School of Engineering and Technology (JA)

Performance evaluation of HEC-HMS for streamflow simulation in a poorly gauged semiarid catchment: Perkerra River, Kenya

2026-01-01T00:00:00Z

Performance evaluation of HEC-HMS for streamflow simulation in a poorly gauged semiarid catchment: Perkerra River, Kenya Jeptum, Irine; Raude, James M.; Kiptala, Jeremiah K.; Cheruiyot, Charles K. Hydrological models are essential for water resources planning, infrastructure development, and drought or flood risk management, yet their application in semiarid, data-scarce catchments remains challenging due to high spatial variability and limited observations. This study evaluated the performance of the HEC-HMS model for streamflow simulation in the Perkerra River catchment, Kenya. Model calibration was conducted using observed streamflow data from the Perkerra gauging station for the period January 2019 to December 2022, while validation was performed using data from January to December 2023. The validated and optimized model was subsequently applied to simulate streamflow at the Tigeri and Narosura stations within the catchment. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), percent bias (PBIAS), RSR, and coefficient of determination (R2 ). NSE values 0.682 and 0.706 with low PBIAS values −4.45 and −1.80% during calibration and validation, respectively, indicated acceptable performance. Sensitivity analysis showed that baseflow parameters were the most influential, followed by impervious area, curve number, time of concentration, and storage coefficient. Overall, the results demonstrated that HEC-HMS could reliably simulate streamflow in the Perkerra catchment and had strong potential for application in poorly gauged semiarid basins, enabling drought preparedness. doi: 10.2166/wcc.2026.061

Demand response-fuzzy inference system controller in the multi-objective optimization design of a photovoltaic/wind turbine/battery/supercapacitor and diesel system: Case of healthcare facility

2023-09-01T00:00:00Z

Demand response-fuzzy inference system controller in the multi-objective optimization design of a photovoltaic/wind turbine/battery/supercapacitor and diesel system: Case of healthcare facility Megaptche, Christelle A.; Musau, Peter M.; Tjahè, Agnès V.; Kim, Hanki; Waita, Sebastian; Aduda, Bernard O. One of the most common causes of power outages in developing countries is a global mismatch between supply and demand. The effects of this phenomenon are especially devastating in the healthcare sector. This paper describes the management of the loads' operation using Demand Response-Fuzzy Inference System Controller (DR-FIS) for the sizing optimization of photovoltaic/wind turbine/battery/supercapacitor and photovoltaic/wind turbine/battery/diesel generator systems operating autonomously in a health center in northern Cameroon using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) methods. The assessment criteria for this optimization are Loss of Power Supply Probability (LPSP), Net Present Cost (NPC), Cost of Energy (COE), Total Greenhouse gases Emission (TGE), Wasted Energy (WE), and Renewable Generation (REG). Implementing a Demand Response-Fuzzy Inference System controller (DR-FIS) has allowed significant energy savings (15.4130% reduction in energy demand) and increased worldwide supply–demand adequacy. This study highlights the techno-economic and environmental significance of using a supercapacitor (SC) as a backup in contrast to a diesel generator (DG), as well as the validation of its compatibility with storage batteries because of the provision of a robust energy management approach. Finally, in this study, MOGA results in better results than MOPSO after evaluating the outcomes of the various multi-objective optimization methods. This strategy enabled the determination of the ideal configuration for the studied Healthcare Center's power supply. This configuration includes the Demand Response-Fuzzy Inference System. It consists of 20 solar panels (PV), 02 wind turbines (WT), 04 batteries (BT), and 07 supercapacitors (SC) for a COE of 0.1691 $/kWh, a NPC of 1.1808e + 03 $, a TGE of 439.7901 Kg, a WE of 4.0066e + 03 Kwh, 100% REG and unfortunately 0.9858 % LPSP. https://doi.org/10.1016/j.enconman.2023.117245

An interdisciplinary overview on biochar production engineering and its agronomic applications

2024-11-01T00:00:00Z

An interdisciplinary overview on biochar production engineering and its agronomic applications Muema, Faith M.; Richardson, Yohan; Keita, Amadou; Sawadogo, Marie Biochar is a porous, carbon-rich material derived from the thermochemical decomposition of biomass materials. Biochars are suitable soil amendments that enhance soil properties and improve crop productivity. Biochar agronomic impact in soils depends on its physiochemical properties. Recent research has shown that feedstock type and pyrolysis temperature are the key factors influencing biochar physiochemical properties. However, an in-depth understanding of the biochar-soil-plant co-relationship governing biochar agronomic performance still needs improvement. A comprehensive overview of the effect of biomass and pyrolysis temperature on biochar properties, mechanisms governing biochar-soil interactions impact on agronomic indices, the long-term effect of biochar, and the viability of large-scale biochar agricultural systems have been discussed. The mechanisms governing the impact of temperature and biomass properties on biochar agronomic properties are different for low temperature (<500 °C) and high temperature (>500 °C). The agronomic benefits of biochar are dependent on biochar-soil-plant interaction mechanisms. The economic and financial feasibility of large-scale production of biochar is case-specific and makes business sense when all co-pyrolysis products are recovered and sold. Understanding biochar-soil-plant-climate interaction mechanisms is key to designing biochars to address specific agronomic needs and requires an interdisciplinary and multiscale approach. Future studies should focus on long-term co-relationships among biochar physiochemical properties, soil conditions, climate, and farm management. https://doi.org/10.1016/j.biombioe.2024.107416

21 - Challenges and future prospects of coated fiber–reinforced polymer composites

2025-01-01T00:00:00Z

21 - Challenges and future prospects of coated fiber–reinforced polymer composites Atalie, Desalegn; Rotich, Gideon K. Coated fiber–reinforced polymer (FRP) composites have attracted attention due to their superior mechanical properties and flexibility. Advances in coating-FRP are improving performance in areas such as interfacial bonding, environmental resistance, and multifunctionality. However, there are still issues in attaining uniform coating thickness, adherence, and long-term durability. This review briefly discusses the current research on coated FRP composites, focusing on recent advancements, preparation techniques, properties, current challenges, and future prospects. DOI:10.1016/B978-0-443-22029-6.00023-X