College of Engineering

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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.
A review on the sustainable energy generation from the pyrolysis of coconut biomass
(Scientific African (Elsevier), 2021) Azeta Osarhiemhen; Ayeni A. O.; Agboola O.; Elehinafe Francis B.
he negative impacts of the extraction and exploration of fossil fuel on the environment and its depletion that has led to environmental degradation have encouraged researchers, stakeholders, and the government to explore alternative and renewable energy sources such as lignocellulosic biomass. Biomass pyrolysis has proven to be a viable energy conversion process over the last decade due to its low carbon footprint on the environment. Pyrolytic products that are bio-char, bio-oil, and bio-gas have several applications and contribute to our society’s industrial, commercial, and economic growth. This paper reviews the different types of pyrolytic processes using coconut biomass as a feedstock while focusing on the biomass properties that make it useful for pyrolysis and the factors affecting the process.
A Simplified Design for Biodiesel Production
(International Conference on Engineering for Sustainable World (IOP Publishing), 2021) Ayoola A. A.; Alagbe E. E.; Agboola O.; Ayeni O. A.; Adeyemi G. A.; Nnabuko D.; Makinwa
Application of mass transfer in the pulp and paper Industry􀀀 overview, processing, challenges, and prospects
(Results in Engineering (Elsevier), 2023) Odunlami Olayemi A.; Amoo Temiloluwa E.; Adisa Hassan A.; Elehinafe Francis B.; Oladimeji Temitayo E.
This study reviews the mass transfer with a focus on the challenges, benefits, processing and prospects in the pulp and paper industry with a scope limited to Kraft pulping which is the dominant pulping process worldwide. The mass transfer usually occurs in various processes that deal with reactions, separation, and heat transfer. All these aforementioned processes occur in the production of pulp and paper from their raw materials. The application of mass transfer to these processes is of great importance in setting target yields, and specifications and improving efficiency. The major processes where mass transfer principles are applied are drying, chemical washing, pulp digestion and pulp bleaching respectively. Understanding the requirements and targets of each of these processes in combination with the mass transfer principles helps in the development of models and design of equipment that operate based on the developed models in meeting the required targets. Studies have indicated that mass and energy balances cannot be done independently in meeting the required targets and equipment design. The drying and stripping of lignocellulosic components of the feed-in paper manufacture constitute a large part of the challenges faced by the industry. Drying techniques have been considered to be inefficient, and lignocellulosic by-products are known to contain toxic components. Green chemistry production processes and newer drying techniques were indicated as possible solutions. It is expected that researchers and investors would find this article useful.
ASSESSMENT OF THE UTILISATION OF SUSTAINABLE ENERGY AND ENVIRONMENTAL PROTECTION IN SOUTHERN NIGERIA
(Covenant University Ota, 2025-01) OBANOR ENOCH IWINOSA; Covenant University Dissertation
This study evaluates renewable energy adoption across Ogun, Lagos, Edo, and Delta states using a mixed-methods approach. A bibliometric analysis of 424 research publications (2014–2024) revealed that solar energy was the most studied topic (35%), followed by hydropower (25%) and bioenergy (20%). The analysis identified a 32% increase in renewable energy publications since 2019, with 62% of highly cited papers focusing on policy and deployment strategies. Citation mapping indicated that the top 10 research institutions contributed 47% of all renewable energy studies, highlighting the concentration of expertise in specific regions. Survey data from 387 respondents indicated that 68% lacked reliable electricity access, while 78% relied on traditional biomass or fossil fuels. Among respondents, 62% expressed willingness to adopt solar energy if installation costs were reduced by at least 40%. However, only 23% were aware of existing renewable energy policies, and 54% rated government efforts as inadequate. In terms of energy satisfaction, only 9% of respondents rated their current energy sources as highly adequate, while 36% described them as moderate, and 21% rated them as low. The study further analysed energy availability across Nigerian states. Lagos, Ogun, Edo, and Delta states experience an average of 12–18 hours of electricity outages per day, forcing 74% of households to rely on generators as backup power sources despite Nigeria’s solar radiation potential of 3.5–7.0 kWh/m². Alignment with Sustainable Development Goals (SDGs) 7 and 13 was assessed, revealing that only 19% of publications explicitly addressed energy access and climate change mitigation, while survey results showed that 69% of respondents were unaware of Nigeria’s commitment to SDGs. Projections based on current adoption rates estimate that, if key policy recommendations, energy access in Southern Nigeria could rise from 32% to over 70% by 2035 and fossil fuel dependency could decline by 55%. This research shows that achieving an efficient renewable energy transition requires urgent policy interventions, enhanced financial incentives, and strengthened institutional frameworks.
