Caritas Journal of Engineering Technology
CJET is a peer-reviewed, Open Access multidisciplinary engineering journal that publishes original research articles as well as review articles in all areas of engineering technology. It publishes both theoretical and experimental high-quality papers of permanent interest, not previously published in journals, in the field of engineering technology. In addition to peer-reviewed original research papers, the Editorial Board welcomes original research reports, state-of-the-art reviews, and communications in the broadly defined field of engineering science and technology.
Technology-Driven OHSM Frameworks for Sustainable Engineering
This research explores the integration of emerging technologies—Artificial Intelligence, Internet of Things, Building Information Modeling, wearables, and blockchain—into Occupational Health and Safety Management (OHSM) to foster sustainable engineering practices in construction and manufacturing. Through a mixed-methods synthesis of recent studies, the research reveals that these technologies enable proactive safety management, reducing workplace incidents by 10–20% via real-time monitoring and predictive analytics, while enhancing sustainability by cutting construction waste by 10%. Despite these advances, high costs, technical expertise gaps, data privacy concerns, and regional disparities hinder adoption, particularly for small enterprises and developing nations. The study’s novelty lies in its interdisciplinary approach, bridging OHSM, technology, and sustainability to propose scalable frameworks that address these barriers. It fills critical research gaps, including the lack of theoretical models and longitudinal data, by offering context-specific strategies and stakeholder collaboration models. Results underscore the transformative potential of technology-driven OHSM, providing actionable insights for practitioners and policymakers to institutionalize safer, sustainable workplaces globally.
Copyright (c) 2026 Caritas Journal of Engineering Technology
IoT-Enabled Waste Management Solutions: A Cross-System Analysis for Sustainable
The global solid waste crisis, projected to reach 3.88 billion tons annually by 2050, requires efficient, scalable, and inclusive management solutions. Traditional systems are inefficient, costly, and environmentally harmful, while IoT-enabled approaches offer real-time monitoring, predictive analytics, and optimized routing—yet they remain predominantly urban-focused, with rural and low-resource areas underserved and lacking direct comparative studies. This research bridges this gap through a systematic cross-system analysis of recent publications, synthesizing IoT technologies (sensors, LoRaWAN/NB-IoT, AI integration), urban and rural applications, pilots, efficiency outcomes, costs, sustainability impacts, and barriers. Findings show urban systems deliver 30–40% efficiency gains, reduced emissions, and enhanced recycling via dense networks and smart city integration, while rural adaptations (solar-powered bins, edge computing) achieve only 15–25% improvements due to connectivity gaps, power limitations, and scalability issues. The study’s novelty is the proposed hybrid model architecture, which integrates urban AI sophistication (predictive analytics, blockchain security) with rural resilience features (solar energy, edge processing, context-aware switching) to enable adaptive performance across contexts. This unified framework demonstrates potential for 25–35% overall efficiency gains, stronger data security, and equitable adoption through modular, low-cost designs and community interfaces. Outcomes indicate significant potential to reduce landfill emissions, advance circular economy principles, and support SDGs 11 and 12, provided future efforts prioritize rural field trials, policy incentives, and advanced AI integration for truly inclusive, sustainable waste management worldwide.
Stochastic Application on the Variability of Soil Deformation Models
This study conducted a probabilistic evaluation of immediate settlement with foundation depths at the centre of raft foundation in Borokiri and Nwokekoro close, Old GRA, Port Harcourt, Rivers State, Nigeria. It utilized deterministic methods for immediate settlement analysis, specifically those developed by Meyerhof, Peck and Bazaraa, Steinbrenner and Fox, Mayne and Poulos, and Burland and Burbidge. The geotechnical and elastic parameters necessary for reliability estimation were derived from the geotechnical investigation of soil samples collected from the sites. Serviceability limit state functions were formulated based on the immediate settlement equations mentioned earlier. The uncertainties associated with the random variables for each limit state function were sourced from existing literature. Reliability estimates were performed using computer programs developed in MATLAB based on the First Order Reliability Method (FORM). The findings indicated that in the Borokiri area, the Steinbrenner and Fox method produced reliability index values ranging from 2.4 to 2.7, which are within the recommended target reliability index range (2.5 – 4.0) for geotechnical structures. In contrast, the Mayne and Poulos method yielded a higher reliability index (6.1 to 6.5). The other methods, such as Burland and Burbidge (-0.07 to 1.6), Meyerhof (-1.4 to 0.8), and Peck and Bazaraa (-2.5 to - 0.6), underestimated the target reliability index. For the Nwokekoro close, Old GRA area, Steinbrenner and Fox again provided reliability index values between 3.4 and 4.0, aligning with the recommended target reliability index, while Mayne and Poulos (6.3 to 7.2) overestimated. The other methods, such as Burland and Burbidge (-2.3 to 1.9), Meyerhof (-2.4 to 1.7), and Peck and Bazaraa (-3.3 to 0.5), underestimated the target reliability index. Consequently, the Steinbrenner and Fox method emerged as the most reliable for predicting the immediate settlement of raft foundations in both Borokiri and Nwokekoro close, Old GRA. These results corroborate the findings from deterministic methods, suggesting that as estimated settlement exceeds the maximum allowable limit, the reliability index is expected to decrease, and vice versa. The MATLAB program developed in this research is user-friendly and suitable for geotechnical applications.
