Faculty of Engineering
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Item Comprehensive Characterization of Some Selected Biomass for Bioenergy Production(ACS Omega, 2023-11-08) Asmau M. Yahya; Adekunle Akanni Adeleke; Petrus Nzerem; Peter Pelumi Ikubanni; Salihu Ayuba; Hauwa A. Rasheed; Abdullahi Gimba; Ikechukwu Okafor; Jude A. Okolie; Prabhu ParamasivamThere is a lack of information about the detailed characterization of biomass of Nigerian origin. This study presents a comprehensive characterization of six biomass, groundnut shells, corncob, cashew leaves, Ixora coccinea (flame of the woods), sawdust, and lemongrass, to aid appropriate selection for bio-oil production. The proximate, ultimate, calorific value and compositional analyses were carried out following the American Standard for Testing and Materials (ASTM) standards. Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy with energy-dispersive X-ray spectroscopy, and X-ray fluorescence were employed in this study for functional group analyses, thermal stability, and structural analyses. The H/C and O/C atomic ratios, fuel ratio, ignitability index, and combustibility index of the biomass samples were evaluated. Groundnut shells, cashew leaves, and lemongrass were identified as promising feedstocks for bio-oil production based on their calorific values (>20 MJ/kg). Sawdust exhibited favorable characteristics for bio-oil production as indicated by its higher volatile matter (79.28%), low ash content (1.53%), low moisture content (6.18%), and high fixed carbon content (13.01%). Also, all samples showed favorable ignition and flammability properties. The low nitrogen (<0.12%) and sulfur (<0.04%) contents in the samples make them environmentally benign fuels as a lower percentage of NOx and SOx will be released during the production of the bio-oil. These results are contributions to the advancement of a sustainable and efficient carbon-neutral energy mix, promoting biomass resource utilization for the generation of energy.Item Comparative studies of machine learning models for predicting higher heating values of biomass(Institution of Chemical Engineers (IChemE), 2024-06-29) Adekunle Akanni Adeleke; Adeyinka Adedigba; Steve Adeshina; Peter Pelumi Ikubanni; Mohammed S. Lawal; Adebayo Isaac Olosho; Halima S. Yakubu; Temitayo Samson Ogedengbe; Petrus Nzerem; Jude A. OkolieThis study addresses the challenge of efficiently determining the higher heating value (HHV) of biomass, a crucial parameter in large-scale biomass-based energy systems. The conventional method of measuring HHV using an oxygen bomb calorimeter is time-consuming, expensive, and less accessible to researchers, particularly in developing nations. To overcome these limitations, we employed four machine learning (ML) models, namely Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). These models were developed by using proximate and ultimate analysis parameters as input features. Up to 200 datasets were compiled from literature and used for the ML models. Our results demonstrate the effectiveness of all ML models in accurately predicting the HHV of biomass materials. Notably, the XGBoost model exhibited superior performance with the highest R-squared (R2) values for both training (0.9683) and test datasets (0.7309), along with the lowest root mean squared error (RSME) of 0.3558. Key influential input features identified for HHV prediction include carbon (C), volatile matter (Vm), ash, and hydrogen (H). Consequently, this research provides a reliable alternative for predicting HHV without the need for costly and time-intensive experimental measurements, facilitating broader accessibility in biomass energy research.Item A Review of the Physical, Optical and Photoluminescence Properties of Rare Earth Ions Doped Glasses(TRENDS IN SCIENCES, 2024-10-22) Serifat Olamide Adeleye; Adekunle Akanni Adeleke; Petrus Nzerem; Adebayo Isaac Olosho; Esther Nneka Anosike-Francis; Temitayo Samson Ogedengbe; Peter Pelumi Ikubanni; Rabiatu Adamu Saleh; Jude A. OkolieDoping glasses with rare-earth ions have garnered significant attention among researchers worldwide. This interest stems from the widespread utilization of rare-earth ions to enhance the optical characteristics of host glasses and exploit the unique spectroscopic properties arising from their optical transitions in the intra-4f shell. Thus, this study reviewed the exceptional potential of rare-earth ion-doped glasses (REIs) in various applications such as solid-state lasers, photonic devices, communication optical fibers, and white light emission. Various methods for the fabrication of glass such as direct melt quenching, sol-gel, ion exchange, sputtering and co-doping techniques were reviewed extensively. The Specific focus was on the physical, optical and photoluminescence properties of glasses produced from glass formers co-doped with rare earth ions. The investigation centers on the comprehensive current applicability of REI-doped glasses. The review concludes based on the physical, optical and photoluminescence