FACULTY OF TECHNOLOGY
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Item Modelling and forecasting periodic electric load for a metropolitan city in Nigeria(2012-01) Eneje, I. S.; Fadare, D. A.; Simolowo, O. E.; Falana, A."In this work, three models are used to analyze the electric load capacity of a fast growing urban city and to estimate its future consumption. Ikorodu, the case-study location is a highly populated city whose energy demand is continuously increasing. The ultimate focus of this study is to establish a basis for the comparison of different electric load consumption for the existing populace and to provide estimates for the future planning of the city. In this work, three different models have been used to present more accurate load predictions and to enhance proper comparison of results. Among numerous mathematical and scientific models that are applicable to this kind of task, the compound-growth method, the linear model approach and the cubic model have been chosen to enhance diversity in load analysis. The futuristic scheme to be harnessed will fall within the ranges of values obtained from the three different models used in forecasting. This paper concludes with issues pertaining to economics of load utilization as it affects substantive planning. "Item Performance evaluation of uncoated carbide cutting tools in turning NST 37-2 steel.(2009) Fadare, D. A.; Asafa, T. B.In metal cutting operations, performance of the cutting tool is the major determinant of the productivity, functionality and production cost of the machined component. In this work, the performance of uncoated cemented carbide tools with International Standard Organisation (ISO) designation SNMA 120406 was evaluated for turning of NST 37.2 steel. Turning operations were conducted on M300 Harrison type lathe driven by 3.0 hp Kapak induction motor. The cutting conditions used were: cutting speed, 20.42, 29.06 and 42.42m/min; feed rate, 1.0, 1.8 and 2.2 mm/rev; and depth of cut, 0.2, 0.4 and 0.6 mm under dry machining. Results showed that both flank and nose wear increased with increase in number of pass, cutting speed, depth of cut and feed rate. The optical surface roughness of the machined workpiece varied from 0.658 - 0.924 and case hardening of the machined surface was observed. Segmented chips with smaller coil radii, which were less voluminous and more manageable were produced at all cutting conditions investigated. Chip breakability tends to increase with increase in cutting speed. The use of uncoated carbide tools has proved to enhance the productivity and surface quality in turning of NST 37-2 steel.Item Organic and organo-mineral fertilizer from wastes(2006-11) Sridhar, M. K. C.; Adeoye, G. A.; Fadare, D. A.; Bamiro, O. A.Item Briquetting of wood and agricultural wastes for energy production(2005) Igbeka, J. C.|; Popoola, L.; Ajayi, S. S.; Onilude, M. A.; Olorunisola, O. A.; Raji, A. G.; Afrifa, E. S. D.; Fadare, D. A.Item Trends of energy input in some Nigerian palm oil mills(2011) Fadare, D. A.; Oni, A. O.; Fadara, T. G.Energy audit was conducted using the energy accounting method in seven palm oil processing mills. The mills were stratified into small, medium and large categories based on the levels of mechanization and daily production capacity. The production process in three mill categories was divided into eight defined unit operations: bunch transportation, detachment and plucking, bunch sterilization, fruit digestion, pulp pressing, oil clarification, oil drying and oil packing. The energy (electricity, thermal and labour) consumption in each unit operation for processing 1,500 kg of fresh fruit bunch was evaluated. Results showed that the total energy intensity in the palm oil processing plants reduced with increase in levels of mechanization and daily production capacity from 344.98 MJ/tones in the small-scale plants to 252.43 MJ/tones in the large-scale plants. Percentage share of electrical energy in the total energy reduced from 96.73 to 95.06, while the thermal energy reduced from 3.27 to 1.84-%. The two identified energy intensive operations in palm oil processing are bunch transportation and fruit digestion, which accounted for over 90% of the total energy consumption in all the three mill categories. The use of fiber sludge as alternate source of energy for the boiler was recommended to reduce the cost of energy.Item Prediction of friction losses in spark-ignition engines: an artificial neural networks approach(2011) Fadare, D. A.; Idialu, E. E.; Igbudu, S. O.Artificial Neural Networks (ANNs) area prormsmg alternative to conventional tools in modeling and prediction of complex and non-linear