Faculty of Engineering, Technology, Applied Design & FineArt (FETADFA)

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Open Access
Battery Management System for Solar Power Plants in Uganda: An IoT-Driven Approach
(MECS Press, 2025) Ssembalirwa, Denis; Cartland, Richard; Bature, U. I.; Kitone, Isaac
In Uganda, the efficiency and reliability of solar power plants are often compromised due to inadequate battery management, leading to reduced battery lifespan and suboptimal performance. To address this challenge, this project develops and prototypes a smart Battery Management System (BMS) tailored for solar power plants. The system continuously monitors key battery parameters, including voltage, load current, and temperature, while leveraging Internet of Things (IoT) technology for real-time data transmission and remote monitoring. Intelligent algorithms autonomously regulate charging and discharging cycles to prevent overcharging and deep discharge, optimizing battery performance. Testing demonstrated that the BMS significantly improved battery lifespan and energy efficiency by disconnecting charging at 100% and isolating the load at 10% discharge to prevent battery degradation. Additionally, the system disconnects power when battery temperature exceeds 30°C (ambient temperature: 25°C) and detects abnormal current levels above 0.16A to mitigate faults such as short circuits. These automated protections enhance battery reliability and longevity. By implementing proactive battery management strategies, the developed BMS contributes to more efficient and resilient energy storage systems, promoting sustainable energy development in Uganda.
Open Access
Bibliometric Insights into Advances in Nondestructive Testing Techniques for Delamination Detection
(Mesopotamian Journal of Civil Engineering, 2025-04-11) Abdulwahd, Abdulrazaq. K.; Mugisha, Simon; Chavula, Petros; Kayusi, Fredrick
This study presents a comprehensive bibliometric analysis of advances in nondestructive testing (NDT) techniques for delamination detection, based on 4,382 publications indexed in Scopus from 2021 to 2025. Using advanced bibliometric methods and the biblioshiny package in R, the analysis evaluates annual scientific production, citation trends, thematic focus, and collaboration patterns. The results reveal a peak in research output in 2024, followed by a marked decline in 2025, alongside a steady decrease in average citations per article. “Delamination,” “composite,” “ultrasonic,” and “infrared thermography” are identified as core research themes. The field is dominated by a few prolific journals, authors, and institutions most notably in China which account for the majority of scientific output and impact. These findings illuminate evolving research priorities, highlight central contributors, and offer critical perspectives on the development, concentration, and future directions of NDT for delamination detection.
Open Access
Geostatistical-based spatial distribution of in-situ groundwater quality parameters in the crystalline basement aquifer in urban and peri-urban city:
(KIU Journal of Science, Engineering and Technology, 2025) Fadugba, O. G.
This study examines the spatial distribution of in-situ groundwater quality parameters in the crystalline basement aquifer in Akure City, Nigeria using geostatistical methods. The area was divided into urban and peri-urban areas. Water samples were taken to the laboratory for characterization of the water quality parameters in the water samples obtained in the study area. The oil/water interphase meter was used to determine the depth to the surface of the selected wells in the study area. The depth of the well is between 3.07 and 7.03 meters. The well depth was divided into four categories: Low (3.07 to 7.03 m), Moderate (4.88 to 5.05 m), High (5.06 to 5.22 m), and Very High (5.23 to 7.03 m). Four categories were used to classify the well depth: Low (2.27 to 4.18 m), Moderate (4.19 to 4.29 m), High (4.30 to 4.41 m), and Very High (4.42 to 6.32 m). The pH scale is 5.48 to 6.71. Four pH ranges were identified: Low (5.48 to 5.91), Moderate (5.92 to 6.20), High (6.21 to 6.41), and Very High (6.42 to 6.71). Four categories were assigned to the ORP: Low (29.71% to 45.63%), Moderate (45.64 percent to 57.42%), High (57.43 percent to 66.16%), and Very High (66.17 percent to 77.95%). There were four categories for the Electrical Conductivity distribution (EC): Low (100.64 µS/cm to 242.50 µS/cm), Moderate (242.51 µS/cm to 347.61 µS/cm), High (347.52 µS/cm to 425.49 µS/cm), and Very High (425.50 µS/cm to 483.20 µS/cm). There were four categories for the Total Dissolved Solid (TDS): Low (50.87 ppm to 120.75 ppm), Moderate (120.76 ppm to 172.53 ppm), High (172.54 ppm to 210.89 ppm), and Very High (210.90 ppm to 239.32 ppm).
