http://localhost/xmlui/handle/123456789/1154 2026-08-20T20:14:13Z 2026-08-20T20:14:13Z Odhiambo, Victor Otieno http://localhost/xmlui/handle/123456789/7110 2026-08-17T11:55:36Z 2026-08-17T00:00:00Z

Optimization of Welding Process Parameters in Flux Cored Arc Welding to Improve Weld Depth of Penetration and Minimize Heat Affected Zone in Pipelines Odhiambo, Victor Otieno Flux Cored Arc Welding (FCAW) is an advanced arc welding process that uses a continuously fed tubular electrode wire with internal flux to generate heat and protect the weld fromcontamination. In the oil and gas industry, ensuring high-quality pipeline welds is critical for structural integrity and public safety. Depth of Penetration (DoP) strongly influences weld joint strength, while excessive Heat Affected Zone (HAZ) can compromise material properties. This study applied Artificial Neural Networks (ANNs) to predict and optimize FCAW process parameters to maximize DoP and minimize HAZ in pipeline welding. The research investigated welding speed, torch angle, contact-tip-to-work distance, welding current, arc voltage, and heat input as key variables. Pipe samples of ASTM A335 Grade B– API 5L Schedule 40 were prepared. The experiments were designed using the Taguchi method. Welding was performed using an MMA/MIG/TIG 200 Prescott FCAW machine. An ANN model was designed and trained in MATLAB, with optimization achieved through Stochastic Gradient Descent (SGD) with momentum. Confirmatory welding trials were conducted at the optimizedparameters. WeldqualitywasassessedusingVickersMicrohardness, Charpy Impact, tensile testing, and microstructural examination. The ANN model achieved an optimum DoP of 7.66 mm and HAZ of 2.90 mm, with prediction accuracies of 99.9948% and 99.9966% respectively, compared to validation results of 7.70 mm DoP and 2.91 mm HAZ at a heat input of 1.13 kJ/mm. Mechanical testing showed a Yield Strength of 276.23 MPa, Ultimate Tensile Strength (UTS) of 483.37 MPa, Engineering Strain of 0.2026 mm/mm, Fusion Zone (FZ) average hardness of 230.7 HV, and Impact toughness of 1.3598 J/mm2. Optimum FCAW process parameters of heat input (1.13 kJ/mm), welding current (126 A), arc voltage (21.5 V), welding speed (115 mm/min), torch angle (450), and contact-tip-to-work distance (5 mm), produced the most desired microstructure, acicular ferrite. The microstructure had refined grains, optimal ferrite-pearlite balance, effective tempering, and minimized brittle phase formation across the Coarse Grained Heat Affected Zone (CGHAZ), Fine Grained Heat Affected Zone (FGHAZ), Inter-Critical Heat Affected Zone (ICHAZ), and Sub-Critical Heat Affected Zone (SCHAZ). The ANNmodeleffectively optimized FCAWparameters, producing welds with deep penetration, narrow HAZ, and superior mechanical properties. This approach offers a reliable predictive tool for improving pipeline weld quality, supporting safer and more durable infrastructure in the oil and gas industry MSc in Mechanical Engineering

2026-08-17T00:00:00Z Kaudza, Chippoh http://localhost/xmlui/handle/123456789/7109 2026-08-11T12:24:41Z 2026-08-11T00:00:00Z

