Machine learning-based prediction of methane production from lignocellulosic wastes

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.

Automated summary

This paper developed a machine learning model to predict the biochemical methane potential during anaerobic digestion. Model analysis identified lignin content, organic loading, and nitrogen content as key attributes for methane production prediction. For feedstocks with high cellulose content, early methane production is lower but can be improved by prolonging digestion time. Moreover, lignin content exceeding a certain value significantly inhibits methane production.