Browsing by Author "Emmanuel I. Epelle"

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    A comprehensive review of hydrogen production and storage: A focus on the role of nanomaterials
    (The University of Edinburgh, 2022-05-20) Emmanuel I. Epelle; Kwaghtaver S. Desongu; Winifred Obande; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Jude A. Okolie; Burcu Gunes
    Nanomaterials are beginning to play an essential role in addressing the challenges associated with hydrogen production and storage. The outstanding physicochemical properties of nanomaterials suggest their applications in almost all technological breakthroughs ranging from catalysis, metal-organic framework, complex hydrides, etc. This study outlines the applications of nanomaterials in hydrogen production (considering both thermochemical, biological, and water splitting methods) and storage. Recent advances in renewable hydrogen production methods are elucidated along with a comparison of different nanomaterials used to enhance renewable hydrogen production. Additionally, nanomaterials for solid-state hydrogen storage are reviewed. The characteristics of various nanomaterials for hydrogen storage are compared. Some nanomaterials discussed include carbon nanotubes, activated carbon, metal-doped carbon-based nanomaterials, metal-organic frameworks. Other materials such as complex hydrides and clathrates are outlined. Finally, future research perspectives related to the application of nanomaterials for hydrogen production and storage are discussed.
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    Enhancing biomass Pyrolysis: Predictive insights from process simulation integrated with interpretable Machine learning models
    (Elsevier, 2024-03-01) Douglas Chinenye Divine; Stell Hubert; Emmanuel I. Epelle; Alaba U. Ojo; Adekunle Akanni Adeleke; Chukwuma C. Ogbaga; Olugbenga Akande; Patrick U. Okoye; Adewale Giwa; Jude A. Okolie
    Waste biomass pyrolysis is a promising thermochemical conversion process for the production of biofuels and sustainable materials. However, it is challenging to accurately predict the properties and yield of products formed during pyrolysis. Machine learning (ML) is a useful tool for predicting the performance of a process. In the present study, ML algorithms integrated with process simulation were explored to accurately model waste biomass pyrolysis based on properties such as H/C, O/ C, oil yield, gas yield, and char yield. Six different ML models including Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD) were used to model pyrolysis process. It was found that the out-of-the-box (without optimization) models for RF, XGBoost, ANN, and GBR performed the best and did not benefit from hyperparameter optimization. The GBR was identified as the most effective among various ML models. It accurately predicted yields of gas, biochar, bio-oil yields, and their H/C and O/C compositions. GBR effectively demonstrated the complex relationships between these variables. The box plot showing the root mean squared logarithmic error (RMSE) revealed that the GBR model had the best overall performance with a value less than 0.03. Also, the partial dependence plot and SHAP feature importance were evaluated to better understand each feature’s effect on the output. Lastly, a shareable graphical user interface (GUI) was created to enable researchers explore and predict pyrolysis yield