http://localhost/xmlui/handle/123456789/1278 2026-05-28T15:50:43Z 2026-05-28T15:50:43Z Mutunga, Jackline Mwende http://localhost/xmlui/handle/123456789/7043 2026-05-28T12:30:55Z 2026-05-28T00:00:00Z

Experimental Study and Numerical Optimization of a Thermal Swing Adsorber for Biogas Upgrading Mutunga, Jackline Mwende Biogas is a renewable energy source that can be adopted as a reliable and sustainable alternative when upgraded. The composition of carbon dioxide in biogas of up to 45% reduces its energy density. Thermal swing adsorption has proven to be a promising technology in the biogas upgrading process, due to its ease of integration with renewable electricity sources and its suitability for water-deficient areas. The experimental study of the biogas upgrading process has been complicated by the dynamic nature of the process and the high sensitivity to operating conditions, such as pressure, temperature, and gas flow rate. To understand the complex interaction between the process parameters, numerical simulation was utilised. There are, however, limited numerical studies evaluating multi-objective optimization of these process parameters. This study assessed the performance of thermal swing adsorption technology, utilising resistive heating, in upgrading biogas produced from the anaerobic digestion of organic waste. Commercial coconut shell-based activated carbon was used as an adsorbent in the experimental cyclic process to capture carbon dioxide. Aspen Adsorption software was used to develop a thermal swing adsorption numerical model. The simulation model was validated using experimental data obtained from a laboratory-scale setup. A good agreement was observed between the simulation and experimental carbon dioxide breakthrough times, with a mean absolute percentage error of 2%. Dynamic adsorption tests were conducted to evaluate the system performance in carbon dioxide capture. The maximum resistive heating regeneration temperature of 60℃ resulted in a peak carbon dioxide concentration of 39% in the waste gas, an energy requirement of 0.1538 kWh per cycle, and an energy efficiency of 87%. This was a good trade-off between adsorbent recovery for subsequent biogas upgrading cycles and system energy efficiency. In the second phase of the study, the adsorbent particle radius, regeneration temperature, and purge-to-feed flow rate ratio were investigated to determine the system's sensitivity. The adsorption and desorption processes were based on methane and carbon dioxide adsorption isotherms, which were fitted to the Langmuir-Freundlich model. The model described the adsorption behaviour on a heterogeneous adsorbent surface, where adsorption sites had different affinities and capacities. Adsorbent particle radius, steam regeneration temperature, and purge-to-feed flow rate ratio range of 1 to 9 mm, 77 to 227℃, and 0.1 to 0.7, respectively, were adopted. Multi-objective numerical optimization of the selected variables was carried out using the Box-Behnken design response surface methodology. The target output responses maximized were the methane purity and recovery. From the analysis of variance, the purge-to-feed flow rate ratio made the highest contribution to both methane purity and recovery, of 92.37% and 99.90%, respectively. While the particle radius had a negligible influence on the methane recovery model, its contribution to the methane purity was significant. The optimal values for maximum methane purity and recovery obtained were 82.12% and 37.21%, respectively, achieved at a particle radius of 9 mm, steam regenerating temperature of 227℃, and a purge-to-feed flow rate ratio of 0.4152. This study offers valuable insights into the design of a thermal swing adsorption biogas upgrading model, as well as the impact of various variables and configurations on the process. The developed model provides practical guidelines for selecting optimal biogas upgrading process parameters to maximize both methane purity and recovery. Doctor of Philosophy in Mechanical Engineering

2026-05-28T00:00:00Z Inyanga, Faith Eseri http://localhost/xmlui/handle/123456789/7037 2026-05-28T11:18:57Z 2026-05-28T00:00:00Z

Optimization of Dynamic Transmission Network Expansion Planning Using Improved Binary Particle Swarm Optimization Algorithm Inyanga, Faith Eseri A transmission system is one of the most important components of a power system for relaying electric power to load centers. Increasing capacities of existing generating units and construction of new generating plants to supply the additional electric power demand has resulted in congestion of transmission networks. Congestion is as a result of reaching or exceeding the voltage, transmission lines’ loading or steady-state stability limits. Persistent congestion is alleviated by construction of additional transmission lines. The Transmission Network Expansion Planning (TNEP) task is needed to determine the best set of transmission lines that can be added to a power system at minimum expansion cost without violating the network constraints during a defined planning period. In this research, voltage limit violations are penalized in a constrained Dynamic Transmission Network Expansion Planning (DTNEP) optimization problem. The number of transmission lines and their optimal location required to minimize the costs of line construction and transmission losses associated with the transmission network operations are determined. Improved Binary Particle Swarm Optimization (IBPSO) algorithm is applied to optimize the DTNEP results. IBPSO algorithm allows discrete TNEP problems to be solved by Particle Swarm Optimization (PSO) algorithm. IBPSO algorithm addresses the limitation of the BPSO algorithm by jumping out of the local optimal position to explore the search space area. The developed model is tested on Garver’s 6-bus and IEEE 30-bus systems using MATLAB. The obtained DTNEP results minimize the costs of constructing new transmission lines when compared to using Linear Programming and Linear population-size reduction Success History Adaptation Differential Evolution Semi-Parameter Adaptation hybrid Covariance Matrix Adaptation (LSHADE-SPACMA). Congestion in the network was alleviated by ensuring the transmission lines’ thermal loading were maintained at 80 % of their capacities and bus voltage limits ( 5 % of nominal voltage) were obeyed. Alleviating congestion in the network improved the adequacy of the transmission network system allowing for increased active power transfer. The developed methodology may be applied to a large power system for further studies. IBPSO algorithm may be applied with other metaheuristic methods to improve speed of convergence for DTNEP problems Master of Science in Electrical Engineering

