预测,不确定性量化和ANN辅助的无氧消化操作,以机器学习为指导
Zhipeng Zhuang1, Xiaoshan Liu2, Jing Jin3
1School of Life Sciences, Zhuhai College of Science and Technology, Zhuhai 519041, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
这项研究引入了以为导向的机器学习框架,以稳定无氧消化 (AD). 人工神经网络 (ANN) 模型显著降低了生物气产量波动,提高了运行稳定性,从而节省了成本并减少了二氧化碳排放.
科学领域:
- 生物技术和生物化学工程 生物技术和生物化学工程
- 机器学习在环境科学中的应用.
- 过程优化和控制过程的控制.
背景情况:
- 无氧消化 (AD) 易发生不稳定,原因是原料的变化和生物波动.
- 现有的方法很难管理AD过程的非线性和干扰敏感性.
- 预测建模和不确定性量化对于提高AD运行稳定性至关重要.
研究的目的:
- 开发一种以为导向的机器学习框架,用于AD参数预测和不确定性量化.
- 评估支持矢量机 (SVM),随机森林 (RF) 和人工神经网络 (ANN) 模型用于AD过程预测的性能.
- 评估开发框架对运营稳定,经济可行性和环境足迹的影响.
主要方法:
- 收集了六个月的工业AD数据 (约10,000个样本) 用于模型培训和验证.
- 将SVM,RF和ANN模型进行比较,以预测生物气产量,发酵温度和挥发性脂肪酸 (VFA) 度.
- 利用基于的不确定性分析和特征重要性分析来评估模型性能并识别关键的影响变量.
主要成果:
- 该ANN模型表现出卓越的性能 (精度=96%,F1=0.95,RMSE=1.2 m3/t),预测误差率最低,表明不确定性降低.
- 特性和位分析确定了料固体,有机物和料速率作为最有影响力的变量 (>85%贡献).
- 实时实施ANN模型将天然气产量波动从±18%降低到±5%,运行稳定性提高了~23%,过程率降低.
结论:
- 将机器学习与基于的不确定性分析相结合,为稳定和低碳的AD运行提供了强大的方法.
- 以为指导的ANN模型有效地提高了AD过程的稳定性,减少了不确定性,并提供了显著的技术经济和环境效益.
- 开发的框架作为一个可靠的决策支持工具,以优化AD工厂的性能和可持续性.
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