AAU Institutional Repository (AAU-ETD)
Addis Ababa University Institutional repository is an open access repository that collects,preserves, and disseminates scholarly outputs of the university. AAU-ETD archives' collection of master's theses, doctoral dissertations and preprints showcase the wide range of academic research undertaken by AAU students over the course of the University's long history.
How to Submit Your Work
The repository contains scholarly work, both unpublished and published, by current or former AAU faculty, staff, and students, including Works by AAU students as part of their masters, doctoral, or post-doctoral research
- All AAU faculty, staff, and students are invited to submit their work to the repository. Please contact the library at your college.
You may contact digirep@aau.edu.et.with any questions about the repository
Colleges,Institutes in AAU-ETD
Select a college,institute to browse its collections.
Recent Submissions
Assessment of Reverse Logistics System for Recyclable and Reusable Waste Generated from Residential and Commercial areas in Addis Ababa
(Addis Ababa University, 2026-05) Hiwot Abebe Gebremariam; Girma Gebresenbet
The study assessed the current reverse logistics practice in selected residential and commercial areas of Addis Ababa, focusing on recyclable and reuseable waste, and mapped the roles of actors with the challenges they face. Data was collected through questionnaires via Kobo Toolbox, field visits, and secondary sources. Analysis was conducted using SPSS and Excel. Findings revealed that despite the growth in recycling rate in both areas 83-90% of waste is still landfilled. In residential areas, micro and small enterprises (MSEs) collect waste door-to-door while private companies collect from commercial centers and transport the waste to the landfill. However, the collection of recyclables and reuseable waste was dominated by informal collectors. In residential areas, the storage areas were relatively larger, but the storage duration is larger due to scarcity of materials, distance from the main road, and limited informal collectors. In commercial areas, due to higher density of informal collectors, the storage duration was shorter, though space shortage and congestion were the main constraints. Crucially, the study found that collection actors operate without systematic route optimization, relying instead on arbitrary, unstudied route paths. Furthermore, primary collection hubs are allocated arbitrarily without any spatial or logistical engineering studies, resulting in a fragmented network that lacks a centralized hub. This structural inefficiency is further compounded by a critical lack of bailing technology, which results in poor vehicle payload utilization. Additionally, transport schedules fail to account for severe urban traffic constraints; in high density commercial areas, loading and transportation activities overlap with peak daytime hours, severely exacerbating traffic congestion. Consequently, these factors directly lead to high transportation cost. The study concluded that current practices are fragmented, reactive, and inefficient, and increasing logistics costs. To improve efficiency and to support the city transition towards a circular economy, the system should shift from a pull to a push system, prioritize route optimization, adopt differentiated practice based on land use, establish transfer stations for recyclable materials, invest in bailing technologies, collection and transport timings must be strategically shifted to off-peak periods (specifically late evenings, early mornings, or Sundays), promote coordination, integrate digital tools, formally recognize the informal actors, and strengthen source segregation is necessary in both areas.
Impact of Electric Vehicle Evolution on Energy System and Its Emission Reduction
(Addis Ababa University, 2026-06) Abdulaziz Nuredin; Fitsum Salehu
Ethiopia has adopted ambitious electric mobility policies, including fiscal incentives for electric vehicles (EVs) and a ban on the import of internal combustion engine (ICE) vehicles. While these measures position transport electrification as a central pillar of the country’s Climate-Resilient Green Economy strategy, their long-term implications for electricity demand, power system investment, emissions mitigation, and economy performance remain insufficiently quantified. Existing studies largely assess transport electrification on grid stress and limited long term energy planning, EVs transition emission mitigation separately from national climate commitment targets, assuming homogeneous vehicles in transport energy modelling, and limited integration of transport demand growth with EVs diffusion. This study address this gaps by developing one of first Ethiopian–specific integrated transport-energy system model to evaluate impact of electric vehicle (EV) evolution on energy system from 2025 to 2050. Its novelty lies in embedding EV diffusion within fleet growth, power system expansion, and climate mitigation objective within unified analytical framework, to assess energy, environmental, investment and economic outcomes.