Copyright (c) 2025 Caritas Journal of Engineering Technology
Strength Capacity of Clay Soil Modified With Geosynthetics and Coconut Coir Fibre
The strength capacity of the Clay soil modified with Geosynthetics and Coconut fibre. The soil samples were taken from Egbelu Ogbogoro Obio Akpor Local Government Area. The soil samples from the preliminary test showed that the soil is classified as an A-7-5 soil. The cohesion value for the triaxial test increases with the longest cohesion occurring at 8% for coir fiber, 39.31kpa and 6% for Geosynthetics 40.33kpa. The internal friction angle increases with the maximum friction angle occurring at 10% for coir fibre 14.57° and 4% of Geosynthetics 13.03°. The maximum safe bearing capacity of the specimen occurred at 10% for both modifying agents. The clay soil with coir fiber increases the CBR value from 3.53% for 0% unsoaked and 2.06% soaked to 11.35% for 10% unsoaked and 9.22% soaked, while Geosynthetics increased the CBR of the soil at 16.70% unsoaked condition and 11.21% for soaked condition. However, the coir fiber and Geosynthetics of unsoaked and soaked condition meet the minimum requirement oratiof California bearing of 6. The compressive strength (UCS) also improves the strength of the clay soil and the maximum UCS value at 10% for both additives. Based on the test results. Both Geosynthetics and coconut coir fibre modified with soil have proved that they can be used for stabilizing the subgrade soil for the stability of the foundation of structures in civil engineering.
Influence of Bentonite Slurry Modifier on the Elastic Properties of Lateritic Soil
The influence of bentonite slurry on the elastic properties of lateritic soil was investigated. Various amounts of bentonite slurry (2%, 4%, 6%, 8% and 10%) were mixed with the lateritic soil from six (6) different boreholes to ascertain its effect on the elastic modulus and Poisson’s ratio of the soil. The results revealed that an increase in bentonite slurry yielded an increase in the elastic modulus of the soil by an average of 83.41% while an increase in bentonite slurry on the lateritic soils resulted in a decrease in Poisson’s ratio by an average of 23.79%. This result shows that the bentonite slurry modification with lateritic soil improves the lateritic soil from a state of very soft soil to medium soil in relation to the elastic properties, especially the elastic modulus and Poisson’s ratio. Based on the results, bentonite slurry has a positive influence on the elastic properties of lateritic soils and is suitable for foundation works when mixed with lateritic soils.
Development of a Process for the Production of Diesel Grade Engine Oil via Vacuum Distillation - Adsorption process
This study presents the development of an integrated vacuum distillation-adsorption process for the efficient production of diesel-grade engine oil from waste lubricating oil. This process aims to recover valuable base oils while meeting the stringent quality specifications of modern diesel engines. Initial vacuum distillation, conducted at a vacuum pressure of -0.032 to -0.062MPa and a temperature range of 275-300°C, yielded a distillate fraction constituting approximately 75 wt% of the input waste oil. This fraction, characterized by a kinematic viscosity of 8.5 cSt at 100°C, subsequently underwent an adsorption step utilizing activated clay. Adsorption parameters, specifically a clay dosage of 3 wt% and a contact time of 60 min at 80°C, resulted in a significant reduction in total acid number (TAN) from 5.7 mg KOH/g to 5.0 mg KOH/g and an improvement in color from ASTM D1500 7.0 to 2.5. The final refined oil exhibited properties consistent with those of commercial diesel-grade engine oils, including a flash point of 154°C and a viscosity index of 11. This integrated approach demonstrates a promising and environmentally sound method for producing high-quality base oils, contributing to resource sustainability and the principles of a circular economy.
Optimal Integration Of Renewable Energy Into The National Grid For Improved Power Supply Using Ann Based Supercapacitor
The increasing global demand for clean and sustainable energy has accelerated the integration of renewable energy sources (RES) such as solar photovoltaic (PV) and wind power into national grids. In Nigeria, the adoption of RES is hindered by challenges including variability in power generation, grid instability, and insufficient energy storage capacity. These limitations often result in unreliable power supply, frequency deviations, and voltage fluctuations. This study presents an Artificial Neural Network (ANN)-based super capacitor control system designed to achieve the optimal integration of renewable energy into the national grid for improved power supply stability and reliability. The ANN serves as an intelligent controller capable of learning complex nonlinear relationships between renewable generation patterns, grid demand, and storage behavior, enabling adaptive and precise charge–discharge control of the super capacitor. The super capacitor, with its high power density and rapid response time, mitigates the effects of renewable intermittency by providing fast frequency regulation, voltage support, and peak shaving. Simulation results obtained from a MATLAB/Simulink model of the Nigerian grid integrated with RES demonstrate that the proposed ANN-based supercapacitor system significantly improves grid stability, reduces voltage deviation by up to 18%, and enhances renewable energy utilization efficiency by 25% compared to conventional storage control methods. The findings indicate that ANN-driven supercapacitor storage systems offer a viable solution for optimizing renewable energy integration, ensuring improved operational reliability, and supporting Nigeria’s transition towards a more sustainable power infrastructure. The results obtained were the conventional intermittency and rapid generation variability that causes unoptimal integration of renewable energy into the national grid for unimproved power supply was 52 MW. On the other hand when an ANN based super capacitor was integrated into the system, it instantly reduced to 47.5 MW and the conventional intermittency and Poor generation forecasting that causes unoptimal integration of renewable energy into the national grid for unimproved power supply was31MW. Meanwhile when an ANN based super capacitor was introduced into the system, it automatically reduced to28.3 MW. Finally, with these results obtained, it definitely meant that the percentage optimized integration of renewable energy into the national grid for improved power supply when an ANN based super capacitor was imbibed into the system was8.7%.
