FACULTY OF ENGINEERING,

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of an IoT- based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller uipped ith wireless transmission capabilities to relay data to the ThingSpeak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily differentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples while one incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained affirm the effectiveness of both processes for corrosion monitoring in remote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-effective and efficient alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

The main purpose of the project was to design a 3.5KVA inverter which makes use of both solar and mains or grid supply for charging the batteries. This is to reduce the frequency of power outages experienced in our homes and businesses. The project was carried out with the use of two 12V batteries connected in series to give a total of 24V DC which would serve as input for the inverter when on inverting mode and give an output of 220V AC for household appliances. Incorporated within the inverter was load control features, such that when the inverter stops charging and starts inverting, at a particular battery level set by the user, the heavy loads would be cut off while supply of power to the light loads continues. But when critical battery level is reached the light loads are also cut off and the inverter shuts down. This was done using Microcontroller in controlling relays which either powers on the load or cuts off the load when the battery is low. The proposed inverter design has two outputs through which load management was achieved. One of the outputs is designated to light loads and the other to heavy loads. The Microcontroller DSPIC30F4012 controls the load stage which can be programmed through the keypad to monitor the output power to the loads in output one and two, to ensure they do not draw power beyond the limits programmed by the user. To achieve this, the Microcontroller cuts off either of the outputs which exceed the set limit. The project was successful and the test results obtained was satisfactory. The inverter's operation was consistent with the design and the desired control of power consumption and power management was achieved.

The aim of this paper is to design and develop an IoT based Smart Monitoring System. The purpose of the current method is to create a safe and secure fish farming that helps the fish pond owners in producing high quality fish by maintaining normal water levels in the fish tank. (Sajal Saha, 2007). In order to implement this design, the Atmega 328p microcontroller is used as well assensors and actuators such as the DSB18b20 temperature sensor, HC-sr04 ultrasonic sensor, HC- sr501 motion sensor and a solenoid valve to automate the process of controlling the water quality parameters, such as the water level, temperature which is best at 24-27 oc (Johnson et al.,2007) and PH range which is acceptable at 6.5 to 9.0 (Will Mosley, 2009). These sensor values are stored in cloud so that farmers can see on their mobiles through mobile app or web application anywhere remotely. Android phone is used as the terminal device. A user can monitor the water condition using an android app through Wi-Fi within Wi-Fi range of 2400-2484 MHz and through Internet from anywhere in the world, A significant cost reduction is achieved as a result of farm equipment and water pumps being operated only when required using optimization schemes to maintain desired waterlevel in fish tank with efficient energy consumption through appropriate selection of pumps and tank filling level (Nirosha et al, 2017). The system consists of various sensors that measure important factors of the water like temperature, pH and water level and the data from these sensors can be accessed by an application through firebase (Weber et al, 2010). The farmer can then act as per the information relayed or the model can automatically act on behalf of the farmer as per the predefined actions. The real time information enables timely intervention by the farmers which eventually helps minimizing or eliminate wastages.

The power supply in developing countries is practically low owing to the inability of public power plants to meet the demand of its population and this has brought in the need for an
alternative source of electrical power. Where this is the case, a transfer switch is needed to transfer the supply of power from the different sources to the load. A manual transfer switch
requires that a user effects the overall process of power changeover from the different supply sources to the load and this could become cumbersome hence, the need for an automatic transfer switch. The objective of this design centers on sensing the primary/main power supply source, to startup the secondary power source (generator) when the main power supply source fails, shutdown the generator when the main power supply source is restored, to startup the secondary power source when power fluctuations from the main power supply source is detected and to automatically transfer the load to the available power source, thereby making the entire process easy and reliable. The design was carried out with low cost solid state electronic components such as; Relays, transformer, microcontroller, voltage regulator, resistors, capacitors, diodes.

The persistent unreliability of the national power grid in Nigeria has significantly hindered economic growth and forced residential consumers to rely on expensive, noisy, and polluting diesel generators to meet their daily electricity needs. This study investigates the technical and economic feasibility of an islanded hybrid photovoltaic (PV)–wind–battery energy system designed to reliably power a residential building in Benin City, Edo State. Using HOMER Pro simulation software, the research modeled and optimized the system for a 3-bedroom apartment with a daily load demand of 21.73 kWh, utilizing local meteorological data. The optimal system configuration was determined to include a 5 kW solar PV array, three 1 kW wind turbines, a 34.8 kWh battery bank, and a 3.5 kW converter. This configuration achieved a 100% renewable fraction and high reliability, with a Loss of Power Supply Probability (LPSP) of 0.78% and a Loss of Load Expectation (LOLE) of approximately 22 hours per year. Economic analysis revealed a Net Present Cost (NPC) of ₦132,850,500 and a Levelized Cost of Energy (LCOE) of ₦1,305/kWh, placing the system within the competitive range of diesel-based alternatives.Furthermore, a comprehensive sensitivity analysis confirmed that the system remained economically viable across all tested scenarios, affirming the suitability of hybrid renewable systems for off-grid residential applications in Nigeria