parameters. However, the selection of appropriate network parameters for optimum performance pose application challenges. In this study, the modeling and predictive performances of six backpropagation learning algorithms: Levenberg-Marquardt (LM), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Fletcher-Powell Conjugate Gradient (CGF), Variable Learning Rate Backpropagation (GDX) and Bayesian Reglarization (BR) in solar radiation forecast were investigated. Multilayer perceptron (MPL) neural network with five, ten and one neuron(s) in the input, hidden and output layers, respectively was designed with MATLAB® neural network toolkit and trained with the six learning algorithms using the daily global solar radiation data of Ibadan (Lat. 7.4° N; Long. 3.90 E; Alt. 227.2m), Nigeria. The network performance was ranked based on the number of iterations required for convergence, and coefficient of correlation (r-value), mean square error (MSE) and mean absolute percentage error (MAPE) between the actual and predicted values of the training and testing datasets. Results showed that the LM and BR learning algorithms are the two best algorithms to be considered for use in modeling and forecasting of solar radiation data.Item Performance ranking of artificial neural network learning algorithms in solar radiation forecast(2010) Fadare, D. A.; Asafa, T. B.Artificial Neural Networks (ANNs) area prormsmg alternative to conventional tools in modeling and prediction of complex and non-linear parameters. However, the selection of appropriate network parameters for optimum performance pose application challenges. In this study, the modeling and predictive performances of six backpropagation learning algorithms: Levenberg-Marquardt (LM), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Fletcher-Powell Conjugate Gradient (CGF), Variable Learning Rate Backpropagation (GDX) and Bayesian Reglarization (BR) in solar radiation forecast were investigated. Multilayer perceptron (MPL) neural network with five, ten and one neuron(s) in the input, hidden and output layers, respectively was designed with MATLAB® neural network toolkit and trained with the six learning algorithms using the daily global solar radiation data of Ibadan (Lat. 7.4° N; Long. 3.90 E; Alt. 227.2m), Nigeria. The network performance was ranked based on the number of iterations required for convergence, and coefficient of correlation (r-value), mean square error (MSE) and mean absolute percentage error (MAPE) between the actual and predicted values of the training and testing datasets. Results showed that the LM and BR learning algorithms are the two best algorithms to be considered for use in modeling and forecasting of solar radiation data.Item Development of indigenous manufacturing infrastructure in Nigeria: a case: study of the pace-setter organic fertilizer plant(2008) Fadare, D. A.; Bamiro, O. A.; Adeoye, G. O.; Sridhar, K. C.This paper presents the overview of the research and development (R&D) of the Pace-setter organic fertilizer plant. The plant, is owned, funded and managed by the Oyo State Government through the Ministry of Environment. The plant is located at the Bodija Market in Ibadan North Local Government area. The 10 tons/day capacity plant, designed and constructed (using locally sourced materials, was installed and commissioned in the year 1998. About 35 - 50 tons/day of solid waste consisting of Market Refuse (MR) and Abattoir Waste (AW) generated within the market are used as raw materials for the production of organic fertilizer. The plant is semi-mechanised as sorting and turning are done manually while the processing of the compost into finish products is done mechanically. The processing plant consists of six different units: shredding, screening; pulverizing, mixing, pelletising and bagging. Two grades of organic fertilizer (A and B) are produced in the plant. Grade A is fortified, grade B is unfortified. Both grades are produced in either powder or pellet form. The estimated man-power and electric-energy requirement of the plant are about 25 persons and 70KW respectively. A 50 kg bag of grade 'A' organic fertilizer is sold for about #700, while the unfortified grade 'B' is sold for about #500 per bag. The plant has proven to be commercially viable in terms of employment and income generation and equally as sustainable solution to the problem of solid waste management.Item Organic fertilizer use in Nigeria: our experience(Department of Agronomy, Univeristy of Ibadan, 2000) Omueti, J. A. I.; Sridhar, M. K. C.||Adeoye, G. O.||Bamiro, O.||Fadare, D. A.; Adeoye, G. O.; Bamiro, O.; Fadare, D. A.Item 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.