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.
CONTROL OF FOSSIL FUEL GAS OPERATING CONDITIONS ON THE PERFORMANCE OF CARBON DIOXIDE CAPTURE PROCESS
(2021) Letsholo I.; Moropeng M. L.; Mavhungu A.; Agboola O.; Fayomi O. S. I.; Moropeng R. C.
CORROSION INHIBITION BEHAVIOUR OF CALF THYMUS GLAND DNA ON MILD STEEL IN SULPHAMIC ACID
(Covenant University Ota, 2025-04) Ekere Isaac E.; Covenant University Thesis
Inorganic acid cleaners based on sulphamic acid are frequently employed in industrial equipment cleaning, descaling and acidizing. This application of sulphamic acid in industrial cleaning is not entirely without its drawback as the cleaning action usually leads to dissolution and loss of base metals. The addition of corrosion inhibitors is one of the industrial practices employed to minimize equipment corrosion damage. The purpose of this work was to assess the viability of deoxyribonucleic acid (DNA), extracted from calf thymus gland, as an inhibitor for mild steel corrosion in sulphamic acid medium, and in comparison, with salmon Fish DNA and INDION 5489, a commercial inhibitor. The inhibition process was investigated using weight loss, potentiodynamic polarisation, SEM/EDX and FTIR measurements. Response surface method (RSM) and artificial neural network (ANN) were employed to determine the optimum corrosion inhibition conditions. The weight loss measurements obtained the highest inhibition efficiency of 82.71% at 303 K and immersion time of 6 h by addition of 2.5 mg/L of calf thymus DNA, CTGDNA. The corrosion rate was also observed to decrease with an increase in inhibitor concentration. Potentiodynamic polarisation curves showed a shift in Ecorr < 85 mV an indication that CTGDNA is a mixed inhibitor, suppressing both cathodic and anodic reactions. An RSM generated polynomial model obtained an optimum efficiency of 72% at 303 K, 5.5 mg/L after 2.12 h immersion. Estimation by ANN, with minimal errors, and a higher R2 of 0.983 in comparison to 0.925 for RSM were close to the experimental inhibition efficiency. CTGDNA adsorption on mild steel modelled the Langmuir isotherm with a linear regression coefficient of 0.99. The increase in the activation energy from 37.54 kJ/mol to 52.5 kJ/mol after 2 h immersion; with a similar trend for 4 and 6 h demonstrated that addition of CTGDNA favoured physioisorption. The small and negative value of entropy was an indication that the adsorption of CTGDNA was spontaneous. FTIR confirmed the presence of protective film formed by CTGDNA inhibitor on the mild steel surface at various concentration. SEM images showed reduction in the degradation of mild steel surface in the uninhibited solution after addition of CTGDNA. The comparative studies obtained a weight loss of 0.0036, 0.0047, 0.0072 and 0.0086 mg in 10% sulphamic acid in the presence of CTGDNA inhibitor, salmon fish DNA, conventional cleaning solution and blank solution of 10% sulphamic acid without an inhibitor, respectively. This confirmed that the CTGDNA inhibitor enhanced the 10% sulphamic acid cleaning solution as a suitable and viable cleaning agent for mild steel in comparison with INDION 5489.
Corrosion mitigating techniques and the mechanisms: Comment
(International Conference on Engineering for Sustainable World (IOP Publishing), 2021) Ayodeji A. I.; Fayomi O.S.I.; Babaremu K. O.; Abioye P. O.; Agboola O.; Daniyan A. A.
A lasting solution is required to curb the corrosion attack because of the very destructive effect it has on engineering materials. Corrosion is a material degrading phenomenon that reduces the significant properties of metallic materials, making them less useful. This paper has been able to highlight the very suitable methods or techniques that help to mitigate the effect of corrosion on metals and other helpful engineering materials. Some of these methods are electrodeposition and protective coatings like organic coatings, inorganic coatings, and metallic coatings.
Corrosion Phenomena and the Occurrences; A comment
(International Conference on Engineering for Sustainable World (IOP Publishing), 2021) Ayodeji A. I.; FayomiO.S.I.; Daniyan A. A.; Babaremu K. O.; Abioye P. O.; Agboola O.
