In this study, a superstructure-based decision making model is presented to determine the most sustainable biorefining pathway of organic fraction of municipal solid waste, i.e., anaerobic digestion, fermentation for bio-succinic acid and lactic acid recovery, and single cell protein production, as well as the best downstream process for the produced biogas. Based on the possible bioprocess routes created from implied technologies, thirteen scenarios were developed to perform mass and energy balances as well as environmental life cycle assessments. Subsequently, a general superstructure-based multi-criteria decision making model was designed representing possible technologies at each stage of biomass processing and their interconnections. The optimal biorefining route for achieving a sustainability solution was selected based on energetic, economic, and environmental aspects; three objective functions for maximizing energy saving and profitability, and minimizing greenhouse gas emissions. The results achieved in this study demonstrated that the scenario including anaerobic digestion of organic fraction of municipal solid waste coupled with a biological biogas upgrading unit for bio-methanation of biogas into fuel-grade bioCH4 was the most sustainable biorefining route. This scenario led to a greenhouse gas saving of 243.4 kg CO2 eq/t biopulp and a primary energy saving of 4664.77 MJ/t biopulp, creating a profit of 145.41 €/t biopulp. On the contrary, biosuccinic acid production with cogeneration of heat & power and bioCH4 with further use as home cooking fuel found to be the least preference for biowaste valorization. The sensitivity analysis showed that fluctuations in production costs and revenue would not change the rank of scenarios however the profitability of scenarios would change to some extent. The framework presented herein is a promising approach to find the most sustainable biorefining rout out of several technically-possible solutions. Such framework would be even more applicable if other important aspects such as spatio-temporal parameters, policy regulations, market-driven factors, etc. to be included in the decision making process.
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