http://hdl.handle.net/20.500.12283/58 Fri, 03 Apr 2026 21:12:50 GMT 2026-04-03T21:12:50Z http://hdl.handle.net/20.500.12283/4426 Optimizing the physical and mechanical properties of cement stabilized rammed earthconstruction blocks Birungi, Aminah This research focuses on the improvement of the physical and mechanical properties of cement- stabilized rammed earth (CSRE) blocks. The approach included characterizing soil samples using sieve analysis to assess soil particle size distribution, moisture content tests, and maximum dry density evaluations to determine the best achievable compaction characteristics.These tested samples were taken from a total of three different soil samples that were prepared with different concentrations of cement (0, 2, 4, 6, and 8 percent) to determine the influence of the cement proportion on the blocks’ strength and durability. The blocks were compacted and cured for a duration of 7,14 and 28 days, then they underwent compressive strength testing using a Universal Testing Machine (UTM) and water absorption measurement for durability. The data was analyzed to find the most beneficial cement-soil ratio that would achieve maximum strength while remaining within economical limits. The results showed the possibility of CSRE blocks being used as a construction material that is cost-effective while being mechanically reliable. Thu, 15 May 2025 00:00:00 GMT http://hdl.handle.net/20.500.12283/4426 2025-05-15T00:00:00Z http://hdl.handle.net/20.500.12283/4425 Prediction of future demand for water distribution systems: A case study of Namayingo water supply system, Namayingo Town council Nagozera, Frank This project focuses on the prediction of future water demand for distribution systems specifically targeting the Namayingo water supply system in Namayingo Town council, Uganda. The growing population and climate variability have led to increasing pressure on water resources, making accurate forecasting crucial for optimizing distribution, ensuring sustainable water supply, and improving infrastructure planning. This research aims to develop a predictive model using Artificial Neural Networks (ANNs) to forecast water demand by integrating real-time data such as weather conditions, population growth, and economic factors. The model will enable water utilities to proactively manage demand fluctuations, reduce inefficiencies, and ensure a consistent water supply. By validating the model through error metrics such as RMSE, MAE, and R², this project will offer a reliable decision-support tool for water resource management. The results of this study will contribute to meeting Uganda's water demand challenges and align with global sustainable development goals related to clean water and sustainable cities. Thu, 15 May 2025 00:00:00 GMT http://hdl.handle.net/20.500.12283/4425 2025-05-15T00:00:00Z http://hdl.handle.net/20.500.12283/4424 Design of an automated dust suppression system for a quary crusher: A case study of ROKO stone quarry Dibya, Hamuza Dust emission from quarry crushers poses health risks and environmental concerns, especially in unregulated industrial settings. This project focused on the design of an automated dust suppression system that detects and controls airborne particulate matter using real-time monitoring. A review of related technologies highlighted the efficiency of sensor-based automation and the effectiveness of water-based suppression methods. The system was developed using a DSM501A dust sensor, an Arduino Uno microcontroller, a relay and a water pump, with data displayed via an LCD and logged on the serial monitor. In the methodology, simulated dust values were used to test the system response to varying concentrations, with the threshold set at 5000 µg/m³ as per OSHA guidelines. Results show a significant reduction in dust levels post-activation of the system, with efficiency above 70% in most trials. Additionally, the economic analysis confirmed the system’s viability with a project pay back period of less than three years. The solution presents a scalable and cost-effective approach for enhancing air quality in quarry environment. Sun, 18 May 2025 00:00:00 GMT http://hdl.handle.net/20.500.12283/4424 2025-05-18T00:00:00Z http://hdl.handle.net/20.500.12283/4423 Experimental Evaluation and process optimization for minimizing hydrogen sulfide emissions in wastewater Ayebale, Jenifer Hydrogen sulfide (H₂S) is a toxic, corrosive, and malodorous gas generated in wastewater treatment systems, especially under anaerobic conditions. Its emission contributes to sewer infrastructure degradation, air quality deterioration, and public health hazards. Conventional approaches to managing H₂S emissions are mostly reactive and do not account for predictive control based on critical operational parameters. This study addresses this challenge by applying statistical modeling using Minitab to investigate and optimize the influence of key wastewater quality parameters temperature, pH, total suspended solids (TSS), chemical oxygen demand (COD), and hydraulic retention time on H₂S emissions. A Box-Behnken experimental design was employed to develop a response surface model, with the following parameter ranges; temperature (20–35 °C), pH (6.0–8.0), total suspended solids (TSS) (165–250 mg/L), chemical oxygen demand (COD) (445–900 mg/L), and retention time (1.0–4.0 days). Hydrogen sulfide concentration (ppm) was the response variable. Analysis of variance (ANOVA) and regression modeling revealed that pH, temperature and retention time were the most influential parameters, while TSS and COD showed minimal impact. Among the studied parameters, pH (26.52%) and temperature (32.82%) and retention time (21.09%) showed the highest contribution to the model, followed by while TSS (0.91%) and COD (0.13%) that had minimal influence on hydrogen sulfide emissions. The final model achieved a coefficient of determination (R²) of 89.32%, indicating strong predictive reliability. The study provides a data-driven decision support framework for wastewater operators to identify operational conditions that minimize H₂S emissions, reduce odor and corrosion risks. Thu, 08 May 2025 00:00:00 GMT http://hdl.handle.net/20.500.12283/4423 2025-05-08T00:00:00Z