properties of rare earth ion-doped glasses that they are extremely useful in photonics, lasers, biomedical and optical communication applications.Item Machine Learning Model for the Evaluation of Biomethane Potential Based on the Biochemical Composition of Biomass(BioEnergy Research, 2023-09-30) Adekunle Akanni Adeleke; Jude A. Okolie; Chukwuma C. Ogbaga; Peter Pelumi Ikubanni; Patrick U. Okoye; Olugbenga AkandeBiomethane potential (BMP) is often used to evaluate the biogas potential during anaerobic digestion (AD). However, BMP tests are complex and time-consuming. Therefore, the present study presents a hybrid model in machine learning (ML) for evaluating and predicting BMP based on biomass biochemical composition. Generative adversarial network (GAN) is combined with different ML models to model and predict BMP for different biomass materials. The models were trained on 64 experimental datasets (original datasets) and a combination of a GAN and the original datasets (augmented datasets). The gradient boost regression (GBR) model performed very well on the training set with both datasets compared to the support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). RF and GBR models performed very well when trained with the combined GAN and original datasets, with RF slightly outperforming GBR on the test datasets (R2 score of 0.9106 vs. 0.9177). This indicates that the models benefit from the additional data generated by the GAN. The GBR model trained with the GAN and original datasets combined outperformed the RF model on the test set, with an R2 score of 0.9177 vs. 0.9106. A comparison between three different hyperparameter optimization methods (grid search, particle swarm optimization, and Bayesian optimization) showed that the grid search optimized model offers a balanced performance with an R2 score of 0.9994 and a marginal improvement on the test set with an R2 of 0.9213. Feature analysis results demonstrate that cellulose has the most influence on BMP.Item 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 GunesNanomaterials 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.Item Influence of sawdust ash on the microstructural and physicomechanical properties of stir‑cast Al6063/SDA matrix composite(The International Journal of Advanced Manufacturing Technology, 2023-02-02) Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Jamiu Kolawole Odusote; Boluwatife B. Olujimi; Jude A. OkolieMechanical, physical, and corrosion properties of pure aluminum cannot meet the requirements of the modern industries. This has led to increase in demand for aluminum alloys and aluminum matrix composites with enhanced properties. These properties make them suitable for most applications. This article analyzes the physicomechanical and microstructural properties of stir cast Al6063 alloy matrix reinforced with different weight fractions (2, 4, and 6 wt.%) of sawdust ash (SDA). The density, porosity, hardness, tensile strength, and impact strength of the unreinforced alloy and developed composite samples were evaluated while microstructural analysis was also carried out. The results showed reduced density values with increased SDA contents while percentage porosity ranged between 1.56 and 2.23%. The hardness (88.3–106.93 BHN) and tensile strength (112.13–132.71 MPa) of the composites were 21.09% and 18.35% better than those of Al6063 alloy. However, the impact strengths (45.48–35.51 J) of the composites were lower when compared to the unreinforced Al6063 alloy with a reduction of 21.92%. Microstructural images showed evenly distributed reinforcement particles within the matrix, while the XRD analysis also revealed the presence of different intermetallic phases in the composite samples. The micrographs of the composites showed plastic deformation during straining. The findings from the study indicate that SDA particulates incorporated into alloy matrix influenced the properties with increased hardness and tensile strength and reduced impact strength. Hence, the aluminum matrix composites will be suitable for use in lightweight engineering applications.Item Pathways for the Valorization of Animal and Human Waste to Biofuels, Sustainable Materials and Value-Added Chemicals(MDPI, 2023-03-06) Jude A. Okolie; Toheeb Jimoh; Olugbenga Akande; Patrick U. Okoye; Chukwuma C. Ogbaga; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Fatih Güleç; Andrew Nosakhare AmenaghawonHuman and animal waste, including waste products originating from human or animal digestive systems such as urine, feces, and animal manure, have constituted a nuisance to the environment. Inappropriate disposal and poor sanitation of human and animal waste often cause negative impacts on human health through contamination of the terrestrial environment, soil, and water bodies. Therefore, it is necessary to convert these wastes into useful resources to mitigate their adverse environmental effect. The present study provides an overview and research progress of different thermochemical and biological conversion pathways