The rapid growth of municipal solid waste presents environmental and health challenges, while manual sorting remains inefficient and hazardous. This study develops a prototype machine learning–driven automated waste sorting system designed for low‑cost deployment. The system integrates an ESP32‑CAM module for image capture, a MobileNetV2 convolutional neural network (CNN) transfer learning technique for classification, and a servo‑actuated mechanical sorting mechanism. Waste items are classified into biodegradable and non‑biodegradable categories, with system control managed through an ESP32 microcontroller and real‑time communication via WebSocket. The model was trained on 90 live images with an additional 2356 images augmented from an online database. The system achieved an accuracy of 93.57% on the test set. While this result confirms the system's feasibility, its generalizability is currently restricted by lighting sensitivity, a limited dataset, and an inability to classify wet waste. The prototype highlights the potential of combining embedded AI and mechatronics for sustainable waste management, supporting circular economy goals. Future work should expand datasets aimed at mitigating overfitting and addressing high intra-class variance, incorporate synthetic data generation to enhance model robustness, and explore edge inference for reduced latency.
The growing demand for sustainable materials has heightened interest in recycling aluminium alloys for engineering applications. This study investigated the recycling of aluminium alloy from automobile cylinder head scrap using semi-solid squeeze casting, optimized via a Taguchi L9(3³) design. Adopted process parameters were squeeze pressure (100–140 MPa), slurry temperature (590–630 °C), and die preheating temperature (220–240 °C). Their effects on tensile strength and hardness were analyzed using signal-to-noise ratios and ANOVA. Also, the recycled alloy was identified as an Al–Si type, containing 6.75% Si and 0.36% Mg, along with trace amounts of Fe, Cu, Mn, and Zn. In addition, tensile strength ranged from 228 to 233 MPa, while hardness ranged from 72 to 78 BHN. The maximum tensile strength of 233 MPa was achieved at 100 MPa squeeze pressure, 590 °C slurry temperature, and 220 °C die preheating temperature. The maximum hardness of 78 BHN occurred at 140 MPa squeeze pressure, 630 °C slurry temperature, and 220 °C die preheating temperature. Similarly, the ANOVA revealed that die preheating temperature had the greatest influence on tensile strength (64.76% contribution), while squeeze pressure and slurry temperature most strongly affected hardness (49.57% each). Furthermore, confirmation tests at optimal parameters yielded 231 MPa tensile strength and 77 BHN hardness values comparable to those of commercial A356 alloy (258.5 MPa tensile strength, 75 BHN hardness). These findings demonstrate that Taguchi-optimized semi-solid squeeze casting can produce recycled aluminium components with excellent mechanical properties, promoting sustainable manufacturing.
This study experimentally investigated the performance of a solar air heater (SAH) constructed using recycled aluminium beverage soda cans as absorber tubes under Maiduguri harmattan climatic season. The research addressed the growing need for low-cost, environmentally sustainable heating systems for applications such as space heating and crop drying in regions with abundant solar radiation. The system was designed as a single-pass solar air collector with dimensions 1.09 m × 0.835 m × 0.15 m and collector area of 0.91 m². The absorber consisted of 96 aluminium soda cans arranged in a 12 × 8 matrix and coated with black paint to improve solar absorptivity. Experiments were conducted in active mode using a 12 V DC fan with air mass flow rate of 0.00113 kg/s and in passive mode airflow movement which occurred naturally through buoyancy-driven convection. The collector was tested at tilt angles of 25°, 30°, 45°, 60°, and 180°. Performance parameters included outlet air temperature, temperature rise, useful heat gain, and collector efficiency. Results indicated that the active system achieved a maximum thermal efficiency of 14 % with useful heat gain of approximately 90W at 45o tilt angle. Also, the passive mode achieved 16% efficiency with optimum performance at 45° tilt angle with maximum useful heat gain of 130.2W. The results confirmed that discarded aluminium cans can be effectively used as low-cost absorber materials for solar air heaters in solar-rich regions, such as Maiduguri.
