Data-Driven Based In-Depth Interpretation and Inverse Design of Anaerobic Digestion for CH4-Rich Biogas Production

Anaerobic digestion (AD) is one of the most widely used bioconversion technologies for renewable energy production from wet biowaste. 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 CH4-rich biogas. In this context, ensemble machine learning (ML) algorithms were employed to develop multitask models for jointly predicting the CH4 yield and content in biogas and understanding this complicated process. Based on the best ensemble model with the R-2 values of 0.82 and 0.86 for the multitask prediction of CH4 yield and content, the top three critical factors for CH4 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 CH4 generation. Moreover, the well-developed ensemble model was integrated with an optimization algorithm to inversely design the AD process for a real-world food waste, in which the CH4 yield was as high as 468.7 mL/gVS and the calculation results were experimentally validated with relative errors of 9-16%. This work provides a creative approach to gain insights and inverse design for AD reactors, which is helpful to waste-to-energy technologists and practitioners.

Automated summary

This study utilized ensemble machine learning algorithms to predict and understand the anaerobic digestion (AD) system and developed a reversible design method. The critical factors influencing methane yield and content were identified and the computational results were experimentally validated in a real-world food waste AD process.