The analysis combines transport demand growth with a Gompertz-based vehicle ownership model, policy-based logistic EVs diffusion model, and the Open Source Energy Modeling System (OSeMOSYS) to represent electricity demand for heterogeneous vehicle segment, technology-specific energy consumption and GHG emission, and long-term power system expansion. Three EVs adoption scenarios are examined in addition to businesses as usual (BAU): Moderate Transition, Ambitious National Target and Net-Zero Mobility by embedding them within a baseline entire economy demand. A key assumption underpinning the analysis is that transport demand continue to expand under rapid population growth and urbanizations, requiring de-carbonization to occur alongside with the increasing mobility rather than through demand saturation.
The results indicates that in ambitious and net-zero scenarios transport electrification reduces transport energy demand and emission substantially with larger economic outcome, despite the rapidly increasing electricity demand after 2035. Under net-zero electrification cumulative energy saving reaches 239TWh, while demanding additional capacity expansion of 25GW from the baseline case of 150 GW for entire economy by 2050, whereas the baseline case emission declined by 65%. Even though significant emission mitigation is observed with rate of EVs adoption in all scenarios, with 95% electricity supply from renewables, transport electrification alone is insufficient to meet national NDC target by 2035. The economic return in net-zero scenario saves $44 billion through reduced fuel imported and lower EVs maintenance costs, which can partially offsets the investment requirement for electricity generation of $64 billion by 2050. The findings indicate that transport electrification represents a strategic pathway for enhancing energy security, reducing fuel import dependence, and advancing Ethiopia’s long-term climate goal.
The results provide a quantitative evidence base for policymakers in setting realistic EV adoption targets, aligning transport policies with power sector expansion plans, and prioritizing investments in electricity generation and charging infrastructure. The analysis highlights that achieving national climate objectives requires coordinated action across the transport, energy, industry, and land-use sectors. These insights can support policymakers in designing integrated, cost-effective, and climate-aligned development strategies that maximize the economic and environmental benefits of Ethiopia’s electric mobility transition.
Coupling Electro-oxidation and UV Photolysis for Amoxicillin and Ibuprofen Synthetic Wastewater Treatment
(Addis Ababa University, 2026-07) Solomon Ali Yimam; Shimelis Kebede; Joon Wun Kang (Co-advisor)
The rapid growth and population increase globally have led to a worsening of water pollution. One of the significant environmental concerns is the presence of pharmaceutical pollutants in water. These pharmaceutical pollutants are recalcitrant and are typically found in low concentrations in water matrices. Conventional treatments used to treat municipal wastewaters are ineffective in removing pharmaceuticals. Recently, advanced oxidation processes (AOPs) such as EO and UV have shown promising results in effectively mineralizing pharmaceutical pollutants. The following studies examined the effectiveness operational parameters in active chlorine generation using EO and coupling EO and UV for the treatment of AMOX and IBU. The first study focused on investigating the combined effects of operational parameters on residual chlorine production using response surface methodology (RSM) and artificial neural network (ANN) techniques. The study evaluated the impact of sodium chloride concentration, electrical potential, electrolysis time, and electrode gap on residual chlorine production and energy consumption. The optimal conditions for residual chlorine production were found to be an electrical potential of 8.8 V, an electrolysis time of 25 minutes, a sodium chloride concentration of 25 g/L, and an electrode distance of 1 cm, resulting in a residual chlorine level of 2450 mg/L and an energy consumption of 21.76 kWh/L. It was revealed that electric potential, sodium chloride concentration, and electrolysis time had a positive influence on residual chlorine production. The study also indicated that the ANN models outperformed the RSM models in terms of prediction ability, suggesting the potential use of electrolysis for active