Artificial-Intelligence on Vince's

Ministry of Defence

Fri, 01 Mar 2024 00:00:00 +0000

Modernizing data modeling through agentic AI automation and a self-service framework

A Machine Learning Model for Predicting Threshold Sooting Index of Fuels Containing Alcohols and Ethers

Mon, 15 Aug 2022 00:00:00 +0000

DOI: 10.1016/j.fuel.2022.123941

Contribution

In this project, my role as an advisor was crucial in the development of the artificial neural networks (ANNs) for predicting the threshold sooting index (TSI) of fuels. My background in computer science and data science was vital in ensuring the accuracy and reliability of the ANN model. This task required a meticulous approach to testing and validating the software, which was essential for proper and efficient data processing, model training, and prediction of outcomes.

A Methodology for Designing Octane Number of Fuels Using Genetic Algorithms and Artificial Neural Networks

Fri, 11 Mar 2022 00:00:00 +0000

DOI: 10.1021/acs.energyfuels.1c04052

Contribution

In this research project, I made significant progress in three key areas. Firstly, I developed precise artificial neural networks (ANNs) for predicting Research Octane Number (RON) and Motor Octane Number (MON), achieving an impressive R² of 0.99 for both, along with low mean absolute error (MAE) values. Secondly, I harnessed the power of genetic algorithms, significantly enhancing the optimization process by systematically reducing high octane component usage. Lastly, my advisory role in developing the polygonal method further refined the optimization process. My multifaceted contributions have led to enhanced fuel blending accuracy and efficiency, providing practical benefits to refineries by reducing costs and minimizing quality issues. My role in the manuscript writing process ensured that these complex ideas were clearly communicated, highlighting the practical applications and implications of our research in a real-world context.

Delft University of Technology

Mon, 01 Nov 2021 00:00:00 +0000

Went from assisting computer science courses to leading the TA team behind a 450-student project

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Wed, 05 May 2021 00:00:00 +0000

DOI: 10.4271/04-14-02-0005

Contribution

In our study, my primary contribution was developing and implementing machine learning models, particularly Artificial Neural Networks (ANNs), to predict the Derived Cetane Number (DCN) of oxygenated fuels. My role was instrumental in processing the data to accurately reflect the diverse chemical compositions of these fuels, a vital step for the ANNs to effectively learn and predict DCN values. Additionally, I introduced innovative techniques for model optimization, including a methodology for tuning hyperparameters and the use of a genetic algorithm, which significantly enhanced the robustness and accuracy of the final models.

Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons

Fri, 10 Jul 2020 00:00:00 +0000

DOI: 10.1021/acs.jpca.0c02785

Contribution

In this study, my role as an advisor was essential, particularly in guiding the development and optimization of machine learning models based on Support Vector Regression (SVR) for predicting the enthalpy of formation in cyclic hydrocarbons. I provided critical insights into the algorithmic design and hyperparameter selection of the SVR model, ensuring its robustness and accuracy. Additionally, I played a key role in reviewing the paper, contributing to the refinement of its scientific communication and ensuring the clarity and precision of the technical content presented.

Machine Learning to Predict Standard Enthalpy of Formation of Hydrocarbons

Thu, 29 Aug 2019 00:00:00 +0000

DOI: 10.1021/acs.jpca.9b04771

Contribution

In this study, my key contribution was developing and implementing Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) models. These models were engineered to accurately estimate the standard enthalpy of formation for a variety of hydrocarbons. I optimized these models using a two-level K-fold cross-validation method, enhancing both accuracy and reliability.

Additionally, I was responsible for defining the hyperparameter search space for these models, ensuring a balance between model complexity and generalizability. This careful tuning was vital to prevent overfitting while maintaining high predictive accuracy. The models I developed notably surpassed conventional methods in predicting standard enthalpy of formation, marking a significant advancement in the fields of chemical kinetics and thermodynamics.

King Abdullah University of Science and Technology (KAUST)

Thu, 01 Nov 2018 00:00:00 +0000

Trained and optimized neural networks for chemical engineering, leading to six published papers

Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks

Tue, 17 Apr 2018 00:00:00 +0000

DOI: 10.1021/acs.energyfuels.8b00556

Contribution

In this work, I focused on harnessing Artificial Neural Networks (ANNs) to accurately predict the Research Octane Number (RON) and Motor Octane Number (MON) for various fuel blends, a significant step forward in understanding complex fuel behaviors. My work centered around developing and fine-tuning the ANN models, integrating molecular parameters obtained from 1H Nuclear Magnetic Resonance (NMR) spectroscopy data. This integration was crucial for capturing the intricate relationships between molecular compositions of fuels and their octane ratings, especially in the context of non-linear variations observed with ethanol-blended gasoline.

Intact Assurance

Fri, 01 Sep 2017 00:00:00 +0000

Prototyped AI and blockchain use cases for Canada’s largest property and casualty insurer