Growing interest in sustainable and low-cost engineering materials has encouraged the use of agricultural waste as reinforcement in aluminium metal matrix composites. In this study, shea nut shell ash (SNSA), an abundant agro-waste material was used as reinforcement in AA6061 aluminium alloy to develop an environmentally sustainable composite. The composites were produced using the stir casting technique with SNSA additions of 0, 5, 10, 15, 20, 25 and 30 wt%. Microstructural and phase characterisation were carried out using scanning electron microscopy with energy dispersive spectroscopy and X ray diffraction, while mechanical properties were evaluated through tensile, hardness and impact tests in accordance with ASTM standards. The results showed that SNSA is predominantly carbon based with 88.15 wt% carbon and 7.75 wt% oxygen, while minor elements such as Fe 2.18 wt%, Si 0.36 wt% and Ca 0.31 wt% were also detected. X ray diffraction revealed the presence of hard phases including Fe₃C and MnO together with graphite. In addition, the Mechanical properties revealed that the unreinforced AA6061 alloy exhibited the highest tensile strength of 119.77 MPa and elongation of 12.91%. With increasing reinforcement content, tensile strength decreased to 83.04 MPa at 5 wt% and further to 32.32 MPa at 25 wt%, before slightly recovering to 66.56 MPa at 30 wt%. Conversely, hardness increased from 7.6 HRB for the base alloy to a maximum of 13.5 HRB at 30 wt% SNSA. However, the impact energy improved from 2.64 J for the matrix alloy to 8.33 J at 10 wt% reinforcement and remained above the base alloy for all reinforced samples. Furthermore, the results indicate that moderate SNSA additions of 5 to 15 wt% provided the most balanced combination of hardness, strength and impact resistance. Therefore, the study demonstrates that SNSA is a promising sustainable reinforcement for aluminium matrix composites suitable for cost sensitive engineering applications.
This study presented the design, fabrication, and performance evaluation of a dual-purpose rice processing machine that integrated threshing and milling operations into a single unit to address the inefficiencies of conventional post-harvest rice processing. The machine was designed using standard mechanical design principles and fabricated with locally available materials to ensure affordability, durability, and ease of maintenance for small- and medium-scale rice processors. A 1.5 hp electric motor served as the prime mover, driving both the threshing drum and milling rollers through a belt and pulley transmission system. Performance evaluation was conducted through a series of preliminary and formal tests using paddy rice samples. Experimental results showed a threshing efficiency of 66%, milling efficiency of 69.69%, and rice husk recovery of 39.6%. When operated as an integrated single-pass system, the machine achieved an overall operational efficiency of 46%, producing 2.3 kg of milled white rice from 5.0 kg of paddy. Although this overall efficiency is moderate and lower than that of many optimized conventional rice processing systems, it remained acceptable for a low-cost prototype and demonstrated the feasibility of integrating threshing and milling in a compact unit. These results highlighted the potential of the machine to reduce processing time, labor, and equipment cost. However, further optimization particularly in grain transfer mechanisms, threshing consistency, and milling clearance adjustment is required to improve performance. It is therefore recommended that future designs should focus on improving component alignment, enhancing separation efficiency, and conducting extended field testing under varying moisture and load conditions to achieve higher overall efficiency and reliability. Overall, the developed machine offered a practical and cost-effective solution for improving local rice processing and enhancing food security in rural communities.