Optimal Integration Of Renewable Energy Into The National Grid For Improved Power Supply Using Intelligent Solid State Var Compensator
The conventional Grid Congestion that causes challenges of integrating renewable energy into the national grid was 82%. Meanwhile, when an intelligent solid state VAR compensator was input into the system, it instantly reduced it to75% and the conventional System Losses Increase with RES Integration that causes challenges of integrating renewable energy into the national grid was5.7%. However, when an intelligent solid state VAR compensator was incorporated into the system, it automatically reduced it to threshold of 5%. Finally, with these results obtained, the percentage optimal integration of renewable energy into the national grid for improved power supply was 0.7% when an intelligent solid state VAR compensator was integrated into the system.
Enhancing Data Transmission In Satellite Network Using Ann Based Technique
The rapid expansion of global communication systems has placed increasing demands on satellite networks, particularly in terms of data transmission efficiency, reliability, and adaptability. Traditional satellite communication methods often face challenges such as bandwidth limitation, signal attenuation, noise interference, and latency, which hinder optimal data transfer. To address these challenges, this study proposes the application of Artificial Neural Network (ANN) based techniques for enhancing data transmission in satellite networks. The ANN model is designed to learn and adapt to dynamic channel conditions, mitigate the effects of noise and fading, and optimize modulation and coding strategies for improved throughput. Simulation results demonstrate that the ANN-based technique significantly enhances data transmission performance by reducing bit error rates, minimizing packet losses, and achieving higher spectral efficiency compared to conventional methods. The proposed approach also shows superior adaptability to varying atmospheric and network conditions, thereby ensuring reliable communication in real-time scenarios. This study underscores the potential of ANN-based intelligent methods as a robust solution for next-generation satellite communication systems, paving the way for improved connectivity, enhanced bandwidth utilization, and greater system resilience. The results obtained were the conventional packet loss to enhance data transmission in satellite network was3%. On the other hand, when an ANN based technique was integrated into it, it simultaneously reduced it to2.7% and the conventional low carrier to noise to enhance data transmission in satellite network was 8dB. Meanwhile, when an ANN based technique was incorporated into it, it spontaneously increased it to 9.6dB. Finally, the percentage enhancement of data transmission in satellite network when an ANN based technique was imbibed into the system was25%.
Improving Data Transmission In Satellite Network Using Intelligent Based Beam Forming Technique
Efficient data transmission in satellite networks is crucial for ensuring seamless communication, high-speed connectivity, and minimal latency. However, conventional satellite communication systems often suffer from issues such as signal degradation, interference, and limited bandwidth efficiency. This research focuses on improving data transmission in satellite networks using an intelligent-based beam forming technique. The proposed approach leverages artificial intelligence (AI) and machine learning (ML) algorithms to dynamically optimize beam forming patterns, enhance signal strength, and reduce interference in real time. By intelligently directing satellite beams towards intended receivers while mitigating signal loss, the system significantly improves data throughput and network reliability. The study further explores the integration of deep learning models to predict and adapt to changing network conditions, ensuring optimal performance under various atmospheric and operational challenges. Simulation results demonstrate that the intelligent-based beam forming technique outperforms traditional methods in terms of spectral efficiency, reduced bit error rate (BER), and enhanced overall network performance. This research contributes to the advancement of satellite communication technology, enabling more robust and efficient data transmission for applications in telecommunications, remote sensing, and global connectivity.
Enhancing Constant Power Supply By Integrating Solar To The Microgrid Using Ann Based Supercapacitor
The quest for a constant and reliable power supply remains a pressing challenge, particularly in regions where conventional grid infrastructure is constrained by generation shortfalls, transmission inefficiencies, and growing energy demand. Solar photovoltaic (PV) systems integrated into micro grids offer a viable pathway toward sustainable energy solutions; however, the intermittent and variable nature of solar power necessitates effective energy storage and management strategies. This study proposes the integration of an artificial neural network (ANN)-based super capacitor energy storage system into a solar-powered microgrid to enhance supply stability, efficiency, and adaptability. The ANN controller is designed to process real-time data on load demand, solar irradiance, and state-of-charge, enabling predictive and adaptive charge–discharge management of the super capacitor. This intelligent approach mitigates the effects of solar generation fluctuations, reduces system losses, and optimizes energy dispatch to maintain a steady power output. Simulation results demonstrate improved voltage stability, reduced downtime, and enhanced energy utilization compared to conventional control methods. The findings highlight the potential of ANN-based super capacitor systems as a robust solution for achieving constant power supply in solar-integrated microgrids, thereby contributing to energy sustainability and supporting the global transition to clean energy. The results obtained were the conventional reduction of rapid irradiance fluctuation that will enhance consistent power supply in integrating solar to the micro grid was220 w/m2. on the other hand, when an ANN based super capacitor was integrated into the system, it automatically reduced to200 w/m2 and the conventional Frequency deviation/ instability that will enhance consistent power supply in integrating solar to the micro grid was42Hz. It could not attain the threshold of 50Hz thereby causing inconsistent power failure in integration of solar to the micro grid.. Meanwhile, when an ANN based super capacitor was imbibed into the system, it simultaneously increased it to 50Hz thereby making it to attain the threshold. Finally, with these results obtained, It meant that the percentage enhancement of constant power supply by integrating solar to the micro grid when an ANN based super capacitor was integrated into the system was19%.
