School of Engineering (SEng.) Collections

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Now showing 1 - 5 of 372
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    Encapsulated and nano-organic fertilizers for water retention and controlled nutrient release.
    (Makerere University, 2026) Nantambi, Hadijah
    Declining soil fertility, low nutrient-use efficiency, and heavy dependence on imported synthetic fertilizers remain critical constraints to agricultural productivity in Uganda. This research developed and evaluated advanced organic fertilizers derived from biochar-blended compost (BBC), specifically focusing on two engineered derivatives: Encapsulated Biochar-Blended Compost (EBBC) and Nano-Biochar-Blended Compost (Nano-BBC). An optimized co-composting matrix (60% Tithonia diversifolia and 5.7% rice husk biochar) was established using Response Surface Methodology and Central Composite Design. The quadratic models developed for nitrogen, phosphorus, and potassium were highly significant (F-values of 33.70, 50.64, and 86.60, respectively) and exhibited a non-significant lack of fit (p < 0.05). Model robustness was confirmed by high coefficients of determination (R2 ≥ 0.97) and adjusted R2 ≥ 0.94, and low coefficients of variation (3.24%–6.24%), indicating high reproducibility. Tithonia diversifolia most influenced N and K enrichment, while P availability depended on quadratic effects of both substrates. The enriched mature compost served as the base for enhancements. Nano-BBC synthesis was optimized via high-energy ball milling, and a reduced quadratic model identified the milling solvent mass and ball-to-powder ratio as key factors for particle size reduction. Chitosan–starch biopolymer encapsulation further enhanced performance. Under simulated 20-mm rainfall, EBBC reduced leachate volume to 6.5 mL (65% less than conventional BBC and mineral fertilizers) while eliminating nitrate-N leaching. Nitrogen-release assays showed controlled release: EBBC pellets released 56.9–70% of total N over 30 days via Fickian diffusion, unlike uncoated BBC, which exceeded 100% by day 25 via non-Fickian kinetics. EBBC also improved soil moisture retention in sandy loam to 4.4% at 30-days via hydrogel effects. In semi-field Zea mays L. (Maize) trials under drought, EBBC produced the highest plant height and shoot biomass, outperforming BBC, Nano-BBC, and synthetics. All formulations met FAO/EU heavy-metal thresholds. This scalable, climate-smart framework transforms organic waste into high-performance fertilizers, synchronizing nutrient delivery with drought resilience in Uganda.
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    Development of an Efficient Path Planning Algorithm for Unmanned Aerial Vehicles - Wireless Sensor Networks in Agricultural Data Collection
    (Makerere University, 2026) Oswaha Matthew Joseph Odiongo
    The increasing demand for precision agriculture has intensified the need for efficient data collection methods across expansive farmlands. This research focuses on the development of an efficient path planning algorithm for Unmanned Aerial Vehicles (UAVs) deployed in Wireless Sensor Networks (WSNs) to facilitate timely and energy-efficient agricultural data collection. The study proposes and evaluates a Particle Swarm Optimization (PSO)-based algorithm to optimize UAV trajectories concerning multiple conflicting objectives, including minimizing mission completion time, reducing energy consumption, and maximizing coverage efficiency through minimizing UAVs’ flight paths. A simulation model was implemented in Matrix Laboratory (MATLAB), considering realistic constraints such as UAV energy limits, communication range, sensor clustering, and agricultural field geometry. The performance of the developed algorithm was compared against the spherical particle swarm optimization algorithm (SPSO), Genetic algorithm (GA), and Random computation method (RCM). Results demonstrate that the developed PSO-based algorithm significantly outperforms others in terms of path length reduction, energy utilization, and mission completion time while maintaining high data collection accuracy. The findings validate the effectiveness of evolutionary optimization techniques in improving UAV-based WSN operations for precision agriculture, outperforming the benchmarking algorithms, with an average optimization efficiency over all the metrics of 23% compared to the random computation method (RCM) as the baseline algorithm. This work contributes a scalable and adaptable algorithmic approach suitable for real-world deployment in resourceconstrained agricultural environments.
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    Modelling the effects of road Geometric design on vehicle emissions.
    (Makerere University, 2026) Itaaga, Emmanuel
    The transport sector is a major contributor to global greenhouse gas emissions, with road transport accounting for nearly 20% of the total CO₂ emissions. In Uganda, rapid traffic growth, reliance on fossil fuels, and declining air quality have intensified environmental and health challenges. Existing emission models, developed mainly for high-income countries, rely on advanced data and modern vehicles, limiting their applicability to Uganda’s older and diverse vehicle fleet. The absence of locally adapted models that integrate road geometry and traffic dynamics constrains the development of sustainable transport infrastructure. This study developed and validated mathematical models to predict CO₂, CO, NOₓ, and hydrocarbon emissions based on geometric and traffic parameters under Ugandan conditions. Vehicle movements were tracked with a high-precision device, and emissions measured directly from tailpipes using a calibrated exhaust gas analyser. Results show that road geometry and dynamic variables significantly affect emissions. In rolling terrain, vehicle speed and higher-order speed terms dominated, producing a Ushaped speed–emissions relationship, particularly for smaller petrol vehicles. In mountainous terrain, gradients, curvature, and interactions between speed, gradient, and curvature were more influential, amplifying emissions and nonlinear effects. Petrol vehicles were more responsive to geometric variations than diesel vehicles. CO₂ and CO models achieved moderate to strong predictive power (adjusted R² up to 0.918), while NOₓ and hydrocarbon models were less predictive. The study concludes that highway geometry, vehicle characteristics, and driver behavior strongly influence emissions, with small-engine vehicles producing disproportionately higher levels. Key gaps in the Uganda Geometric Design Manual were identified, including missing emission-sensitive parameters, environmentally optimal speed ranges, and fleet heterogeneity considerations. Recommendations include integrating environmental criteria into road standards, promoting sustainable transport, and enforcing emission regulations.
