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Advancements in sorption-enhanced steam reforming for clean hydrogen production: a comprehensive review
Get PDFThe sorption-enhanced steam methane reforming (SE-SMR) process, which integrates methane steam reforming with in situ CO2 capture, represents a breakthrough technology for clean hydrogen production. This comprehensive review thoroughly explores the SE-SMR process, highlighting its ability to efficiently combine carbon capture with hydrogen generation. The review evaluates the mechanisms of SE-SMR and evaluates a range of innovative sorbent materials, such as CaO-based, alkali-ceramic, hydrotalcite, and waste-derived sorbents. The role of catalysts in enhancing hydrogen production within SE-SMR processes is also discussed, with a focus on bi-functional materials. In addition to examining reaction kinetics and advanced process configurations, this review touches on the techno-economic aspects of SE-SMR. While the analysis does not provide an in-depth economic evaluation, key factors such as potential capital costs (CAPEX), operational expenses (OPEX), and scalability are considered. The review outlines the potential of SE-SMR to offer more efficient hydrogen production, with the added benefit of in situ carbon capture simplifying the process design. Although a detailed economic comparison with other hydrogen production technologies was not the focus, this review emphasizes SE-SMR's promise as a scalable and flexible solution for clean energy. With its integrated design, SE-SMR offers pathways to industrial-scale hydrogen production. This review serves as a valuable resource for researchers, policymakers, and industry experts committed to advancing sustainable and efficient hydrogen production technologies.The authors would like to acknowledge the support received from King Fahd University of Petroleum & Minerals (KFUPM) through the KFUPM Consortium for Hydrogen Future under project no. H2FC2315. The support received from the Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management (IRC-HTCM) to perform this work through project no. INHE2308 is also appreciated.Carbon Capture Science & Technolog
Optimization of combined hydrothermal and mechanical refining pretreatment of forest residue biomass for maximum sugar release during enzymatic hydrolysis
Get PDFThis study aimed to investigate the effect of chemical-free two-stage hydrothermal and mechanical refining pretreatment on improving the sugar yields during enzymatic hydrolysis of forest residue biomass (FRB) and optimize the pretreatment conditions. Hot-water pretreatment experiments were performed using a central composite design for three variables: temperature (160–200 °C), time (10–20 min), and solid loading (10–20%). Hydrothermally pretreated biomass was subsequently pretreated using three cycles of disk refining. The combined pretreatment was found to be highly effective in enhancing sugar yields during enzymatic hydrolysis, with almost 99% cellulose conversion for biomass pretreated at 213.64 °C, 15 min, and 15% solid loading. However, the xylose concentrations in the hydrolysate were found to be low under these conditions due to sugar degradation. Thus, less severe optimum pretreatment conditions (194.78 °C, 12.90 min, and 13.42% solid loading) were predicted using a second-order polynomial model. The response surface model optimized the hydrothermal pretreatment of FRB and predicted the glucan, xylan, and overall conversions of 94.57%, 79.78%, and 87.84%, respectively, after the enzymatic hydrolysis. The model-predicted biomass conversion values were validated by the experimental results.SUNY College of Environmental Science and ForestryThis research was funded by the McIntire Stennis Program at SUNY ESFEnergie
Supply Chain Disruptions and Stock Prices: Chapter 6 - Conclusions
No full textThis chapter concludes the book, summarising key takeaways for both practitioners and academics on managing Hurricane-Related Risks based on our research findings. The summary encapsulates the critical outcomes of our research, shedding light on the intricate relationship between hurricanes, supply chain disruptions, and firm performance. Our study draws from a diverse range of academic sources, offering profound insights into the imperative of proactive management and transparent communication during hurricane-induced supply chain disruptions.Supply Chain Disruptions and Stock Prices: The effects of hurricane-induced disruptions on company stock pric
Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm
Get PDFAccurate flight delay prediction is fundamental to establishing an efficient airline business. It is considered one of the most critical intelligent aviation systems components. Recently, flight delay has been a significant cause that deprives airlines of good performance. Hence, airlines must accurately forecast flight delays and comprehend their sources to have excellent passenger experiences, increase income and minimise unwanted revenue loss. In this paper, we developed a novel approach that is an optimisation-driven deep learning model for predicting flight delays by extending a state-of-the-art method, DeepONet. We utilise the Box-Cox transformation for data conversion with a minimal error rate. Also, we employed a deep residual network for the feature fusion before training our model. Furthermore, this research uses flight on-time data for flight delay prediction. To validate our proposed model, we conducted a numerical study using the US Bureau of Transportation of Statistics. Also, we predict the flight delay by selecting the optimum weights using the novel DeepONet with the Gradient Mayfly Optimisation Algorithm (GMOA). Our experiment results show that the proposed GMOA-based DeepONet outperformed the existing methods with a Root Mean Square Error of 0.0765, Mean Square Error of 0.0058, Mean Absolute Error of 0.0049 and Mean Absolute Percent Error of 0.0043, respectively. When we apply 4-fold cross-validation, the proposed GMOA-based DeepONet outperformed the existing methods with minimal standard error. These results also show the importance of optimisation algorithms in deciding the optimal weight to improve the model performance. The efficacy of our proposed approach in predicting flight delays with minimal errors well define from all the evaluation metrics. Also, utilising the prediction outcome of our robust model to release information about the delayed flight in advance from the aviation decision systems can effectively alleviate the passengers’ nervousness.UKRI for the COVID-19 recovery grant under the budget code SA077N. This research was heavily affected by the COVID-19 pandemic during the first authors' PhD studies. This lead to an extension to registration for 3 months, which was funded by the UKRI doctoral extension recovery grant. (PTDF main funder of PhD).Expert Systems with Application
Real-time on-the-fly motion planning for urban air mobility via updating tree data of sampling-based algorithms using neural network inference
Get PDFIn this study, we consider the problem of motion planning for urban air mobility applications to generate a minimal snap trajectory and trajectory that cost minimal time to reach a goal location in the presence of dynamic geo-fences and uncertainties in the urban airspace. We have developed two separate approaches for this problem because designing an algorithm individually for each objective yields better performance. The first approach that we propose is a decoupled method that includes designing a policy network based on a recurrent neural network for a reinforcement learning algorithm, and then combining an online trajectory generation algorithm to obtain the minimal snap trajectory for the vehicle. Additionally, in the second approach, we propose a coupled method using a generative adversarial imitation learning algorithm for training a recurrent-neural-network-based policy network and generating the time-optimized trajectory. The simulation results show that our approaches have a short computation time when compared to other algorithms with similar performance while guaranteeing sufficient exploration of the environment. In urban air mobility operations, our approaches are able to provide real-time on-the-fly motion re-planning for vehicles, and the re-planned trajectories maintain continuity for the executed trajectory. To the best of our knowledge, we propose one of the first approaches enabling one to perform an on-the-fly update of the final landing position and to optimize the path and trajectory in real-time while keeping explorations in the environment.Aerospac
Digital transformation in food supply chains: an implementation framework
Get PDFPurpose
Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA).
Design/methodology/approach
A systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research.
Findings
The combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation.
