Modeling and experimental validation of the Co-gasification of plastic and biomass waste to estimate product yields

Co-gasification represents a viable method for addressing the challenges posed by the rapid accumulation of plastic and biomass waste. The present study advances the thermodynamic modeling of plastic and biomass co-gasification using the Aspen Plus simulation tool. The predictions from the non-modified original Aspen Plus model (which utilizes the Gibbs free energy minimization approach to simulate the gasifier) and the modified Aspen Plus model (enhanced with super-equilibrium conditions and multiple constraints) are compared with existing models. These modifications account for the interactions and synergistic effects between plastic and biomass during the decomposition phase, enabling the prediction of char and tar yields, tar composition, and hydrocarbon concentrations in the gas stream. Projected outcomes from the modified model closely match experimental findings, with the majority of variances falling within a ±5 % margin of error. On comparing root mean squared error (RMSE) values for various plastic/biomass co-gasification scenarios, under the worst-case circumstances, the maximum deviation in gas composition by the model was 1.93 vol% and 4.45 wt% for product yields relative to experimental results. An analysis of RMSE values indicated that the modified model offered at least a 95 % improvement to the accuracy of gas composition predictions compared to the original thermodynamic Aspen Plus model. The modified model also more accurately forecast char, tar, and hydrocarbon yields, which is often unattainable in thermodynamic-based models. The results of sensitivity analysis performed with predictive tools aligned well with the experimental observations. A higher co-gasification temperature, reduced equivalence ratio (ER) and lower plastic-to-biomass ratio (P/B) were identified by the modified model as favourable conditions for enhancing hydrogen production while simultaneously minimizing char and tar formation. These findings enhance simulation accuracy, supporting future economic and environmental assessments for waste management.