Abstract：
The biochemical methane potential test is a standard method to determine the biodegradability of lignocellulosic wastes (LWs) during anaerobic digestion (AD) with disadvantages of long experiment duration and high operating expense. This paper developed a machine learning model to predict the cumulative methane yield (CMY) using the data of 157 LWs regarding physicochemical characteristics, digestion condition and methane yield, with the coefficient of determination equal to 0.869. Model interpretability analyses underscored lignin content, organic loading, and nitrogen content as pivotal attributes for CMY prediction. For the feedstocks with a cellulose content exceeding about 50%, the CMY in the early AD stage would be relatively lower than those with low cellulose content, but prolonging digestion time could promote methane production. Besides, lignin content in feedstock surpassing 15% would significantly inhibit methane production. This work contributes to valuable guidance for feedstock selection and operation optimization for AD plants.
摘要：
生物甲烷潜力测试是一种用于确定厌氧消化（AD）过程中木质纤维素废物（LWs）生物降解性的标准方法，其缺点是实验时间长，运行费用高。本文开发了一种机器学习模型，使用157个LW的数据在物理化学表征和甲烷产量方面预测累积甲烷产量（CMY），决定系数等于0.869。模型可解释性分析强调了木质素含量，有机负载，氮含量是CMY预测的关键属性。对于纤维素含量超过约50%的原料，AD早期的CMY可能比纤维素含量低的CMY低，但是延长消化时间可以促进甲烷的产生。此外,原料中木质素含量超过15%将显著抑制甲烷的产生。这项工作有助于为AD工厂的原料选择和操作优化以及LW的充分利用提供有价值的指导。
