Investigating A Clean Natural Gas-based Hydrogen Production Process for Electricity Generation in Power Plants

This study investigates a clean hydrogen production process (based on a CH4 feedstock flow rate of 1000 kmol/h) integrated with an onsite hydrogen-combustion power plant. A rate-based kinetic model is used to develop steam methane reforming (SMR) and water gas shift (WGS) reactions in the reformer. The impact of auto thermal reforming (ATR) on hydrogen purity and the generated power is investigated by analysing the correlation between temperature, pressure, and steam-to-methane ratio. A full factorial design matrix is used to investigate the potential interactions among the operational variables with a set of key performance indicators (KPIs) i.e. hydrogen purity and generated power. The ATR leads to higher hydrogen purity and generated power at lower feed temperatures Also, increasing the steam-to-methane ratio leads to increased hydrogen purity and generated power in both scenarios. Pressure is found to play a critical role in power generation but has a less pronounced effect on hydrogen purity in comparison. Employment of ATR has been found to be beneficial to achieve higher hydrogen purity and increased power generated at lower feed temperatures, while simultaneously minimizing CO 2 emissions.10.13039/501100000266-Engineering and Physical Sciences Research Council

Probing into the interactions among operating variables in blue hydrogen production: A new approach via design of experiments (DoE)

Data availability: Data will be made available on request.Copyright © 2023 The Authors. Anthropogenic CO2 emission is a key driver in global warming and climate change. Worldwide, H2 production accounts for 2.5% of this CO2 emission. A shift to clean methods of hydrogen production is required to reduce CO2 emissions, and to mitigate the effects of climate change. Developing optimised process models of H2 production processes is required in order to investigate the effects of operational variables of the process and their impacts on key performance indicators (KPIs). Within this study, a detailed rate-based model was implemented to simulate the reformer in Sorption Enhanced Steam Methane Reforming (SE-SMR), as well as Sorption-Enhanced Auto-Thermal Reforming (SE-ATR) processes. The results indicate that the SE-ATR/ATR corresponds to a significantly improved performance over the SMR with the optimal operating conditions for achieving the desired KPIs, including hydrogen purity (86%), hydrogen yield (36%), methane conversion (99%), and carbon capture rate (50%) at a temperature of 720 °C, a pressure of 20 bara, and an S/C ratio of 6. Whereas with SMR, the temperature, pressure, and S/C ratio should be adjusted to 975 °C, 20 bara, and 6, respectively, to achieve a hydrogen purity of 84%, a hydrogen yield of 42%, a methane conversion of 96%, and a carbon capture rate of 48%. The study provides insights into the optimal operating conditions to achieve maximum efficiency in the reformer, and demonstrates the effectiveness of incorporating DoE within process modelling as a tool for optimisation.Engineering and Physical Sciences Research Council (EPSRC) under the project titled “Multiphysics and Multiscale Modelling for Safe and Feasible CO2 Capture and Storage - EP/T033940/1

The rise of the machines: A state-of-the-art technical review on process modelling and machine learning within hydrogen production with carbon capture

Data availability: No data was used for the research described in the article.Copyright © 2023 The Authors. This study aims to present a compendious yet technical scrutiny of the current trends in process modelling as well as the implementation of machine learning within combined hydrogen production and carbon capture (i.e. blue hydrogen). The paper is intended to accurately portray the role that machine learning is anticipated to play within research and development in blue hydrogen production in the forthcoming years. This covers the implementation of machine learning at both material and process development levels. The paper provides a concise overview of the current trends in blue hydrogen production, as well as an intro to machine learning and process modelling within the same context. We have reinforced our paper by first summarising a brief description of the key “tools” used in machine learning and process modelling, before painstakingly examining the implementation of these techniques in blue hydrogen production and the less-discovered merits and de-merits. Ultimately, the paper depicts a clear picture of the advancements in machine learning and the major role it is expected to play in accelerating research and development in blue hydrogen production on both material and process development fronts. The paper strives to shed some light on the key advantages that machine learning has to offer in blue hydrogen for future research work.UK Engineering and Physical Sciences Research Council (EPSRC) via the grant “Multiphysics and multiscale modelling for safe and feasible CO2 capture and storage - EP/T033940/1”; EPSRC Doctoral Training Partnerships (DTP) award, EP/T518116/1 (project reference: 2688399)

Chemical Activation of Recycled Carbon Fibres for Application as Porous Adsorbents in Aqueous Media

UK Engineering and Physical Sciences Research Council (Doctoral Training Programme (DTP) Award (2020)), Brunel Research Initiative and Enterprise Fund (BRIEF)

Latest advances and challenges in carbon capture using bio-based sorbents: A state-of-the-art review

