Advances in Science and Technology Vol. 160

Abstract: This research investigates the application of AI-enhanced structural health monitoring (SHM) systems to predict and optimize the performance and reliability of wind turbines in Sub-Saharan Africa power generation. Supervisory Control and Data Acquisition (SCADA) systems data from a Turkish wind turbine were leveraged to develop a predictive model using the eXtreme gradient boosting (XGBoost) algorithm. Wind speed data from the Turkish wind turbine was substituted with wind speed data of some selected locations in Sub-Saharan Africa (Katsina, Nigeria; Addis Ababa, Ethiopia; Dakar, Senegal; and Cape Town, South Africa). The performance of the models was evaluated using Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R²). The findings show a 0.95% decrease in predicted power output for all the selected locations. The adapted model achieved a MAPE of 1.1% for Addis Ababa, 1.25% for both Cape Town and Katsina, and 1.17% for Dakar, while achieving a high R² of 0.96 for all the locations, indicating high predictive accuracy. In scenarios with high wind speed, Dakar has the highest prediction of 3691.09kW, achieving a 1.03% increase compared to Turkey with a predicted power output of 3583.69kW. Cape Town achieved better prediction accuracy, with a MAPE of 0.78% and R² of 0.98, while yielding a power output of 3545.67 kW. The model achieved F1-Score, Accuracy, sensitivity, precision, and selectivity scores of 99.87%, 99.75%, 99.93%, 99.81%, and 4.85% respectively. This study shows there is vast potential for employing machine learning models in enhancing the operational efficiency of wind turbines. Future study is recommended to incorporate local SCADA data across different wind farms in Sub-Saharan Africa.

Abstract: The integration of renewable energy sources, such as photovoltaic (PV) and wind turbines, has gained significant attention due to the growing demand for reliable and clean energy solutions. This paper presents a comprehensive modelling and optimization approach for hybrid PV and wind turbine systems to maximize system performance at the same time minimizing cost and surplus energy. The proposed model incorporates detailed mathematical formulations that capture the interactions between PV modules, wind turbines, and the storage system which is a battery energy storage system (BESS). The model also considers economic factors and methods to reduce the surplus energy of the system. The optimization scheme utilized the African vulture optimization algorithm (AVOA). The AVOA is a nature-inspired meta-heuristic algorithm created based on the hunting patterns of African vultures. In addition, the AVOA was constructed to handle a multi-objective optimization with size and costs as the objective functions. The optimized system provides the best system size to support the electricity supply of a coastline town (4.7231°N, 6.77881°E). The optimized system can deliver 41.80 GWh of energy annually, meeting 98.3% of the energy demands of the community; while the optimized system has a cost savings of 45.11%, with 92.9% penetration. The work provides valuable insights for system designers, energy planners, and policymakers in their efforts to promote renewable energy integration and address the challenges associated with the transition to a low-carbon future.

Abstract: Solar PV sizing is the process of determining the quantity and capacity of solar PV system components to meet a given energy demand. This process is needed to ensure that the components are not undersized resulting in insufficient energy or oversized increasing the system cost. This study has reviewed seven solar PV sizing frameworks currently in use in the market including intuitive frameworks, numerical, and analytical frameworks. However, these frameworks have neglected some key adaptability factors unique to off-grid areas such as the ability of the household to pay, the roofing structure, and the ability of the system to be relocated. This neglect has seen development of solar PV systems that are beyond the budget of most households in off-grid areas and with specifications that technically inhibit their effective use in the off-grid setup. Therefore, for enhanced adaptability, there is need to develop a new solar PV sizing framework that considers the unique adaptability factors of off grid areas. This study identified these unique adaptability factors and categorized them as economic, technical, location and mobility factors. The study further investigated how these factors influence the size of a solar PV system and revealed that economic, factors of income and system cost play a major role in sizing an adaptable solar PV system. Demand as a technical factor was also found to significantly influence the system size while temperature and irradiation as location factors were found to be significant when comparing systems that need to be used in different geographical areas. Mobility factors such system type was revealed to influence the adaptation of the PV system in nomadic setups. Through the modification of the existing numerical sizing framework, these adaptability factors were integrated in the sizing process within the context of this study. It was established that by integrating these factors, the resultant PV systems were more adaptable to off-grid areas in terms of cost, mobility, durability and reliability.

Abstract: Nigeria fondly regarded as the ‘Giant of Africa′ is the most populous country in Africa and the largest economy on the continent. However, despite the abundance of renewable energy sources, Nigeria has been identified as one of the countries with the largest energy access deficit in the world. This study therefore sought to elucidate various off-grid renewable energy opportunities that could be harnessed to engender rural electrification. The study adopted an exploratory research methodology to identify tenable off-grid renewable energy solutions that could address the prevailing energy deficit in Nigerian agro-rural communities. This study found that off-grid renewable energy solutions such as solar, biomass, hydro and wind technologies are cost-effective alternative energy sources with immense potential to enhance agro-rural community development in Nigeria significantly.