2026-05-28T00:00:00Z Chikamai, Rinah Munyelele http://localhost/xmlui/handle/123456789/7001 2026-05-21T08:31:42Z 2026-05-21T00:00:00Z

Impact of Exceptional Events on Cost Performance of Highway Projects in Kenya: Chikamai, Rinah Munyelele The implications of cost overruns in road infrastructure projects are wide ranging. The most notable effect being the strain on the resources due to additional costs over and above the budgeted costs as well as reduced benefits to the intended users. In some cases, these costs are passed on to the road users. The problem in this study is that highway construction projects in Kenya experience huge cost overruns. However, the contribution of exceptional events to cost performance of highways projects is not well understood. Further, the current standard forms of contract have not been clear on how to address the aftermath of exceptional events considering the uncertainty of the occurrence. Therefore, this study was conducted to address these concerns. The main objective of the study was to assess the impact of exceptional events on the cost performance of highway projects in Kenya with a case study of Kenya National Highway Authority (KeNHA). The study was guided by specific objectives which include: to determine the effect of global pandemics on the cost performance of highway projects in Kenya; to determine the influence of economic recession on cost performance of highway projects in Kenya; to determine the effect of exceptional climatic conditions on the cost performance of highway projects in Kenya; to determine influence of mitigation measures on the cost performance of highway projects in Kenya. The research design adopted was both qualitative and quantitative. The target population was active KeNHA projects within Nairobi Metropolitan area where 14 projects were considered. From the target population, a sample size of 42 respondents was selected. The respondents were drawn from three levels of workmanship in the projects which included a project manager, project contractor and site engineers. In this study, emphasis was given to both primary data and secondary sources. The primary data was collected using structured questionnaires. Secondary data was sourced from journals, published reports and articles. Data collected was analysed with the aid of Statistical Package for Social Science (SPSS) Version 20. The raw data was edited and then entered in the SPSS computer program by assigning symbols in a process referred to as coding. Thereafter, the data was analysed with the output being presented in form of frequencies, descriptive charts and graphs. The findings showed that exceptional events had a significant effect on cost of infrastructure projects in Kenya. The correlation and regression analysis indicated that global pandemic, economic recession, climatic conditions and mitigating factors had significant effect on cost of projects. The study concluded that all the variables were relevant in explaining variation in cost of projects. From the regression model it was noted that the correlation of variables was explained by R2 of 90.3% and adjusted R2 of 90.0%. This means that the exceptional events would cause the variation of cost performance of the project by over 90%. This study therefore recommends the improvement of this model by including more variables that are relevant in explaining the variation. This study also recommends further research to include studies in other government departments on other factors other than exceptional events that affect cost performance of projects. of Master of Science in Construction Engineering and Management

2026-05-21T00:00:00Z Maroko, Josiah Teyah http://localhost/xmlui/handle/123456789/6956 2026-05-12T11:33:01Z 2026-05-12T00:00:00Z

Development of a Hybrid Fuzzy Logic and Linear Programming Algorithm for Optimal Load Shedding in Islanded Microgrid Maroko, Josiah Teyah The islanded microgrid (IMG) entirely depends on Distributed Generations (DGs) like Micro Hydro Power (MHP), solar photovoltaic (PV), wind, and fuel cells among other sources of energy. The stochastic nature of solar PV, wind and local loads creates an imbalance between generation and the loads. These disturbances can plunge the IMG into an emergency power crisis which can lead to a cascaded blackout if no remedy strategy is brought on board to restore the power to a balance. To avert the power crisis in the IMG load shedding (LS) is done as a last resort after all control mechanisms have been exhausted. The conventional methods of LS used in grids perform poorly when applied to the IMG because of low convergence and settling time. Recent researchers have found that adaptive methods for LS specifically the hybrid method perform optimal LS to curb the power system from collapsing in times of contingencies. The hybrid method of LS using a Fuzzy Logic Controller (FLC) and Linear Programming (LP) was used to optimize the amount of LS in the IMG. In this method, the objective function was formulated and solved by the Fuzzy Linear Programming (FLP) algorithm. The inputs to the controllers are power generated and power demand of the IMG. The loads were classified according to priorities using fuzzy membership functions while optimization of loads shed was achieved by the LP algorithm. The simulations consisted of generation contigencies, power demand in which 10 overload contingencies were simulated, voltage profiles and power losses. The results depict FLP algorithm finds the best steady-state operating point with a minimal amount of load curtailment. The scheme minimizes loading at the buses until total load demand matches generation to restore power balance within a single LS step. In comparison to GA 77.04%, ABC-ANN 84.03%, PSO-ABC 85.50% the proposed FLP algorithm was able to shed optimal amount of load quantities resulting in 86.10% voltage profile recovery. The developed algorithm was tested by performing simulations on IEEE 14 bus systems on a Matlab Simulink platform. MSc in Electrical Engineering

2026-05-12T00:00:00Z