Fuel theft and frequent vehicle abductions across many African regions inflict heavy economic losses on transportation providers, demanding reliable yet affordable monitoring solutions. Traditional ultrasonic sensors for fuel level detection fall short due to inaccuracies from fuel foaming and the need for invasive tank drilling during installation. This study addresses these challenges by introducing a low-cost, non-invasive telematics retrofit that leverages existing vehicle fuel sensors for real-time fuel level display and geo-location tracking, which are critical tools for fleet management in developing economies with meager resources. The proposed system intercepts the signal cable from the fuel tank sensor to the vehicle dashboard, integrating it with a microcontroller-based data acquisition system. This setup displays fuel levels and GPS coordinates on an LCD screen and streams data to Google Maps via a web application, requiring no tank modifications. The results showed GPS data from the device correlated strongly with mobile phone benchmarks, yielding coefficients of 0.911 for latitude and 0.941 for longitude, validating its precision. These findings demonstrate a practical, retrofit-friendly alternative to ultrasonic methods, enabling theft prevention and efficient fleet oversight without high costs or disruptions. Future work could integrate machine learning to predict fuel consumption, vehicle paths, and driver behaviour.
Household energy needs should be governed according to the global warming challenge. However, current IoT solutions for measuring carbon footprint are usually costly, inaccurate or have no user-friendly feedback. The key objective of this study was the development of a new low-cost IoT system that tracks real-time electricity consumption and indoor CO₂ concentration to promote sustainable behaviour. A key design feature of the system is its modularity, with an ESP32 microcontroller at the heart of the system allowing easy integration of calibrated SGP30 gas sensor, energy sensors (ZMPT101B, ZMCT103C), and multi-channel communication. The new software solution used for this study was the CallMeBot API that transmits the data directly to WhatsApp, making it very user-friendly by removing the need to use a mobile app only for this purpose. The tests carried out demonstrated that the average error of the voltage sensor was 1.8%, the Wi-Fi data transmission success was 99.2%, and the SGP30 sensor had a successful dynamic baseline calibration, ensuring accuracy to within ±50 ppm of reference values in steady-state conditions. The experiments revealed the existence of energy consumption differences by a substantial margin: the LED bulb (8.7 W) consumed 92% less energy than the incandescent bulb (107.46 W) and the DC fan (7.5 W) 91% less than the traditional AC fan (82.0 W). The indoor CO₂ levels correlated with the occupancy and ventilation, from a base of approximately 400 ppm to over 1250 ppm in areas with no ventilation. This study demonstrated that a powerful open-source IoT platform can enable households to monitor and reduce their ecological footprint to save energy and improve the indoor environment.
This study evaluates the mechanical and durability of grade 25 concrete modified with Rice Husk Ash (RHA) and Periwinkle Shell Ash (PSA) under crude oil exposure. RHA and PSA were incorporated at 0–10% replacement of cement by weight, while maintaining a constant water-to-cement ratio of 0.55. Concrete specimens were subjected to two curing regimes: 28 days of pre-curing in water and then followed by 62 days post-curing in crude oil immersion (totaling 90 days), and continuous crude oil immersion for 90 days. Mechanical and Durability were assessed through compressive strength and moisture absorption tests at 90 days of curing in line with BS standards. Microstructural investigations using Scanning Electron Microscopy (SEM) were carried out to examine pore refinement, crack morphology, and hydration products. The results indicate that optimal blends of ≈5–6% RHA and 4–5% PSA produced the best performance. They achieved higher density (eg.C7=2730kg/mm3 and W7=2803 kg/mm3), lower moisture absorption (e.g. W7=0.1785% and C6-0.486%), and improved retention of mechanical strength (e.g. W7=20.5N/mm2 and C9=16.0N/mm2) compared to control samples. Water-cured specimens consistently outperformed those in crude oil by 7.87% in strength, yet RHA–PSA concretes exhibited slower strength loss and reduced microcracking under crude oil exposure. SEM images confirmed denser C–S–H gel networks and refined pore structures at optimal dosages, whereas higher ash contents increased porosity and susceptibility to deterioration. The findings demonstrate that moderate incorporation of RHA and PSA enhances both the mechanical and durability of concrete in crude oil environments (C7=15.0N/mm2), making these materials promising sustainable additives for infrastructure exposed to petroleum-polluted conditions.
