Targeting carbon-neutral waste reduction: Novel process design, modelling and optimization for converting medical waste into hydrogen

This study proposes a novel system designed to turn waste masks (WM) into hydrogen while aiming at carbon neutrality. The system utilizes plasma gasification to primarily convert WM into syngas, which is subsequently upgraded through water gas shift to enhance hydrogen fraction. To mitigate carbon emissions, carbon capture techniques including monoethanolamine-based carbon absorption and oxy-fuel combustion are incorporated into the system. A high-fidelity model of the designed process is developed in Aspen Plus. Machine learning-driven optimization is applied to identify the optimal operating conditions. Utilizing Gaussian process regression-based surrogate optimization, the system achieves around 0.5 kg CO2 eq/kg H2 of levelized emissions per unit hydrogen (LEH) in the reaction stage and the execution time required for optimization is only 4 s, outperforming detailed process models-based optimization. Energy assessment highlights plasma gasification and CO2 absorption as the primary sources of energy loss. Furthermore, life cycle analysis indicates that CO2 absorption is the primary contributor to carbon emissions, accounting for approximately 60% of the total emissions. With levelized emissions of 1.27 kg CO2 eq/kg WM and 5.18 CO2 eq/kg H2, the designed system demonstrates superiority over conventional medical waste treatment and hydrogen production methods.