The framework of a multi‐scale model that couples a deep neural network, a widely used machine learning approach, with a partial differential equation solver and provides understanding of the relationship between the pore‐scale electrode structure reaction and device‐scale electrochemical reaction uniformity within a redox flow battery is introduced. A deep neural network is trained and validated using 128 pore‐scale simulations that provide a quantitative relationship between battery operating conditions and uniformity of the surface reaction for the pore‐scale sample. Using the framework, information about surface reaction uniformity at the pore level to combined uniformity at the device level is upscaled. The information obtained using the framework and deep neural network against the experimental measurements is also validated. Based on the multi‐scale model results, a time‐varying optimization of electrolyte inlet velocity is established, which leads to a significant reduction in pump power consumption for targeted surface reaction uniformity but little reduction in electric power output for discharging. The multi‐scale model coupled with the deep neural network approach establishes the critical link between the micro‐structure of a flow‐battery component and its performance at the device scale, thereby providing rationale for further operational or material optimization.

Carbon dioxide capture via solvent absorption in packed columns has emerged as a potential technology to mitigate coal‐fired power plant CO₂ emissions. Parameters, including packing types, solvent properties, and operating conditions, could potentially affect the packed column CO₂ capture efficiency. To understand the importance of those parameters and help packed column optimization, a design of experiments (DoEs) method was proposed to generate input parameter matrix. Combined with multiphase computational fluid dynamics (CFD), the random packed column parameter influence on the liquid holdup and interfacial area can be efficiently investigated. Surrogate‐based sensitivity analysis shows that the solvent flow rate and contact angle are key factors dictating liquid holdup and interfacial area. On the other hand, solvent viscosity has a marginal impact on the interfacial area. The sensitivity scores were calculated for each input parameter to guide the selection of dimensionless numbers for the liquid holdup and interfacial area correlation development.