Corrosion refers to the deterioration of metallic materials resulting from the reactions between the components and their environe. However, corrosion occurs every day in the human world, both in industrial operations and domestically. This happens in different ways, making it well categorized into various types. This paper has concisely reported the basic phenomenon of corrosion, its environmental effect, and the various types like a crevice, pitting, erosion, and others. The Electrochemical behavior of corrosion was also reported as reactions that include oxidation and reduction reactions; oxidation reactions increase the valence number of materials by removing particles from the material, making them positively charged. In other ways, technological advancement has provided several attempts to understand this catastrophe, providing consistent mitigating measures and control toward attaining less cost. This overview studies the electrochemical corrosion phenomena and the prospect of materials selection in curtailing the ranging challenges.
Cuckoo search algorithm approach for optimal placement and sizing of distribution generation in radial distribution networks
(International Journal of Electrical and Computer Engineering Vol. 15, No. 3,, 2025-06) Ojo Kayode; Fanifosi Seyi; Awelewa Ayokunle A.; Samuel Isaac A.
Radial distribution networks (RDNs) often experience power loss due to improper distribution generation (DG) allocation. Strategic DG placement can reduce power loss, minimize costs, and improve voltage profiles and stability. This research optimizes DG placement and sizing in RDNs using the cuckoo search algorithm (CSA). The objective function considers losses across all network branches, and CSA identifies optimal DG locations and sizes. Tested on IEEE 33-bus, IEEE 69-bus, and Nigeria's Imalefalafia 32-bus RDN, the Cuckoo Search technique results in optimal DG locations at buses 6, 50, and 18 with corresponding sizes of 2.4576, 1.852, and 2.718 MW, respectively. Voltage improvements are 0.9509, 0.9817, and 0.9821 p.u, while total active and reactive power losses for IEEE 33-bus are reduced by 49.03% and 45.00%, and for IEEE 69-bus by 63.67% and 61.14%. The CSA approach significantly enhances voltage profiles and reduces power losses in these networks.
Data on the effect of Ibruprofen drug derivative on 430T1 stainless steel in acid solutions
(International Conference on Engineering for Sustainable World (IOP Publishing), 2021) Sanni O.; Fayomi O. S. I.; Popoola A. P. I.; Agboola O.
DEVELOPMENT OF A HIERARCHICAL ANOMALY DETECTION MODEL IN A FEDERATED CLOUD INFRASTRUCTURE USING ENHANCED GRAPH SAMPLING AND AGGREGATION
(Covenant University Ota, 2025-08) LAWAL, Comfort Oluwaseyi; Covenant University Dissertation
Modern distributed computing systems generate massive volumes of log data, making manual analysis infeasible. Existing methods treat log entries as independent events, failing to leverage structural dependencies and temporal correlations. This limitation is critical in federated cloud infrastructures where anomalies propagate across interconnected services. This research developed a hierarchical anomaly detection model that employs Federated Hierarchical Graph Sampling and Aggregation (Fed-HiGraphSAGE) techniques to enable multi-level anomaly classification in distributed cloud environments while preserving data privacy. FedHiGraphSAGE was built on an Enhanced Hierarchical GraphSAGE architecture, incorporating node features, edge attributes, and hierarchical structure to classify anomalies across five semantic levels: Anomaly, Anomaly-Type, Cloud Component, Application-Type and Specific-cloud-module. The model employs federated learning capabilities, dynamic graph management, hierarchical diagnostic capabilities, adaptive thresholding, and memory-efficient training. It also implemented a HierarchicalStratifiedBalancer to address class imbalance. The model was trained and evaluated using federated learning across three data-contributing regions: Afe Babalola University, Landmark University, and DRC_Congo, with Covenant University serving as the federated learning coordinator. A total of 54,919 system logs were processed from these three regions to simulate real-world federated deployment. The model demonstrated exceptional performance with region-specific accuracies of 91.97% (Afe Babalola), 98.27% (Landmark), and 98.76% (DRC_Congo). Hierarchical metrics confirmed effective multi-level classification with h-precision ranging from 91.82% to 98.99%, h-recall from 90.60% to 98.53%, and h-f1 from 89.95% to 98.66%. The model generated detailed hierarchical anomaly classifications and demonstrated significant performance adaptability across regions while maintaining global model coherence, with federated training reducing the global client’s loss from approximately 0.47 to 0.02 over 15 rounds. This research advances automated system monitoring by demonstrating that federated learning with graph-based representations and hierarchical classification significantly improves anomaly detection performance while enabling cross-regional knowledge sharing. The model’s ability to maintain exceptional performance across multiple classification levels while providing explainable results establishes a new benchmark for automated log analysis in complex distributed systems
