Battery Electrolyte | Catalysis | Machine Learning
The need for clean energy and environmental sustainability are challenges facing the world. My focus lies in comprehending the conversion and storage of energy and applying this knowledge to develop innovative and environmentally sustainable solutions.
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Project Title: Machine Learning for Battery Electrolyte Discovery
Duration: 10 weeks
Project Description:
The advancement of next-generation batteries is hindered by several challenges, encompassing issues like low energy density, safety hazards, and sluggish charge-discharge rates. To address these challenges, this project focused on the discovery of innovative electrolytes with outstanding ionic conductivity—a vital property that enhances both energy density and safety in batteries. We achieved this by training a Chemprop model using an extensive dataset featuring over ten thousand unique electrolytes. Rigorous model performance assessments were conducted, including comparisons with alternative models, to ensure its suitability for our project's objectives.
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Project Title: Data-Driven Intelligent Modeling, Optimization, and Global Sensitivity Analysis of a Xanthan Gum Biosynthesis Process
Duration: 6 months
Project Description:
Xanthan gum, a commonly used thickening agent in various fast-moving consumer goods (FMCG) products, was efficiently synthesized from agricultural waste materials. Machine learning models were designed to consider three crucial input factors for xanthan gum production: fermentation time, potassium phosphate concentration, and ammonium nitrate concentration.
To identify the most effective predictive model, a comprehensive evaluation of several machine learning techniques, including extreme learning machines, extreme gradient boosting, kernel ridge regression, random forest, support vector machines, and artificial neural networks was conducted. Additionally, assessment of the optimization capabilities of two metaheuristic algorithms: genetic algorithm and particle swarm optimization was done.
This research aimed to streamline and enhance xanthan gum production from agricultural waste, contributing to both sustainability and cost-efficiency in the FMCG industry.
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Project Title: Optimization of Lipase Yield from Agricultural Waste Using Machine Learning
Duration:
Project Description (final year thesis):
Lipase, a vital enzyme utilized in the transesterification process for biodiesel production, was synthesized using a ternary substrate mixture comprising both solid and liquid feedstock components. To achieve the most favorable fermentation medium formulation, a constrained mixture design approach was employed.
In addition to optimizing the feedstock blend, experiments to enhance lipase yield by harnessing the inductive effects of surfactants were conducted. This comprehensive research contributes to the efficient and sustainable production of biodiesel, offering promising solutions for the biofuel industry.
Stanley Aimhanesi Eshiemogie, Ritesh Kumar, Chibueze V. Amanchukwu, "Data Preprocessing and Machine Learning Modelling for Battery Electrolyte Discovery," 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG), Omu-Aran, Nigeria, 2024. Link
Amenaghawon, A. N., Omede, M. O., Ogbebor, G. O., Eshiemogie, S. A., Igemhokhai, S., Evbarunegbe, N. I., Ayere, J. E., Osahon, B. E., Oyefolu, P. K., Eshiemogie, S. O., Anyalewechi, C. L., Okedi, M. O., Chinemerem, B. A., Kusuma, H. S., Darmokoesoemo, H., & Okoduwa, I. G. (2024). "Optimized biodiesel synthesis from an optimally formulated ternary feedstock blend via machine learning-informed methanolysis using a composite biobased catalyst." Bioresource Technology Reports, 25, 101805. Link
Ekpenyong Maurice, Ikharia Eloghosa, Edeghor Uwamere, Ubi David, Amenaghawon Andrew, Akwagiobe Ernest, Eshiemogie Stanley, Antigha Richard, Asitok Atim, Antai Sylvester (2024). "Enhanced production, artificial intelligence optimized three-phase partitioning extraction, and in silico characterization of extracellular neutral Bacillus cereus proteinase." Biocatalysis and Agricultural Biotechnology, 61, 103389. Link
Andrew Nosakhare Amenaghawon, Shedrach Igemhokhai, Stanley Aimhanesi Eshiemogie, Favour Ugbodu, Nelson Iyore Evbarunegbe (2024). "Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process." Heliyon, 10(3), e25432. Link
Steve Oshiokhai Eshiemogie, Joshua O. Ighalo, Michael Adekanbi, Titilope Banji, Stanley Aimhanesi Eshiemogie, Raymond Okoh, Chinenye Adaobi Igwegbe, Adewale George Adeniyi, Adedapo O. Adeola & Kanika Dulta (2023). "Current Effect and Projected Implications of Climate Change on Nigeria’s Sustainable Development Plan". Springer Climate, 1–17. Link
Stanley Aimhanesi Eshiemogie, Majirioghene Enaye, Shedrach Igemhokhai, Andrew Nosakhare Amenaghawon. "Surfactant-Facilitated Metabolic Induction Enhances Lipase Production from an Optimally Formulated Waste-Derived Substrate Mix using Aspergillus niger." The Nigerian Society for Microbiology, 45th Scientific Conference and Annual General Meeting (November 2023).
Introduce second-year diploma students at the School of Maritime Studies in the field of organic chemistry.
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Led a volunteering team of tutors responsible for teaching a variety of courses to students at all levels within the Department of Chemical Engineering, University of Benin. Go to project site