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Cranfield University
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Basic Info

Latest Publications
Journal Article
Carbon Capture Science & Technology
Published: 01 September 2024 in Carbon Capture Science & Technology

Hydrogen has a key role to play in decarbonising industry and other sectors of society. It is important to develop low-carbon hydrogen production technologies that are cost-effective and energy-efficient. Sorption-enhanced steam methane reforming (SE-SMR) is a developing low-carbon (blue) hydrogen production process, which enables combined hydrogen production and carbon capture. Despite a number of key benefits, the process is yet to be fully realised in terms of efficiency. In this work, a sorption-enhanced steam methane reforming process has been intensified via exergy analysis. Assessing the exergy efficiency of these processes is key to ensuring the effective deployment of low-carbon hydrogen production technologies. An exergy analysis was performed on an SE-SMR process and was then subsequently used to incorporate process improvements, developing a process that has, theoretically, an extremely high CO2 capture rate of nearly 100 %, whilst simultaneously demonstrating a high exergy efficiency (77.58 %), showcasing the potential of blue hydrogen as an effective tool to ensure decarbonisation, in an energy-efficient manner.

ACS Style

William George Davies; Shervan Babamohammadi; Yongliang Yan; Peter T. Clough; Salman Masoudi Soltani. Exergy analysis in intensification of sorption-enhanced steam methane reforming for clean hydrogen production: Comparative study and efficiency optimisation. Carbon Capture Science & Technology 2024, 12 .

AMA Style

William George Davies, Shervan Babamohammadi, Yongliang Yan, Peter T. Clough, Salman Masoudi Soltani. Exergy analysis in intensification of sorption-enhanced steam methane reforming for clean hydrogen production: Comparative study and efficiency optimisation. Carbon Capture Science & Technology. 2024; 12 ():.

Chicago/Turabian Style

William George Davies; Shervan Babamohammadi; Yongliang Yan; Peter T. Clough; Salman Masoudi Soltani. 2024. "Exergy analysis in intensification of sorption-enhanced steam methane reforming for clean hydrogen production: Comparative study and efficiency optimisation." Carbon Capture Science & Technology 12, no. : .

Journal Article
Expert Systems with Applications
Published: 01 August 2024 in Expert Systems with Applications

Accurate 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.

ACS Style

Desmond Bala Bisandu; Irene Moulitsas. Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm. Expert Systems with Applications 2024, 247 .

AMA Style

Desmond Bala Bisandu, Irene Moulitsas. Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm. Expert Systems with Applications. 2024; 247 ():.

Chicago/Turabian Style

Desmond Bala Bisandu; Irene Moulitsas. 2024. "Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm." Expert Systems with Applications 247, no. : .

Journal Article
Ocean Engineering
Published: 01 August 2024 in Ocean Engineering

In the global pursuit of Net Zero emissions by 2050, wind turbines have become a leading solution. These renewable energy generators offer a trifecta of benefits, significantly reducing CO2 emissions, minimizing environmental impact, and delivering cost-competitive clean power. However, the key to maximizing their potential lies in the aerodynamic design of the turbine blades. By improving the blade performance, researchers and engineers can significantly increase wind energy capture, propelling wind turbines to the forefront of the global transition to a sustainable future. Higher power generating wind turbines are needed to reach the Net Zero target. By upscaling the “DTU 10 MW Reference Wind Turbine”, this research has achieved an aerodynamically stable 20 MW offshore wind turbine blade design. Variable rotation speed and variable pitch angle configurations have been considered to achieve an ideal power curve. The aerodynamic performance has been evaluated and quantified for a length optimised blade design, wherein the power and thrust have been increased by 80.84% and 88.67%, respectively, at a rated wind velocity of 12 m/s.

ACS Style

Pavana Koragappa; Patrick G. Verdin. Design and optimisation of a 20 MW offshore wind turbine blade. Ocean Engineering 2024, 305 .

AMA Style

Pavana Koragappa, Patrick G. Verdin. Design and optimisation of a 20 MW offshore wind turbine blade. Ocean Engineering. 2024; 305 ():.

Chicago/Turabian Style

Pavana Koragappa; Patrick G. Verdin. 2024. "Design and optimisation of a 20 MW offshore wind turbine blade." Ocean Engineering 305, no. : .

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