http://ir.mu.ac.ke:8080/jspui/handle/123456789/26 2026-07-14T08:00:39Z http://ir.mu.ac.ke:8080/jspui/handle/123456789/10322 Ergonomic redesign of sewing workstation for garments manufacturing: a case study of Rivatex East Africa Limited, Eldoret, Kenya Abdalla, Esmaeel E. A. Worldwide, the design of a workstation in many garment industries is usually done with minimal consideration of anthropometric data. This may cause hazards and reduce work efficiency as a result of sitting for long periods of time in uncomfortable positions. Ergonomically redesigned workstations are known to reduce Musculoskeletal Disorders (MSDs) and improve the motivation of the garment workers in the workstation environment. The main objective of this study was to redesign an ergonomic sewing workstations for garment manufacturing using selected anthropometric data collected at Rivatex East Africa Limited (REAL), Eldoret, Kenya. The specific objectives were: to assess work-related risks and hazards for garments making workers at sewing workstations; to determine the relevant anthropometric dimensions from garments-making workers for a sewing workstation; to redesign a sewing workstation for garments-making workers; to simulate the redesigned sewing workstation; to optimize the redesigned workstation for garments-making workers at sewing operations; and to fabricate the redesigned sewing workstation. Rapid Entire Body Analysis (REBA) method was used to assess the ergonomic risk of the existing workstation. The sample size of 100 was determined. Eleven anthropometric measurements were taken from workers using ISO 7250-1:2017 and compared using one-way analysis of variance (ANOVA). Using the anthropometric data, a redesigned sewing workstation model was proposed. The model was analyzed using Computer Aided Three-Dimensional Interactive Application (CATIA V5) software based on Rapid Upper Limb Analysis (RULA). The model was simulated using SolidWorks 2024 software based on Finite Element Analysis (FEA). Aluminum alloy 1060 was selected for FEA. The FEA criteria included: stress, displacement, strain and Factor of Safety (FOS). The model was optimised using design study. The model was then fabricated in accordance with the necessary manufacturing process. ANOVA tests results failed to reject the null hypothesis in the data sets (P > 0.05), thus, there was no significant difference between the anthropometric data. The recommended dimensions for workstations are redesigned, significantly reducing the mismatches between workstation dimensions to the relevant body dimensions. Analysis results of the workers' posture for the existing sewing workstation had a final REBA score of 5, implying existence of medium ergonomic risk, hence, changes were necessary. The proposed sewing workstation had a final RULA score of 1, meaning that the ergonomic risk is negligible. The FEA results showed that the maximum stress was 7.175E-01 MPa and did not exceed the yield strength; the maximum deformation was 0.03209 mm, which was below the assigned safety level; the maximum strain was 6.258E-06 and within the range for the material; and the minimum FOS distribution was 3 implying that the model was within the safety range limits. The optimisation results showed that the optimal dimension of the model was 416.5 mm for seat height, 457 mm for seat depth and 472.8 mm for seat width; the optimal stress of the model was 2.662E+01 MPa; and the optimal mass of the model was 19865.24 g. In conclusion, the dimensions of the redesigned sewing workstation was recommended. The proposed redesigned workstation should be suitable for all garment workers in Kenya. 2026-01-01T00:00:00Z http://ir.mu.ac.ke:8080/jspui/handle/123456789/10208 Process simulation and machine learning modeling of biomass wastes co-gasification for syngas and biochar production Bongomin, Ocident The urgency of climate change has accelerated research into renewable energy, including biomass co-gasification. While biomass waste offers a sustainable resource for producing syngas and biochar, the conversion process remains complex due to variability in feedstock properties, reactor design, and operating conditions. Additionally, traditional experimental and mechanistic modeling approaches are often time-consuming, costly, and limited in generalizability. This study addresses this gap by integrating experimental characterization, process simulation (PS), and machine learning (ML) to enhance understanding and prediction of autothermal biomass co-gasification outcomes. The primary objective of this research is to develop predictive models to optimize syngas and biochar production. Specifically, the study characterizes the physico-chemical and thermo-kinetic properties of five