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AbstractThis study aims to investigate the significance and biodegradation pathways of PHB-based bioplastic in anaerobic digesters treating food waste, where the reactor performance of changed methane generation, bioplastic biodegradation efficiency, and bioinformatic analysis of functional microbes were emphasized. The results showed that PHB-based plastic film could be partially biodegraded in the food waste digester, and a bioaugmentation use of Alcaligenes Faecalis (AF) and Bacillus Megaterium (BM) was beneficial to largely accelerate the degradation process through a beneficial shift of both the functional bacterial and archaeal species. Microbial community analysis indicated that the major bacterial species belonged to genera Candidatus_Cloacimonas, Rikenellaceae, and Defluviitoga, while the dominant methanogenic archaeal species belonged to genera Methanomassiliicoccus, Methanosarcina, and Methanosaeta. Bioplastic biodegradation analysis suggested that the optimal fractions of AF and BM for PHB-based plastic degradation were 50%AF and 75%BM, respectively, which deserves further optimization and scale-up validation. The finding of this study would contribute to the combined management of PHB-based bioplastic with food waste for clean energy recovery and a greener environment.

AbstractThe utilization of biochar derived from biomass residue to enhance anaerobic digestion (AD) for bioenergy recovery offers a sustainable approach to advance sustainable energy and mitigate climate change. However, conducting comprehensive research on the optimal conditions for AD experiments with biochar addition poses a challenge due to diverse experimental objectives. Machine learning (ML) has demonstrated its effectiveness in addressing this issue. Therefore, it is essential to provide an overview of current ML-optimized energy recovery processes for biochar-enhanced AD in order to facilitate a more systematic utilization of ML tools. This review comprehensively examines the material and energy flow of biochar preparation and its impact on AD is comprehension reviewed to optimize biochar-enhanced bioenergy recovery from a production process perspective. Specifically, it summarizes the application of the ML techniques, based on artificial intelligence, for predicting biochar yield and properties of biomass residues, as well as their utilization in AD. Overall, this review offers a comprehensive analysis to address the current challenges in biochar utilization and sustainable energy recovery. In future research, it is crucial to tackle the challenges that hinder the implementation of biochar in pilot-scale reactors. It is recommended to further investigate the correlation between the physicochemical properties of biochar and the bioenergy recovery process. Additionally, enhancing the role of ML throughout the entire biochar-enhanced bioenergy recovery process holds promise for achieving economically and environmentally optimized bioenergy recovery efficiency.
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