废水污泥和食品废物的无氧共消化:一种机器学习方法来进行过程建模和优化
Maryam Ghazizade-Fard1, Ehssan H Koupaie1
1Waste & Wastewater Biorefinery Lab (WWBL), Department of Chemical Engineering, Queen's University, 19 Division Street, Kingston, K7L 2N9, ON, Canada.
Journal of environmental management
|August 16, 2025
概括
机器学习准确地预测了废水污泥和食品废物 (FW) 无氧共消化 (An-CoD) 的甲产量. 优化的混合比率最大限度地提高了沼气产量,根据原料质量,产量高达510mL/g VS.
科学领域:
- 环境工程 环境工程
- 生物技术是生物技术.
- 机器学习应用 机器学习应用
背景情况:
- 由于复杂的微生物相互作用,预测无氧共消化 (An-CoD) 的结果是复杂的.
- 现有的模型往往缺乏通用性,仅限于单个设施的数据.
- 现实世界的运作需要模型来考虑诸如固定的消化器条件和可变的原料比率等约束因素.
研究的目的:
- 开发一个通用和可转移的机器学习 (ML) 框架,用于建模和优化废水污泥和食品废物 (FW) 的An-CoD.
- 预测甲产量并确定在现实的操作约束下最佳原料混合比率.
- 通过使用多源文献数据集,提高An-CoD模型在各种污泥和FW特征中的适用性.
主要方法:
- 从多个文献来源编制了多样化的数据集,用于废水污泥和FW的An-CoD.
- 训练并比较了三个ML模型:随机森林 (RF),XGBoost和人工神经网络 (ANN) 以预测甲产量.
- 利用规范化的ANN,实现R2为0.86和NRMSE为0.31,并将其与全局优化算法 (双) 结合起来,以找到最佳的混合比率.
主要成果:
- 规范化的ANN表现出卓越的性能,有效地减少了过度装配.
- 优化的模型预测甲产量在134至510mL/gVS之间.
- 根据原料特性和运行条件,最佳混合比率差异很大 (0.99%至88%VS),其中高VS的污泥和FW混合物产生了最多的生物气.
结论:
- 机器学习提供了一个强大的方法来建模和优化An-CoD过程,克服与微生物复杂性相关的挑战.
- 开发的框架是可转移和适用于各种污泥和FW来源,反映实际的操作限制.
- 原料质量和工艺强度是甲产量的关键决定因素,突出了通过优化共消化来实现显著生物气生产的潜力.
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