The rising cost of grid electricity and the global push for sustainable energy solutions have heightened interest in renewable-based power systems. This project presents a comprehensive reliability assessment and techno-economic analysis of an islanded (standalone) Solar Photovoltaic (PV) and Battery Energy Storage System (BESS) designed to meet the entire electrical load of the Department of Electrical and Electronics Engineering at the University of Benin. The study utilized HOMER Pro software to model, simulate, and optimize the system. A detailed load profile of the department was developed and used as the primary input, alongside solar irradiation data for the Benin City location. The system was designed to operate without any grid connection, making reliability the paramount design constraint. The optimization process aimed to find the most cost-effective system configuration that minimizes the Net Present Cost (NPC) while adhering to a strict maximum allowable capacity shortage of 1%. Using HOMER Pro software, an optimal system configuration was determined: a 180 kW solar PV array coupled with a 100 kWh Lead-acid battery bank. The system demonstrates high reliability, meeting 98.98% of the annual load demand while maintaining complete energy independence. Economic analysis shows the system achieves a Levelized Cost of Energy of ₦619.5/kWh, proving it to be a technically feasible and financially viable sustainable energy solution for the department. The study confirms that islanded PV-Battery systems can provide reliable power while offering long-term economic benefits compared to conventional alternati

The age-less interaction between man and his built environment has always had positive and negative impacts on the two. Environmental disaster of varying origin from man-made to natural is one of the most negative effects of the built environment on man. An assessment of the magnitude of these disasters and an evaluation of the existing capacities to prevent, mitigate or
prepare for them are necessary tools to provide future safe living for man in his built environment. Building collapse established to be caused by many factors is one of such disasters wielding its great impact of loss of lives and properties on man .This study evaluated the causes of building collapse in Edo State by identifying the major causes of collapse, assessing its effect, and also assessing the roles of government and construction professionals in mitigating the occurrence of building collapse in Edo State.
The method use to carry out this study involves materials harvested from the different building collapse sites, structural analysis of the collapse building and distribution of questionnaires to gather information relevant to the study. Findings from the study show that building failure and collapse in Edo State arise mainly from a combination of technical weaknesses and human negligence. Poor structural design emerged as a major cause, followed closely by flooding, which weakens foundations, reduces soil
stability, and accelerates material deterioration. Low compressive strength of blocks, inadequate supervision, and the use of inexperienced or unqualified personnel also contributed significantly to building failures. Other notable factors include ignorance of construction standards, deliberate avoidance of building regulations, and weak enforcement of quality control measures on construction sites and in the building materials market.

Post-harvest losses in developing Countries have contributed to the unavailability of foodstuffs. Estimation of these losses is generally cited to be of the order of 4% but under very adverse conditions, it is estimated as high as 100%. A significantpercentage of these losses are related to improper and or untimely preservation of foodstuffs such as fish, meat. Fish being an important component of the diet for people throughout the World, has high protein and nutritional value. It supplies 6% of global protein and may be classed as either white, oily, or shell fish. In most developing countries where there is high rate of malnutrition, fish provides nutritious food which is often cheaper than meat and therefore available to a larger number of people. Fish being an extremely perishable food which in most cases becomes inedible within twelve hours at tropical temperature, spoilage therefore begins as soon as the fish dies and processing should therefore be done quickly to prevent the growth of spoilage bacteria. Fish is a low acidfood and is therefore very susceptible to the growth of food poisoning bacteria hence another reason why it should be processed quickly. Basically the moisture content of fish stands around 80%; if this is reduced to around 25% bacteria cannot survive and autolytic activity will be greatly reduced. Further moisture content of 15% or less mould and bacteria will cease to grow; well dried fish if stored under the right conditions can be kept for several months. The use of appropriate methods of preservation creates the possibility of having greater increase in the amount of fish available for human consumption. The purpose of preservation is to reduce the moisture content of the fishy because micro-organisms that are responsible for spoilage and wastage cannot survive without moisture. Some of the preservation methods/ techniques include the following; cooking (boiling or frying), salting, smoking and drying collectively known as curing (lowering the moisture content) and fermentation (lowering the pH). However, it should be noted that the scope of this research project paper is basically aimed on drying as a means of fish preservation via a multipurpose solar powered fish drying mechanism devise. Drying is a dual process of heat and mass transfer of moisture from the interior of the product to the surrounding air. Drying involves the abstraction of moisture from the product by heating and the passage of air mass around it to carry away the released vapour. The basic essence of drying is to reduce the moisture content of the product to a level that preventsdeterioration within a certain period of time normally regarded as the “safe period” There are basically two common methods of drying fish namely; open air/sun drying and smoking. Open air/sun drying is probably the oldest method used for preserving fish and other foodstuffs primarily used in developing countries because it is the simplest and cheapest method of conserving fishes. This traditional method involves spreading of products on the ground or on rack in the open air/sun drying or on local three stone stove for smoking. Some disadvantages ofopen air/sun drying and smoking are; exposure of the fish to rain and dust, uncontrolled drying;exposure to direct sunlight which is undesirable for some foods; infestation by insect; attacks by animals etc. The use of solar powered drying mechanisms has not gained popularity in developing countries and reasons can be attributed to; poor problem definition which makes the developed dryers technically inadequate and economically unviable; inappropriate understanding of dryers designs due to the choice of construction materials; inadequate understanding of the operations of solar powered dryers and lack of design procedures. Herein, a forced convection solar powered fish dryer is designed, constructed andcharacterized to yield a better means of drying fish. The designed solar powered dryer would incorporate a heat storage unit which would variably compliment the drying rate of the product during off sunshine hours. As envisaged the device would be characterized during rainy and dry seasons, however results, calculations and analysis clearly indicate the influence of seasonal variation on the performance of the device. Average hourly variation of the dryer and ambient temperatures measured on selected days of different months of measurement during the two seasons, clearly indicated that temperatures were significantly enhanced by the device. This was due to the high ability of the designed solar collector to trap large amount of long waves. Average daily moisture loss from the drying fish was notably high, especially during dry season which was due to the high dryer temperatures and decrease in atmospheric humidity.

