Optimizing the microbial community composition in anaerobic digesters to improve biogas yields from food waste

Methods: Food waste collected from local restaurants and households in New York city will be used in this study. The food waste will be characterized to analyze its composition. Batch anaerobic digesters will be inoculated with microbial communities from existing food waste digesters. DNA analysis using 16S rRNA gene sequencing will be done to show the initial microbial consortia. Various perturbations will be applied to the digesters including changes in temperature, pH, feeding rates, mixing intensities to select for microbial populations yielding higher biogas production. Biogas production will be monitored over time. DNA analysis will be repeated after perturbation to analyze changes in the microbial community structure. Results: Preliminary results show Food waste characterized was found to contain 35% proteins, 45% carbohydrates and 20% fats. The initial microbial consortia in the digesters was dominated by bacteria from the genera Clostridium, Bacteroides and Methanothermobacter. Increasing feeding rates led to a 40% increase in biogas yields. Microbial analysis indicated a shift in populations with Clostridium becoming the most abundant genus. Decreasing pH from 7 to 6.5 further enhanced biogas by 25% with Clostridium and Syntrophomonas becoming dominant. Discussion: The study demonstrates that optimizing operational conditions can effectively manipulate the microbial communities in anaerobic digesters processing food waste. By applying selective pressures, populations better suited to degrade the local food waste and produce higher methane yields can be enriched. Further experiments will aim to construct stable, optimized consortia through controlled perturbation for robust and efficient food waste digestion. The approach holds promise for improving biogas production from food waste globally