biomass feedstocks (coffee husks, groundnut shells, macadamia nutshells, rice husks, and tea wastes); develops a PS model to represent biomass co-gasification dynamics; develops and validates ML models to predict co- gasification outcomes; assess model robustness using new biomass blends; and evaluates the impact of model interpretability techniques on feature importance rankings. Proximate analysis method followed ASTM E1131-08, while ultimate analysis was conducted using a Carbon-Hydrogen-Nitrogen-Sulfur (CHNS) analyzer. Thermogravimetric analysis under a nitrogen atmosphere was employed to study thermal degradation, and kinetic parameters were estimated using the Coats-Redfern method. Aspen plus PS method was used to simulate a pilot-scale downdraft gasifier. ML models (Random Forest, Artificial Neural Networks, Gradient Boosting Regression, Support Vector Regression, and SuperLearner ensembles (SLE)) were developed and validated in MATLAB. Robustness was tested by validating the models with new feedstock blends. Interpretability was evaluated using permutation importance, Gini importance, and partial dependence plots. Proximate and ultimate analysis results revealed variability among feedstocks. Volatile matter (63.96% ±3.57%) indicated high syngas and tar potential, fixed carbon (19.62% ±2.69%) contributed to char formation, and carbon content (47.69% ±4.80%) suggested high energy conversion efficiency. Thermo-kinetic analysis showed peak devolatilization temperatures between 345°C and 380°C, activation energies ranging from 39 to 46 kJ/mol, and Gibbs free energy values between 151 and 162 kJ/mol, indicating favorable decomposition behavior. PS model achieved high accuracy with temperature deviations of 2°C (pyrolysis), 4°C (combustion), and 7°C (reduction), and syngas yield deviations of 0.21 Nm3/kg (Equivalence Ratio (ER) 0.17) and 0.34 Nm3/kg (ER 0.29). Sensitivity analysis showed that increasing ER enhanced hydrogen concentration by 10–15% and reduced carbon dioxide by 20–25%. All ML models performed well with Coefficient of Determination (R2) > 0.90 and Root Mean Square Error (RMSE) < 5%, confirming their effectiveness. Robust analysis showed that SLE maintained superior generalizability (R2 > 0.75, RMSE < 5%) for predicting char and syngas yields. However, hydrogen and cold gas efficiency predictions were less robust, indicating needs for more diverse datasets to improve generalization. Interpretability analysis identified ER and steam-to-biomass ratio as key predictors, with macadamia nutshells playing a critical role in enhancing char yield. In conclusion, this study demonstrates biomass waste co-gasification using integrated experiments, simulation, and ML, providing insights for sustainable energy. The findings recommend applying the developed ML models in academic research and practical gasification systems to support reliable process prediction and decision-making. 2025-01-01T00:00:00Z http://ir.mu.ac.ke:8080/jspui/handle/123456789/10122 Development, characterization and evaluation of selected transition metal doped zinc sulphide nanostructure surface layers decorated with graphene for water splitting Kiptarus, Joan Jeptum Water splitting (WS) is the dissociation of Water (H2O) into Hydrogen (H2) and Oxygen (O2). Zinc Sulphide (ZnS) provides an excellent option for the hydrogen reduction cathode in photo electrochemical (PEC) cells for WS. However, its low sensitivity to visible range in electromagnetic spectrum limits its practical appli cability. Few comprehensive studies consider a wide range of transition metals as potential dopants to meet future energy requirements for greater PEC WS. The main objective of this research was to develop, characterize and evaluate the selected Transitional metal (TM) doped ZnS nanostructure (NS) surface layers decorated with graphene (rGO) for WS. The specific objectives were to: simu late the optimal dosage of TM dopants for ZnS nanostructure layers, synthesize TM doped ZnS NS layers decorated with graphene, characterize TM doped ZnS NS layers decorated with graphene and to evaluate the photocatalytic hydrogen production of TM doped ZnS NS layers decorated with graphene. Theoretical f irst principles Ab-Initio calculations based on Density functional theory (DFT) method was employed to examine the electronic structure of ZnS nanostructures (NSs) doped with selected TM dopants including; manganese (Mn), copper (Cu), cobalt (Co) and iron (Fe) in order to modify the structural properties of ZnS NSs. Highly distributed cobalt doped ZnS NSs were effectively fabricated on the surfaces of graphene sheets via simple hydrothermal technique. The structural, electronic and optical properties of the cobalt doped ZnS decorated with graphene (Co-ZnS-rGO-NS’s) were examined using X-ray diffraction (XRD), X-ray pho tocurrent spectroscopy (XPS), Raman spectroscopic (RS), Fourier transmission infrared spectroscopy (FTIR), Scanning electron microscopy (SEM) and Ultra violet visible absorbance spectroscopy (UV-vis). The photocatalytic activity of CoxZn1−xSrGO NS’s at (x = 0, 1, 2, 4 and 6) atomic percentage (atm.