DEVELOPMENT OF A PAIRING-ANOMALY DETECTION SYSTEM IN VIDEO SURVEILLANCE USING OBJECT DETECTION MODEL
(2025-03) SODIPO, QUEEN BUSAYO; Covenant University Diseertation
Anomaly detection in social behaviours among students is critical to maintaining a safe and respectful academic environment. This research focuses on developing an object detection-based pairing-anomaly detection system to identify and classify unusual social behaviours, such as hand-holding, which can be perceived as an anomaly and potentially lead to more serious issues like sexual misconduct. Object detection, a most significant branch of computer vision, attempts to locate and identify objects in images or video frames. The other is anomaly detection, identifying data sets that do not follow expected behaviour configurations. Combining object detection and anomaly detection approaches provides a powerful solution to detect and flag anomalous or problematic behaviour in any setting. Object detection-based models have recently gained significant attention for their efficiency in detecting and identifying anomalies of interest in complex scenes at high precision. This study shows the application of deep learning for anomalous event detection with an object detection architecture. Utilising the YOLO (You Only Look Once) architecture, the system is designed to detect and localise anomalies in real-time. The model is trained on a custom holding hands dataset, specifically designed to capture instances of hand-holding alongside the Pascal VOC (Visual Object Classes) benchmark dataset, to ensure versatility across varied scenarios. This approach incorporates transfer learning and data augmentation techniques to and optimise model performance with limited labelled data. Evaluation metrics, including mean accuracy precision (mAP), recall, F1 score, and AUC (Area Under Curve), demonstrate the model's effectiveness, with the Custom Holding Hands Dataset achieving an impressive mAP score of 99.5%. The system is integrated into a web application, enabling real-time anomaly detection and classification. This research contributes to developing computer vision-based pairing-anomaly detection systems for social behaviour analysis, with potential applications in maintaining a safe and respectful academic environment.
DEVELOPMENT OF AN AUTONOMOU AGENT FOR A NUMBER STRATEGY GAME USING DEEP Q-NETWORK
(Covenant University Ota, 2025-03) NKWOR, JANE CHINELO; Covenant University Dissertation
Deep Q-Networks (DQNs) have emerged as a pivotal reinforcement learning algorithm for training autonomous agents in complex decision-making tasks. This study investigates the application of Deep Q-Networks in Numero, a number strategy game that requires logical reasoning and iterative feedback processing. Numero is a number strategy game where players predict an opponent's secret four-digit number in the fewest steps possible by analysing feedback and refining strategies. The study explores Numero's unique challenges, such as sparse reward structures, high-dimensional state-action spaces, and non-deterministic feedback mechanisms. To address these challenges, a Deep Q-Network algorithm augmented with Prioritised Experience Replay(PER) was designed and developed to enhance sample efficiency by prioritising critical experiences during training. The autonomous agent interacts with the custom environment, sampling mini-batches from the replay buffer, performing backpropagation, and updating Q-values to improve decision-making. Hyperparameters, such as learning rate, discount factor, replay buffer and exploration rate, were tuned to optimise the agent's learning efficiency. Comparative analysis was conducted using Reservoir Sampling without Replacement and the Minimax algorithm as a baseline approach. Experimental results show that the algorithm achieved a higher success rate (correctly predicted numbers) and faster convergence than Minimax, reducing the average number of steps required to guess the secret number by more than 100%. Additionally, this algorithm demonstrated superior adaptability in handling dynamic feedback, outperforming Reservoir sampling in long-term decision-making. These findings reveal the effectiveness of Deep Q-Networks in structured feedback-driven environments, suggesting their potential application in logical reasoning and decision-making tasks and that the autonomous agent learns effective decision-making strategies through iterative training and fine-tuning, demonstrating improved performance in predicting the opponent's secret number. Further research directions include extending this approach to multi-agent settings where multiple autonomous agents can compete or collaborate to refine their strategic reasoning and explore its application in real-world scenarios requiring structured feedback processing.