Copyright © 2022 The Authors. Effective decarbonisation is key to ensuring the temperature rise does not exceed the 2 °C set by the Paris accords. Adsorption is identified as a key technology for post-combustion carbon capture. This rise in prominence of such processes is owed to the fact that application of solid sorbents does not lead to the generation of secondary waste streams. In fact, sorbents can be produced from waste material (e.g. bio-based sorbents). Bio-based sorbents have become an increasingly attractive option; food waste, agricultural and municipal sources can be employed as precursors. These sorbents can be physically and chemically activated and then further modified to produce sorbents that can capture CO2 effectively. The employment of these types of sorbents, however, often entails geological and operational challenges. Understanding how these sorbents can be deployed at scale and the geological challenges associated with bio-based sorbents are key research areas that must be further investigated. Process modelling and machine learning can provide insights into these challenges especially within optimization of adsorption processes and sorbent development. This paper aims to provide a state-of-the-art review of the synthesis of bio-based sorbents and their application within post-combustion carbon capture processes as well as the recent trends of utilizing machine learning for the development of these sorbents, and the design of the corresponding adsorption processes alike.UK's Engineering and Physical Sciences Research Council (EPSRC) under the project titled “Multiphysics and Multiscale Modelling for Safe and Feasible CO2 Capture and Storage - EP/T033940/1″; UK Carbon Capture and Storage Research Centre (EP/W002841/1) through the flexible funded research programme “Investigation of Environmental and Operational Challenges of Adsorbents Synthesised from Industrial Grade Biomass Combustion Residues”

Mixture of piperazine and potassium carbonate to absorb CO<inf>2</inf> in the packed column: Modelling study

Supplementary data are available online at https://www.sciencedirect.com/science/article/pii/S0016236121019098?via%3Dihub#s0135 .A rate-based non-equilibrium model is developed for CO2 absorption with the mixture of piperazine and potassium carbonate solution. The model is based on the mass and heat transfer between the liquid and the gas phases on each packed column segment. The thermodynamic equilibrium assumption (physical equilibrium) is considered only at the gas–liquid interface and chemical equilibrium is assumed in the liquid phase bulk. The calculated mass transfer coefficient from available correlations is corrected by the enhancement factor to account for the chemical reactions in the system. The Extended-UNIQUAC model is used to calculate the non-idealities related to the liquid phase, and the Soave-Redlich-Kwong (SRK) equation of state is used for the gas phase calculations. The thermodynamic analysis is also performed in this study. The enhancement factor is used to represent the effect of chemical reactions of the piperazine promoted potassium carbonate solution, which has not been considered given the rigorous electrolyte thermodynamics in the absorber. The developed model showed good agreement with the experimental data and similar studies in the literature

Exergy Analysis in Intensification of Sorption-enhanced Steam Methane Reforming for Clean Hydrogen Production: Comparative Study and Efficiency Optimisation

Data Availability Statement: All the generated data in this work has been presented and integrated within the paper in form of tabulated data....The research presented in this work has received financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) through the project “Multiphysics and multiscale modelling for safe and feasible CO2 capture and storage - EP/T033940/1”, as well as via the EPSRC Doctoral Training Partnerships (DTP) award, EP/T518116/1 (project reference: 2688399). The authors would also like to acknowledge the UKCCSRC ECR Collaboration Fund 2022 (call 4) for the financial support of the collaboration between the researchers in this work

Mass transfer coefficients of carbon dioxide in aqueous blends of monoethanolamine and glycerol using wetted-wall column

There is an urgent need for CO2 capture development because of the global warming crisis. Recently CO2 absorption by the mixture of monoethanolamine (MEA) and glycerol, as an eco-friendly solvent, has been considered due to its promising performance and low technical and environmental impacts. However, more aspects of this process, especially mass transfer coefficients, need to be studied further. In this work, a bench-scale wetted-wall column was used to find the CO2 mass transfer coefficients in the aqueous blends of MEA (25 wt%) and glycerol (5–20 wt%). The experiments were performed nearly to the industrial conditions of flue gas at atmospheric pressure and three different temperatures (313, 323, and 333 K). The gas flow rate was maintained around 0.17 ± 0.01 stdL/s, and the CO2 partial pressure was in the range of 1–15 kPa. The findings revealed that increasing the glycerol to 10 wt% improves the overall mass transfer (), and adding more glycerol up to 20 wt% decreases the . The gas-side mass transfer resistance () found to be negligible. Thus, the primary mass transfer resistance was in the liquid phase. It is also found that the solution with 10 wt% glycerol and 25 wt% MEA (10G25M) had the highest liquid-side mass transfer coefficient () among the other solutions. The 10G25M showed a comparable and even better absorption rate than solutions with a higher concentration of MEA studied in the literature. Compared with industrial-grade, the of the 10G25M was over two times higher than the 30 wt% MEA solution

A seven-year study on head injuries in infants, Iran---the changing pattern

OBJECTIVE: Head injury (HI) is the leading cause of mortality and life-long disability in infants. Infants have different anatomical and pathophysiological brain structures from other age groups. The aim of this study was to survey infant HI patients admitted to Shahid Behest Hospital in Kashan, Iran from 2004 to 2010, and to identify the causes of HIs in this age group. METHODS: In this retrospective study, all HI patients under the age of two who were hospitalized for more than 24 hours between January 2004 and January 2010 were enrolled in the study. Demographic, etiologic, and injury data were collected and a descriptive analysis was performed. RESULTS: Infants comprised 20.8 of all children (under 15 years old) with HIs and 65.1 of the injuries occurred in the home. Falls were the most common cause of injury (63.4). In hospital mortality was 6.6 per 100 000 infants. A decreasing trend was seen in home events, but HIs caused by traffic accidents were increasing during the study period. The amount of HI infants resulting from car accidents has tripled from the years 2004 to 2010. CONCLUSION: Although home events and falling are the main causes of infant HIs and need attention, our study showed an increase of HIs caused by road traffic accidents, especially by car accidents, thus legislation for the implementation of protective equipment such as child safety seats and programs is urgently needed