Open Access
Mechanical performance of structural concrete utilising porcelain insulator ceramic waste as partial replacement for coarse aggregates
(2025) Kabiru, R. U; Abbas, U.; Hassan, A.; Muhindo, D.
The increasing environmental impact of natural aggregate extraction and the growing accumulation of ceramic waste have prompted the search for sustainable construction materials. This study investigates the feasibility of using ceramic waste from waste electric insulators as partial replacement for natural coarse aggregates in concrete production. The ceramic waste from waste electric insulators termed here as porcelain insulator ceramic waste (PICW) sourced from local dumpsites was processed and incorporated into concrete mixes at replacement levels of 0%, 15%, 30%, 50% and 75%. Coarse aggregates of maximum size 20 mm were used in this study. The ceramic waste from waste electricity insulators was crushed using a hammer up to size 20 mm as indicated in the particle size distribution. Grade 25 of concrete was designed for in the mix design. Laboratory tests, including sieve analysis, moisture content, specific gravity, water absorption, workability (via slump testing), and compressive strength were conducted to assess the mechanical and physical properties of both fresh and hardened concrete at curing intervals of 7, 14, and 28 days. The findings indicate that concrete containing up to 30% ceramic waste exhibits highest cempressive strength and workability comparable to conventional concrete without compromising its durability and workability, demonstrating its potential as a viable and eco-friendly alternative. The highest compressive strengths were recorded with 15% and 30% at 25.7 and 25.5 MPa respectively. Conversely, tensile strength declines with increasing PICW replacement at 28 days thus 3.32, 3.16, 2.99, 2.31 MPa. The study underscores the dual benefits of reducing construction costs and promoting sustainable waste management, making ceramic waste a promising material in the pursuit of green construction practices i.e., sustainable construction by mitigating environmental degradation and promoting circular waste utilization. The study offers valuable insights for future standards development and large-scale industrial applications.
Open Access
An expert analytical approach to reducing construction project delays from ineffective scheduling
(KIU Journal of Science, Engineering and Technology, 2025) Joseph, O. S; Fadugba, O. G; Oluyemi-Ayibiowu, B. D; Uduebor, M. A; Olu-Matins, O. A
The study investigated the effects of ineffective scheduling on the completion of construction projects, identified causes of poor planning and scheduling, and provided expert-recommended solutions. Using a mixed-method approach including online surveys, physical questionnaires, and statistical analysis (Relative Importance Index - RII, Reliability Test, and SPSS), the research analyzed data from 130 construction professionals in Nigeria. The most significant effects of ineffective scheduling were "Time Overrun" (RII = 0.8892) and "Cost Overrun" (RII = 0.8246), followed by "Compromise of Project Quality" (RII = 0.8077). The top causes identified were "Poor Decision-Making Regarding Activity Criticality" (RII = 0.8631), "Lack of Finance for Project Execution" (RII = 0.8492), and "Lack of Expertise in Scheduling" (RII = 0.8123). The study concludes by offering a comprehensive roadmap for stakeholders to enhance scheduling efficiency, reduce delays, and improve overall project performance through practical strategies such as accurate cost estimation, effective planning, stakeholder engagement, and the use of scheduling software.
