College of Engineering

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End date: 2024

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    Bridging the Artificial Intelligence Knowledge and Skill Gaps in Africa: a Case of the 3rd Google Tensorflow Bootcamp and FEDGEN Mini-Workshop
    (2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, 2024) Adetiba Emmanue; Wejin John S.; Oshin Oluwadamilola; Ifijeh Ayodele H.; LAWAL, Comfort Oluwaseyi; Thakur Surendra Colin; Awelewa Ayokunle A.; Kala Raymond Jules; Ajayi Priscilla O.; Akanle Matthew B.; Sweetwiliams Faith O.; Nnaji Uche; Owolabi Emmanuel; Idowu-Bismark Olabode; Sobola Gabriel
    In transiting from one civilization to another, technology has played a vital and positive role. In the 21st century, one of the digital developments that is paving ways for human life improvement is machine-assisted technology using Artificial Intelligence (AI). Artificial Intelligence has successfully enhanced man’s capacity in solving complex problems and processes. However, as developed nations continue to reap from the adoption of AI in various fields of human endeavors, the continent of Africa has remained behind, especially in AI-based skills and research. Various governments in developing nations have encouraged the adoption of AI, especially in institutions of learning. However, theoretical adoption without practical experience has remained an ineffective way of bridging the digital divide. In this paper we present the outcome of a practical approach to bridging the AI divide among students and researchers in Africa through funding support from the Google TensorFlow College Outreach Award. A 3-day hybrid bootcamp was organized (11th to 13th December, 2023) using the Google funding in order to equip postgraduate students and researchers with AI and collaborative research skills. A pre-survey method was employed to ascertain the knowledge level of the bootcamp participants. From the pre-surveyed feedback, training sessions on various AI domains were presented, and participant equipped with practical AI skills using a deployed AI-based cloud programming platform running on the private Federated Genomic Cloud (FEDGEN) infrastructure at Covenant University. A post-survey feedback was used to ascertain the effectiveness of this approach. A comparative analysis of the pre-survey and post-survey reveals a 70% improvement of AI skills among participants. This shows that having continuous training session for students and researchers is an effective method in closing the AI skills gap between developed and developing nations.
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    Optimal Maintenance Strategy for Power Transmission Infrastructure
    (Conf. Series: Earth and Environmental Science 1492, 2024) Somefu T. E.; Oluseyi P. O.; Babatunde O. M.; Somefun C. T.; Longe O. M.; Samuel Isaac A.; Awelewa Ayokunle
    In modern electricity markets, reducing operational costs while improving reliability is a primary concern for power system operators. However, Nigeria's aging power transmission network remains vulnerable, necessitating the implementation of optimal maintenance strategies to enhance system reliability. This study introduces a method for mitigating degradation in transmission components through condition-based maintenance, using a hybrid approach that combines the nonhomogeneous continuous time Markov chain (NHCTMC) for system state detection and the differential evolution (DE) algorithm for optimizing maintenance actions. The method is tested on a substation transmission network under various maintenance scenarios. Results indicate a significant improvement in system reliability (90.3%) and an efficient condition-based maintenance strategy achieving 91.3% power delivery. This approach offers promising potential for enhancing the power delivery capacity of the network.
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    FAULT IDENTIFICATION SYSTEM FOR ELECTRIC POWER TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORKS
    (International Journal of Scientific & Engineering Research Volume 9, Issue 2, 2018-02) Mbamaluikem Peter O.,; Aderemi Oluwaseun S.,; Awelewa Ayokunle A.
    Electric power transmission line faults hinder the continuity of electric power supplied and increase the system downtime thereby increasing the loss of electric power transmitted. Early fault detection and classification leads to prompt clearance of faults with an attendant effect of improved reliability and efficiency of the power system network. In view of this, this paper develops an arti-ficial neural network (ANN)-based detector and classifier to indicate and classify respectively a fault on Nigeria 33-kV electric power transmission lines. The transmission lines are modeled in Simulink using SimPowerSystems toolbox in MATLAB. Fault simulations are carried out, and the resulting instantaneous values of voltages and currents are used to develop the proposed fault identification sys-tem using multilayer perceptron feedforward artificial neural networks with backpropagation algorithm. Results are presented to vali-date the effectiveness and efficiency of the developed identification system for detecting and classifying faults. The Mean Square Error (MSE), linear regression and the confusion matrix are used as performance evaluators for the system. The ANN-based identification system achieved MSE of 4.77399e-10 and an accuracy of 100% for fault detection. This indicates that the performance of the developed ANN-based identification system is highly satisfactory and may be practically implemented on the Nigeria transmission lines.
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    Sulphamic Acid Corrosion Inhibition: A review Isaac
    (ASEN Journal of Chemical Engineering Vol. 24 No 2, 2024) Ekere Isaac E.; Agboola O.; Ayeni Augustine O.
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    A comprehensive review of biofuel utilization for household cooking in developing countries: Economic and environmental impacts
    (Process Safety and Environmental Protection Volume 191, Port A,, 2024-11) Dirisu joseph 0.; Oyedepo Sunday 0.; Olowole Olukunle C.; Somefun Tobilobo E.; Peter Nkoliko J.; Babatunde Damilola; Nwaokocha Collins N.; Onokwai Anthony 0.; Obano Enoch; Alam Md Mahbub; Kale Sandip A.
