Techno-economic assessment of optimised vacuum swing adsorption for post-combustion CO2 capture from steam-methane reformer flue gas

Enhanced CH4/N2 Separation Efficiency of UiO-66-Br2 through Hybridization with Mesoporous Silica

Efficient separation of CH4 from N2 is essential for the purification of methane from nitrogen. In order to address this problem, composite materials consisting of rod-shaped SBA-15-based UiO-66-Br2 were synthesized for the purpose of separating a CH4/N2 mixture. The materials were characterized via PXRD, N2 adsorption-desorption, SEM, TEM, FT-IR, and TGA. The adsorption isotherms of CH4 and N2 under standard pressure conditions for the composites were determined and subsequently compared. The study revealed that the composites were formed through the growth of MOF nanocrystals on the surfaces of the SBA-15 matrix. The enhancements in surface area and adsorption capacity of hybrid materials were attributed to the structural modifications resulting from the interactions between surface silanol groups and metal centers. The selectivity of the composites towards a gas mixture of CH4 and N2 was assessed utilizing the Langmuir adsorption equation. The results of the analysis revealed that the U6B2S5/SBA-15 sample exhibited the greatest selectivity for CH4/N2 adsorption compared to the other samples, with an adsorption selectivity parameter (S) of 20.06. Additional research is necessary to enhance the enrichment of methane from CH4/N2 mixtures using SBA-15-based metal-organic framework materials.

Performance-Based Screening of Porous Materials for Carbon Capture

Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

Oxygen Enrichment Membranes for Kuwait Power Plants: A Case Study

Power plants are considered as the major source of carbon dioxide pollution in Kuwait. The gas is released from the combustion of fuel with air to convert water into steam. It has been proven that the use of enriched oxygen can reduce fuel consumption and minimize emissions. In this study, UniSim (Honeywell, Charlotte, NC, USA) was used to estimate the fuel savings and carbon dioxide emissions of the largest power plant in Kuwait (Alzour). Results showed that at 30 mol% oxygen, the fuel consumption was lowered by 8%, with a reduction in carbon dioxide emissions by 3524 tons per day. An economic analysis was performed on the use of a membrane unit to produce 30 mol% oxygen. At current market prices, the unit is not economical. However, the system can achieve a payback duration of 3 years if natural gas price increases to USD 6.74 or the compressor cost decreases to USD 52 per kW. Currently, the research and development sector is targeting a membrane fabrication cost of less than USD 10 per m² to make the membrane process more attractive.

Guidelines for Techno-Economic Analysis of Adsorption Processes

Techno-economic analyses (TEAs) of CO2 capture technologies have risen in popularity, due to growing interest in meeting CO2 emissions reduction targets. Adsorption processes are one of the technologies proposed for CO2 capture, and although difficult, standardization of TEAs for adsorption should be attempted. The reason is that TEAs are often referred to as input data to other forms of modeling, to guide policy, and to act as summaries for those unfamiliar with adsorption processes. Herein, we discuss the aspects that should be considered when conducting TEAs for CO2 adsorption processes, and we present the implications of choices made at the TEA stage and offer guidance on best practice. our aim is to make TEAs of adsorption processes widely accessible to the adsorption community, and more generally to communities engaged in the evaluation of CCS technologies.

The Role of Machine Learning in the Understanding and Design of Materials

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.