DEVELOPMENT OF SUSTAINABLE ECO-CONCRETE WITH KENAF FIBRE AND COATED RECYCLED CONCRETE AGGREGATE
(Covenant University Ota, 2025-06) TAIWO-ABDUL DAMILOLA OMOZUAWO; Covenant University Dissertation
The urgent global demand for sustainable infrastructure has driven innovations in eco-efficient construction materials. This study explores the development of high-performance, sustainable concrete by integrating pozzolanic-treated recycled concrete aggregates (RCA) and kenaf fibre as eco-friendly alternatives to natural coarse aggregates and synthetic reinforcements. The research addresses the inherent limitations of RCA—such as high porosity, residual mortar, and weak interfacial zones—through a surface modification technique involving a blended calcined clay-cement slurry. Simultaneously, kenaf fibre is incorporated to enhance the tensile and flexural properties of the concrete matrix. Concrete mixes were produced with varying RCA replacement levels (30%, 45%, 60%, and 90%) using both untreated and pozzolanic-treated RCA. Comprehensive characterisation, including X-ray fluorescence (XRF), X-ray diffraction (XRD), and scanning electron microscopy (SEM), was employed to assess material and microstructural properties. Mechanical performance was evaluated through compressive, tensile, and flexural strength tests, alongside water absorption and density tests for durability analysis. Statistical optimisation using Response Surface Methodology (RSM) and ANOVA determined the influence of treatment and fibre incorporation on concrete performance. The results indicate that pozzolanic treatment significantly improved RCA concrete properties, with optimal performance observed at 45–60% RCA replacement. Treated mixes achieved a 28-day compressive strength of 36 MPa, a 5.3 MPa split tensile strength, and reduced water absorption to 3%, reflecting improved durability and structural integrity. These enhancements demonstrate the synergy between calcined clay treatment and natural fibre reinforcement. This study substantiates the viability of producing eco-concrete with treated RCA and kenaf fibre, promoting circularity, reducing carbon footprint, and contributing to sustainable development goals. It provides a framework for future applications in structural concrete, aligning with low-carbon construction practices.
Empirical assessment of ammonia and urea concentrations in wastewater from a pharmaceutical plant: A case study
(International Conference on Energy and Sustainable Environment (IOP Conf. Series), 2021) Sanni S E.; Odigure J. O.; Agboola O.; Emetere M. E.; Okoro E. E.; Audu C.
In several ways, urea is one of the most prominent sources of fixed nitrogen due to its relative abundance in waste water treatment plants. In this study, the wastewater effluent from X-Chemical Industries was considered for hazard analysis in order to ascertain the water quality and impact at the outfall effluent of company X relative to environmental standards. The study period is for December, 2013 – February 2014. Based on the results, it was observed that at the company’s sluice gate, the desorber (primary treatment unit) did not perform optimally. Also, the variation of the parameters measured i.e. urea concentration, pH and ammonia concentrations exceeded the standards established by the World Bank, International Finance Corporation (IFC) and the National Environmental Standards and Regulations Enforcement Agency (NESREA), i.e. against the specified standard of 100 ppm, higher concentrations include December 3, 9, 11, 12, 19, 20, 21, 24, 25, 27, 28 and 29 with corresponding urea concentrations of 1457, 1970.4, 122.7, 163.2, 150.3, 171.4, 148.76, 270.78, 178, 123, 101.33 and 250.43 ppm respectively, whereas that of ammonia is higher than 5 ppm on December 3, 21, 24, 25, 26, 27, 28 and 29 with corresponding concentrations of 8.4, 9.69, 8.13, 9.45, 12.5, 6.98, 22.95 and 9.95 respectively, whereas, it was lowest on other days. It was also observed that the treated waste water advancing the creek (jetty) close to the plant, will have environmental consequences on marine lives such as marine micro-flora as well as fishes.
ENHANCEMENT OF FINGERPRINT TEMPLATE PROTECTION AND PRIVACY PRESERVATION USING FULLY HOMOMORPHIC ENCRYPTION
(Covenant University Ota, 2025-03) ITUH NICOL IGNATIUS; Covenant University Dissertation
The transition from conventional or token-based passwords to biometric technologies because of the advantageous characteristics of biometrics traits is increasing daily. Nowadays, biometric technologies are utilised in applications such as border control, e-banking, e-health, etc. Biometric traits comprise biological traits (iris, face, fingerprint, etc) and behavioural traits (keystroke, signature, voice, etc). In contrast to other biometric traits, the fingerprint is the most utilised in most applications. Despite the advantages, biometric technologies have their drawbacks. The biometric data of an individual is unique since no two people have the same biometrics, and compromising this biometric data could have devastating results. This issue was addressed using the implementation of the Paillier cryptosystem, a partial homomorphic encryption scheme which only involves addition operations. This implementation suffers drawbacks when faced with complex computations such as the multiplication of two ciphertexts and faces ciphertext noise growth due to these complex computations. Thus, a need for fully homomorphic encryption which handles complex computation and manages noise growth through several techniques. This research work is aimed at enhancing fingerprint template protection and privacy preservation using fully homomorphic encryption. The proposed system was developed utilising the Brakerski/Fan-Vercauteren fully homomorphic encryption scheme implemented using the OpenFHE-Python library. The system was evaluated using the Neurotechnology CrossMatch dataset according to performance metrics including Accuracy, Genuine Acceptance Rate (GAR) and Equal Error Rate (EER). Results indicated that the Neurotechnology CrossMatch dataset achieved an accuracy of 84%, GAR of 84%, and EER of 16%. Therefore, the implementation of fully homomorphic encryption in biometrics achieves adequate accuracy despite both the encryption and decryption processes, thereby safeguarding the template, and preserving the user’s privacy.
