在子加工废物和城市固体废物的堆肥中进行生物过程建模和优化:1型模糊回归,基于神经网络的方法和遗传算法
Murat Kahraman1, Fulya Aydın Temel1, Ozge Cagcag Yolcu2
1Department of Environmental Engineering, Faculty of Engineering, Giresun University, Giresun, 28200, Türkiye.
Journal of environmental management
|December 12, 2025
概括
用子堆肥有机废物,可以提高堆肥的成熟度. 一个新的混合机器学习模型准确地预测了堆肥效率和堆肥质量,帮助实现绿色转型和循环经济目标.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 计算机科学 计算机科学
背景情况:
- 有机废物的堆肥对于绿色转型和循环经济至关重要.
- 机器学习模型可以预测堆肥效率和堆肥质量.
- 现有的模型缺乏处理不确定性和深度学习集成等能力.
研究的目的:
- 模拟使用子堆肥市场废物对堆肥成熟度的影响.
- 开发和验证一种新的混合机器学习模型,用于堆肥过程的预测.
- 评估子贝添加对堆肥质量参数的影响.
主要方法:
- 开发了一个混合机器学习模型,结合了线性/非线性关系,模糊逻辑和深度学习.
- 使用市场废物和不同百分比的子进行了堆肥实验.
- 监测了关键参数,包括温度,pH,C/N比,湿度,NH4+/NO3-和发芽指数.
主要成果:
- 使用12.5%子的堆肥达到所需的成熟度标准.
- 最终堆肥的发芽指数随着子的添加而显著增加.
- 与基准方法相比,混合模型显示出更高的预测准确性 (超过70-80%的改进) 和稳定性.
结论:
- 开发的混合模型是预测堆肥过程参数和堆肥质量的可靠工具.
- 加入子可以提高堆肥的成熟度和质量.
- 该模型的准确性和稳定性支持其在优化循环经济的堆肥过程中的应用.
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