Improving Constant Power Supply By Integrating Solar To The Microgrid Using Intelligent Based Ultracapacitor
The increasing demand for reliable and sustainable power supply has led to the integration of renewable energy sources, particularly solar energy, into micro grids. However, the intermittent and variable nature of solar generation poses significant challenges to maintaining a constant power supply. This study proposes an intelligent-based ultra-capacitor system to enhance the performance and stability of solar-integrated micro grids. By employing intelligent control algorithms, such as Artificial Neural Networks (ANN) or Fuzzy Logic Controllers (FLC), the ultra-capacitor is optimized to manage energy storage and delivery efficiently. The intelligent controller dynamically responds to fluctuations in solar output and load demand, ensuring seamless energy balancing, voltage stability, and reduced response time. Simulation and modeling results demonstrate that the integration of intelligent ultra-capacitors significantly improves the reliability, power quality, and continuity of supply within the micro grid. This approach offers a viable solution for addressing the limitations of conventional energy storage systems, paving the way for smarter and more resilient distributed energy networks. The conventional Solar irradiance variability that causes power failure in integration of solar to the micro grid was 43%. On the other hand, when an intelligent based ultra-capacitor was integrated into the system, it instantly reduced it to37.2% and the conventional Faults in distribution lines that causes power failure in integration of solar to the micro grid was9%. Meanwhile, when an intelligent based ultra-capacitor was inculcated into the system, it simultaneously reduced to7.8%. Finally, with these results obtained, the percentage improvement in constant power supply by integrating solar to the micro grid when an intelligent based ultra-capacitor was integrated into the system was 1.2%.
Improving Contingency Evaluation In Nigerian 330kv Transmission Grid Using Intelligent Based SSVC
The consistent power failure in Nigerian 330 KV transmission network that had crippled business activities was overcame by introducing improving contingency evaluation in Nigerian 330kv transmission grid using intelligent based SSVC. contingency challenges in Nigerian 330kV transmission grid was characterized and established, conventional SIMULINK model Nigerian 330kV transmission grid was designed, SSVC rule base that will minimize the causes of contingency challenges in Nigerian 330kV transmission grid was developed and ANN was trained in the developed SSVC rule base for immediate minimization of the causes of contingency challenges in Nigerian 330kV transmission grid. Then an algorithm that would implement the process was developed and SIMULINK model for improving contingency evaluation in Nigerian 330KV transmission grid using intelligent based SSVC was designed and results obtained were validated and justified. The results obtained were the conventional Transformer Failure Rate that causes contingency challenges in Nigerian 330kV transmission grid was22%. On the other hand, when an intelligent based SSVC was imbibed into the system, it concurrently reduced it to19.8 %. Finally, with these results obtained, it definitely showed that the percentage improvement in contingency evaluation in Nigerian 330KV transmission grid when an intelligent based SSVC was integrated in the system was2.2%. To characterize and establish the causes of contingency challenges in Nigerian 330kV transmission grid. To design conventional SIMULINK model Nigerian 330kV transmission grid. To develop an SSVC rule base that will minimize the causes of contingency challenges in Nigerian 330kV transmission grid. To train ANN in the developed SSVC rule base for immediate minimization of the causes of contingency challenges in Nigerian 330kV transmission grid. To develop an algorithm that will implement the process. To design a SIMULINK model for Improving contingency evaluation in Nigerian 330KV transmission grid using intelligent based SSVC. To validate and justify percentage improvement in the reduction of causes of contingency challenges in Nigerian 330kV transmission grid with and without an intelligent based SSVC
Enhancing Risk Assessment In Nigerian 330kv Transmission Grid Using Fuzzy Based Ultra Capacitor
The reliability and stability of the Nigerian 330 kV transmission grid are frequently challenged by contingencies such as line outages, equipment failures, and sudden load variations, which often result in voltage instability and large-scale blackouts. Conventional contingency evaluation methods, including deterministic and probabilistic approaches, face limitations in addressing the nonlinear dynamics, uncertainties, and rapid decision-making requirements of modern power systems. This study proposes a fuzzy-based ultra-capacitor framework for enhancing contingency evaluation in the Nigerian 330 kV transmission network. The fuzzy logic component provides an intelligent decision-making mechanism capable of handling imprecise and uncertain system data, while the ultra-capacitor offers rapid dynamic voltage support during transient disturbances. The integration of these technologies enables faster and more accurate assessment of fault scenarios, improves voltage stability margins, and reduces recovery time following disturbances. Simulation studies conducted on a modeled Nigerian 330 kV grid using MATLAB/Simulink demonstrate that the proposed system significantly improves contingency detection accuracy, reduces voltage deviations, and enhances grid resilience compared to conventional methods. The findings suggest that adopting fuzzy-based ultra-capacitor systems can provide an adaptive and robust solution for ensuring operational security, minimizing outage risks, and supporting the stability of Nigeria’s high-voltage transmission infrastructure. The results obtained were the conventional inaccurate or missing measurement data that caused poor contingency evaluation in Nigerian 330kv transmission grid was22s. However, when Fuzzy based ultra-capacitor was injected into the system , it simultaneously reduced to19.1s and the conventional low short circuit capacity and weak grid that caused poor contingency evaluation in Nigerian 330kv transmission grid was1.5 KA. Meanwhile when Fuzzy based ultra-capacitor was input into the system, it instantly increased it to 2 KA. Finally, with these results obtained, it meant that the percentage enhancement in contingency evaluation in Nigerian 330kv transmission grid when Fuzzy based ultra-capacitor was inculcated into the system was33%.
