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Multiscale Dynamics in Advanced Cogasification of Solid Fuels and Green Energy Carriers: Integrating Microstructural Kinetics, CFD Aerodynamics and AI-Driven GHG Mitigation
Summary
This review paper looks at how factories can turn waste materials like coal, plants, and even plastic trash into clean-burning gas fuel using computer modeling and AI, instead of relying on more traditional fossil fuels. This matters for human health because these industrial processes can reduce air pollution and greenhouse gas emissions compared to standard fossil fuel burning, while also offering a way to responsibly dispose of plastic waste rather than letting it break down into microplastics in the environment. It's important to note this is a technical engineering summary of modeling techniques, not a study measuring direct health outcomes in people.
The transition of heavy industries toward lower carbon emission intensity requires the substitution of conventional fossil fuels with renewable and alternative feedstocks. Co-gasification provides a thermochemical pathway to convert heterogeneous blends of high-ash coal, biomass, plastic waste, and alternative energy carriers into syngas. The introduction of these varied feedstocks induces nonlinear variations in reaction kinetics, multiphase aerodynamics, and ash fusion behavior. Understanding these complex interactions is essential for optimizing reactor design, ensuring stable operation, and maximizing syngas yield while mitigating operational bottlenecks like agglomeration and slag deposition. This chapter examines advanced co-gasification systems through a comprehensive multiscale modeling framework. By integrating microstructural kinetic data, three-dimensional Computational Fluid Dynamics (CFD), and Artificial Intelligence (AI) models, this work connects microscopic particle degradation with macroscopic reactor operation. The synergistic approach allows for the accurate prediction of carbon conversion efficiencies and the spatial distribution of multiphase flows. Furthermore, the application of machine learning algorithms enhances process control by rapidly predicting syngas profiles. The chapter also synthesizes recent advancements in entrained flow and fluidized bed gasifier designs, emphasizing the simultaneous control of tar and particulate matter. It evaluates the downstream integration of gasification networks with Solid Oxide Fuel Cells (SOFCs) and green hydrogen-coupled renewable methanol synthesis to form efficient polygeneration systems. Finally, techno-economic and life cycle assessments (LCA) are presented to validate Greenhouse Gas (GHG) mitigation strategies in the power generation and cement sectors. Consequently, this integrated framework provides critical insights for the model-based design, optimization, and industrial scale-up of advanced, low-carbon gasification technologies.