The increasing demand for concrete has intensified the exploitation of natural river sand used as fine aggregate, resulting in material scarcity, rising construction costs and environmental degradation from excessive sand mining. This has encouraged the search for sustainable alternative materials, including locally sourced soils, for partial replacement of river sand in concrete production. This study presented a structured review of experimental investigations on the use of lateritic, sandy, silty and clayey soils as fine aggregates in concrete. A total of 10 experimentally validated studies published majorly between 2015 and 2024 were systematically reviewed. The review evaluated the effects of these soils on workability, compressive strength, durability and cementitious interactions within the concrete matrix. Findings indicated that well-graded sandy soils and lateritic soils with low clay content can replace approximately 10–30% of river sand without significant reduction in compressive strength. Quantitative analysis showed that optimum replacement levels generally ranged between 15–25%, producing compressive strengths of approximately 24–30 MPa suitable for normal structural concrete applications. The review further revealed that soils containing appreciable silica (SiO₂) and alumina (Al₂O₃) may contribute to secondary pozzolanic reactions and improved matrix densification. However, excessive clay content (>8–10%) increased water demand, reduced workability, weakened cement–aggregate bonding and negatively affected durability performance. The use of locally sourced soils may reduce dependence on river sand and lower transportation costs in developing regions. Although preliminary durability performance at moderate replacement levels was satisfactory, further investigations involving permeability, sulphate resistance, shrinkage, carbonation and long-term durability are required before widespread structural application. The study highlighted the potential of locally sourced soils as sustainable supplementary fine aggregate materials when proper characterization, grading and mix proportioning are adopted.
This work presents the experimental study to investigate the geotechnical properties of the collapsible soils within Maiduguri Metropolis, Borno state, Nigeria. Ten Soil sampling points (test pits) were considered from five different locations within the town and labelled as A1-A5 (Dikwa, Damboa, Baga, Jos and Bama roads). The samples were obtained with the aid of a hand auger, shovel, 100mm Polyvinyl chloride (PVC) pipes, scrapper and preserved in Polyethene bags and prepared wooden boxes to avoid moisture loss. Particle size distribution revealed that the soils were predominantly sand with traces of gravel and classified as A-3(0) according to the American Association of State Highway and Transportation Officials (AASHTO) and SP in accordance with the Unified Soil Classification System (USCS). The compaction characteristics showed that optimum moisture content (OMC) ranged between 9.0-15.1% and maximum dry densities (MDD) ranging between 1.57-1.92g/cm3 while colours ranged from brown to rich brown. The oedometer test affirmed the existence of collapsible soils in the studied locations while collapse potential (CP) ranged between 1.50-12.20%. The shear strength test revealed low values of cohesion (c) which ranged between 0-1 kPa with an angle of internal friction (ᶲ) ranging between 5-9º. The ultimate bearing capacity result revealed a range between 14.40-86.80 kN/m2 and safe bearing capacity ranging between 20.20-86.80 kN/m2 with a mean safe bearing capacity of 46.26 kN/m2. Hence the soils were classified as cohesionless.
This study investigated the effects of drying temperature on the nutritional and phytochemical components of Gongronema latifolium (Bush Buck) leaves; a widely spread edible indigenous leafy vegetable of West Africa. The leaves were dried at room temperature (RTD), in the open sun (OSD) and at 30, 40, 50, 60 and 70 °C in a convective oven (OD). Proximate and phytochemical analyses were done on the products of each OD temperatures, while for RTD and OS drying, the tests were done at their equilibrium moisture content (𝑀𝑖) of the ambient. Statistical analysis was done using MATLAB. The parameters of the fresh leaves with 82.95% moisture content served as the control. Drying decreased the product 𝑀𝑖 to 12.95%, 7.65%, 6.95%, 5.79% and 5.03% in the listed order of the drying temperatures. The proximate and phytochemical contents increased with drying temperature. 70 °C drying temperature gave the highest contents of ash (16.35%), fibre (27.85%), protein (22.70%), carbohydrate (32.84%) and fat (2.42%) in the dried leaves. Same went for the phytochemicals: alkaloid (4.50%), flavonoid (3.92%), saponin (1.46%), tannin (1.39%) and terpenoids (2.72%). Second order polynomial models fitted the data well and gave coefficient of determination (R2) of 0.789 for fibre, 0.869 for fat, 0.895 for protein, 0.921 for ash, 0.923 for terpenoids, 0.941 for moisture, 0.943 for flavonoids, 0.982 for saponnins, 0.987 for alkaloids and 0.992 for tannins content. These results show that there are strong relationships between the proximate and phytochemical composition and the drying temperatures. At their corresponding temperatures, OSD yielded the highest value of saponins (1.45%), tannins (0.64%) and terpenoids (2.45%), RTD gave highest value of alkaloids, flavonoids, carbohydrate (58.38%), protein (17.07%) and fat (1.10%), while OD yielded the highest fibre content (28.32%). The results show that drying methods and conditions should be chosen based on the intended use of the products.
Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Adsorption of toxic metals has been achieved using Vermicompost, but there is dearth of knowledge in adsorption of crude oil using Vermicompost. This study brings to knowledge the effectiveness of earthworm waste (vermicompost) use for the remediation of crude oil contaminated soils. The remediation methods adopted were batch and column processes conditions. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the data from laboratory experiments. Machine learning models [Artificial neural network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS). ANN and ANFIS were evaluated on the observed and predicted percentage removal of crude-oil using the coefficient of determination (R2) and mean square error (MSE)]. Removal efficiency ranged from 29% to 98.9% for batch process remediation and 56% to 92% for column process remediation. Optimum values of the experimental factors were absorbent dosage of 34.53 g, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, for batch and column processes. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation and 56% to 92% for column process remediation with the same optimum values of factors. Coefficient of determination (R2) for ANN was (0.9974) and (0.9852) for batch and column process, respectively. This result show strong correlation between the observed and predicted values for batch and column process, respectively, the coefficient of determination (R2) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between observed and predicted values. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. Machine learning models appear to be capable of predicting the removal of crude oil from polluted soil using vermicompost.
The trouble with a developing national grid is compounded by the fact that the power system supplies power to a vast number of loads which is fed by a number of generating units sometimes far from the load centers. Variations of loads bring about power losses and corresponding increase in the reactive power requirements of the transmission systems. Thus, the paper is aimed at establishing the point of collapse of transmission lines to enable a control system operator to take proactive measures in the event of small/large system disturbances. Voltage collapse proximity indicator is exploited to show that the variations in load close to the maximum load require extremely large amounts of reactive power at the sending end in order to support the increase in load. In the work, it was identified that Katemkpe-Shiroro transmission line is the most critical transmission line contributing immensely for voltage collapse scenarios in Nigeria national grid. Consequently, it ranked first because it has the highest voltage collapse proximity index. Computation involving voltage collapse proximity indicator obtained the collapse point or the knee point as 2.8684. This serves as a criterion that will enable control system expert to take proactive measure before the impending voltage collapse.
The study examines the residents’ socio-economic characteristics as correlates of groundwater source in Ikenne Local Government Area, Ogun State, Nigeria. The objectives addressed in this study are analysis of the socio-economic characteristics of the residents; sources of groundwater; and relationship between residents’ socio-economic attributes and groundwater source in the study area. A total of two hundred and seventy-two (272) respondents across all the six political wards of Iperu and Ilisan, were administered using questionnaires. Data collected were analysed using frequency distribution, percentage, ANOVA (it is used to establish if there is significance different between variable), Correlation and Regression. The study revealed that there is no significant difference in socioeconomic background of the residents (p > 0.05) except for educational attainment. It was found that correlation exists between groundwater source and marital status, education, income and occupation with exception of gender. Regression results established that 82 % of variability in groundwater source is predicted by residents’ socioeconomic characteristics. It could be concluded that socioeconomic background of the residents significantly influences their source of groundwater. It is therefore imperative that governments at all levels need to come up with policies and programmes that will enhance peoples’ socioeconomic status, thereby enhancing their ability to provide an improved water source.