ENHANCING THE PERFORMANCE OF WATER BASED MUD FOR HIGH TEMPERATURE HIGH PRESSURE (HTHP) APPLICATIONS USING BIO-BASED POLYMER AND NANOPARTICLES
(SSRN, 2022-12) Abiodun Adeyemi Gbadegesin; Samson Fadairo Adesina; ling Kegang; Ayodeji Ayoola A.; Owen Erhabor Lewis
The increased need for energy and petroleum products has led to the exploration of deep and ultradeep wells such as geothermal and harsh formation. Oil-Based Muds (OBM) fluid has the best qualities because of its stability but its environmental challenge is of great concern. In many parts of the world, environmental rules continue to ban the use of oil-based muds, although very effective yet polluting and expensive. Water-Based Muds Fluids (WBMFs) which are environmentally friendly and inexpensive to produce are considered as alternatives, but under the high temperature and high-pressure conditions, their stability is compromised. Addition of biopolymer to WBMFs was consider since it is ecologically acceptable, but at elevated temperature the biopolymer is break down and reduces the drilling fluid's viscosity, suspension, and fluid loss capacity. As a result, there is need to formulate of high-performance water-based drilling fluids that are stable at high temperatures. This research paper investigated the thermal stability of Polyethylene-Glycol (PEG) in the formulated drilling fluid with biopolymers (Potato Peel powder, PPP) suspended in bentonitewater and buffered with eggshell nanoparticles (ESNP). The resultant samples were evaluated at normal temperature 25oC and high temperature150oC, and it was discovered that the addition of ESNP and PEG improved the drilling fluids' rheological behavior by 70.8% and 46.2%, respectively. Also, the filtration properties of the various samples were also analyzed at different concentrations and varying temperatures from 40oC to 220oC. These additives ESNP and PEG greatly slowed down the degradation of the biopolymer formulated mud up to 220 °C. This newly formulated fluid system, is stable at high temperatures, can meet the demands of high-temperature formation during drilling.
Evaluation and improvement of power quality of distribution network: a case study of Covenant University, Ota
(Frontier Energy Efficiency, 2025-01-09) Samuel Isaac A.; Daudu Afah Toyin; Somefun Tobiloba E.; Awelewa Ayokunle A.; Abba-Aliyu Shehu
Power quality is a global concern, particularly as electronic devices are increasingly supporting modern economies. This research evaluates and proposes improvements for power quality of the distribution network at Covenant University, Ota, Nigeria, where electrical equipment usage contributes to power quality challenges. Measurements and evaluations were carried out in three stages: first, measuring power quality at five campus powerhouses using a Circutor aR6 power analyzer; second, assessing these measurements with Power Vision software; third, simulating the evaluated network with NEPLAN software. The study was conducted during an active school session, with measurements taken at 500 kVA, 11 kV/415 V/230 V on the outgoing circuits for each transformer. The results were benchmarked against IEEE power quality standards and identified issues such as harmonics, total harmonic distortion (THD), overload, and a lagging power factor. The proposed improvements, derived from NEPLAN simulation, included active harmonic filters to reduce harmonics, a shunt capacitor for power factor correction, and load sharing for managing transformer overloads. Simulation results demonstrated that THD was significantly reduced across all powerhouses: CDS from 7.28% to 0.91%, EIE from 10.52% to 3.54%, CST from 16.03% to 0.58%, the Library from 11.92% to 0.12%, and the Male Hostel from 16.71% to 0.24%. These adjustments enhanced THD within specified limits. Additionally, the shunt capacitor increased the power factor to 0.96 from −0.96. These enhancements are expected to extend equipment life, reduce heat loss, and lower utility costs.