Open Access
Developing High-Performance Low-Carbon Concrete Using Ground Coal Bottom Ash and Coconut Coir Fibre
(Elsevier, 2025) Ahmed, Muneer; Khan, Suliman; Bheel, Naraindas; Awoyera, Paul.O; Fadugba, Olaolu George
This study addresses the need for eco-friendly concrete by incorporating agro-industrial waste, ground coal bottom ash (GCBA) and coconut coir fibre (CF), as partial replacements for Ordinary Portland Cement. A total of150 samples were tested using Scanning Electron Microscopy (SEM), Fourier-transform infrared (FTIR), X-ray Fluorescence (XRF) and mechanical methods. Optimal results were achieved with 4.31 % CF, 0.4 % superplasticizer, flexural performance. The addition of CF reduced cracking and improved durability and GCBA enhanced long-term performance through improved particle packing and pozzolanic reactions. The GCBACF mixtures also lowered embodied carbon by 194 kg CO₂/m³ and energy by 970 MJ/m³, achieving higher Eco efficiency than traditional concrete. This research supports the development of sustainable, high-performance concrete aligned with circular economy and sustainability goals.
Open Access
Improving productivity and efficiency in banana processing: Advancements and challenges in matooke peeling techniques
(KIU Journal of Science, Engineering and Technology, 2025) Edeh, J. C; Kizanye, Stella; Onyeukwu, K. J; Nnamani, L.C
The processing of green bananas into various food products involves several critical post-harvest operations, with matooke fruit peeling being one of the most crucial steps to ensure quality, safety, and overall integrity of the derivative products. This study presents a comprehensive review of matooke peeling techniques, analyzing traditional, thermal, and mechanized methods, while also exploring the theoretical principles and operational concepts underpinning these processes. Parameter evaluation of crop-related physical and mechanical properties of the banana fruits is provided to identify key factors influencing the effective design of an efficient mechanized peeling system. These parameters, including fruit size, geometric mean diameter, peel thickness, moisture content, angle of repose, and shear stress, are shown to significantly impact peeling efficiency and system performance. The review emphasizes the potential of mechanical peeling as a viable solution for the full mechanization of matooke processing, eliminating the drudgery and contamination-prone manual intervention within the process. The success of the mechanized systems is highly contingent on the precise understanding and integration of these crop-specific characteristics. Based on these insights, the paper proposes a crop parameter-based design for an automated peeling system aimed at improving productivity, enhancing hygiene standards, reducing labor costs, and ensuring consistent peeling quality. This mechanized approach is positioned as a reliable, efficient, and cost-effective solution for small, medium and large-scale matooke processing, with good potential for value addition and significant advancements in the processing industry
Open Access
Advanced machine learning models for the prediction of ceramic tiles’ properties during the firing stage
(Scientific Reports, 2025) V. Vasic, Milica; Awoyera, Paul O.; Fadugba, Oladlu George; Barisic, Ivana; Nettinger Grubeša, Ivanka
The firing stage is a critical phase in ceramic tile production, where the interplay of raw material composition and thermal treatment determines essential properties such as water absorption (WA) and bending strength (BS). This study employs advanced machine learning (ML) models to accurately predict these properties by capturing their complex nonlinear relationships. A robust dataset of 312 ceramic samples was analyzed, including variables such as particle size distribution, chemical and mineralogical composition, and firing temperatures ranging from 1000 to 1300 °C. Among the four ensemble ML models evaluated, CatBoost demonstrated the highest predictive performance. Model accuracy was assessed using multiple evaluation metrics, including the coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). To enhance interpretability, SHapley Additive exPlanations (SHAP) were used, revealing that clay mineral content and SiO₂ concentration were the most influential factors for WA, contributing approximately 40% and 30%, respectively. For BS, firing temperature (35%) and Al₂O₃ content (25%) were identified as the key predictors. Partial dependence plots further illustrated critical thresholds, such as a significant drop in WA above 62% SiO₂ and optimal BS values near 1200 °C, findings that align with known ceramic processing principles while offering new, data-driven formulation insights. These results demonstrate the value of explainable artificial intelligence (AI) in enabling real-time process optimization, enhancing product consistency, and supporting energy-efficient ceramic manufacturing. Future work will focus on extending the dataset to include a wider variety of clay compositions and investigating hybrid modeling approaches to further improve predictive performance.
