Abstract
The transition to sustainable energy sources has heightened interest in biomass gasification as a viable thermochemical conversion process for producing syngas, a flexible and renewable fuel with applications in power generation, hydrogen production, and synthetic fuel synthesis. Despite its potential, the efficiency of biomass gasification remains highly sensitive to reactor design, operating conditions, and feedstock properties, requiring advanced computational modeling to optimize performance. Entrained flow (EF) and bubbling fluidized bed (BFB) gasifiers represent two of the most widely employed reactor configurations for biomass conversion, yet accurately predicting their hydrodynamics, gas-solid interactions, reaction kinetics, and heat transfer mechanisms remain a challenge. Existing modeling approaches often rely on simplified assumptions that limit predictive accuracy, particularly in capturing the complex multiphase flow dynamics and the intricate chemical reactions during gasification.
This research bridges this gap by developing and confirming high-fidelity Eulerian-Lagrangian Computational Particle Fluid Dynamics (CPFD) models for biomass gasification to enhance the representation of gas-solid interactions, reaction kinetics, and thermal energy transport. To address the limitations of existing models, this study develops an advanced CFD framework capable of simulating biomass gasification processes in EF and BFB reactors with improved accuracy. The models incorporate detailed gas-solid interaction physics, including particle residence time, char conversion, and volatile release, ensuring a strong representation of biomass conversion mechanisms. For the BFB gasifier, a 3D CPFD model was established to simulate air–steam biomass gasification. The BFB model was used to investigate the effects of key parameters such as reactor temperature and steam-to-biomass (S/B) ratio on syngas composition. The simulations demonstrated that increasing temperature and S/B ratio led to enhanced hydrogen and carbon monoxide yields, while methane and tar content decreased, validating the model’s predictive accuracy against experimental data from earlier BFB campaigns.
In parallel, an advanced CPFD framework was developed for EF gasification processes, incorporating detailed gas-solid interaction physics such as particle residence time, char conversion, and volatile release. The computational framework is validated against experimental data from oxygen-blown, steam-injected gasification of lignin and entrained flow gasification of sewage sludge digestate (SSD). The research further explores the optimization of key operating parameters such as temperature, equivalence ratio (λ), steam-to-biomass ratio (S/B), and reactor pressure to assess their influence on syngas composition, hydrogen yield, carbon conversion efficiency (CCE) and cold gas efficiency (CGE). Particular attention is given to the role of gasifying agents, including air, steam, CO₂, and O₂, in determining the efficiency and quality of syngas production. The study identifies CO₂-steam gasification as a promising strategy for enhancing hydrogen production by promoting water-gas shift reactions, resulting in an increase in hydrogen yield of 15-20% compared to conventional air gasification.
In addition to optimizing operating conditions, the research investigates the feasibility of multi-feedstock gasification as a strategy for improving syngas quality and waste valorization. The co-gasification of SSD, and wood powder (WP) is analyzed to assess its impact on carbon conversion efficiency, reactor stability, and phosphorus retention in ash. Experimental and numerical results demonstrate that blending SSD with WP enhances CGE beyond 100%, significantly improving syngas yield and hydrogen production. The CFD simulations provide critical insights into the synergistic effects of biomass blending, revealing that co-gasification mitigates operational challenges such as agglomeration and excessive char formation while enabling more stable reactor performance. The findings underscore the potential of co-gasification as an effective strategy for using heterogeneous biomass resources while enhancing gasifier efficiency.
The results of this research highlight the effectiveness of high-fidelity CFD modeling in optimizing biomass gasification processes, offering a predictive tool for improving reactor design, process control, and fuel flexibility. The validated Eulerian-Lagrangian CPFD models developed in this study provide a reliable foundation for optimizing biomass-to-energy conversion at an industrial-scale. The identification of optimal gasifying agents, feedstock blends, and operating conditions contributes to the advancement of sustainable bioenergy technologies, with significant implications for hydrogen production, waste-to-energy conversion, and emissions reduction. This work provides a comprehensive modeling framework that supports the transition towards more efficient and cleaner biomass gasification systems. Future research should focus on further refining kinetic mechanisms, scaling up CFD simulations for industrial applications, and integrating real-time optimization techniques to enhance process efficiency and reliability.