Adsorptive Removal of Sulfamethoxazole and Trimethoprim from Aqueous Solutions Using White-Rot Fungus Biochar, Corncob Biochar, and Their Composite Mixtures Kaudza, Chippoh Pharmaceutical residues, particularly antibiotics, are emerging contaminants of environmental concern because of their persistence in aquatic environments and their contribution to antibiotic resistance. Adsorption using biochar has attracted considerable attention as a sustainable and cost-effective treatment technology. This study evaluated the removal of sulfamethoxazole (SMX) and trimethoprim (TMP) from aqueous solutions using phosphoric acid-activated white-rot fungus biochar (WR700), corncob biochar (CB700), and their mixtures. Specifically, the study characterized the physicochemical properties of WR700 and CB700, evaluated their adsorption behavior using kinetic and equilibrium studies, and assessed the performance of their mixtures under varying operating conditions using a Taguchi L25 orthogonal array. Biochar characterization using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), elemental analysis, proximate analysis, and zeta potential analysis confirmed that both WR700 and CB700 possessed porous structures, oxygen-containing functional groups, and favorable surface characteristics for adsorption. Batch adsorption experiments showed that WR700 achieved maximum removal efficiencies of 99.5% for SMX and 89.8% for TMP, while CB700 achieved maximum removal efficiencies of 89.8% for SMX and 87.7% for TMP. The maximum adsorption capacity of WR700 was 0.22 mg g⁻¹ for both antibiotics, whereas CB700 achieved adsorption capacities of 0.12 mg g⁻¹ for SMX and 0.15 mg g⁻¹ for TMP. Adsorption was influenced by solution pH, contact time, and adsorbent dosage, with kinetic and equilibrium analyses indicating that both physical and chemical interactions contributed to antibiotic removal. The mixtures of WR700 and CB700 did not improve adsorption performance. Instead, maximum removal efficiencies of 65.56% for SMX and 27.37% for TMP were obtained, indicating an antagonistic interaction between the two biochars. Overall, the study demonstrates that WR700 and CB700 are effective adsorbents for the removal of sulfamethoxazole and trimethoprim from aqueous solutions, whereas combining the two biochars does not enhance adsorption performance. These findings provide useful information for the development of sustainable biochar-based technologies for antibiotic removal from contaminated water. MSc in Soil and Water Engineering

2026-08-11T00:00:00Z Wanjagi, Jedidah Agnes http://localhost/xmlui/handle/123456789/7108 2026-08-11T12:13:41Z 2026-08-11T00:00:00Z

Capital Adequacy, Asset Quality, Earnings and Liquidity on Operational Efficiency of Commercial Banks in Kenya Wanjagi, Jedidah Agnes The study investigates the capital adequacy, asset quality, earnings quality, liquidity on operational efficiency of commercial banks in Kenya. This was guided by analyzing the effect of capital adequacy, asset quality, earnings quality, bank liquidity and operational efficiency while mark share was used as the moderating variable. Operational efficiency is viewed as a pre-requisite for the financial soundness of any banking institution. In banking literature, empirical review indicated mixed findings on the effect of capital adequacy, asset quality, earnings quality, bank liquidity and market share on the operational efficiency of the respective banks. Thus the issue of agency problems between managers and shareholders, asset liability mismatch and inefficiency remain unclear in Kenyan commercial banks. Thus this study is anchored on theories from agency theory, conventional economic efficiency theory and asset liability management theory. Quantitative research design was embraced as it aligns with the choice of positivism philosophy. Positivism philosophy was used as it allows the researcher to explore measurable and credible results from the financial statements to establish the cordial relationship amongst the variables. A census was applied with the target population being all the 43 banks listed under the Central Bank of Kenya. The study covered a period of 14 years from 2008-2022. The period is appropriate for the research since it incorporates banking sector financial reforms and issues of financial efficiency that shook the Kenyan financial system in 2008. Data was extracted from the verified audited financial statements of the banks available in the Central Bank of Kenya and the official websites of respective commercial banks. The independent variables were capital adequacy, asset quality, earnings quality and liquidity; moderating variable was market structure and dependent variable operational efficiency. In testing Panel regression model, normality, heteroscedasticity and autocorrelation, Shapiro Wilk Test, Variance Inflation Factor (VIF) and Breauch Pagan Test were used. The study used a two-step model of analysis. The first step involved the use of the Stochastic Frontier Analysis approach, where scores were estimated for each of the cross-sections under study. Second, the panel Generalized Method of Moments (GMM), was applied to regress efficiency scores on the regression model. The regression results indicate that capital adequacy demonstrates a substantial positive influence on operational efficiency of banks; however, liquidity has no significant influence on efficiency while asset quality and earnings quality lead to an increase in operational efficiency. Furthermore, the results indicated previous years’ earnings are important in determining the current year’s operational efficiency. Market structure was found to moderate effect of capital adequacy, earnings quality and liquidity on operational efficiency of banks. This implies that large banks with higher core capital often benefit from economies of scale, allowing them to spread fixed costs over a larger asset base. This can lead to lower average costs per unit of output and greater operational efficiency. Furthermore banks should seek mechanisms to improve these variables to enhance operational efficiency and ensure market readiness. PhD in Finance