Open Access
Development of a fundamental model for pelleting efficiency of an innovative hybrid fish feed processing system
(KIU Journal of Science, Engineering and Technology, 2025) Daniel C., Nnadi; John Chijioke, Edeh; Offiong Alexander, Aniekan; Aniekan, Offiong
The development of a fundamental model for predicting pelleting efficiency at variable feed rates and number of orifices was central to optimizing the performance of an innovative hybrid fish feed processing system. The system was designed for simplicity, quality, and precision in fish feed production. Machine parameters, derived from comprehensive design and parametric analysis, were used to establish input variables for the pelleting efficiency model, including feed rate and number of orifices. With a constant driving force of 713.38 N from a 3 hp electric motor, the system demonstrated pelleting efficiencies of 55 %, 70 %, and 88 % for 15, 20, and 25 orifices, respectively. At a fixed die orifice, increasing the feed rate from 10 to 20 mm/rev at interval of 5 mm/rev resulted in efficiencies of 60 %, 80 %, and 110 %. Evaluation of the combined effect of the factors predicted an optimum efficiency of 86.9 % at optimal settings of 20mm/rev and 15 orifices. The model’s experimental validation, conducted under optimized conditions, showed that the 20-orifice die produced a higher pelleting efficiency (97%) but with reduced pellet floatability, whereas the 15-orifice die yielded an efficiency of 86.21 % and better floatability. The prediction error of 0.69% validated the model’s accuracy at 99 %. In addition, an introduction of cassava starch constituent improved pellet floatability and surface finish. This study therefore, highlights the potential of the developed model to enhance pelleting performance, balancing efficiency and pellet quality, and providing a robust foundation for optimizing fish feed production processes.
Open Access
Mechanisms and modelling of diffusion in solids: a multiscale framework with industrial case studies and AI enhancements
(Discover Sustainability, 2025) Barah, Obinna Onyebuchi; Natukunda, Faith; Bori, Ige; Ukagwu, Kelechi John
Diffusion in solids is a fundamental mechanism governing mass transport, phase transformations, and microstructural evolution in metals, alloys, and functional materials. This review presents a comprehensive overview of key atomic-scale diffusion mechanisms, including substitutional, interstitial, grain boundary, and surface/pipe pathways, within the classical framework of Fick’s steady-state and non-steady-state laws. The roles of temperature, crystal structure, defect density, and concentration gradients in controlling diffusivity are critically analyzed, with emphasis on activation energies and transport regimes. The limitations of classical Fickian models at the nanoscale are examined, where transport often deviates from equilibrium behaviour and becomes dominated by interfaces, anisotropy, and confinement effects. Addressing these challenges requires alternative modelling frameworks and multiscale simulation strategies. Recent advances are highlighted in computational-experimental integration, including first-principles calculations, phase-field modelling, and in situ characterization under service-relevant conditions. The review also explores the emerging role of artificial intelligence (AI) and machine learning (ML) in predicting diffusion coefficients, activation barriers, and optimal processing conditions. These tools enable inverse design workflows and are increasingly applied in surface treatment design, grain boundary engineering, and coating development. Case studies in carburization, alloy homogenization, and high-temperature coating systems illustrate how diffusion modelling informs real-world process optimization. Looking forward, the convergence of physics-based models, AI-driven analytics, and experimental feedback loops is expected to accelerate the development of diffusion-aware materials design strategies, advancing applications in structural alloys, protective coatings, and digital manufacturing.
Open Access
Analyses of Bioretention Systems for Removal of Stormwater Pollutants
(Journal of Ecological Engineering, 2024) Sholagberu, Abdulkadir T.; Nuwagaba, Emmanuel; Tibenderana, Philip; Terseer, Ako; Bainomugisha, Jonan; Twesigye-omwe, Moses N.; Agwe, M. Tobby; Oluwatosin, Olofintoye O.