chlorine production from saline solutions in industrial and water disinfection applications. In the subsequent experiment, electro-oxidation (EO) of synthetic wastewater containing amoxicillin (AMOX) and Ibuprofen (IBU) was carried out using Ti/IrO2 electrodes in a batch reactor setup. The optimization of the EO process was performed using response surface methodology (RSM) to examine the effects of pH, current density, and initial concentrations of AMOX and IBU on the degradation of pollutants and Chemical Oxygen Demand (COD) removal. The optimal conditions for the degradation of AMOX and IBU were determined to be pH 3, current density of 10 mA cm-2, and initial concentrations of AMOX and IBU at 276 μg/L
TechnoEconomicFeasibilityofInstalling AfloatingSolarPanelSystemOnHdropowerReservoirinEthiopa
(Addis Ababa University, 2026-06) Haleluya Alemayehu; Solomon Tesfamariam
While minimizing land use, floating photovoltaic (FPV) systems are emerging as an effective method to enhance renewable energy production. This study investigate the efficiency and technical feasibility of floating solar PV system integrated with hydropower at the Grand Ethiopian Renaissance Dam (GERD) reservoir. The dam was decided as the case study region due to its enormous water surface and considered importance in Ethiopia’s energy sector. In this work, a floating solar PV system with an installed capacity of 5.06 MWp was designed using PVSyst simulation software. After accounting for paths, pitch, and structural spacing, an area of 2.3% of 1 km² closed water surface area was seen as suitable for FPV installation which is 23,000 m² were utilized for the floating array. The system consists of fifteen central inverters to medium voltage transformer and the GERD high voltage substation at 220 KV. Number of modules are 11,500, 440 Wp solar panels arranged in many sub-arrays. The floating structure uses pontoon technology designed to adjust to the reservoir's water depth variations, which range from 594.16 meters to 623.38 meters, and has an anchoring cable length of 29.22 meters.
According to simulation results, the performance ratio (PR) of 80.18% with system losses of 0.17 kWh/kWp/day. The open reservoir was estimated to lose 96.587 Mm³/year to evaporation. Including floating photovoltaic (FPV) systems reduced evaporation to 89.521 Mm³/year for the small-footprint configuration and 77.270 Mm³/year for the large-footprint configuration. These reductions correspond to annual water savings of 7.066 Mm³/year (7.32%) and 19.317 Mm³/year (20.0%), respectively, demonstrating that FPV coverage can substantially mitigate reservoir evaporation losses.
A Hybrid Two-Stage Neuro-Symbolic Framework for Explainable Recommendation in Sparse Data
(Addis Ababa University, 2026-02) Ermias Alemayehu; Beakal Gizachew
Large-scale recommender systems commonly employ two-stage pipelines in which a fast retriever generates a candidate set and a more expressive reranker produces the final top-K recommendations. In sparse implicit-feedback settings, however, end-to-end performance is constrained by candidate availability, and explanation mechanisms are often weakly connected to ranking decisions. This thesis proposes a hybrid two-stage neuro-symbolic framework for explainable recommendation in sparse data that integrates a LightGCN retriever, train-only symbolic candidate expansion based on association-style co-occurrence rules, and an evidenceaware MLP reranker under a fixed candidate-budget constraint. The framework is evaluated using Candidate Recall@C, HR@K, NDCG@K, explanation coverage (EC@K), and a fidelity-style evidence-removal diagnostic to assess whether symbolic features influence ranking behavior under fixed candidates. Experiments\ on Amazon Books, MovieLens-1M, and Yelp show that the impact of symbolic expansion and explainable reranking is dataset-regime dependent; in retrievalbottlenecked settings, improvements in Candidate Recall@C are accompanied by corresponding gains in HR@K and NDCG@K, consistent with a retrieval-ceiling interpretation of two-stage recommendation. On Amazon Books, the full configuration achieves Candidate Recall@C = 0.3135/0.2527, HR@10 = 0.0698/0.0388, and NDCG@10 = 0.0391/0.0201 on validation/test, with EC@10 approximately 0.86. Overall, the thesis contributes a neuro-symbolic two-stage recommendation framework and an evaluation strategy that jointly analyzes retrieval constraints, ranking effectiveness, explanation coverage, and evidence dependence under fixed
serving budgets.