Faculty of Engineering
Permanent URI for this communityhttps://repository.nileuniversity.edu.ng/handle/123456789/14
Browse
78 results
Search Results
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 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 Chemical and Mechanical Properties of Reinforcing Steel Bars from Local Steel Plants(Springer, 2019-06-06) Jamiu Kolawole Odusote; Wasiu Shittu; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Olumide AdeyemoSteel bars are important engineering materials for structural application. In Nigeria, due to incessant building collapse occurrences, it is important to further investigate some of the mechanical and chemical properties of reinforcing steel bars produced from scrap metals in order to ascertain their compliance with the required standard. Three diameters (10, 12 and 16 mm) of the reinforcing steel bars were chosen from each of the eight steel plants (A–H). Chemical composition analyses and mechanical tests (yield strength, ultimate tensile strength and percentage elongation) were performed using optical emission spectrometer and Instron Satec Series 600DX universal testing machine, respectively. Hardness values of the samples were obtained by conversion of tensile strength based on existing correlation. The results showed that carbon contents, hardness values, yield and ultimate tensile strengths of some of the steel bars were found to be higher than the BS4449, NIS and ASTM A706 standards. The steel bar samples were also found to possess good ductility with samples from steel plants C and D. By observation, all the 12 mm steel bars from steel plants A to H met the required ASTM and BS4449 standards except samples from plant G. This study revealed that most of the investigated reinforcing steel bars have reasonable yield strength, ultimate tensile strength, ductility and hardness properties when compared with the relevant local and international standards. Therefore, they are suitable for structural applications where strength and ductility will be of paramount interestItem 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 Essential basics on biomass torrefaction, densification and utilization(Wiley, 2020-09-24) Adekunle Akanni Adeleke; Jamiu Kolawole Odusote; Peter Pelumi Ikubanni; Olumuyiwa A. Lasode; Madhurai Malathi; Dayanand PaswanTorrefaction and densification are crucial steps in upgrading biomass as feed-stock for energy generation and metallurgical applications. This paperattempts to discuss essential basics on biomass torrefaction and densification,which can propel developing nation to take full advantage of them. The mostpromising clean energy sources that have found applications in various areasare biomass materials, that is, both the lignocellulosic and non-lignocellulosi c.However, high moisture contents, low energy density, hydrophilic nature, poorstorage and handling properties are the major drawbacks limiting its useful-ness. Therefore, torrefaction as one of the major thermal pre-treatment pro-cesses to upgrade biomass in terms of improved energy density, hydrophobic,moisture content and grindability has been discussed. The influence of temper-ature, residence time, particle sizes and gas flow rates on the properties of tor-refied biomass has also been discussed. The advantages and disadvantages ofvarious torrefaction technologies have also been highlighted. The possibleareas of application of torrefied biomass especially densification into pelletsand briquettes alongside the equipment required for it have been reviewed inthis paper. The torrefied biomass can be deployed in the metallurgical indus-tries as reducing agent in the development of sponge iron from iron ores ofvarious grade including lean ones. The information gathered in this paperfrom peer-reviewed articles will reduce the burden of seeking to understandthe preliminaries of torrefaction process and its importanceItem Electrochemical Studies of the Corrosion Behavior of Al/SiC/PKSA Hybrid Composites in 3.5% NaCl Solution(MDPI, 2022-09-30) Peter Pelumi Ikubanni; Makanjuola Oki; Adekunle Akanni Adeleke; Olanrewaju Adesina; Peter Omoniyi; Esther AkinlabiThe corrosion behavior of metal matrix composites (MMCs) is accelerated by the inclusion of reinforcements. Hence, this study investigates the corrosion behavior of MMCs produced from Al 6063 matrix alloy with reinforcement particulates of silicon carbide (SiC) and palm kernel shell ash (PKSA) inclusion at different mix ratios. The MMCs were synthesized using the double stir casting technique. The corrosion behaviors of the composites in NaCl solutions were studied via gravimetric analysis and electrochemical measurements. The gravimetric analysis showed fluctuating dissolution rate of the samples in NaCl solution to indicate flawed film as well as corrosion product formation over the surface of the specimens. The observed corrosion mechanism of the samples was general and pitting corrosion. The presence of reinforcements within the Al6063 matrix acted as active sites for corrosion initiation. The range of values for Ecorr and Icorr obtained in 3.5% NaCl at 24 h was between −220.62 and −899.46 mV and between 5.45 and 40.87 µA/cm2, respectively, while at 72 h, the Ecorr values ranged from 255.88 to −887.28 mV, and the Icorr ranged from 7.19 to 16.85 µA/cm2. The Nyquist and Bode plots revealed the electrochemical corrosion behavior of the samples under investigation, with predominant reactions on the surface of the samples linked to charge transfer processes. The relative resistance to corrosion of the samples depends on the thin oxide film formed on the surface of the samples.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.