Stormwater transports directly into rivers eroded soil, animal wastes, pesticides, fertilizers and other potential pollutants. Bioretention is often designed to capture and treat it using the natural properties of soil and plants. However, selection of appropriate media structure and plants need to be adequately studied. This study investigated the performance of bioretention system in removing stormwater pollutants using Dracaena, a local plant, also called Song of India Plants. Physical model of three Columns A, B and C were developed having five layered filter media of different configurations whose materials were analyzed to meet the design standards. The plants were introduced into Columns A and C leaving out B as control experiment. The quality parameters were determined before and after treatments at ages 10, 20, 30and 40 days when Dracaena plants were introduced into bioretention models. The results showed that the bioretention model drastically improved stormwater quality by reducing values of electrical conductivity, total coliforms (TC), fecal coliforms (FC), total suspended solids (TSS) and biochemical oxygen demand (BOD) as compared to the tested raw stormwater samples. The filter media in both Columns A and C substantially reduced the pollutant levels to standard discharge limits for all parameters tested such as TSS, TC, FC, BOD and nitrates. BOD fell within the recommended standard after 20 days of treatment in Column C with considerable reduction in TC and FC by 68.9% and 75.4% respectively when compared to raw stormwater sample. However, Column C completely removed TC and FC at 40 days which are pathogen indicators in wastewater. This study would be useful to the stakeholders for sustainable stormwater treatment and management.
Open Access
Performance evaluation of lime improved lateritic soil with the addition of pulverised snail shell and sawdust ash for sustainable highway infrastructure
(Discover Civil Engineering, 2024-02-03) Fadugba, Olaolu George; Ojo, Adeyemi Amos; Oluyemi Ayibiowu, Bamitale Dorcas; Omomomi, Oladapo Jayejeje; Bodunrin, Michael
This research investigated the effects of lime, Pulverized snail shell (PSS), and sawdust ash on the mechanical proper ties of lateritic soil for soil stabilization in construction. The use of this waste materials aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly Goal 12 on responsible consumption and production and Goal 9 on sustainable infrastructure development. Reusing waste materials for soil stabilization supports a circular economy approach, diverting these materials from landfills and promoting their sustainable use as valuable resources. Various tests, including maximum dry density, moisture content, unconfined compressive strength (UCS), triaxial, permeability, compressibility, and California Bearing Ratio (CBR) tests, were conducted on soil samples with different proportions of additives. The results show that the addition of additives reduced maximum dry density and increased moisture content. The sample with 6% lime and 7.5% PSS exhibited the highest UCS of 302 kPa after 28 days of curing, while the untreated sample had a UCS of 121 kPa. Triaxial tests revealed reduced cohesion and increased angle of internal friction with higher additive content. The 6% lime and 7.5% PSS sample displayed the highest shear strength of 60.6 kPa and elastic modulus of 181.8 MPa. Permeability tests demonstrated that the 6% lime and 6% sawdust ash sample had the lowest permeability (6.67 × 10–7 m/s) among the stabilized samples. The untreated soil exhibited high compressibility, whereas the 6% lime and 7.5% PSS sample exhibited the highest resistance to compression and deformation. The untreated soil had a soaked CBR value of 8%, while the 6% lime and 7.5% PSS sample achieved the highest soaked CBR value of 38%, making it suitable as a sub-base material. These findings highlight the effectiveness of lime, PSS, and sawdust ash in enhancing the mechanical properties of lateritic soil and offer valuable insights for soil stabilization in construction of Sustainable Highway Infrastructure.
Open Access
Industrial Monitoring System with Real-time Alerts and Automated Protection Mechanisms
(Modern Education and Computer Science Press., 2025-04-08) Nabusha, Alice; Asiimwe, Julius; Bature, U. I.; Mugisha, Simon; Tusiime, Meron
This work presents the design and prototyping of an Industrial Monitoring and Protection System aimed at enhancing safety and operational efficiency in industrial environments. The system integrates multiple sensors with a GSM module to monitor and respond to critical environmental parameters, such as ambient light levels, temperature, and smoke detection. A Light Dependent Resistor (LDR) is configured to detect excessive lighting levels, interfacing with a microcontroller to activate the GSM module and send alert messages when thresholds are exceeded. The temperature sensor continuously monitors ambient temperature, and upon detecting overheating, the microcontroller triggers the GSM module to notify operators. Similarly, a smoke sensor detects the presence of harmful smoke and initiates an alert through the GSM module for early fire hazard detection. These sensors are connected to the microcontroller via analog and digital input pins, with their outputs processed to enable condition-based responses. A relay switch, controlled by the microcontroller, automatically disconnects connected loads when safety thresholds are breached, preventing equipment damage and ensuring personnel safety. Real-time sensor readings and system status are displayed on an OLED screen, providing operators with comprehensive, up-to-date information on the monitored environment. The system dynamically responds to environmental conditions by triggering alerts and actions based on customizable safety thresholds for light intensity, temperature, and smoke levels. This integrated architecture ensures seamless communication between sensors, the microcontroller, and the GSM module, delivering real-time monitoring, automated protective mechanisms, and early warning capabilities. The proposed system demonstrates the feasibility of affordable and scalable solutions for industrial safety, offering immediate responses to hazardous conditions while minimizing downtime. Furthermore, its adaptable design allows for customization across different industrial environments, making it suitable for a wide range of applications.
