Browsing by Author "Oni, A. O.|"

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Assessment of household energy utilization in Ibadan, Southwestern Nigeria
(Scientific Research, 2012) Waheed, M. A.; Oni, A. O.|; Fadare, D. A.; Sulaiman, M. A.
Energy and exergy analysis was conducted for a vegetable oil refinery in the Southwest of Nigeria. The plant, powered by two boilers and a 500 kVA generator, refines 100 tonnes of crude palm kernel oil (CPKO) into edible vegetable oil per day. The production system consists of four main group operations: neutralizer, bleacher, filter, and deodorizer. The performance of the plant was evaluated by considering energy and exergy losses of each unit operation of the production process. The energy intensity for processing 100 tonnes of palm kennel oil into edible oil was estimated as 487.04 MJ/tonne with electrical energy accounting for 4.65%, thermal energy, 95.23% and manual energy, 0.12%. The most energy intensive group operation was the deodorizer accounting for 56.26% of the net energy input. The calculated exergy efficiency of the plant is 38.6% with a total exergy loss of 29,919 MJ. Consequently, the exergy analysis revealed that the deodorizer is the most inefficient group operation accounting for 52.41% of the losses in the production processes. Furthermore, a critical look at the different component of the plant revealed that the boilers are the most inefficient units accounting for 69.7% of the overall losses. Other critical points of exergy losses of the plant were also identified. The increase in the total capacity of the plant was suggested in order to reduce the heating load of the boilers. Furthermore, the implementation of appropriate process heat integration can also help to improve the energy efficiency of the system. The suggestion may help the company to reduce its high expenditure on energy and thus improve the profit margin.
Modeling of solar energy potential in Africa using an artificial neural network
(Science Hub Publishing, 2010) Fadare, D. A.; Irimisose, I.; Oni, A. O.|; Falana, A.
In this study, the feasibility of an artificial neural network (ANN) based model for the prediction of solar energy potential in Africa was investigated. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using NeuroSolutions®. Geographical and meteorological data of 172 locations in Africa for the period of 22 years (1983-2005) were obtained from NASA geo-satellite database. The input data (geographical and meteorological parameters) to the network includes: latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity while the solar radiation intensity was used as the output of the network. The results showed that after sufficient training sessions, the predicted and the actual values of solar energy potential had Mean Square Errors (MSE) that ranged between 0.002 - 0.004, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available in Africa. The predicted and actual values of solar energy potential were given in form of monthly maps. The solar radiation potential (actual and ANN predicted) in northern Africa (region above the equator) and the southern Africa (region below the equator) for the period of April – September ranged respectively from 5.0 - 7.5 and 3.5 - 5.5 kW h/m2/day while for the period of October – March ranged respectively from 2.5 – 5.5 and 5.5 - 7.5 kW h/m2/day. This study has shown that ANN based model can accurately predict solar radiation potential in Africa.