This study introduces a system based on the gasification of medical wastes to produce synthesis gas, which is further processed for delivering power, storing hydrogen fuel, and capturing carbon dioxide in compliance with zero-emission goals. In the core of such a system, a series of reactions takes place within a gasifier reactor, steam methane reformer, and water-gas shift reactor, followed by hydrogen separation via a Palladium membrane. Different machine learning algorithms are developed to predict system outputs with remarkable accuracy up to 100 % R-squared value for hydrogen storage and carbon dioxide capturing, and 99.84 % for power production. The required surface area for the Palladium membrane is estimated with high accuracy at an R-squared value of 99.47 %. Statistical analysis shows that medical waste rate and reactor pressure are the most influencing parameters leading in the minimum area of 5 m2 at low values of both parameters. The scalability and adaptability of such a system assure that the present work will represent a useful basis for any future developments in waste-to-energy systems. Integration with machine learning algorithms further enhances the efficiency and reliability of the system, hence setting a new benchmark for the solution of sustainable energy.