Open Access
A Regression Model to Enhance the Profitability of Local Construction Contractors in Uganda
(Journal of Construction in Developing Countries, 2021-08-23) Buhamizo, Isaac; Muhwezi, Lawrence; Sengonzi, Ruth
Doubtlessly, the primary goal of every construction company is to maximise profitability. Without this, construction companies cannot survive. Incidentally, Ugandan local construction contractors (LCCs) continue to collapse in a short period, despite enormous public and private investments in the construction sector. This study investigates the profitability of LCCs in Uganda. An investigation was conducted to develop a regression model that would enable LCCs to enhance their profitability and minimise business failure. A questionnaire survey was conducted to collect primary data from 47 local construction companies registered with the Uganda National Association of Building and Civil Engineering Contractors (UNABCEC) and secondary data were collected from audited books of accounts covering from year 2016 to 2018. Thirty-five valid responses were received, representing a response rate of 74%. Data were coded into SPSS version 25, analysed and displayed using the relative importance index (RII), statistical correlation and regression analysis. The findings indicated that the profitability of LCCs was unsatisfactory when compared to the profitability ratios recommended for the construction industry and those of contractors in other countries. The results also indicate that the profitability of LCCs is significantly affected by the timeliness of payments, cost of f inance, competitive bidding environment, project delays, price fluctuations and corruption tendencies, in that order. The findings of this study will benefit construction industry players by providing awareness about the factors affecting the profitability of LCCs. A regression model to enhance profitability was developed using regression analysis. This will help LCCs enhance their profitability by developing mitigation strategies that prevent low profitability; consequently, business failure will be minimised.
Open Access
Mathematical Modeling of Traffic Flow in Kampala City Using the Moving Observer Method
(East African Journal of Interdisciplinary Studies, 2024-08-25) Okiza,Humphrey; Muhwezi, Lawrence; Omwonylee, Okello Joseph; Awichi, Richard Opaka; Nuwagaba, Savannah
The purpose of the study was to investigate the variables affecting traffic flow in Kampala Central Business District (CBD), employing a quantitative approach. The rapid urbanization has led to a huge increase in the number of vehicles, resulting in traffic congestions, delays, and financial losses especially in the Kampala CBD area. Data on traffic density, speeds, and driver behaviors were collected for a period of 20 days from five selected road sections leading into and out of the city which included traffic on Entebbe Road, Jinja road, Sir Apollo Kaggwa Road, Yusuf Lule and Wandegeya roads using the moving observer method. Regression analysis was done to identify the relationships between the variables, leading to the development of a predictive model for traffic flow. The study found out that the flow tends to increase as the day progresses and as well flow rate increases with increase in density. As the week progressed, the flow rate decreased as number of people coming to town on weekends is low since there is no work. A mathematical model was generated which could be used to predict the traffic intensity on the road at a given day and time. The model shows that changing from weekdays to weekend, the flow decreases by about 29%, and as density increases by 1%, the flow also increases by 1.5% over time. The study recommends prioritizing public transportation improvement, establishment of out of city parking yards, utilizing the other various means of transport other than road and promoting non-motorized modes of transportation in order to reduce traffic density on the road and subsequently manage congestion.