Abstract: This study evaluates the energy efficiency, cost-saving opportunities and nearly zero emissions for the residential sectors of Ghana and Nigeria, focusing on three household appliances: fridges, freezers and bulbs. It analyses the integration of renewable energy systems in residential households, comparing energy-efficient and non-energy-efficient scenarios in both countries. The study uses field data, existing data on RETScreen software and analytical methods to examine the energy consumption patterns of key household appliances and their impact on renewable energy system design, cost, and emissions reduction. As for the energy consumption patterns, Ghana showed higher daily usage of household appliances, with longer operational hours for fridges and freezers averaging 20.3 hours/day, while Nigeria had lower daily usage, averaging 11.8 hours/day for similar appliances. For Nigeria, the annual electricity cost in the non-energy-efficient scenario is approximately 109.69 USD, while for energy-efficient households, the cost is reduced to 79.31 USD. In Ghana, non-energy-efficient households spend around 379.97 USD annually, compared to 317.55 USD for energy-efficient homes. The results highlight the significant cost-saving opportunities of adopting energy-efficient technologies. This higher energy demand in Ghana and its higher electricity tariffs lead to greater overall consumption and higher costs. Despite similar appliance wattages, Nigeria's lower tariff results in comparatively lower energy expenses. Using the energy consumption patterns for both countries under the energy-efficient and non-energy-efficient scenarios, system sizing for solar PV and battery storage was conducted to know the economic viability of renewable energy integration through Levelized Cost of Energy (LCOE) and Net Present Value (NPV) assessments. In both scenarios, the payback period for solar PV and battery systems in Nigeria is 22 years, making it not economically viable under current electricity tariffs. In contrast, Ghana shows a payback period of 10.3 years, making solar PV systems financially viable. The RETScreen simulation examined two important scenarios for energy efficiency in Nigeria and Ghana: compact fluorescent lamps (CFLs) as the baseline and LED lighting as a proposed alternative. The results show different GHG (greenhouse gas) reduction equivalences for the number of automobiles that are not driven, the number of individuals who cut their energy use, the number of hectares of forests that absorb GHG, etc. The study's conclusions highlight the significance of energy efficiency in lowering overall energy usage, tariff rates, and expenses associated with solar and battery systems. The results have also provided more insights for both countries to create more comprehensive policies that will encourage adopting energy-efficient practices and make it cheaper for homes to integrate and use renewable energy systems.

Abstract: Universities are significant energy consumers, and effective energy management is important for sustainability and cost reduction. This article explores the role of energy audits in helping universities improve their energy management and sustainability practices. It analyzes research from 2010 to 2024 to identify various energy auditing techniques, tools, and methodologies used by different institutions. The article demonstrates how diverse auditing approaches can uncover energy inefficiencies and propose practical solutions. Key findings indicate that thorough energy audits are important for promoting sustainability goals through improved energy efficiency and waste reduction. However, universities face challenges in implementing audit recommendations due to infrastructure limitations and technological constraints. The review emphasizes the essential role of energy auditing in enhancing both environmental and economic performance and underscores the importance of ongoing innovation and adaptation in energy auditing practices. In conclusion, the article suggests exploring new technologies and improving auditing techniques to better support sustainability efforts in universities.

Abstract: Energy harvesting is an effective technique for optimizing Wireless Body Area Network (WBAN) devices used for continuous healthcare services delivery. Despite the growing popularity of WBANs in recent years due to their potential to transform healthcare, energy consumption remains a critical issue. This is due to several factors such as the limited capacity of batteries in smaller sensor nodes, the continuous operation that drains batteries and renders the nodes inoperable, and the impracticality of replacing batteries in situations where the sensors are implanted in the human body and would require surgical procedures to remove them. Thus, the need for research into scavenging, harvesting and utilizing available energy sources. This work proposed energy optimization of WBAN using Time Division Multiple Access (TDMA) duty cycling and thermal energy harvesting. The proposed model aims to enhance energy efficiency in a WBAN using TDMA and Thermoelectric Harvesting (TEH) techniques. At the heart of this model is an IoT controller that runs on a single-sensor activation principle at all times, controls the sensor function and stores the sensor data in its internal memory (buffer), enabling efficient data management and transfer. The TDMA scheduling ensures that multiple sensors are engaged in a coordinated manner whereby a node is enabled only when needed reducing idle time, network collisions and contention, hence contributing to energy savings which is critical to our energy optimization plan. The proposed optimization model shows a 52.40% improvement in the energy conversation of the WBAN device, thus increasing the battery’s useful life by more than 50%.

