使用混合机器学习模型在可变料下模拟无氧消化过程
Ayeh Karami1, Ayoub Karimi-Jashni1, Mohammad Reza Nikoo2
1Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran.
Chemosphere
|August 29, 2025
概括
本研究使用人工智能模拟无氧消化 (AD) 性能. 一种结合机器学习和贝叶斯模型的混合方法,平均可以显著改善生物气生产和污泥稳定性的预测.
科学领域:
- 环境工程
- 生物技术
- 人工智能
背景情况:
- 无氧消化 (AD) 对于稳定废水污泥至关重要.
- 精确模拟AD性能对于优化处理过程至关重要.
- 人工智能的进步为复杂的生物系统提供了新的可能性.
研究的目的:
- 研究一种结合机器学习 (ML) 和贝叶斯模型平均值的混合方法,以模拟无氧消化 (AD) 的性能.
- 评估五种ML技术 (DT,RF,GB,SVR,MLP) 在预测生物气产量,挥发性固体去除和总固体输出方面的有效性.
- 通过实验室规模的数据来提高AD系统的预测准确性.
主要方法:
- 使用了五种机器学习技术:决策树 (DT),随机森林 (RF),梯度增强 (GB),支持向量回归 (SVR) 和多层感知器 (MLP).
- 使用实验室规模的污泥条件来预测生物气产量,挥发性固体的去除和总固体的输出.
- 应用贝叶斯模型的平均值来结合ML模型的结果以提高准确性.
主要成果:
- 各个ML模型的性能不同,最佳的生物气输出模型达到0.90的R2
- 总固体输出预测的表现最低 (R2为0.53).
- 混合方法显著提高了预测准确性,将总固体输出的R2增加到0.98,并减少了错误.
结论:
- 机器学习模型可以有效模拟无氧消化系统的关键性能指标.
- 结合贝叶斯模型与机器学习模型的混合方法大大提高了AD过程的预测准确性.
- 这项研究展示了人工智能驱动的混合模型在优化废水污泥稳定方面的潜力.
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