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[Display omitted] • Effects of various microaerobic pretreatments on AD of tobacco straw were studied. • Biogas slurry as pretreatment agent dosing 4 mL/g VS of O 2 (BS-4) performed better. • BS-4 had a good performance in the lignocellulosic decomposition of tobacco straw. • O 2 dosing in microbial pretreatment process could change the microbial diversity. Tobacco straw is an abundant biomass in China's agricultural ecosystems, and has high potential for methane production. However, the anaerobic digestion (AD) efficiency is limited by the recalcitrant lignocellulose structure of the tobacco straw. In this study, three microaerobic pretreatments were performed for the AD of tobacco straw to increase methane production. Among them, microbial pretreatment with biogas slurry at an oxygen concentration of 4 mL/g VS resulted in the highest methane production of 349.1 mL/g VS, increasing by 19.8 % than that of untreated. During this pretreatment, the relative abundances of Enterococcus and Clostridium sensu stricto 12 , which are closely related to acetic acid production and cellulose degradation, were high, and these bacteria might have an important contribution to substrate hydrolysis and the methanogenesis efficiency of the AD process. This study advances the understanding of microaerobic pretreatment processes and provides technological guidance for the efficient utilization of tobacco straw. [ABSTRACT FROM AUTHOR]

[Display omitted] • The machine learning model for methane yield prediction was constructed. • Lignin content, organic loading and nitrogen content were vital predictive features. • The interaction of digestion time, cellulose and lignin contents was uncovered. • An online platform for methane yield prediction was created for public access. 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. [ABSTRACT FROM AUTHOR]