for the transformation of human- and animal-derived waste into valuable resources. The physicochemical properties of human and animal waste are meticulously discussed as well as nutrient recovery strategies. In addition, a bibliometric analysis is provided to identify the trends in research and knowledge gaps. The results reveal that the U.S.A, China and England are the dominant countries in the research areas related to resource recovery from human or animal waste. In addition, researchers from the University of Illinois, the University of California Davis, the Chinese Academy of Science and Zhejiang University are front runners in research related to these areas. Future research should be centred on developing technologies for the on-site recovery of resources, exploring integrated resource recovery pathways, and exploring different safe waste processing methods.Item Synthesis and Characterization of Eggshell-derived Hydroxyapatite for Dental Implant Applications(EDP Sciences, 2023-01-01) Jamiu Kolawole Odusote; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Peter Omoniyi; Tien-Chien Jen; G. Odedele; Jude A. Okolie; Esther Titilayo AkinlabiHydroxyapatite (HAp) production from eggshells for dental implant purposes involved a novel approach utilizing a wet chemical precipitation technique. The eggshells, finely ground to a size below 250 μm, underwent calcination at a high temperature of 900°C for 2 hours. This thermal treatment facilitated the conversion of calcium carbonate into calcium oxide (CaO) while eliminating any organic components in the eggshell. To initiate the synthesis of HAp, a solution comprising 0.6 M phosphoric acid was added to the CaO dispersed in water. The resulting mixture was allowed to undergo aging at different time intervals ranging from 0 to 24 hours, promoting the formation of HAp. Subsequently, the HAp particles were oven-dried at 100°C for 2 hours to remove residual moisture. Finally, the dried particles were sintered at 1200°C in a muffle furnace to achieve the desired properties for dental implant applications. XRD peaks at 25, 33, 40, and 50° confirm the synthesized material as HAp. Vibrational modes of phosphate (PO43-), hydroxyl (OH-), and carbonate (CO32-) groups indicate carbonated HAp. Synthesized HAp holds potential for biomedical applications.Item Comparative Analyses of Lean Grade Coal and Carbonized Antiaris toxicaria for Energy Generation(Petroleum and Coal, 2022-02-02) Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Ayokunle O. Balogun; Jude A. Okolie; Chiebuka T. Christopher; Ayobami O. Olawale; Joseph C. OkonkwoThe current study focused on characterizing lean grade coal and carbonized biomass (at 400oC) for energy generation. Samples were pulverized using a ball mill and then mixed with a mechanical mixer at two mixing ratios. Proximate, ultimate and calorific value analyses were carried out on the samples using different ASTM standards and some available linear regression models. Lean grade coal has the highest ash content (79.58%) while raw biomass has the least (2.29%). Carbonized biomass samples have the highest heating value (9.49 MJ/kg). The O/C and H/C atomic ratios shows that carbonized biomass is the best fuel compared to coal and blended samples. The FTIR spectra of coal and blended samples shows peaks representing Si-O-Si while C-H bonds were the predominant ones in raw and carbonized samples. Lean grade coal and blended samples contain silicon as displayed by the EDX spectra. The coal and blended samples have grey-like silica and carbide microstructure. The coal and blended samples are not good for energy generation but may serve well as raw material for silicon recovery. Carbonized biomass has good fuel properties that can be useful in existing coal-fired plants.Item Inhibition potential of silver-gold nanoparticles on mild steel in 3.5% NaCl solution(Engineering and Applied Science Research, 2023-01-01) Peter Pelumi Ikubanni; Adekunle Akanni Adeleke; Jamiu Kolawole Odusote; Tesleem B. Asafa; Sharafadeen K. Kolawole; Victor O. Ogbesanya; Jude A. OkolieThis study investigates the corrosion behaviour of silver-gold nanoparticles as an inhibitor on the degradation of mild steel in 3.5% NaCl (saline environment) using gravimetric analysis and potentiodynamic measurement. The inhibitor Ag-AuNPs was synthesized from Kola nut pod. Five different concentrations of the Ag-AuNPs solution (0, 5, 10, and 20 μg/ml) were added to the saline environment. The gravimetric result showed that inhibition efficiency of 83.33% was the highest at 20 μg/ml of Ag-AuNPs inhibitor concentration. The Tafel polarization result showed that the solution with 20 μg/ml of Ag-AuNPs had the highest inhibition efficiency of 99.465%. At 0 μg/ml of Ag-AuNps, the surface morphologies of the mild steel sample did not show the existence of Ag-AuNps constituent in the saline environment containing the nanoparticles. The outcome showed that the saline environment with 15 and 20 μg/ml of Ag-AuNPs could successfully limit the corrosion of the mild steel.