Abstract: Decarbonisation is instrumental in attaining sustainable mobility. To actualize the Ghana Nationally Determined Contribution (NDC) set emission reduction target of 15% relative to Business-As-Usual (BAU) scenario by 2030, sustainable transport actions should be encouraged. Thus, promoting the use of Liquified Petroleum Gas (LPG) and Compressed Natural Gas (CNG) in automobiles is very crucial to ensure efficient and green mobility. Nonetheless, existing policies in Ghana overlook autogas (LPG/CNG) as prospective decarbonizing solution in the transport sector. The study employed survey analysis and analytical modeling approach to elicit the benign effects of autogas in the transport sector using Accra as a case study. The ecological, economic and social dimensions of autogas were expatiated to extrapolate effective measures to facilitate their smooth implementation. Survey was carried out in the central business district of Accra to attain the percentage of autogas and gasoline used by taxis operators for the first time per author’s knowledge. A purposive sample of 500 taxi drivers was selected and data analysis was conducted using R statistical package. From the survey, 14% of taxis were powered with LPG whilst 86% were gasoline, however, the LPG-powered taxis were retrofitted gasoline engine vehicles. The analytical model was based on physics principles involving three resistance forces- aerodynamic, rolling resistance and inertia. CO₂ emission savings of 29% and 18.4% were elicited from the use of CNG and LPG relative to gasoline fuel at the end of the simulation using the ambient conditions in Accra. Thus, use of autogas will limit global warming impact and aid the country to fulfill its pledged emission target by 2030. The government is entreated to regulate autogas use in the transport sector and increase its patronage by promoting flexible policies like meager custom duty on imported CNG/LPG vehicles as well as tax credits.

Abstract: Chitosan/clay materials derived from periwinkle shells and clay soil at a 50:50 ratio were prepared as adsorbents, characterized, and used for the adsorption of CO₂ from flue gas at elevated temperatures (500°C - 5000°C) in a fixed bed column (1.5 m in length and 0.02 m in internal diameter). The flue gas, with a composition of Methane (0.003), Ethane (0.002), Hydrogen (0.05), CO2 (0.15), Water Vapor (0.02), and Nitrogen (0.76), at a pressure of 49 KPa, a temperature of 5000°C, and a flow rate of 75 L/min from the exhaust tank, entered the fixed bed column for the adsorption process, where the adsorbent had already been placed. Fourier Transform Infrared spectroscopy revealed the presence of halogen, alcohol, nitro, and amine compounds in the nanoparticles, indicating a strong affinity for CO₂ particles in the flue gas. Additional analysis showed the presence of elements (Ca, Si, Al, and Sr) in significant compositions (0.470, 0.202, 0.186, and 0.092, respectively), suggesting that the adsorbent was resistant to high temperatures. X-ray diffraction analysis of the adsorbent identified Ca(OH)₂, CaCO₃, and TiO₂ with compositions of 0.78, 0.19, and 0.026, respectively, further confirming the strong affinity of the adsorbent for CO₂. Surface morphology analysis revealed that the adsorbent’s surface was rough and contained a variety of pores or holes with different capacities, indicating that more CO₂ was captured and accommodated within the surface. Thermal analysis using the Barrett-Joyner-Halenda method showed that the adsorbent could withstand high temperatures of up to 9000°C. At this temperature, the adsorbent accounted for only about 18% of the material that entered the fixed bed column for adsorption, but 100% of it could remain active within the temperature range of 0°C - 3000°C. The characterization of the adsorbent showed that a pore width of 5.283 nm, a pore diameter of 2.64 nm, a micropore surface area of 434.7 m²/g, a pore volume of 0.202 cc/g, and a porosity of 56.73% were the optimal values for the adsorbent. Finally, it was revealed that 95% of CO₂ was adsorbed at optimal conditions within the temperature range of 500°C - 3500°C, time range of 0.5 - 5 hours, and bed height range of 1 - 6 cm.

Abstract: This paper discusses the feasibility of geothermal power as a sustainable solution within the agri-food chain focusing on relational to global decarbonization plans. As the world shifts focus towards sourcing Renewable energy for minimal emission of greenhouse gases. Geothermal energy is receiving a lot of attention for its reliability. The Geotto system, which was piloted in the Eburru community of Kenya, showed how geothermal energy – IoT – AI interaction could be utilized in agricultural economies to the optimal level. The project of developing locally-owned incubators powered by geothermal energies helped to increase the hatch ratios as well as the energy, but not the fossil fuel consumption. The cross-sectional utilization of this resource in, for instance, crop drying, greenhouses, and fish farming was discussed as a prospect. Some of the challenges that were observed in Geotto’s case are technical barriers and the opposition to the adoption of new technology by the public and relevant authorities. The Geotto team tackled these problems in cooperation with the community and local government. From this pilot study, it is evident that geothermal power holds promise and value to be pursued as a long-term investment in the pursuit of higher food productivity with less pollution in addition to aiding the case for decarbonization. The pilot findings indicate that the kind of systems being implemented can likely be extended to other agricultural segments to support grassroots initiatives and strengthen world economies.