Predicting the subsurface temperature distribution within sedimentary and petroleum‐bearing formations is essential for accurate hydrocarbon maturation modeling, well‐bore stability, and drilling‐fluid design. Traditional approaches—relying on sparse bottom‐hole temperature (BHT) measurements and one‐dimensional conductive models—often misestimate true formation
temperatures by 5–10 °C in heterogeneous settings such as the Niger Delta. To address these limitations, this study develops a data‐driven workflow that employs machine learning algorithms trained on routinely acquired drilling logs to produce continuous, high‐resolution temperature profiles. First, we assembled a dataset from Niger Delta wells comprising wireline logs (gamma‐ray, four‐pad resistivities, density, neutron porosity, sonic velocity), drilling parameters (equivalent circulating density, rate of penetration, exposure time), and corrected BHT readings. After replacing sentinel values (–9999) with NaNs and depth‐referencing all curves to true vertical depth (TVD), each log was clipped to its 1st–99th percentile range to mitigate extreme outliers. Features were standardized to zero mean and unit variance. Derived attributes—such as depth‐derivatives and moving‐window averages—were also explored to enhance lithofacies and thermal signal detection. We compared three regression models: a multilayer perceptron neural network (ANN), a Random Forest (RF) ensemble, and Support Vector Regression (SVR). Hyperparameters were tuned via grid search with k-fold cross‐validation, and models were evaluated on a hold‐out subset (20 % of depths). Error metrics (RMSE, MAE, R²) and well‐log–style scatter and depth‐track plots quantified predictive performance and bias. The ANN achieved an RMSE of 0.16 °F and R² = 0.97, producing smooth temperature gradients. The RF delivered an RMSE of 0.25 °F and R² = 0.985, with feature‐importance analysis highlighting mid‐pad resistivities and drilling parameters as primary predictors. SVR, with an RMSE of 0.47 °F and R² = 0.955, was less competitive but still captured over 95 % of temperature variance. Well‐log plots demonstrated that both ANN and RF closely track actual temperature profiles across lithologic transitions. This end‐to‐end pipeline—from data cleaning and feature engineering to model training, validation, and interpretation—demonstrates that machine learning can offer accurate, cost‐effective alternatives to physics‐based thermal models. Future work will explore hybrid physics–ML models, temporal drilling‐data integration, expanded feature fusion (e.g; seismic attributes), and explainable‐AI techniques, with the goal of operationalizing these tools in real‐time drilling workflows

Drilling mud, also known as drilling fluid, is a vital component in the oil and gas industry. As the primary medium for drilling oil and gas wells, its importance cannot be overstated. However, in Nigeria, the procurement of drilling mud is often costly, as the required materials for its formulation are largely imported. This project investigates the suitability of a locally sourced clay, Iyi−Ogene, obtained from one of Nigeria’s numerous clay deposits, as a potential substitute to imported bentonite in drilling
mud formulation. The study aims to promote local material utilization, reduce import dependency, and minimize overall operational costs. Guided by API specifications, rheological properties of the local clay were determined upon preparation using standard procedures. Additionally, carboxymethyl cellulose (CMC) was incorporated to some samples to enhance performance toward API standards. The results indicate that the local clay possesses promising potential for drilling mud formulation, provided adequate beneficiation and optimization of activation conditions are applied. The findings also emphasize the importance of maintaining optimal base concentration during chemical activation, as excessive amounts may yield adverse effects. Overall, this laboratory−based study demonstrates that certain local clays, when properly treated and modified with suitable additives, can perform comparably to imported bentonite. It further underscores the need for field−scale evaluation to validate laboratory results and support the wider adoption of local materials in drilling fluid formulation.