ABUAD Journal of Engineering Research and Development (AJERD)

Characterization and Comminution Energy Determination of Anka Manganese Ore for Steelmaking Applications

2025-07-04T10:50:11+00:00

The global demand for manganese continues to rise due to its critical role in steelmaking and emerging battery technologies. This study investigates the physicochemical properties and comminution energy requirements of manganese ore from the Anka deposit in Zamfara State, Nigeria. A comprehensive characterization was conducted using Energy Dispersive X-Ray Fluorescence (ED-XRFS), X-Ray Diffraction (XRD), and Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS). Particle size analysis was carried out on the as-received sample, followed by a grindability test. The chemical analysis confirmed a high MnO content (52.112%), while mineralogical assessments identified pyrolusite and jacobsite as dominant phases. SEM-EDS analysis revealed the interlocking nature of manganese, iron, silicon, and aluminium within the ore matrix. Particle size analysis identified the optimal liberation size at -125+90 µm, assaying 53.973% MnO. Grindability tests using quartz and iron as reference ores yielded work index values of 11.34 kWh/ton and 14.62 kWh/ton, respectively. The average energy expended during grinding was 5.06 kWh. These findings provide a basis for designing energy-efficient beneficiation processes for Anka manganese ore, contributing to sustainable resource utilization and industrial cost reduction.

Evaluation of Optimized Carbonized Wheat Husk-Reinforced AA6061 Composite for Automotive Components Applications

2025-10-03T15:37:28+00:00

In the pursuit of sustainable and lightweight materials for automotive applications, metal matrix composites (MMCs) have emerged as promising candidates due to their superior strength-to-weight ratios, enhanced wear resistance, and tailored mechanical properties. Aluminum-based composites, particularly those with AA6061 as the matrix, are widely recognized for their excellent corrosion resistance, weldability, and mechanical performance. However, there remains a need to improve the environmental sustainability, mechanical properties and lightweight properties of these materials through the incorporation of eco-friendly, sustainable, light and low-cost reinforcements. In this work, the reinforcement particulate, carbonized wheat husk (CWH) was gotten after pulverizing wheat husks to increase the surface area and charging it into a muffle furnace, subjected to a temperature of 900 ⁰C for 3 hours. Thereafter, AA6061 reinforced composites (AA6061-CWH) were produced using the stir casting method, optimized through the Taguchi's L9 orthogonal array. The composite developed at optimum parameters was then selected and compared with selected automotive components. The optimized AA6061-CWH composite offers a well-balanced mechanical profile. It delivers decent tensile strength, exceptional hardness, good impact resistance, and a lower density, making it an appealing material choice for a broad spectrum of automotive applications. Its application could support the automotive industry’s ongoing pursuit of improved performance, efficiency, and sustainability through the use of affordable and eco-friendly materials.

Optimising Logistics Operations using Geospatial Algorithms with Adaptive Route Evolution

2025-10-03T15:37:09+00:00

Rapid urbanisation in cities like Lagos, Nigeria has impaired traffic congestion and route-planning, causing delivery delays stemming from logistic inefficiencies leading to operational costs; to overcome these challenges, an Adaptive Route Evolution (ARE) model that integrates A* for initial shortest-path generation, a Genetic Algorithm (GA) for offline route refinement, and Reinforcement Learning (RL) for real-time adjustments based on live traffic data is proposed. In simulations of two vehicles traversing standard delivery corridors with geospatial road-network data and real-time traffic APIs, Vehicle 1’s delivery time decreased from 78 min to 70 min after A* (–10.3%), 65 min after GA (–17%), and 61 min after RL (–21.8%), while Vehicle 2’s time fell from 120 min to 90 min after A* (–25%), 83 min after GA (–30.8%), and 79 min after RL (–34.2%), yielding an average 27.5% reduction in delivery time and an estimated 22% drop in fuel and driver costs. ARE provided a complete end-to-end optimisation framework by combining RL’s adaptability with A*’s shortest-path planning and GA’s sequence optimisation. It demonstrated scalability and effectiveness in dynamically optimising urban logistics, reducing both transit delays and carbon emissions, and enabling a more sustainable and cost-efficient transportation system in line with Sustainable Development Goals (SDGs) 7, 11, and 13.

Application of Artificial Intelligence in Chatbot and Social Media Community for a Mental Health Monitoring Web-Application

2025-10-03T15:36:51+00:00

Mental health is a major challenge for modern society affecting people of working age and their families, jobs, and communities. To provide continuous emotional support, personalized assistance, and remote mental health tracking, chatbots and social media platforms are increasingly using Artificial Intelligence (AI). There is little or no previous literature that has documented a fully functional mental health monitoring web-app that uses AI to provide all these features: chatbot, e-community, video conferencing, and email automation. Chatbots are software systems that offer various interactive online services, including people with mental health needs and have been successfully integrated into the field of mental healthcare. Similarly, social media can help monitor the mental health situation by extracting information from posts for sentiment analysis as people often post their feelings on social media, so analysing these posts can reveal their mood, emotion, cognition, or mental state. This paper solves the problem sentiment by using the MERN stack to develop an interactive mental health monitoring application which features: social media community, chatbot named ‘Dave the HappyBot’, Email automation and video conferencing. The results showed that among the 13 users of the web-app from a performance indices class of A, B, C, D, and E, majority indicated that they felt ‘happier’ and ‘somewhat mentally relieved’ after interacting with the Chatbot. The A class recorded 18% responses, B class obtained 31%, the C class got 19%, the D class achieved 26%, and the E class gained 6%. The users were open to recommending the App to their peer and meeting their psychologist for constant mental health monitoring sessions. This paper can be further improved documenting the developments of web-app with more users who possess higher knowledge of software development and have a history of mental health struggle. Higher utility of the MERN stack would also be an improvement.