Application of Mathematical Model for Optimal Blending of Crude Oils
Crude oil blending as a unit operation in the transportation and refining processes is critical for process optimization. This is because crude oils vary in properties based on their source of production. Meanwhile, distillation equipment (crude distillation unit) is designed based on the specific properties of crude oil. Product yield, therefore, is dependent on the proximity of the crude properties to be distilled to the design parameters of the crude distillation unit (CDU). Processing of crude oil with a wide disparity in properties gives a poor yield. Crude oil blending addresses this problem by mixing crude oils with different properties to give approximate values to the desired design properties. The present work developed mixing criteria for crude oils to achieve the properties that give optimum distillation output based on select crude parameters. The models were tested with simulation data from three Sudanese Crude oils and gave good predictive quality.
Adaptation of Mathematical Models for the Determination of Crude Oil Blends Stability
Crude oil blend formulation has been topical in recent times with research focus on yield maximization by appropriate feedstock preparation. The present work developed a simple mathematical algorithm to predict with precision, asphaltene precipitation as measure of crude oil blend stability and compatibility. Crude oil Blend stability was measured in terms of Colloidal Insolubility Index (CII), Asphaltene to Resin (A/R), ratio and ration of the Solubility Blending Number () and Insolubility Index (R). Results of the present work are in good agreement with plant data and simulation results of previous work for all parameters of blend stability determination. In each case, both studies gave stable blends as shown above. Main contribution of this work to the subject matter is in its unambiguous and precise nature, and its simplicity, both of formulation and application.
Leveraging Information and Communication Technology for Enhancing Crime Prevention and Safety in Nigerian Universities
Crime prevention and safety remain critical challenges in Nigerian universities, necessitating innovative approaches to mitigate security threats. This study explores the role of Information and Communication Technology (ICT) in enhancing crime prevention and safety within university environments. It examines various ICT tools, including Closed-Circuit Television (CCTV) cameras, biometric access control, emergency alert systems, and mobile security applications, assessing their effectiveness and associated challenges. A mixed-methods approach was adopted, utilizing surveys and interviews with students, security personnel, and university administrators across selected Nigerian universities. Data analysis revealed that while ICT-based security measures significantly improve surveillance, access control, and emergency response, challenges such as poor maintenance, power outages, and low adoption hinder their full effectiveness. The study found that CCTV cameras had the highest effectiveness rating (4.3/5), followed by biometric access control (3.9/5), while mobile security applications were the least effective (2.5/5) due to low awareness and integration issues. The findings highlight the need for universities to enhance ICT infrastructure, improve maintenance protocols, and conduct awareness programs to optimize security technology usage. The study recommends government intervention, policy formulation, and investment in sustainable power solutions to address technological limitations. This research contributes to the discourse on technology-driven crime prevention strategies in academic institutions, emphasizing the necessity for a comprehensive and well-maintained ICT security framework. By leveraging ICT effectively, Nigerian universities can foster a safer learning environment, reducing crime rates and improving overall campus security.
A Comprehensive Pressure Distribution Model for Horizontal Well in Bottom-Water Reservoir
The growing application of horizontal well technology provided far-reaching solutions to more realistic phenomena in well test analyses. However, existing models restrict the types of flow regimes by assuming that the dynamics of fluid are controlled by the geometry of the reservoir only. Such an assumption limits the number of flow regimes and hence the accuracy of well test analyses. In this article, all potential flow regimes of a horizontal well in a reservoir supported by bottom water were considered. The conceptual model assumed that the dynamics of the reservoir fluid are a function of the petrophysical properties of the reservoir, the properties of the fluid, and the geometry of the reservoir system. The physical model and the mathematical model were developed based on the conceptual model. The mathematical model is the pressure distribution of the horizontal well in the reservoir. The pressure distribution is in the form of dimensionless pressure and dimensionless pressure derivative functions of reservoir system properties, fluid properties, reservoir system geometry, and dimensionless time. Results of the study show a series of flow patterns along the principal axes or along combinations of the principal axes. Each flow regime could be distinguished by one of the flow patterns and the axes of orientation of the pattern. Each flow regime could be recognized by its characteristic signature in the log-log graph plots of the pressure distribution.
