Angielski
Advanced foam materials featuring highly interconnected pore structures substantially improve heat and mass transfer performance in catalytic reactors and energy conversion systems, thereby facilitating process intensification that is both energy-efficient and aligned with carbon-neutral objectives. This book adopts a multidisciplinary methodology, integrating high-fidelity computational modeling, physics-informed machine learning, and experimentally validated characterization, to address persistent technical challenges across catalytic reactors, proton-exchange membrane fuel cells, solar-thermal energy systems, and low-emission combustion technologies. Key methodological and technological contributions include: (i) The key applications of foam structures in the field related to energy; (ii) AI-enhanced porous media modeling for predictive optimization of transport processes; and (iii) thermally adaptive design strategies for safe and efficient management of highly exothermic processes, including CO₂ methanation and hydrogen-blended combustion. Grounded in application-driven research, the work demonstrates quantifiable advances in emission reduction, system-level energy efficiency gains, and scalable implementation pathways.