Modelling and Simulation of Spray Pyrolytic Graphene and Graphite Coatings on Polymeric Substrates for enhanced Oil and Gas Pipeline Repair Applications

2025-10-03T15:36:33+00:00

The oil and gas industry faces significant challenges in pipeline corrosion management, with high corrosion resistance and electrical conductivity for effective cathodic protection (CP). This study focuses on the mathematical modelling of electrical conductivity in nanoparticle-sized graphene, graphite, and graphene-graphite coated polyvinyl chloride (PVC) and glass reinforced polymer (GRP) substrates, prepared via low-temperature spray pyrolysis (50–60 °C). Single- and double-layer coatings with graphene-graphite blend ratios of 1:0.5 and 1:1 were applied on 50 × 10 × 2 mm substrates to obtain coating thickness of 0 µm, 56.1 µm, 77.2 µm, 80.6 µm, 80.8 µm, 92.6 µm, and 97.9 µm respectively for the PVC samples while the GRP samples have coating thickness of 0 µm, 110.3 µm, 114.0 µm, 109.0 µm, 115.7 µm, 117.3 µm, 124.7 µm. Electrical conductivity was measured using an LCR meter, and polynomial models were developed to correlate conductivity with coating thickness. Cathodic protection simulations assessed the performance of coated composites in a 15 km pipeline, highlighting the impact of non-conductive sections and the efficacy of bypass wire designs. Results showed that single-layer graphene-coated GRP achieved the highest conductivity (1.8 × 10⁻⁶ S/m), while double-layer hybrid coatings (1-0.5D) offered optimal durability. The mathematical models accurately predicted conductivity trends, with GRP exhibiting superior performance compared to PVC due to better graphene integration. CP modelling revealed that non-conductive GRP sections cause localized underprotection, mitigated by optimized bypass designs. These findings demonstrate that graphene-graphite-coated GRP composites, supported by predictive conductivity models and CP simulations, are promising alternatives to steel for pipeline repairs, enhancing corrosion control and longevity in oil and gas applications.

Process-Composition Design of Hypoeutectic Aluminum-Silicon Alloy for High Performance Wear Resistance Application

2025-05-25T22:22:52+00:00

This study investigates the process-composition design of hypoeutectic aluminium-silicon alloys aimed at improving the wear behaviour of the alloy for tribological application. Hypoeutectic Al-Si alloys with percentage composition of silicon ranging from 3 – 7.5% were cast at varying pouring temperatures of 700, 750 and 800⁰C. The impact of the process-composition parameters on wear rate and the microstructures of the alloy were determined using tribor testing apparatus and scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) respectively. The results obtained show that increasing silicon content from 3 – 7.5% significantly improved the wear rate of the alloy from 0.0360 – 0.0120 mg/m with optimum pouring temperature value at 700⁰C. The SEM micrographs indicate that higher percentage composition of silicon yielded the formation of more numbers of primary silicon phases with intermetallic phases that reduced material loss while optimum pouring temperature influenced solidification rate leading to a refined uniform homogeneous phases distributed in the microstructures. It was concluded that process parameter optimization carefully tailored through combination of silicon percentage composition and pouring temperature enhances the wear performance of Al-Si alloys for engineering applications in wear-critical environment.

Intelligent Web App for Flash Flood Prediction in Nigeria’s Coastal Regions

2025-10-20T15:38:57+00:00

Many coastal cities in Nigeria and around the world are faced with the menace of flash floods and many times, it temporarily disrupts the socio-economic activities of residents. This project aims to address this challenge by developing a smart web application using machine learning to intelligently predict flash flood occurrence and offer recommendations. In order to achieve this, the random forest machine learning algorithm is utilized to analyze environmental data such as rainfall, river levels, and soil moisture necessary for the prediction of a flood which are captured in real-time using the OpenMeteo API. The machine learning model is then trained using these environmental variables and integrated into a web application for easy user interaction. The frontend of the web application is built with TypeScript, React.js, and Tree.js, providing an interactive and user-friendly interface for visualizing flood predictions, while the backend is built using MongoDB and python (FLASK framework). The goal is to offer accurate, real-time flood forecasts to help individuals prepare and respond effectively. This project demonstrates the integration of data science and web development to create a practical tool for disaster risk management. The random forest model was evaluated using the standard metrics for evaluating machine learning models and showed the following results; Accuracy of 96%, precision of 75% and recall of 91%. In addition, the model, showed a Real-time latency of less that one second, which is indicative of a fast response to changing environmental data input. Since flood conditions can change rapidly, this low real-time latency shows that the web is able to respond quickly to new sensor or satellite data input.