Abstract：
Chemical pretreatment is a common method to enhance the cumulative methane yield (CMY) of lignocellulosic waste (LW) but its effectiveness is subject to various factors, and accurate estimation of methane production of pretreated LW remains a challenge. Here, based on 254 LW samples, a machine learning (ML) model to predict the methane production performance of pretreated feedstock was constructed using two automated ML platforms (tree-based pipeline optimization tool and neural network intelligence). Furthermore, the interactive effects of pretreatment conditions, feedstock properties, and digestion conditions on methane production of pretreated LW were studied through model interpretability analysis. The optimal ML model performed well on the validation set, and the digestion time, pretreatment agent, and lignin content (LC) were found to be key factors affecting the methane production of pretreated LW. If the LC in the raw LW was lower than 15%, the maximum CMY might be achieved using the NaOH, KOH, and alkaline hydrogen peroxide (AHP) with concentrations of 3.8%, 4.4%, and 4.5%, respectively. On the other hand, if LC was higher than 15%, only high concentrations of AHP exceeding 4% could significantly increase methane production. This study provides valuable guidance for optimizing pretreatment process, comparing different chemical pretreatment approaches, and regulating the operation of large-scale biogas plants.
摘要：
化学预处理是提高木质纤维素废物(LW)累积甲烷产率(CMY)的常用方法,但其效果受多种因素影响。准确估计预处理LW的甲烷产量仍然是一个挑战。这里,基于254个LW样本，使用两个自动ML平台（基于树的管道优化工具和神经网络智能）构建了机器学习（ML）模型来预测预处理原料的甲烷生产性能。此外，预处理条件的相互作用效应，原料性质，通过模型可解释性分析，研究了消化条件对预处理LW产甲烷的影响。最优ML模型在验证集上表现良好，和消化时间，预处理剂,发现木质素含量（LC）是影响预处理LW甲烷产量的关键因素。如果原始LW中的LC低于15%，使用NaOH可以达到最大CMY，KOH,KOH和碱性过氧化氢（AHP），浓度为3.8％，4.4%,和4.5%，分别。另一方面,如果LC高于15%，只有超过4%的高浓度层次分析法才能显著提高甲烷产量。本研究为优化预处理工艺提供了有价值的指导,比较不同的化学预处理方法，并规范大型沼气厂的运行。
