http://localhost/xmlui/handle/123456789/1278 2026-08-26T16:35:05Z http://localhost/xmlui/handle/123456789/7110 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 http://localhost/xmlui/handle/123456789/7109 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 http://localhost/xmlui/handle/123456789/7107 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 http://localhost/xmlui/handle/123456789/7103 Evaluation of Lateritic Soil and (Fungal) Agaricus Impudicus Biomass for Adsorption–Desorption of Nitrogen and Phosphorus from Human Urine Chepkwony, Faith Human urine contains high concentrations of nitrogen and phosphorus, whose uncontrolled discharge contributes to eutrophication and nutrient losses in agricultural systems. Recovering these nutrients using low-cost natural materials offers a sustainable alternative to synthetic fertilizers. This study evaluated the performance of lateritic soil (LS) and fungal biomass (Agaricus impudicus) as potential adsorbents and desorbents for the recovery of ammonium nitrogen (NH₄⁺–N) and phosphorus (P) from human urine. The physicochemical properties of the adsorbents were characterized using Fourier Transform Infrared Spectroscopy, scanning electron microscope, X-ray Fluorescence (XRF), and elemental analysis. Batch adsorption experiments were conducted under varying operational conditions to evaluate nutrient recovery performance. Physicochemical properties of human urine were evaluated using atomic absorption spectrometry (AAS), nesslerization method, Molybdenum antimony colorimetric, pH meter and electrical conductivity meter. Time-dependent data were fitted using Pseudo first order (PFO), Pseudo second order (PSO), Elovich and Intra-Particle diffusion kinetic models. Equilibrium data were analyzed using Langmuir, Freundlich, and Dubinin–Radushkevich (D–R) isotherm models to elucidate adsorption mechanisms and capacities. Batch and column desorption experiments were conducted to evaluate the desorption behavior of lateritic soils and fungal adsorbent. Elemental and XRF analysis revealed that lateritic soils possess aluminium and iron oxides that facilitate ligand exchange and inner sphere complexation during adsorption. Fungal biomass on the other hand, contains carboxyl and hydroxyl that enable adsorption of 〖NH〗_4^+-N and P via electrostatic bonding. PFO best described the time-dependent data for 〖NH〗_4^+-N and P adsorption for the two adsorbents indicating physisorption was the dominating adsorption mechanism. For 〖NH〗_4^+-N adsorption, the D–R model best described lateritic soil (R² = 0.968), indicating physical adsorption, whereas the Freundlich model best fitted A. impudicus biomass (R² = 0.813). Lateritic soil exhibited higher adsorption capacity (16.3 mg/g) than fungal biomass (12.0 mg/g). For phosphorus, the Langmuir model provided the best fit for both materials (R² = 0.939–0.946), with lateritic soil demonstrating a substantially higher capacity (24.6 mg/g) than fungi (6.2 mg/g), likely due to iron and aluminum oxides. Desorption tests showed that fungal biomass achieved the highest nutrient release (43.93% 〖NH〗_4^+-N and 44.83% P). Overall, lateritic soil and A. impudicus biomass exhibited promising adsorption–desorption performance, demonstrating their potential as low-cost, environmentally sustainable materials for nutrient recovery and circular fertilizer production. Thus, these results provide comparative evaluation of lateritic soil and fungal biomass for simultaneous nitrogen and phosphorus recovery from human urine. These findings also demonstrate the potential integration of locally available natural materials into decentralized sanitation and nutrient recycling systems for sustainable agriculture. MSc in Soil and Water Engineering 2026-08-11T00:00:00Z