Development of selective microbial consortia for optimizing anaerobic digestion using food waste and understanding their interactions with molecular profiling

Abstract

Food waste contributes significantly to global greenhouse gas emissions (8-10%) and constitutes 24% of municipal solid waste in landfills. Annually, around 1.05 billion tonnes/ annum of food is wasted globally, with India responsible for 78 million tonnes. A large portion remains dumped in landfills, leading to greenhouse gases in the atmosphere. Given its rich biochemical composition (carbohydrates, lipids, proteins, and essential nutrients), FW presents an ideal feedstock for anaerobic digestion (AD) to generate renewable energy. This study explores the AD of food waste (FW), a multi-stage microbial process that converts organic matter into biogas. The efficiency of this process depends on the diversity of microbial consortia used as inoculum. The research focuses on developing optimized microbial consortia, evaluating their interaction with substrates, and analysing how microbial diversity and metabolism influence FW digestion. For this, three different mixed consortiums were considered: cow dung (R1), sewage sludge (R2), and a mixture of cow dung and sewage sludge (R3) for AD of FW. It was observed that the mixed consortia (R3) produced maximum biogas for 5 gTS/L (310 mL) or 62 mL/g TS. Metagenomics analysis of R3 showed better microbial diversity than individual ones (R1 and R2), and the experimental results were corroborated with the potential metabolic mechanisms involved in the AD of the FW process. To optimize AD using two widely used optimization methods: Response Surface Methodology with Box Behnken Design (RSM-BBD) and Taguchi orthogonal array L9. Organic load, oxidation-reduction potential, pH, and hydraulic retention time were evaluated for biogas production and volatile solids removal (VSr). The study found that Taguchi s design provided approximate optimization response values and did not allow parameter interaction. In contrast, RSM-BBD provided precise optimal values and details on the impact of individual and combined parameters, making RSM-BBD the preferred choice for optimizing AD systems fo