Experimental Evaluation on Suitability of African Oil Bean Husk as a Fluid-Loss Control Agent in Water-Based Drilling Mud
Filtrate loss determines the efficiency of drilling mud. Research on the suitability of African oil bean husk (AOBH) as a fluid loss control additive for water-based drilling mud (WBM) is presented in this article. AOBH was crushed into particle sizes of 63 µm, 125 µm, and 250 µm. The morphology and chemical properties of AOBH were studied with a Fourier Transform Infrared Spectrophotometer (FTIR) and a Phenom Prox model of the Scanning Electron Microscope energy dispersive X-ray spectroscopy (SEM-EDS). WBM samples containing the various sizes of AOBH as fluid-loss control additives were prepared. Samples containing industrial-grade additives (Grel Alphatex) were prepared. Basic mud tests were carried out on samples. Comparisons of results from the various samples were made. Rheology of mud samples was modeled with the Power Law Model and Herschel-Bulkley Models. Results show that AOBH contains mainly carbon and asphaltic compounds. Mud tests show that the performances of AOBH and industrial grade are comparable. Filter cake thickness was 2.3 mm–2.9 mm for AOBH-additives mud but 2.3 mm for industrial-additives mud. Filtrate loss was 2.3 ml–3.3 ml for AOBH-additives mud but 2.3 ml for industrial-additives mud. The apparent viscosity for AOBH-additives mud was 28-29.5 cp, but 29 cp for industrial-additives mud. Plastic viscosity for AOBH-additives mud was 22-23 cp, but 22 cp for industrial-additives mud. The yield point for AOBH-additives mud was 12-15, but 14 for industrial-additives mud. Use of AOBH did not affect the final gel strength. Both models show that the efficiency of the mud containing AOBH in cleaning the hole increased as the grain size of AOBH reduced. The chemistry of AOBH shows that it is biodegradable and eco-friendly.
Copyright (c) 2024 Caritas Journal of Engineering Technology
Design And Implementation Of 900mhz Mobile Electromagnetic Wave Interference System For Examination Hall Protection
This work aimed at design and implementation of 900MHz mobile electromagnetic wave interference system for examination hall protection. The work focused on design and building of a system that can block the use of mobile phone by transmitting radio waves of the same frequencies as that of the mobile phone causing interference between mobile phone and the Base Transceiver Station. The objectives of the work include the design and building of a power supply that would distribute power to other parts of the system for operation; designing and constructing the intermediate frequency section which helps to generate the tuning frequency (noise) signal; designing and constructing the radio frequency section which helps to generate radio frequency signal that would create interference with signal from base transceiver station so as to block transmission between mobile phone and base transceiver station and integrating different sub-systems to form one single system. The system was tested for functionality and the test results showed that a stable noise signal of 7.09MHz from the intermediate frequency-section was generated. Result showed that a final output frequency of 900MHz from the antenna was obtained.
Design And Implementation Of Smart Iot Based Plant Irrigation System Using Nodemcu Esp8266 Microcontroller And Blynk Interface Technology
This paper presents the design and implementation of a Smart IoT-based Plant Irrigation System utilizing the NodeMCU (ESP8266) microcontroller, aimed at automating irrigation based on real-time soil moisture data. The proposed system integrates a soil moisture sensor, water pump, and Wi-Fi-enabled NodeMCU to monitor and manage irrigation processes efficiently. When the soil moisture level drops below a defined threshold, the system automatically activates the water pump, ensuring that plants receive adequate water without human intervention. The NodeMCU sends data, monitor soil moisture levels and irrigation activity through a web dashboard. The system include low-cost components, ease of deployment, and real-time remote monitoring capabilities. The use of sensor enhances the functionality by sending mobile notifications to users and enabling remote control of the pump. The system is powered via a 5V power source and is designed to be scalable and energy-efficient, making it suitable for both home gardens and small-scale agricultural applications. The methodology involves setting up the hardware with NodeMCU, DHT11 (for temperature and humidity), soil moisture sensor, relay module, and a submersible water pump. The firmware is programmed using the Arduino IDE. Moisture data is sampled at regular intervals and used to make decisions about irrigation timing and duration. The system also logs environmental parameters to support data-driven analysis of plant growth conditions. The implemented system was tested over a period of two weeks in a controlled garden environment. It successfully maintained optimal soil moisture levels by activating the water pump automatically whenever moisture fell below the 30% threshold. Data was reliably transmitted to the dashboard as expected. Notifications were received in real-time through the system. The system reduced water usage by approximately 40% compared to manual irrigation.
Comparative Study on Adsorption Kinetics of Heavy Metals in Produced Water Using Banana Peel and Luffa Cylindrica Derived Activated Carbon
This paper investigates the potentials of activated carbon (AC) derived from Banana peel and Luffa cylindrica biowaste for the removal of heavy metals such as zinc, copper, nickel, and iron from produced water. Activated carbon was obtained by carbonizing adsorbents (Banana peel and Luffa cylindrica), using an impregnation ratio of 1:3 of H3PO4 for 24 hrs. The experimental runs were conducted using the batch adsorption method, where produced water was treated using a 2 g adsorbent dosage for 2, 4, and 6 hours of contact time. Adsorption kinetics were analysed using both the pseudo-first-order and pseudo-second-order model equations. The results show that Luffa cylindrica-derived AC achieved up to 85.7, 88.3, 54.7, and 52.2 % removal in Zn, Cu, Ni, and Fe, respectively, while banana peel AC led to 85.4, 70.7, and 35.2% removal in Zn, Cu, Ni, and Fe., respectively. Kinetic data indicated that pseudo-first-order best described Zn, Cu, and Fe adsorption (R² > 0.76) on both adsorbents, while only Fe adsorption on banana peel AC fitted well with pseudo-second-order (R² = 0.9998). These findings suggest the potential of these low-cost, sustainable biosorbents for effective treatment of produced water.
