Leveraging machine learning in porous media

\ua9 2024 The Royal Society of Chemistry.The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML), has had a significant impact on engineering and the fundamental sciences, resulting in advances in various fields. The use of ML has significantly enhanced data processing and analysis, eliciting the development of new and improved technologies. Specifically, ML is projected to play an increasingly significant role in helping researchers better understand and predict the behavior of porous media. Furthermore, ML models will be able to make use of sizable datasets, such as subsurface data and experiments, to produce accurate predictions and simulations of porous media systems. This capability could help optimize the design of porous materials for specific applications and improve the effectiveness of industrial processes. To this end, this review paper attempts to provide an overview of the present status quo in this context, i.e., the interface of ML and porous media in six different applications, namely, heat exchanger and storage, energy storage and combustion, electrochemical devices, hydrocarbon reservoirs, carbon capture and sequestration, and groundwater, stressing the advances made in the application of ML to porous media and offering insights into the challenges and opportunities for future research. Each section also entails a supplementary database of the literature as a spreadsheet, which includes the details of ML models, datasets, key findings, etc., and mentions relevant available online datasets that can be used to train ML models. Future research trends include employing hybrid models by combining ML models with physics-based models of porous media to improve predictions concerning accuracy and interpretability

An innovative four-objective dragonfly-inspired optimization algorithm for an efficient, green, and cost-effective waste heat recovery from SOFC

This work proposes a novel yet practical dragonfly optimization algorithm that addresses four competing objectives simultaneously. The proposed algorithm is applied to a hybrid system driven by the solid oxide fuel cell (SOFC) integrated with waste heat recovery units. A function-fitting neural network is developed to combine the thermodynamic model of the system with the dragonfly algorithm to mitigate the calculation time. According to the optimization outcomes, the optimum parameters create significantly more power and have a greater exergy efficiency and reduced product costs and CO2 emissions compared to the design condition. The sensitivity analysis reveals that while the turbine inlet temperatures of power cycles are ineffective, the fuel utilization factor and the current density significantly impact performance indicators. The scatter distribution indicates that the fuel cell temperature and steam-to-carbon ratio should be kept at their lowest bound. The Sankey graph shows that the fuel cell and afterburner are the main sources of irreversibility. According to the chord diagram, the SOFC unit with a cost rate of 13.2 $/h accounts for more than 29% of the overall cost. Finally, under ideal conditions, the flue gas condensation process produces an additional 94.22 kW of power and 760,056 L/day of drinkable water.</p