A hybrid artificial neural network–Aspen Plus framework for CO2 -enhanced biomass gasification and gas turbine performance

Concerns over climate change and fossil fuel dependency have intensified the development of renewable energy and sustainable energy globally. However, conventional mechanistic models often struggle to capture the complex nonlinear behavior of integrated gasification and turbine systems, limiting their predictive accuracy and suitability for real-time applications. This study presents a hybrid modeling framework that integrates a two-stage biomass gasification and gas turbine model with artificial neural networks (ANNs) to enhance prediction accuracy for integrated biomass-to-power performance. More than 5,000 simulation cases were generated using a parametric grid sampling strategy and validated against experimental data from the literature to ensure model fidelity. Spearman’s rank correlation analysis identified the air flow rate and the carbon dioxide (CO₂)-to-fuel ratio as the most influential parameters. The optimized ANN models achieved coefficients of determination (R²) exceeding 0.99 and root mean square errors (RMSE) below 5 %, substantially outperforming conventional modeling approaches. The proposed hybrid ANN–Aspen Plus framework enables rapid and reliable performance prediction, facilitates real-time optimization, and promotes carbon dioxide (CO₂) utilization in advanced biomass-to-power systems.