The rampant global energy demand is predominantly met by fossil fuels, resulting in the
reduction of their supply and an alarming increase in greenhouse gas (GHG) emissions …

This work reports the results of a perspective workshop held in summer 2021 discussing the
current status and future needs for multiscale modeling in reaction engineering. This …

Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …

This work aims to evaluate the diffusion limitation for the steam methane reforming process
over spherical catalyst particles (Ni/α-Al 2 O). Numerical calculations based on a CFD model …

The intensification of catalytic reactors is expected to play a crucial role to address the
challenges that the chemical industry is facing in the transition to more sustainable …

This study presents conventional and artificial neural network‐based data‐driven modeling
(DDM) methods to model simultaneously the filtered mesoscale drag, heat transfer and …

Biogas from anaerobic digestion (AD), as an important alternative to fossil fuels, has
contributed to energy recovery and environmental sustainability. Incomplete or inefficient …

Increasing the global energy demand motivates the search for renewable and clean energy
resources. Fischer-Tropsch synthesis (FTS) is one of these sources, which converts syngas …

The catalyst particle packing characteristics have a significant impact on improving the
comprehensive energy conversion performance and economic benefits of sustainable solar …

Improving the radial heat transport or the heat distribution is a paramount aspect for the
sustainable and safe operation of catalytic gas-solid reaction systems. In this perspective …