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    A freight transport demand forecasting model for the Northern Economic Corridor with future transport options
    (Makerere University, 2024) Mugarura, Edgar
    Many developing countries, including Uganda, face challenges in appraising transport policies/schemes and making evidence-based decisions due to the lack of appropriate transport demand models. This study aimed to contribute to freight transport demand analysis by developing a freight mode choice model to assess the influence of transport cost, transport time, reliability, and rail accessibility on freight mode choices along the corridor. Stated preference data collected from 42 logistics companies was used under Random Utility Maximization (RUM) framework to develop Multinomial Logit (MNL) and Mixed Multinomial Logit (MMNL) models to forecast choices between road and rail on the Northern Economic Corridor in Uganda in light of the future transport options (development of the Standard Gauge Railway (SGR) infrastructure). The model results indicate huge demand potential for the proposed SGR with mode shares estimated to be 29%,16%, 55% for road, MGR and SGR respectively. The study also estimated willingness-to-pay metrics along the corridor, including the Value of Transport Time (VTT) determined to be 195.8 USD per day per TEU which is comparable to 261.06 USD/TEU reported by a similar study in South Africa. The Value of Reliability is also determined to be 7.81 USD for each 1% improvement in reliability. The study is used to forecast future market scenarios for the different modes including the one without rail accessibility where SGR market share reduces to 44%. Furthermore, the model results were also used to forecast road-to-rail traffic diversion under the different policy scenarios and to simulate the contribution to increased pavement life on a road section of the Northern Economic Corridor. This study notes the inherent preference for road for freight transport and recommends several policy measures including improving rail accessibility to increase its competitiveness and contribute to the development of sustainable transport along the corridor.
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    Evaluation of transition pathways from traditional bioenergy to improved domestic cooking energy services in Uganda using a systems dynamics approach.
    (Makerere University, 2026) Kakebe, Sekajja Robert
    Uganda’s energy sector is heavily dependent on traditional biomass, primarily firewood and charcoal, which supplies over 89% of the country's total energy demand. This overreliance contributes to deforestation, indoor air pollution, and social burdens associated with fuel collection, particularly for women and children. Rapid population growth, urbanization, and limited access to clean energy services exacerbate these challenges, while policy progress has been slow. Addressing such systemic issues requires a holistic framework that captures the complexity and dynamics of the bioenergy sector. This study employed a System Dynamics Approach to evaluate sustainable transition pathways from traditional to clean cooking energy services in Uganda. Guided by constructivist and systems thinking paradigms, it integrated qualitative systems mapping, through literature review and stakeholder engagement, with quantitative modeling and simulation in STELLA. The research defined the sector’s temporal (1990–2050), spatial (national), and structural boundaries, and developed the Uganda Bioenergy System Dynamics Model using Causal Loop Diagrams and Stock–Flow Diagrams. The model captured key stocks and flow variables, with feedback loops and policy levers representing the system’s internal dynamics. Model verification and validation were conducted through structural checks, historical data comparison, and extreme condition testing. Four policy scenarios, Efficient Charcoal Production (ECP), Efficient Cookstoves (EC), Fuel Switching (FS), and Comprehensive Transition (CT), were simulated against a Business-as-Usual (BAU), and evaluated using a cost–benefit analysis (CBA) framework. Results showed that all intervention scenarios deliver significant environmental and socio-economic improvements compared to BAU, with the CT scenario performing best overall. By 2050, CT reduces woody biomass harvesting by 81% relative to BAU and by 84% compared to 2030 levels. It also cuts household energy costs by about 70% and generates an estimated US$115 million annually in carbon credit revenues. The EC and FS scenarios yield 50% and 47% cost reductions, respectively, with substantial carbon finance potential. Policy implications highlighted that scaling up efficient charcoal production, promoting improved cookstoves, and incentivizing fuel switching through subsidies and infrastructure investment can accelerate Uganda’s clean energy transition. An integrated, comprehensive strategy, aligned with Vision 2040, NDP IV, and NDC commitments, offered the strongest pathway for achieving sustainable energy access and environmental protection. The study contributed a validated, replicable SD framework for modeling bioenergy transitions in low-income contexts, offering insights into feedbacks, delays, and socio-technical interactions that shape sustainable energy futures.