Open Access
Effects of seasonal variations of the physio-chemical properties of municipal solid waste on effective materials and resources recovery
(Scientific Reports, 2025) Agwe, Tobby Michael; Twesigye-omwe, Moses N.; Ukundimana, Zubeda; Rotimi, Davies; Gupta, Sneha
Municipal solid waste (MSW) generation rate is on the rise as it is estimated to reach 3,539 million tonnes by 2050 from the 1,999 million tonnes in 2015. The seasonal variations of the physio-chemical properties of the MSW among others exacerbates its management challenges. This study aimed to conduct in-depth investigations on the seasonal variations of physio-chemical properties of the MSW generated in Kabale Municipality, southwestern Uganda to inform sustainable MSW management systems. This study revealed that this MSW is majorly plastics, with concentrations of 21.45% and 26.94% in the dry and wet seasons, respectively, which presents a more recycling potential for these plastics in the wet season. The biodegradable MSW fraction (food, paper, cardboard and garden trimming wastes), which were 35.6% and 35.34% for the dry and wet seasons, respectively, supports energy recovery from the waste in the form of biogas, with a higher potential in the wet season as supported by its higher volatile solid content for the same of 48.92% as compared to that of the dry season of 34.92%. Based on these findings, it is recommended among others that the masses be sensitized on how to generate biogas from the biodegradable fraction of this MSW.
Open Access
Investigating the cost of mechanized unpaved road maintenance operations in Uganda
(Elsevier Ltd., 2024) Obeti, Andrew Moses; Byaruhanga, Chris Bic; Muhwezi, Lawrence; Kakitahi, John Muhumuza
Force Account Mechanism (FAM) is the predominant road maintenance system in Uganda’s local government setup and a similar, though slightly different approach, is used in some large private sector agriculture planta tions. With the Uganda Road Fund (URF) 2021/2022 annual report and previous research citing challenges in cost management and efficiency of the FAM method of road maintenance, it becomes paramount to analyse how FAM is implemented in government-led operations, in comparison to similar private sector approaches, while proposing possible solutions to these challenges. This research offered to analyse unpaved road maintenance cost drivers alongside providing a cost model solution to improve on cost prediction of the FAM system. Gulu District Local Government (DLG) and Kakira Sugar Limited (KSL) were selected as case study areas. Two descriptive research methods were used: observations and case study approach. The selected case study areas were accessible and reachable in terms of data. Control parameters affecting unpaved mechanized road maintenance were identified as machine repair costs, tool costs, labour costs, material costs, fuel costs and machine fuel costs. Unpaved mechanized road maintenance costs at KSL and Gulu DLG were computed as a cost/km ratio of 26,442,032Ugx/km (6,958.4USD/km) and 32,674,895Ugx/km (8,598.65USD/km) respectively. The Uganda National Roads Authority (UNRA) unpaved road maintenance costs were calculated as an average of 34,987,122.9Ugx/km (9,165USD/km) while the World Bank ROCKS database provided a comparable figure of 7,971USD/km (30,553,440.83Ugx/km). A USD to Ugx conversion rate of 3,800 was used. Two linear regression cost models with a 0.679 and 0.687 R 2 value were computed, and these can be used in preliminary road maintenance cost prediction. The study recommends the need for an effective, digital road maintenance cost database system for mechanized unpaved road maintenance works, cost driver analytics and management, alongside improvement in aspects of maintenance processes at both the DLG and KSL. Further research can be conducted on equipment condition level prediction and analytics in the private sector and at the DLG.
