Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, United States; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing 100083, PR China; Beijing Engineering Research Center of Process Pollution Control, National Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Innovation Academy for Green Manufacture, Chinese Academy of Sciences, Beijing 100190, PR China.
Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97333, United States.
Waste Manag. 2021 Feb 15;121:59-66. doi: 10.1016/j.wasman.2020.12.003. Epub 2020 Dec 22.
The use of zero-valent iron (ZVI) to enhance anaerobic digestion (AD) systems is widely advocated as it improves methane production and system stability. Accurate modeling of ZVI-based AD reactor is conducive to predicting methane production potential, optimizing operational strategy, and gathering reference information for industrial design in place of time-consuming and laborious tests. In this study, three machine learning (ML) algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and deep learning (DL), were evaluated for their feasibility of predicting the performance of ZVI-based AD reactors based on the operating parameters collected in 9 published articles. XGBoost demonstrated the highest accuracy in predicting total methane production, with a root mean squared error (RMSE) of 21.09, compared to 26.03 and 27.35 of RF and DL, respectively. The accuracy represented by mean absolute percentage error also showed the same trend, with 14.26%, 15.14% and 17.82% for XGBoost, RF and DL, respectively. Through the feature importance generated by XGBoost, the parameters of total solid of feedstock (TS), sCOD, ZVI dosage and particle size were identified as the dominant parameters that affect the methane production, with feature importance weights of 0.339, 0.238, 0.158, and 0.116, respectively. The digestion time was further introduced into the above-established model to predict the cumulative methane production. With the expansion of training dataset, DL outperformed XGBoost and RF to show the lowest RMSEs of 11.83 and 5.82 in the control and ZVI-added reactors, respectively. This study demonstrates the potential of using ML algorithms to model ZVI-based AD reactors.
利用零价铁（ZVI）来增强厌氧消化（AD）系统被广泛提倡，因为它可以提高甲烷产量和系统稳定性。准确地对基于 ZVI 的 AD 反应器进行建模有助于预测甲烷产生潜力、优化操作策略，并为工业设计提供参考信息，以替代耗时费力的测试。在这项研究中，评估了三种机器学习（ML）算法，即随机森林（RF）、极端梯度提升（XGBoost）和深度学习（DL），以评估它们基于 9 篇已发表文章中收集的操作参数预测基于 ZVI 的 AD 反应器性能的可行性。XGBoost 在预测总甲烷产量方面表现出最高的准确性，其均方根误差（RMSE）为 21.09，而 RF 和 DL 的 RMSE 分别为 26.03 和 27.35。平均绝对百分比误差表示的准确性也呈现出相同的趋势，XGBoost、RF 和 DL 的准确率分别为 14.26%、15.14%和 17.82%。通过 XGBoost 生成的特征重要性，确定了进料总固体（TS）、sCOD、ZVI 剂量和粒径是影响甲烷产量的主要参数，其特征重要性权重分别为 0.339、0.238、0.158 和 0.116。进一步将消化时间引入到上述建立的模型中，以预测累积甲烷产量。随着训练数据集的扩大，DL 在控制和添加 ZVI 的反应器中分别表现出最低的 RMSE，分别为 11.83 和 5.82。本研究表明，使用 ML 算法对基于 ZVI 的 AD 反应器进行建模具有潜力。
