Integration of gasification with CO2 capture using CaO sorbent is proposed as an alternative treatment to convert municipal solid waste (MSW) into energy. Aspen Plus process simulator was employed to study the process. Two models were built to represent the non-sorbent and the sorbent-enabled MSW gasification. The model validation against available experimental data shows high accuracy of the simulation result. The effect of CO2 capture using CaO sorbent on the syngas composition and lower heating value (LHV) was observed by comparing the two models, and sensitivity analysis was performed on both models. Several process parameters affecting the syngas composition and LHV were investigated, including CaO/MSW ratio, temperature, equivalence ratio, and steam/MSW ratio. The addition of CaO sorbent for CO2 capture was found to successfully reduce the CO2 content in the syngas, increase the H2 composition, and improve the syngas LHV at the temperature below 750 oC. The maximum H2 composition of 56.67% was obtained from the sorbent-enabled gasification. It was found that increasing equivalence ratio leads to a higher H2 concentration and syngas LHV. Raising steam/MSW ratio also increases the H2 production, but also reduces the LHV of the syngas. Observation of the temperature effect found the highest H2 production at 650 oC for both non-sorbent and sorbent-enabled gasification.
Machine learning based modelling for lower heating value prediction of municipal solid waste