The development of porous materials as highly efficient CO2 adsorbents is a promising and continuously expanding research area, aiming to provide more efficient energy alternatives and help maintain a clean environment. Currently, fossil fuel combustion is the primary cause of the increase in atmospheric CO2 content and a significant contributor to global warming, because CO2 is a major greenhouse gas considered to pose a large environmental threat. The current level of atmospheric CO2 exceeds 415 ppm, which highlights the need to reduce the further release of this gas and prevent it from entering the carbon cycle. Many porous adsorbents have been developed as CO2 capture agents; however, they are only partially effective and need to be replaced with more effective porous materials to tackle the global climate concerns due to the presence of CO2 in the atmosphere. Herein, we review the existing CO2 capture techniques as well as related tools and materials, highlighting the recent progress in the use of zeolites for this purpose and indicating their significant potential for CO2 capture owing to their high porosity, ultra-small pores, structural diversity, high stability, and excellent recyclability and chemical reactivity.

Utilization of zeolites as CO2 capturing agents: Advances and future perspectives

Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: Plant data learned by deep neural network and least square support vector machine

The use of solid oxide fuel cells (SOFCs) is a promising approach towards achieving sustainable electricity production from fuel. The utilisation of the hydrocarbons and biomass in SOFCs is particularly attractive owing to their wide distribution, high energy density, and low price. The long-term operation of SOFCs using such fuels remains difficult owing to a lack of an effective diagnosis and optimisation system, which requires not only a precise analysis but also a fast response. In this study, we developed a hybrid model for an on-line analysis of SOFCs at the cell level. The model combines a multi-physics simulation (MPS) and deep learning, overcoming the complexity of MPS for a model-based control system, and reducing the cost of building a database (compared with the experiments) for the training of a deep neural network. The maximum temperature gradient and heat generation are two target parameters for an efficient operation of SOFCs. The results show that a precise prediction can be achieved from a trained AI algorithm, in which the relative error between the MPS and AI models is less than 1%. Moreover, an online optimisation is realised using a genetic algorithm, achieving the maximum power density within the limitations of the temperature gradient and operating conditions. This method can also be applied to the prediction and optimisation of other non-liner, dynamic systems.

Towards online optimisation of solid oxide fuel cell performance: Combining deep learning with multi-physics simulation

Parallel and series multi-bed pressure swing adsorption processes for H2 recovery from a lean hydrogen mixture

High-purity hydrogen production via a water-gas-shift reaction in a palladium-copper catalytic membrane reactor integrated with pressure swing adsorption

Combined approach using mathematical modelling and artificial neural network for chemical industries: Steam methane reformer

Adsorption equilibria and kinetics of silica gel for N2O, O2, N2, and CO2

Separation of propane and propylene by desorbent swing adsorption using zeolite 13X and carbon dioxide

A 13X column Vacuum Swing Adsorption (VSA) has been widely studied as a promising separation process for post-combustion carbon capture, as it has been claimed that it would be more economical than...

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Experimental and Simulation Study on CO2 Adsorption Dynamics of a Zeolite 13X Column during Blowdown and Pressurisation: Implications of Scale-up on CO2 Capture Vacuum Swing Adsorption Cycle

The adsorption equilibria of water vapor on surface-modified activated carbons (ACs) and activated alumina (AA) were measured at 283.15, 293.15, and 303.15 K at relative pressures of up to 0.9. The...

Suk Hoon Hong

Papers

Combined approach using mathematical modelling and artificial neural network for chemical industries: Steam methane reformer

Adsorption equilibria and kinetics of silica gel for N2O, O2, N2, and CO2

Separation of propane and propylene by desorbent swing adsorption using zeolite 13X and carbon dioxide

Experimental and Simulation Study on CO2 Adsorption Dynamics of a Zeolite 13X Column during Blowdown and Pressurisation: Implications of Scale-up on CO2 Capture Vacuum Swing Adsorption Cycle

Adsorption Equilibria of Water Vapor on Surface-Modified Activated Carbons and Alumina