Originality/value
The study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully
Recent advances in bio-based production of top platform chemical, succinic acid: an alternative to conventional chemistry
Get PDFSuccinic acid (SA) is one of the top platform chemicals with huge applications in diverse sectors. The presence of two carboxylic acid groups on the terminal carbon atoms makes SA a highly functional molecule that can be derivatized into a wide range of products. The biological route for SA production is a cleaner, greener, and promising technological option with huge potential to sequester the potent greenhouse gas, carbon dioxide. The recycling of renewable carbon of biomass (an indirect form of CO2), along with fixing CO2 in the form of SA, offers a carbon-negative SA manufacturing route to reduce atmospheric CO2 load. These attractive attributes compel a paradigm shift from fossil-based to microbial SA manufacturing, as evidenced by several commercial-scale bio-SA production in the last decade. The current review article scrutinizes the existing knowledge and covers SA production by the most efficient SA producers, including several bacteria and yeast strains. The review starts with the biochemistry of the major pathways accumulating SA as an end product. It discusses the SA production from a variety of pure and crude renewable sources by native as well as engineered strains with details of pathway/metabolic, evolutionary, and process engineering approaches for enhancing TYP (titer, yield, and productivity) metrics. The review is then extended to recent progress on separation technologies to recover SA from fermentation broth. Thereafter, SA derivatization opportunities via chemo-catalysis are discussed for various high-value products, which are only a few steps away. The last two sections are devoted to the current scenario of industrial production of bio-SA and associated challenges, along with the author's perspective.Biotechnology for Biofuels and Bioproduct
Effect of blade shape on aerodynamic and aeroacoustic characteristics of vertical axis wind turbines using mid-fidelity and high-fidelity methods
Get PDFThis research paper investigates the effect of different blade shapes on the aerodynamic and aeroacoustic characteristics of Darrieus Vertical Axis Wind Turbines (VAWTs). Three different VAWT blade shapes are investigated: Straight, Troposkein, and Helical, considering a chord-based Reynolds number of 1.73e+5 and at a constant tip speed ratio for all. The mid-fidelity Lifting Line Free Vortex Wake (LLFVW) method and the high-fidelity Lattice Boltzmann/Very Large Eddy Simulation (LB-VLES) method are employed. Power performance analysis reveals that the straight-bladed VAWT generates the highest power output (about 11% higher), followed by the helical and troposkein blade configurations. The helical-bladed rotor exhibits smoother thrust and torque distribution over a wider azimuthal angle range, as predicted by both methods. While both methods capture the same trends in thrust and torque values, the mid-fidelity LLFVW method predicts approximately 22% higher thrust and torque values and lower near-wake streamwise velocities as compared to the high-fidelity LBM. The LLFVW is unable to accurately capture the inherent 3D vortices in the VAWT flow-field and the effect of blade-vortex interaction (BVI) on the VAWT force-field, as compared to LBM. In terms of aeroacoustics, the troposkein VAWT produces the highest noise at lower frequencies (20-30 Hz), followed by the straight and helical VAWTs. However, the troposkein and helical VAWTs emit more noise at higher frequencies (500-2000 Hz) than the straight VAWT due to the higher intensity of BVI observed for the former.For the part of high-fidelity simulations, this project has received funding from the European Union’s Horizon 2020 Marie Curie zEPHYR research and innovation programme under grant agreement No EC grant 860101.AIAA SCITECH 2024 Foru
Interferometric robot joint angle measurement: data
No full textTime series of angle measurement performed on 3 joints of a robotic arm instrumented with an array of optical fibre based angle sensors, exploiting fibre segment interferometry, Data presetned and analysed in: A fibre optic angle sensing tape for applications in robotics and automationDTP 2018-19 Cranfield Universit
Dataset DrivAer hp-F: Force Measurements at Various Rear Wing Angles of Attack
No full textDataset for the aerodynamic force measurements on the 35% scale DrivAer hp-F rear wing configuration using the moving ground facility in the 8x6 Wind Tunnel at Cranfield University. The dataset includes aerodynamic force coefficients results from the moving ground experiments on the DrivAer hp-F with rear wing angle of attack settings ranging from 0°-27.5°. The measurements have been conducted three times at each angle of attack setting for repeatability. In reference to the publication: Steven Rijns, Tom-Robin Teschner, Kim Blackburn, James Brighton; Effects of cornering conditions on the aerodynamic characteristics of a high-performance vehicle and its rear wing. Physics of Fluids 1 April 2024; 36 (4): 045119. https://doi.org/10.1063/5.0204204 CAD files for the DrivAer hp-F rear wing configuration are available at: Rijns, Steven; Teschner, Tom-Robin; Blackburn, Kim; Ramos Proenca, Anderson; Brighton, James (2024). DrivAer hp-F: Spoiler & Rear Wing Configurations Geometry Pack. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.25715202