Based on the best ensemble model with the R 2 values of 0.82 and 0.86 for the multitask prediction of CH 4 yield and content, the top three critical factors for CH 4 yield/contents were identified and their interactions with process acid generation and microbial community in the AD process were comprehensively interpreted to unveil their importance on CH 4 generation. In this context, ensemble machine learning (ML) algorithms were employed to develop multitask models for jointly predicting the CH 4 yield and content in biogas and understanding this complicated process. However, such an AD system is so complicated that it is challenging to fully comprehend this process and design the operational conditions for a specific biowaste to achieve CH 4-rich biogas. Anaerobic digestion (AD) is one of the most widely used bioconversion technologies for renewable energy production from wet biowaste.
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