%) was determined in lab experiments using water and visible light. The stability of 3d orbital transitional metal dopant (TMD’s)’s in ZnS NSs were shown to be depen dent both on the dopant concentrations and the d orbital character of the TMD’s. Evidently, the 3d orbital TMD’s’s (Cu, Co,Mn and Fe) showed low formation energies and appropriate band edge states due to their low lattice strain, hence absorbed into ZnS NSs. ZnS doped with 4 atm.% of Cu and Co was shown to be optimal for photocatalytic hydrogen generation based on theoretical studies. The f indings of XRD, FTIR, RS, XPS and SEM investigation suggest that graphene oxide (GO) was successfully transformed into graphene sheets, CoxZn1−xSrGO NS’s possessed a crystalline, cuboidal and spheroidal form of structure displaying a paper like appearance. UV-vis spectrophotometric analysis verified a notable rapid increase in transmittance and high transparency (≈ 90%) within (180-800) vi nm wavelength range. Calculations of transmittance spectra revealed a direct allowable band gap range of (1.26-5.46) eV, demonstrating a band gap decrease as cobalt content increased, consistent with theoretical predictions. Furthermore, the optimal cobalt loading of 0.04 atm.% generated a maximum hydrogen yield of 7649µmolh−1 after 720 minutes of Ultra Violet (UV) light exposure, indicating that the ZnS NSs’s electronic and optical characteristics were influenced by their stability with respect to dopant concentration. In conclusion, the results show that improved transfer of photo-generated electrons, increased surface area and better dispersion-absorption properties all contributed to higher photocatalytic hydrogen generation activity. The study recommended synthesis optimization for commercially viable technology. 2025-01-01T00:00:00Z http://ir.mu.ac.ke:8080/jspui/handle/123456789/10114 Technical assessment of large-scale integration of Solar electrification in energy systems in kenya Dominic, Ondieki Samoita Kenya has witnessed a significant increase in electricity demand, reaching 1.5 GW in 2022 compared to a production of 12.65 TWh. This growth is primarily driven by population expansion and industrialization. However, continued reliance on fossil fuels remains environmentally unsustainable. To address this, the Kenyan government has set a target of achieving 100% renewable energy integration by 2030, with a strong emphasis on solar and wind energy. With its abundant solar resources, Kenya has the potential to generate more solar power than its total electricity demand. This thesis investigates the feasibility and impact of large-scale integration of solar power systems into Kenya’s energy mix. EnergyPLAN tool was employed to simulate hourly energy production and demand, enabling a comprehensive assessment of the technical, economic, and environmental implications. Cross-sectoral analysis was conducted to evaluate interdependencies and sectoral dynamics. A novel Whale Optimization Algorithm (WOA) based Maximum Power Point Tracking (MPPT) algorithm was developed in MATLAB and benchmarked against conventional methods, including Incremental Conductance, Fuzzy Logic, and Particle Swarm Optimization (PSO). Simulation results showed a 32% increase in solar power capacity—from 212.5 MW (6.8% of total generation) to 4,601 MW—at an annual cost of KSh 145.5 billion, compared to KSh 186.9 billion under the baseline scenario. With further solar power integration, optimal generation reached 10.01 TWh (39.56% of total), while renewable electricity output increased from 11.90 TWh to 19.76 TWh. CO₂ emissions dropped significantly from 0.134 Mt to 0.021 Mt, and total annual production costs decreased to KSh 134.3 billion. These findings demonstrate that optimized solar power integration offers substantial benefits in cost savings, emissions reduction, energy security, and system reliability. Sectoral Innovation System (SIS) analysis revealed that global cost declines primarily drive solar power adoption, with minimal local adaptation needed. The proposed WOA-based MPPT algorithm achieved a tracking efficiency of 99.95% with a steady-state error of 0.04%, outperforming PSO (99.7% efficiency, 0.2% error). Although PSO successfully tracked the global maximum power point, its dynamic response was inferior to that of the developed WOA-based MPPT system. 2025-01-01T00:00:00Z