以获得高质量的有机肥料.
- 确定影响堆肥效率和损失的关键操作参数.
主要方法:
- 从猪堆肥中收集的多维参数数据.
- 构建了六个机器学习模型,包括CatBoost和XGBoost.
- 使用遗传算法优化模型参数,并使用夏普利添加式扩展进行解释.
主要成果:
- 对湿度,温度和通风的最佳控制显著改善了堆肥质量 (GI ~198%).
- 氨- (NH3-N) 和氧化- (N2O-N) 的排放量分别减少了35.17%和9.70%.
- 通过增加微生物社区活动,增强了转化.
结论:
- 机器学习为优化猪的有氧堆肥提供了一种有效的方法.
- 控制的堆肥参数对于提高肥料质量和减少环境排放至关重要.
- 这种方法提供了高效的农业废物利用,减少对化学肥料的依赖.
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