Uncertainty and influence of input parameters and assumptions on the design and analysis of thermochemical waste conversion processes: A stochastic approach

Process design is a challenging task for researchers and engineers. Incomplete information and variation in input data affect the outputs and reliability of key performance indicators (KPIs) of the designed process. The efficient utilization of waste is becoming increasingly important, and researchers use simulation and modelling tools for design and assessment of waste conversion processes. The complex nature of modelling of waste conversion processes and uncertainty of technical and financial data result in substantial variation in the KPIs of the designed process. In this study, we identified the critical parameters and assumptions that cause uncertainty in the process design analysis of waste-to-biofuels conversions. We used a stochastic modelling approach to address these methodological challenges and performed Monte Carlo simulations on waste-to-biofuel processes. The identified uncertain parameters and inputs were varied for a whole year with a one-minute time step. Different thermochemical conversion pathways were modelled by varying uncertain inputs and assumptions over the year by applying Monte Carlo simulations. Variations in the system's technical and economic KPIs were observed and compared. The results show that the heterogeneous nature of waste is a highly sensitive parameter, and a small change in its elemental analysis varies the technical performance significantly. Similarly, operating hours, plant size, capital investment, waste, and biofuel price are also very influential parameters on process design. Furthermore, the feasibility of waste-to-biofuel systems depends largely on how researchers and engineers select these parameters. Overall, the results reveal that by including the uncertainty of input parameters and assumptions in process design, the biases in results could be addressed transparently, making the overall assessment more reliable.