2026-08-11T00:00:00Z Karugu, Kevin Aaron http://localhost/xmlui/handle/123456789/7107 2026-08-11T11:54:09Z 2026-08-11T00:00:00Z

Machine Learning Based Rice Yield Prediction and Rice Growth Stages Identification Using Sentinel-1 SAR and Auxiliary Data in Mwea Irrigation Scheme, Kenya Karugu, Kevin Aaron Rice is considered the third most important staple food in Kenya after maize and wheat making its productivity of significant concern. Additionally, conventional preharvest crop yield estimation techniques are dependent on data collection from ground-based field visits which are often subjective, costly and prone to huge errors resulting to poor crop estimates. Satellite remote sensing has been widely accepted as a solution to overcoming these challenges. Therefore, the primary objective of this research was to identify rice growth stages and develop a rice yield-prediction model using Sentinel-1 SAR data and climatic auxiliary data with machine learning (ML) in Mwea Irrigation Scheme (MIS), Kenya. Hence, this study focused on the characterization of rice growth stages in MIS using spectral data derived from Sentinel satellites on Google Earth Engine (GEE) cloud. Thereafter, the SAR backscatter data, coupled with rainfall, and temperature data covering the period 2021-2024 were used to predict rice yield applying four ML models including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Linear Regression (LR) using python programming language on the web-based Jupyter Notebook platform. Finally, evaluation of the performance of RF, XGBoost, SVM models in predicting rice yield for the 2024 season was done using LR as a benchmark. The proposed method produced a rice extent map having an overall accuracy of 83% with a kappa coefficient of 0.53 and effectively identified the stages of rice growth during the 2024 main rice growing season in the study area. From the identified rice clusters, it can be shown that transplanting takes place in July and August while the maturity phase is attained in October and November, giving an indication that the main cropping season was approximately 120 days/ 4 months. The findings from this study illustrate that the RF model outperforms the other models with lower error values, achieving a RMSE of 246.91 kg/acre, MAE of 202.90 kg/acre and MAPE of 7.67% for rice yield predictions utilizing both SAR backscatter and auxiliary climatic datasets. On the other hand, LR model shows suboptimal performance of the four models as indicated by its higher error values attaining a RMSE of 384.09 kg/acre, MAE of 290.86 kg/acre, and MAPE of 11.06%. Furthermore, in predicting yields for the upcoming 2024 season using datasets from previous seasons, the RF model emerged with the highest accuracy obtaining the lowest RMSE of 288.52 kg/acre, MAE of 195.13 kg/acre and MAPE of 8.43%, followed by XGBoost with a RMSE of 290.44 kg/acre, MAE of 201.45 kg/acre and MAPE of 8.56% and finally the SVM model with a RMSE of 493.66 kg/acre, MAE of 395.21 kg/acre and MAPE of 16.73%. The findings show that integration of Sentinel SAR backscatter and optical time series data applying a phenology-driven framework on the GEE platform provides an effective approach for rice mapping and growth stage identification. The study demonstrates that ML models calibrated using previous seasons’ datasets can reasonably predict rice yield for subsequent growing seasons. RF provided the most accurate predictions one month prior to harvesting, followed by XGBoost, and SVM models. The LR model failed to generalize to an unseen growing season, indicating that linear relationships are inadequate for capturing seasonal variability in rice production. Machine learning-based rice yield prediction, particularly using the RF model, should be incorporated into agricultural monitoring frameworks to provide early estimates of rice production before harvest. Reliable predictions would support government and state agencies in evidence-based decision-making on grain procurement, import and export planning, food reserve management and market stabilization. Availability of quality weed control, fertilization and pest management data for rice production was a major limitation in this study which future studies could incorporate for rice yield predictions. MSc in Soil and Water Engineering

2026-08-11T00:00:00Z