With partial shading conditions, it is essential to acquire Maximum Power Point at which the Photovoltaic systems (PV) operate effectively despite the variation in the cell temperature and incident angle of sunlight rays on the panels. This study explores the use of a Smell Agent Optimization (SAO) algorithm for Maximum Power Point Tracking (MPPT) in partial shaded PV systems. The proposed MPPT system is composed of a PV model, a DC-DC converter model and a control part. The Smell Agent Algorithm (SAA) was adopted in the control part of the MPPT system to implement the optimization algorithm using four different shading patterns (SPs) and to calculate the optimal switching duty cycle of the DC-DC converter. The effectiveness of the proposed system was verified using simulations in the MATLAB/Simulink environment. The SAO respectively track maximum values for Power, Voltage and Current as 845.8476 W, 211.7308 V, 3.99492 A while the maximum values for Power, Voltage and Current for Perturb and Observe (P and O) are 845.0465 W, 211.6305 V, 3.993028 A respectively during SP1. The results showed that the SAO algorithm has excellent tracking results in terms of convergence speed, accuracy, power extracted stability, and dynamic response in reaching the optimum point.
This research was carried out to model and optimize the efficiency of disc harrow on clay-loam soil in South – East Nigeria to assist farmers scrutinize and select appropriate harrowing implement reliant on soil type for efficacious and magnificent production. The harrowing operation was conducted at selected effective working widths, operational speeds and cutting depths using 2-gangs tandem disc harrow. The experimental design adopted in the research was a three level – three factor full factorial design. The experiment consists of three factors which were varied at three levels of harrowing depths which include 10, 20, 30 cm; three levels of effective working widths of 60, 120 and 180 cm and three levels of operational speeds (6, 7 and 8 km/hr). Central Composite Response Design which gives 17 test runs was performed for each sample. The results found that the highest field efficiency of 98.50% was obtained when the harrow was operated at the pulverizing depth of 20cm under operational speed of 7 kmh-1 and working width of 120 cm. The quadratic model equation was statistically significant (P ˂ 0.05) for the prediction of the field efficiency. Additionally, the results show that the coefficient of determination; R2 for the field efficiency was 0.9139, which indicated adequate correlations among the factors. The Predicted R² of 0.7695 was reliable with the Adjusted R² of 0.8031 which identified excellent interactions between the factors (effective working width, operational speeds and harrowing depths). The adequacy Precision (10.3749) obtained indicated seemly indicator and that the model could navigate the design space. The optimum field efficiency and the desirability of 96.66% and 0.697 were respectively attained at optimum depth of 30 cm, working width of 180 cm and speed of 6.16 kmh-1. Therefore, farm operators can henceforth, evaluate and select the harrow implements using the developed model.
This study focused on the optimization of vitamin A in guinea corn and millet mix. The concentration of vitamin A was investigated under the following conditions: blending time (1.5 - 5 hours), amount of red guinea corn (10 - 50g) and amount of agro residue (50-100 g) using Box-Behnken design. Statistically significant model (p<0.05) was developed to represent the relationship between the response (concentration of vitamin A) and the independent variables. The model showed a significant fit with experimental data with R2 values of 0.94. Analysis of variance (ANOVA) results showed that the concentration of vitamin A was influenced by the blending time, amount of red guinea corn and amount of millet used. Additionally, response surface methodology (RSM) was used to optimize the concentration of vitamin A. The results showed that maximum concentration of 98.76 µg/100g for vitamin A was obtained at the optimum production conditions of blending time of 5hours, 49.79g of red guinea corn and 100g of millet. The blend produced at the optimized conditions satisfied the World Health Organization (WHO), Food and Agricultural Organization (FAO) specification for recommended safe intake for all age groups, pregnant and nursing mothers.
