https://ir.cut.ac.zw/xmlui/handle/123456789/3 Thu, 16 Jul 2026 21:44:33 GMT 2026-07-16T21:44:33Z https://ir.cut.ac.zw/xmlui/handle/123456789/815 Microwave-assisted pyrolysis of pine sawdust (Pinus patula) with subsequent bio-oil transesterification for biodiesel production Makepa, Denzel Christopher; Chihobo, Chido Hermes; Musademba, Downmore This study aims to thermochemically convert pine sawdust to crude bio-oil via the microwaveassisted pyrolysis technique with subsequent bio-oil transesterification. American Society for Testing and Materials (ASTM) standards were followed in the characterization of the feedstock and pyrolysis products. The thermal degradation behaviour of pine sawdust was studied using thermogravimetric analysis. The components in the bio-oil organic phase were upgraded to fatty acid methyl esters via the transesterification process. The composition of the organic phase and the fatty acid methyl esters was analysed using gas chromatography–mass spectrometry (GC-MS) and Fourier transform infrared (FT-IR). The thermal degradation behaviour of pine sawdust showed three distinct phases of weight loss. These were the drying stage (30–200 C), the devolatilization stage (200–450 C), and the char formation stage (>450 C). The process yielded 42.28wt.% of bio-oil, constituting 24 and 76wt.% of the organic and aqueous phases, respectively. GC-MS and FT-IR compositional analysis identified various organic compounds and functional groups, with phenolics contributing a greater percentage. Transesterification improved the bio-oil properties by converting the organic acids and oxygenated compounds to methyl esters with a concentration of 510.05mg/L. The bio-oil has proven to be a promising sustainable raw material for the production of biofuels and value-added biochemicals Fri, 04 Aug 2023 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/815 2023-08-04T00:00:00Z https://ir.cut.ac.zw/xmlui/handle/123456789/812 Implementation of robotics in improving safety and efficiency in mining operations Simango, Doubt; Jinya, Last E; Musaidzi, H; Makusha, D. T. Mining operations present hazardous conditions, particularly in post-blast environments where the accumulation of toxic gases poses significant risks to worker safety. This study presents a robotic system designed to autonomously navigate underground mining terrains and detect harmful gases such as methane, carbon monoxide, and carbon dioxide. The robot is built with a rocker-bogie mechanism for stability on uneven surfaces and uses IR sensors for obstacle detection. Gas detection is carried out using a sensor-integrated circuit. A GSM module is used to transmit the gas concentration data to surface-level receivers. Simulation results confirmed the robot's capability to navigate autonomously and detect gas presence with reliable accuracy. Wed, 01 Jan 2025 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/812 2025-01-01T00:00:00Z https://ir.cut.ac.zw/xmlui/handle/123456789/811 AI and computer vision-based pest and disease detection for greenhouse crops in Zimbabwe Simango, Doubt; Mahungwe, Panashe G.; Makusha, D. T.; Musaidzi, H. Pests and diseases remain a major constraint to agricultural productivity in Zimbabwe, causing significant losses and limiting farmers’ income. In greenhouse farming, early detection of infestations is critical to ensure healthy crops and reduce pesticide overuse. This study presents a computer-vision system powered by artificial intelligence (AI) for early identification of common pests and diseases in greenhouse crops. A convolutional neural network (CNN) trained on a regionally curated dataset of healthy and diseased plant images automatically classifies visual symptoms from digital photographs. The system is implemented through a MATLAB desktop application, enabling offline classification for users with limited internet access. The CNN achieved 83 % training accuracy and 82 % validation accuracy, with high precision and recall across multiple crop categories. Testing confirmed reliable detection of leaf curl, septoria leaf spot, and related infections in tomatoes and peppers. This work demonstrates that locally trained deep-learning models can effectively support greenhouse farmers in Zimbabwe, enhancing early response and minimizing losses due to pest and disease outbreaks. Wed, 01 Jan 2025 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/811 2025-01-01T00:00:00Z https://ir.cut.ac.zw/xmlui/handle/123456789/810 Autonomous disinfection robot with Robotic Arm for precision cleaning Simango, Doubt; Ndavambi, Denzel C.; Chihota, Kelvin; Makusha, D. T. Hospital-acquired infections (HAIs) remain a major challenge to patient safety and healthcare efficiency. This project presents the design and implementation of an autonomous disinfection robot equipped with a 6-degree-of-freedom robotic arm for precision sanitization in healthcare environments. The robot integrates a LiDAR-based SLAM system for navigation, mecanum wheels for omnidirectional mobility, and an electrostatic spray system for targeted disinfection. A database-linked user interface allows authorized personnel to monitor, schedule, and control disinfection processes remotely. Simulation and prototype results confirm that the system effectively maps environments, avoids obstacles, and performs adaptive surface cleaning with minimal human intervention. Wed, 01 Jan 2025 00:00:00 GMT https://ir.cut.ac.zw/xmlui/handle/123456789/810 2025-01-01T00:00:00Z