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    Utilization of Bioresources: Towards Biomass Valorization for Biofuels
    (Bioresource Utilization and Management, 2021) Sanni Samuel Eshorame; Agboola Oluranti; Moses Emetere; Okoro Emeka; Adefila Sam Sunday; Sadiku Rotimi; Alaba Peter
    Biomass valorization has recently gained wide attention owing to the vast availability of wastes from which essential oils can be extracted, processed, and subsequently converted to energy utilities and value-added products by taking advantage of the free fatty acid contents of the parent bio-oils. The waste materials undergo a conversion process to give the prospective fuels. The two major final products of the conversion process are bioethanol and biodiesel. Recent findings have also indicated the usefulness of these products as intermediate products/raw materials for end products such as simple sugars or biolubes; this goes further to emphasize the huge potential in these substrates (agricultural wastes) when used as raw materials. In 252recent times, hydrogen from biomass is being sought as a means of energy to power cars, and this also brings to bear the need to underscore how far efforts have been made to introduce bioethanol-driven cars. Fossil fuels, besides being nonbiodegradable relative to bio-oils, are gradually being depleted, with no measures put in place for their replacement. This then suggests the need to look beyond the current situation for viable alternatives. This chapter provides background information on biofuels, their production processes, and their physicochemical characterization, application, and sustainability, since the world is like a vicious circle that currently tilts in the direction of "engineering for a sustainable world". Furthermore, the chapter unveils some gray areas of research that still remain untapped, thus opening up doors for revenue generation.
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    Surface effect of environmentally assisted corrosion growth of automotive welded steel performance
    (Materials Proceedings Volume 38, Part 5, 2021-03) Fayomi O. S. I.; Samuel Olusegun David; Mashilo Matsobane; Popoola Abimbola Patricia; Agboola Oluranti; Balasubramanian Dhinesh
    Failure of steel in automobile industries due to environmentally assisted medium in service is alarming. In this study an effort has been made to investigate critically the corrosion induced performances of automotive welded part. The experimental correlation on the effect of corrosive media (NaCl and HCl solutions) on the base metal, heat affected zone (HAZ) and weld metal formed were examined using linear potentiodynamic polarization technique. The hardness, microstructural and phase rationale of the studied joint was evaluated using, diamond base indenter, Scanning electron microscope and x-ray diffractograms respectively. The results revealed that the base metal has no detrimental phase transformation unlike the HAZ and the weld joint region. The hardness value of 185 HV welded joint was obtained compared to 235.95 HV of base region. The corrosion propagation in the 3.5NaCl and 1 M HCl shows that pitting deterioration occurs at the welded position in all case as a result of the shift in grain refinement and micro-straining during welding process.
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    Short review on the prospect of laser cladding for aluminum based alloys composite for automotive industries
    (RESEARCH ARTICLE, 2022-08) Agboola O.; Fayomi O. S. I.; Ayoola A.; Ayeni A. O.; Alagbe E. E.; Sadiku E. R.; Popoola P.
    Laser cladding is a growing technological method that utilizes a navigating high power laser for melting a small region of the substrate; it has a function of trapping and melting entering powder particles. Hence, the process leads to the development of a new stratum. Aluminum alloy Matrix composites afford properties suchlike high wear resistance, high tensile strength, lightweight; this composites is finding wide applications in automotive industry. This review presents a brief discussion on laser cladding for aluminum base alloys for automotive industry with emphasis on aluminum alloys matrix composite for automotive industry.
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    Safety and Environmental Aspects of Cellulose Nanoparticles
    (Cellulose Nanoparticles: Chemistry and Fundamentals, 2021-06-01) Fasiku Victoria Oluwaseun; Agboola Oluranti; Owonubi Shesan John; Revaprasadu Neerish; Sadiku E. R.
    Nanoparticles made from cellulose are increasingly gaining attention in several fields for diverse applications, which can be attributed to their excellent properties. The physical, chemical, and mechanical properties of cellulosic nanomaterials are being studied to investigate their application in areas such as drug delivery, wound dressing, artificial tissues, and others. However, the safety and environmental aspects of cellulose nanoparticles (CNPs) are yet to be fully understood and established. The ambiguity surrounding their environmental health and safety remains a serious concern to the standards community. This chapter, therefore, provides information about the types of cellulose nanoparticles, and their various properties and the characterization techniques employed to provide useful information about their properties. It further proceeds to focus on the safety and environmental issues associated with cellulose nanoparticles and the handling practices which can be implemented to ensure maximum safety. This includes various safety techniques that have been developed as precautionary measures against the release and exposure of cellulose nanoparticles into the environment.
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    Influence of synthetic carbon grade on the monomeric constitution of polyhydroxyalkanoate produced by Bacillus cereus AAR-1
    (2024) Akinwumi A. R.; Nwinyi O. C.; Ayeni A. O.; Mohan S. Venkata
    Three synthetic sucrose samples with varying manufacturing purity levels were selected as carbon substrates to synthesize diverse polyhydroxyalkanoates (PHAs) using a wild-type Bacillus cereus AAR-1. Comparative analyses of the extracted biopolymers were characterized using Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (1H and 13C NMR), gas chromatography-mass spectrometry (GC-MS), Thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), CHNS/O elemental analysis, and X-ray diffraction (XRD). The biopolymers identified include Poly(3-hydroxytetradecanoate) (P3HTD), Poly(3-hydroxybutyrate-co-2-hydroxytetradecanoate) [P(3HB-co-2HTD)], and Poly(3-hydroxybutyrate) (P3HB). The carbon content of the biopolymers ranged from 39-53%, with no nitrogen element detected. The thermal decomposition temperature of [P(3HB-co-2HTD)] was 279 °C, surpassing the corresponding values for the other two biopolymers. Notably, the homopolymer P3HTD exhibited an increased melting temperature of 172.4 °C and a reduced crystallinity percentage (Xc %= 20%), crucial properties for bioplastics and medical sector applications. The results suggest that different carbon purity grades influenced homopolymer accumulated in Bacillus cereus AAR-1.