Abyssinia Journal of Engineering and Computing

Analyze Climatic and Edaphic Factors for Ethiopia Cotton Production and Quality

2025-01-10T19:06:07+00:00

Cotton is crucial to Ethiopia's economy, providing livelihoods through production, processing, and trade, impacting millions nationwide. The demand for cotton is projected to rise significantly due to expanding spinning mills and textile industry parks. However, Ethiopia continues to import cotton due to insufficient domestic production and quality issues. Factors such as soil
fertility, type, temperature, and rainfall are pivotal in determining cotton production and quality, akin to other agricultural crops. Understanding these climatic and demographic variables is essential for identifying optimal locations for cotton cultivation. This study delves into how Ethiopia's climatic and soil conditions affect cotton quality and yield. The research underscores climate
change's profound impact on cotton output. Lower temperatures during germination phases reduce productivity, while adequate rainfall is critical for growth and flower maturation. Conversely, excessive rainfall during fiber maturation and harvest diminishes productivity and quality. Elevated temperatures during the seedling, growth, and blooming phases benefit cotton production when accompanied by sufficient rainfall. Conversely, high evaporation rates, elevated temperatures, low humidity, and minimal rainfall negatively correlate with cotton flower and boll production, possibly due to water stress induced by evaporation. In conclusion, selecting suitable areas for cotton cultivation in Ethiopia requires a thorough understanding of how climatic and soil conditions influence productivity. The findings from this research can empower farmers to make informed decisions about cotton farming practices, potentially leading to improved production outcomes and sustainability. Addressing these challenges and leveraging favorable climatic conditions could reduce Ethiopia's reliance on cotton imports, bolstering the local cotton industry and benefiting both the economy and livelihoods. Continued research and targeted agricultural strategies are essential for navigating climate variability complexities and optimizing cotton cultivation practices in Ethiopia.

Attribution and Streamflow Sensitivity Analysis in the Upper Blue Nile River Basin Using a Top-down Modeling Framework

2025-01-10T19:44:38+00:00

This study applied the Budyko framework and elasticity method to quantify the relative contribution of climate and watershed characteristics to streamflow changes in 95 watersheds in the Upper Blue Nile River Basin (UBNRB). The Water and Energy Processes (WEP) model was successfully verified and used to simulate the streamflow and potential evapotranspiration of the watersheds. The study period was divided into base (1983-1998) and change (1999-2018) periods based on Pettitt's test to assess the changes in streamflow. Precipitation showed moderate fluctuations during the study period (1983-2018) with an increasing trend. However, potential evapotranspiration exhibited very low fluctuation, leading to a lower contribution to streamflow changes than precipitation. The watershed characteristics coefficient (ω) decreased overall during the study period. Nearly 81% of the UBNRB watersheds had aridity index values between 0.6 and 1.1, indicating humid conditions. Watersheds with higher aridity index and lower ω were more sensitive to streamflow change. Streamflow in the UBNRB tended to increase more due to changes in watershed characteristics than due to climate variations. Nearly 68% of the watersheds showed an increase in streamflow during the change period due to the combined effects of climate and watershed characteristic variations. Overall, the Eastern and Southern source regions and the
Northwestern lowlands of the basin were highly affected by these changes. These areas need more attention to sustainably manage the basin's water resources.

Printing of Cotton Fabric with Reactive Dye using Eco-Friendly Natural Thickener

2025-01-10T20:02:24+00:00

The primary objective of this research was to investigate the use of a thickening agent derived from a protein-based product and mixed with sodium alginate. Additionally, the study aimed to compare this combination with fabric printed using sodium alginate alone. Using native starch-based thickeners in various industrial applications presents significant challenges, such as low water solubility and difficulties in crosslinking with reactive colors due to the high concentration of hydroxyl groups on the molecules. The print paste was prepared with SA/GT at five different mixing ratios to evaluate the impact of these ratios on the viscosity of the printing paste and the physical qualities, color strength, and fastness properties of the printed cotton fabric. The research findings indicated an enhancement in the color strength and color fastness properties of the printed cotton fabric. However, some physical properties exhibited a slight deterioration as the gelatin content in the thickener mixture increased. Encouraging outcomes were observed when sodium alginate and a gelatin-based thickener were combined in a 50:50 ratio.

Analysis and Prediction of Electric Energy Consumption Using a Deep Learning Approach: A Case Study of the Dessie District

2025-01-10T20:10:48+00:00

Predicting electric power consumption is essential for modern energy management, addressing challenges like cost optimization, resource allocation, and sustainability. This study offers a thorough analysis of power consumption prediction to tackle the prevalent issue of inaccurate energy usage forecasts. A real dataset from the Ethiopian Electric Utility in the Dessie district, covering the years 2019 to 2023, forms the foundation of this research. Using advanced deep learning models, specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM. This study proposes a robust methodology based on cutting-edge neural network architecture. The research includes detailed experimentation in data preprocessing, feature extraction, model development, and evaluation to showcase the potential of these models to transform energy management. The findings highlight these models' capabilities to improve operational efficiency, reduce costs, and enhance grid management. Despite challenges such as model overfitting and the need for precise hyperparameter tuning, model performance is evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Among the models, GRU demonstrated superior performance with minimal prediction error: 0.105 for MSE, 0.21 for RMSE, and 0.018 for MAE on testing data. This study emphasizes the potential of deep learning models to drive advancements in the energy sector, despite the existing challenges.

Numerical and Experimental Performance Study of Bladeless Wind Turbine

2025-01-10T20:21:35+00:00

Bladeless Wind Turbines (BWTs) represent an innovative and environmentally friendly approach to wind energy conversion, utilizing vortex-induced vibrations rather than traditional blades. This study aimed to evaluate the performance of BWTs by investigating key aerodynamic parameters through both numerical simulations and experimental methods. A comprehensive 3D analysis was conducted using the k-ω SST turbulence model in ANSYS FLUENT, alongside a 2D Fast Fourier Transform (FFT) analysis in Tecplot. These analyses provided valuable insights into critical factors such as frequency synchronization, amplitude ratios, and force coefficients. A prototype was 3D-printed and tested in a wind tunnel to validate the theoretical findings. The experimental results demonstrated a maximum amplitude ratio of 0.155 and a nominal power output of 0.43 milliwatts at a wind speed of 3 meters per
second, suggesting significant potential for small-scale applications. Aditionally, direct amplitude measurements were taken using a custom-designed stand to corroborate the 2D FFT results. The overall findings indicate that BWTs could serve as effective alternatives for urban environments, where traditional wind turbines may not be feasible The findings highlight BWTs as promising alternatives for urban settings, with further optimization needed for increased efficiency.