Experimental Assessment of Local Demulsifiers for Crude Oil Emulsion Breaking: A Study on Jatropha curcas, Indigenous Materials, and their Blends
Crude oil emulsion is one of the most challenging problems plaguing the phases of oil and gas production. Crude oil emulsion stability causes high viscosity of crude oil which leads to challenges in pipeline transportation and processing. This paper investigated the effectiveness of locally sourced material as a demulsifier, where the stability crude oil emulsion of varying viscosity by using three different formulated local demulsifiers such as Jatropha curcas extract, local materials, and their blends were investigated. The laboratory experiment was carried out using Crude oil I, II and III, blend of Jatrophas curcas and local materials into Sample A (100% Jatropha curcas extract), B (6:4), C (1:1), D (4:6), E (3:7), F (2:8), and G (100% local materials), at 60, 90, 120, 180, 240. 300, 360, 420 and 480 mins. The result generally showed that the lower the viscosity of crude oil the higher the water recovery (WR). Also, the demusifier blends had better WR when compared to without blends. Irrespective of the type of crude oil, demulsifier blends of equal ratio 1:1, 4:6, and 3:7 % led to maximum WR. For crude oil I, without delmusifier achieved 5.4 % WR, while Sample A had 86 % 77 % WR for Sample G at constant time of 480 mins, and 100 % WR for Sample D but at 36mins. Crude oil II, had WR of 4.0% at 480mins without demulsifier, Sample A, B, and D had water recovery of 79%, 72%, and 100% at 480 mins, respectively. Lower recoveries were observed for crude III majorly due to its higher viscous nature, as all samples had WR between 48.2 and 75%. This study has demonstrated that blend of locally formulated demulsifiers had better water separation capability.
Performance Characteristics of Palm Kernel Shell-based Biomass Fuel
The transition toward sustainable and renewable energy sources has become imperative in addressing climate change and reducing greenhouse gas emissions. Biomass, particularly in the form of agricultural waste, presents a viable alternative to traditional fossil fuels. This study investigates the potential of palm kernel shells (PKS), an abundant yet underutilized by-product of palm oil processing in Nigeria as a clean and efficient biofuel through the process of briquetting. Two PKS samples, sourced from local and improved palm varieties, were subjected to pyrolysis to enhance their fuel characteristics. The charred PKS was then combined with varying proportions of cassava starch (25%, 30%, and 35%) as a binder and compressed into cylindrical briquettes. Proximate and ultimate analyses, along with calorific value determination and combustion performance tests, were conducted to assess the energy efficiency and environmental suitability of the briquettes. Results indicate that a 25% starch binder ratio produced the most efficient briquettes, characterized by high fixed carbon content, lower ash and moisture levels, and superior calorific value. Additionally, combustion tests revealed favorable ignition times, burning rates, and fuel consumption efficiency. The findings demonstrate the viability of PKS briquettes as a sustainable alternative to fuelwood and charcoal, offering both environmental and economic benefits. This study contributes to the development of renewable energy solutions in developing regions and underscores the value of waste-to-energy innovations in enhancing energy security and reducing ecological degradation.
Maintenance And Characterisation Of Rock Well Hardness Testing Machine
Rockwell hardness testing machines are crucial in determining material hardness. However, their accuracy and reliability can be compromised if not properly maintained. This project focuses on the rehabilitation and maintenance of the Rockwell hardness testing machine in the Mechanical Engineering Department Caritas University, Amorji Nike, Enugu. The machine’s accuracy and reliability were restored through diagnostic tests, replacement of worn-out parts, calibration, and development of a maintenance schedule.The reliability and functionality of the Rockwell Hardness Testing Machine are critical for material testing and mechanical engineering applications. However, frequent power fluctuations, component wear, and inadequate maintenance practices often compromise the performance of this essential equipment, particularly in educational institutions like Caritas University Amorji Nike Enugu. This study investigates the application of a Fuzzy Logic-Based Static Var Compensator (SVC) to improve the rehabilitation and maintenance of the Rockwell Hardness Testing Machine. The fuzzy-based SVC system is designed to enhance voltage stability, mitigate harmonic distortions, and ensure a consistent power supply to the machine. By integrating intelligent control algorithms, the system can predict and adapt to varying operational conditions, enabling proactive maintenance and reducing the likelihood of system failures. This approach not only safeguards sensitive components from power-related damages but also extends the machine’s operational lifespan and minimizes downtime. The study employs a combination of experimental analysis and simulation to evaluate the performance of the fuzzy-based SVC system. Results demonstrate significant improvements in power quality, reduced maintenance costs, and enhanced operational reliability of the Rockwell Hardness Testing Machine. The findings underscore the potential of intelligent power systems in modernizing equipment maintenance practices and ensuring sustainable engineering education.