Open Access
Antistripping potential of cement kiln dust on recycled asphalt pavement. Innovative Infrastructure Solutions
(Springer Nature., 2025) Usman, Kabiru Rogo; Ali, Atheer Muhammed; Wakawa, Yakubu Mamman; Usman, Abbas
Cement kiln dust (CKD) is the by-product of cement manufacturing. It is collected using air pollution control devices (APCDs) also known as electrostatic precipitators in the form of fue dust to minimize environmental hazards. This study investigates the potential use of CKD as a fller material and its novel antistripping properties on recycled asphalt pavement (RAP). CKD's chemical properties, make it desirable for improving stripping resistance of asphalt in areas prone to high rainfall or moisture exposure, but its application in RAP remains a grey area to explore. Its dual role in improving both adhesion and mechanical properties of asphalt makes it particularly advantageous, in terms of sustainability, cost and resource efciency. The rising production cost, environmental safety concerns, and the push towards sustainable consumption/ production seek alternatives for traditional antistripping agents for asphalt production, thus, CKD. This study prepared densegraded asphalt concrete with nominal maximum aggregate size (NMAS) of 14 mm with 1%, 3%, and 5% of CKD by weight of RAP according to Malaysian standard. A total of fve (5) asphalt concrete (AC14) mixtures were produced with an optimal 3% CKD used in the modifed mixtures at the optimum binder content (OBC). The antistripping properties of CKD in hot mix asphalt (HMA) were assessed through indirect tensile strength test (ITS), indirect tensile stifness modulus (ITSM) and boiling tests on the asphalt mixtures. In addition to the physical, mechanical, chemical, and structural/morphological tests, the safe inclusion of CKD in terms of heavy metals was evaluated by applying toxicity characteristic leaching procedure (TCLP) test. The fndings confrm that CKD meets ASTM C150 standards for type II and type IIA hydraulic cement for use as a fller in asphalt. The fatigue cracking resistance, antistripping resistance in terms of the tensile strength ratio (TSR) & indirect tensile stifness modulus (ITSM) tests indicated that CKD modifed RAP mixes performed better than the control (CNTRL), RAP only and CKD modifed RAP mixes. It also compares favourably with CNTRL+CKD mixture. Ultimately, the boiling test results indicated that CKD blended RAP mix surpassed the minimum 80% TSR for moisture damage resistance.
Open Access
A state-of-the-art review on the modeling and probabilistic approaches to analysis of power systems integrated with distributed energy resources.
(Ain Shams Engineering Journal, 2024) Wanjoli, Paul; Abbasya, Nabil H.; Moustafa, Mohamed M. Zakaria
Modern power systems are shifting toward decarbonization and incorporation of distributed energy resources (DERs) to replace fossil fuel generators. Although promising, DERs introduce uncertainty because of their intermittent nature. This study provides a comprehensive survey of current approaches for modeling system uncertainties and methods of analysis, particularly in the context of static voltage stability studies within modern power systems. Emphasis is placed on evaluating various models applied to different system random variables (RVs), focusing on their suitability for those particular RVs. Additionally, the study examines the characteristics and frameworks of prominent probabilistic methods (PM), evaluates their efficacy, and discusses static voltage stability analysis approaches, emphasizing solution structures and appropriate applications. It concludes by thoroughly reviewing both numerical and analytical PM methods and offering insights into their strengths and limitations. The provided comprehensive survey reveals that, considering system uncertainties, voltage stability studies have gained the most share, followed by small-signal stability studies, whereas the frequency stability studies have gained the least share.
Open Access
A new combined analytical–numerical probabilistic method for assessing the impact of DERs on the voltage stability of bulk power systems
(Energy reports, 2024) Wanjoli, Paul; Moustafa, Mohamed M. Zakaria; Abbasy, Nabil H.
The integration of distributed energy resources (DERs) and unpredictable loads has increased uncertainty in power systems. Traditional methods struggle to assess performance under these uncertainties, and existing probabilistic methods face challenges with complexity and accuracy. This paper introduces a new combined analytical–numerical probabilistic method to assess the impact of DERs on voltage stability. Using Bayesian Parameter Estimation (BPE), the method derives the analytical properties of random variables (RVs) associated with DERs and loads, obtaining posterior distributions. The Metropolis–Hastings sampling technique then estimates these posteriors numerically, enabling accurate predictions of DERs and load outputs. Voltage stability analysis was performed using the continuation power flow method and validated on the IEEE 59- bus test system in MATLAB/Simulink. The results show that integrating DERs significantly improves voltage stability. The proposed method outperforms the Monte Carlo simulation (MCS)-based method in accuracy and computational speed, increasing DERs penetration and voltage stability limits by 3%. It closely matches MCS voltage estimates but requires fewer iterations (500 per loading increment) compared to MCS’s 1000, leading to faster computation times (a few hours to one day versus up to three days for MCS). This method provides an efficient solution for managing uncertainties in power systems.