Faculty of Engineering
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Item Characterization and assessment of selected agricultural residues of Nigerian origin for building applications(COGENT ENGINEERING, 2024-12-22) Esther Nneka Anosike-Francis; Gina Odochi Ihekweme; Paschal Ateb Ubi; Ifeyinwa Ijeoma Obianyo; Seun Jesuloluwa; Adekunle Akanni Adeleke; Prabhu Paramasivam; Azikiwe Peter Onwualu; Rasoamalala VololonirinaThe high rate of agricultural residue generation in Nigeria in recent times poses a serious environmental hazard. Thus, there is a need to valorize these residues for various engineering applications. Five Nigerian agricultural residues (okro, plantain, jute, kenaf, and sisal) were studied to determine their potential for forming natural fiber composites for building applications. The samples were subjected to a process of peeling and immersion in water for 15–20 days to facilitate the degradation of microbial cells and ease the extraction of fibers. Proximate and lignocellulose analyses of the samples were conducted according to the American Standard for Testing and Materials (ASTM) specifications. The physico-mechanical and thermal properties of the agricultural residues were examined using an Intron universal testing machine and a thermogravimetric analyzer. The fiber phase analysis revealed a crystallinity index range of 41.20–66.08% and a crystallite size of 30.79–84.00 nm, indicating that the fibers were thermally stable above 280 °C. Fourier Transform Infrared analysis provided conclusive evidence of the presence of distinct chemical compositions and their associated functional groups. The study contributes a reliable database for agricultural residues in Nigeria, particularly for construction applications. It is also being utilized to inform the design and implementation of manufacturing processes for roofing tiles and boards intended for general applicationsItem 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 Understanding casting behaviour of low carbon high manganese steel through detailed characterization of mould powder and mould top slag(Taylor and Francis, 2023-02-02) D. Paswan; J. K. Ansu; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; C. T. Christopher; T. K. Roy; P. Palai; M. MalathiThis study focused on multistage characterization techniques in developing an understanding of the abnormal casting behaviour of low carbon high manganese (LCHMn) steel. In addition to raw mould powder used for casting LCHMn steel, mould top slag samples were also collected for normal and abnormal casting conditions. Raw mould powder and top slag samples were characterized using XRF, XRD, and SEM-EDS to determine chemical composition, crystallinity and morphology. The chemical composition results revealed deviation of normal and abnormal behaviours from the mould powder due to the pickup of oxides of Al, Mn, and Ti. The SEM analyses of raw mould powder showed different granular particle sizes while pores and glassy/crystalline structure were seen for normal and abnormal behaviour at casting. CaF2, CaSiO3, and Na2CaSi3O9 were revealed as the mineralogical phases. There was a modified crystalline phase present in the abnormal behaviours at casting due to pickup of other oxides.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 Simulation Technology in Renewable Energy Generation: A Review(International Conference on Multidisciplinary Engineering and Applied Sciences (ICMEAS), 2023-11-01) Adekunle Akanni Adeleke; Petrus Nzerem; Ayuba Salihu; Esther Nneka Anosike-Francis; Adebayo Isaac Olosho; Kpabep Kerein Kalenebari; Yuguda Abdullahi Muhammad; Waliyi Adekola Adeleke; Moses Oluwatobi FajobiThe escalating energy consumption rates and the alarming environmental impacts associated with fossil fuel usage have driven global attention towards alternative energy sources. While nuclear power has emerged as one such alternative, concerns about past reactor accidents and the health effects of radiation release have limited its widespread adoption. Renewable energy, on the other hand, offers a promising solution with minimal environmental harm compared to nuclear power. However, the intermittent nature of renewable energy sources and their inability to consistently supply power present significant challenges for nations aiming to harness these abundant resources. To address these challenges, the integration of simulation technology into energy generation processes has proven instrumental. By employing simulation tools, it becomes possible to identify, control, and even eliminate factors that may hinder energy generation and efficiency. Furthermore, simulation technology enables accurate predictions of the expected energy output from renewable sources. This paper presents a comprehensive review of the recent advancements and applications of simulation technology in renewable energy generation. It elucidates how simulation technology has been successfully integrated into renewable energy systems and discusses its potential to enhance the efficiency of renewable energy generation.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 DEVELOPMENT AND ASSESSMENT OF PARTICLE REINFORCED ABRASIVE GRINDING DISCS FROM LOCALLY SOURCED MATERIALS(Journal of Chemical Technology and Metallurgy, 2024-09-09) Jamiu Kolawole Odusote; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Timothy Adekanye; Adeiza Avidime Samuel; Chinedum OjiManagement of waste materials is a serious concern to researchers and scientists. Waste materials cause health and environmental hazards. Hence, they should be properly managed. The aim of this study is to develop a grinding disc using agricultural wastes (palm kernel shell and snail shell), granite, aluminium oxide, and polyester resin. The particles of snail shell, palm kernel shell, aluminium oxide (abrasive) and granite (friction modifier) were measured in percentages varying between 8 - 29 wt. % and were mixed with 27 wt. % polyester resin (binder), 3 wt. % methyl ethyl ketone peroxide (hardener) and 3 wt. % cobalt naphthalene (accelerator) to produce a grinding disc. The micrograph, hardness, wear rate, and water absorption tests were carried out on the grinding disc samples. The result showed that the composition with the highest palm kernel shell particle content (29 wt. %) had the best values for hardness and wear resistance, making it the most suitable material for grinding discs. The environmentally-friendly palm kernel shell-based discs could be used for soft metals, wood grinding and finishing processes.Item Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model(Hindawi, 2023-02-06) Moses Oluwatobi Fajobi; Olumuyiwa A. Lasode; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Ayokunle O. Balogun; Prabhu ParamasivamOne of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40 days), the agglomerate that comprised 50% cow dung, 25% mango pulp, and 25% Chromolaena odorata produced the highest volume of biogas, 4750 m3/kg, while the one with 50% cow dung, 12.5% mango pulp, and 37.5% Chromolaena odorata produced the lowest volume of biogas, 630 m3/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m2) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R2 values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R2 values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78% of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately.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 Mechanical properties and stress distribution in aluminium 6063 extrudates processed by equal channel angular extrusion technique(Taylor and Francis, 2023-08-08) T.M. Azeez; L. O. Mudashiru; Tesleem B. Asafa; Adekunle Akanni Adeleke; Adeyinka Sikiru Yusuff; Peter Pelumi IkubanniThis study investigated the effect of temperature and load on the mechanical properties and stress distribution in ECAE processed Al6063. Samples were extruded at temperatures of 350, 425 and 500 under applied loads of 1000, 1100 and 1200 kN and ram speed of 5 mm/s. Hardness and tensile strength of all the extrudates were measured with Rockwell hardness tester and universal testing machine, respectively. Stress distribution in the extrudates was simulated with qform software under different extended applied loads and temperatures. Experimental results showed that billet temperature influenced tensile strength and hardness more significantly than applied load. The grains in the extrudates also became more refined as billet temperature increased. Simulation results indicated that more uniform stress distribution with lower magnitude was observed in the extrudates with increased billet temperature. The hardness and tensile strength of the extrudates as revealed through the uniform stress distribution were enhanced.