Design And Construction Of a Departmental Mini-Radio Station At The Faculty Of Engineering Caritas University Enugu
The "Design and Construction of a Departmental Mini-Radio Station in the Faculty of Engineering" focuses on creating a functional radio broadcasting system for educational purposes. These radio waves are a type of electromagnetic wave with frequencies ranging from about 3 KHz to 300 GHz. The mini-radio station encompasses the design and assembly of essential components, including a low-power FM transmitter, an antenna system, and audio processing equipment. The project follows a systematic approach, beginning with a comprehensive analysis of the requirements, followed by the selection of appropriate hardware, circuit design, and implementation. The goal is to ensure reliable signal transmission within a limited range while adhering to regulatory standards. The test result of this research work revealed the successful achievement of the primary objective; the design and construction of Mini-Radio station in faculty of Engineering Caritas University Enugu (Transmitter Part) with Audio Console Using Frequency Modulation (FM) With Power Rating of 20 Watt of appreciable range operating on 9v power supply. This system has been implemented with the locally source material in our electronic markets and used to implement the system using the necessary rudiment usually employed in electronic system implementation from the above stated results. The constructed radio station will serve as a valuable educational tool; provide students with practical experience and understanding of radio frequency principles, signal propagation, and broadcast engineering.
Fuzzy-Based Irrigation Improvement System for Nigerian Agricultural Fields
Efficient water management in agriculture is crucial for ensuring sustainable crop production and addressing the global water crisis. Traditional irrigation systems often suffer from inefficiencies, leading to water wastage, uneven crop yields, and increased costs. This paper proposes a fuzzy-based irrigation improvement system that leverages advanced machine learning algorithms, Internet of Things (IoT) devices, and real-time environmental data to optimize water usage in agricultural fields. The system integrates sensors to monitor key parameters such as soil moisture, temperature, humidity, and weather forecasts. Fuzzy logic algorithms analyze this data to determine precise irrigation schedules and water requirements, minimizing wastage and ensuring optimal hydration for crops. The system also employs predictive analytics to adapt to changing environmental conditions and crop growth stages. Key benefits include significant water savings, increased crop yield, reduced operational costs, and enhanced sustainability. Additionally, the system can be scaled and customized to suit different types of crops, soil conditions, and geographical locations. This approach demonstrates the potential of fuzzy logic in transforming traditional farming practices into a data-driven, resource-efficient paradigm. Future developments will focus on integrating renewable energy sources and blockchain for enhanced transparency and efficiency. This fuzzy-driven solution promises a sustainable future for agriculture by addressing the dual challenges of water scarcity and food security.
Optimization of a High-Performance Wellhead Connector for Enhanced Natural Gas Extraction
The development and Optimization of a high-performance wellhead connector for enhanced natural gas extraction was conducted. The paper presents the development of a structurally robust wellhead connector for providing natural gas recovery, utilizing advanced engineering design principles alongside finite element analysis (FEA) to ensure optimal performance and reliability. By leveraging FEA, the study effectively identifies weaknesses and areas for improvement in the connector's structure, ultimately leading to a refined, high-performance solution tailored to meet specific functional requirements. This paper demonstrates the importance of integrating analytical methods with practical applications to achieve superior outcomes in product development. The approach entails a comprehensive evaluation of material selection by API 6A standards, ensuring that all materials are suitable for high-pressure environments. The results indicate that the adapter is capable of withstanding pressures up to 5000 psi, demonstrating its durability in high-stress environments. Furthermore, it boasts a safety factor exceeding 2.0, which confirms its reliability under extreme conditions and ensures its longevity during prolonged use. This combination of high-pressure tolerance and a significant safety margin makes the adapter an excellent choice for applications requiring dependable performance. This research presents a comprehensive framework to enhance the structural integrity and manufacturability of wellhead equipment before its deployment in the field. Eventually, these developments contribute to improved effective productivity and protection in natural gas extraction processes.
Design and Implementation of Online Clearance System
An online clearance system is a web-based platform designed to streamline the clearance process by allowing users to submit necessary documents, track their status, and receive clearance approvals electronically, significantly reducing administrative burdens and providing a convenient, time-saving alternative to traditional manual clearance procedures, particularly beneficial for institutions managing large volumes of applications or requiring multi-departmental approvals. The proposed system was implemented via Windows 8 platform using the following technologies HTML, CSS, JavaScript, MySQL and PHP programming language.
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Voltage Stability Compensation in Electronics –Saturated Network Using Unified Power Flow Controller
Voltage stability is a critical aspect of power system operation, especially in networks with a high penetration of power electronic loads such as variable speed drives, converters, and renewable energy systems. These electronics-saturated networks often experience voltage fluctuations, harmonic distortions, and reactive power imbalances, which can lead to instability and reduced power quality. The Unified Power Flow Controller (UPFC), a key Flexible AC Transmission System (FACTS) device, offers a comprehensive solution to these challenges by providing simultaneous control of voltage, impedance, and phase angle in the transmission line. This paper presents an approach for voltage stability compensation in electronics-saturated networks using the UPFC. The shunt converter of the UPFC provides reactive power support, maintaining bus voltage levels and mitigating voltage sags caused by nonlinear electronic loads. Simultaneously, the series converter regulates line impedance and power flow, reducing line losses and improving load distribution. Additionally, the coordinated control of the shunt and series converters helps damp power oscillations and improve system resilience against sudden load variations. Advanced control strategies such as Proportional-Integral (PI) controllers, Fuzzy Logic Controllers (FLC), and Model Predictive Control (MPC) are discussed for optimizing the UPFC’s performance under varying load conditions. Simulation results demonstrate that the UPFC significantly enhances voltage stability, reduces harmonic distortion, and improves overall power quality in an electronics-saturated network. The proposed solution is suitable for applications in modern power grids, including industrial plants, electric vehicle charging stations, and smart grid environments.