Optimization and Process Modeling of Plasma Gasifier via Aspen Plus and Surrogate Model for Treatment of Municipal Solid Waste

Plasma gasification is a sustainable and advanced technology for the safe and efficient treatment of municipal solid waste (MSW). In this process, a plasma torch serves as the primary heating source to convert MSW into syngas and inert vitrified slag. The produced syngas can be used for various downstream applications, including power generation. In this study, an updraft plasma gasifier is modeled using the Aspen Plus process simulator, with municipal solid waste from Lahore, Pakistan, used as the feedstock. Air is selected as a plasma-forming gas due to its low cost and widespread availability. The primary aim of this research is to analyze the effect of specific torch power and the air-to-feed mass flow ratio on syngas molar composition, syngas higher heating value (HHV), and cold gas efficiency (CGE), and to maximize gasifier performance. CGE of the gasifier is optimized using a surrogate-based model integrated with a genetic algorithm (GA). An artificial neural network (ANN) is employed as the surrogate model for the optimization of CGE. The novelty of this work lies in two key aspects: firstly, this is among the first studies to specifically model and simulate plasma gasification of Lahore’s MSW, capturing its unique waste composition characteristics; and secondly, the integration of process simulation with a data-driven optimization framework using an ANN surrogate model. A total of 1521 data points were generated from the Aspen Plus simulation to train the ANN model and perform optimization in MATLAB. The optimized CGE was found to be 90.6%. Validation of the ANN-GA optimization was carried out by implementing the optimized input parameters in the Aspen Plus gasifier model. The resulting CGE shows a percent relative error of only 0.11% compared to the MATLAB-predicted value, confirming the accuracy of the surrogate model. Furthermore, comparison with the base case simulation reveals that the optimized operating conditions lead to an 8.6% increase in cold gas efficiency, demonstrating the effectiveness of the proposed optimization approach.