scholarly works
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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.Item Energy and exergy analysis of a vegetable oil refinery(Scientific Research, 2012-09) Sulaiman, M. A.; Oni, A. O.; Fadare, D. 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.Item Comparative analysis of different inocular conditions on the performance of a bioreactor in the treatment of operationally exhausted metal working fluids (MWFs)(2011-01) Ogunjobi, A. A..; Fadare, D. A.; Dauda, O. F.; Fagade, O. EThis work studied the biodegradation of the spent MWFs by using two inocular conditions: Indigenous microorganisms and seeding with Pseudomonas and Bacillus species (both earlier isolated from MWFs) both in a locally designed bioreactor. The performance of each inoculum condition was monitored by total viable bacteria counts, chemical oxygen demand (COD), biological oxygen demand (BOD), total organic carbon (TOC), oil and grease content and heavy metals residue over a 15-day period. For indigenous organisms alone, the total viable counts ranged from 30 × 107 to 5 × 103 cfu/ml; COD, BOD & TOC showed reduction of approximately 34%, 37% and 24% respectively; oil and grease showed approximately 8% reduction and the results for heavy metal residues showed about 20% reduction for all heavy metals analyzed (Cu, Pb, Cd, Cr). Inoculation with the laboratory isolates showed high total viable counts throughout from initial 10 × 107 cfu/ml to 3 × 107cfu/ml at the end of the period; COD, BOC, and TOC showed 44%, 51% and 37% reduction respectively; oil and grease content was reduced by 33% and result for heavy metals showed over 30% reduction for Pb and Cu while Cd and Cr were below 20% reduction. The results showed that bio-augmentation with the laboratory isolates performed better than indigenous microorganism in the degradation of MWFs.Item Solving difference equations by forward difference operator method(Asian Research Publishing Network, 2010-07) Odior, A. O.; Charles-Owaba, O. E.; Fadare, D. A.In this paper a forward difference operator method was used to solve a set of difference equations. We also find the particular solution of the nonhomogeneous difference equations with constant coefficients. In this case, a new operator call the forward difference operator Δr,s, defined as Δr,s yn = r yn+1 - s yn, was introduced. Some of the properties of this new operator were also investigated.Item Application of Neuro- Fuzzy to palm oil production process(Nigerian Association of Mathimatical physics, 2009-11) Odior, A. O.; Fadare, D. A.Palm oil is an important nutritional food requirement and in order to facilitate the production of palm oil for consumption, the production process of palm oil has been investigated. The basic operations involved in the production of edible palm oil include; purchase, transportation and reception of oil palm bunches; bunch threshing and fruit fermentation; sorting and weighing of oil palm fruits; boiling, digestion and pressing of palm oil fruits; clarification and drying of palm oil and palm oil storage. A Neuro-Fuzzy model was used to analyze the performance of palm oil production process as it affects the basic operations involved in the production of edible palm oil. The research work can be applied to any other small or medium scale production firm for better efficiency.Item A statistical analysis of wind energy potential in Ibadan, Nigeria, based on weibull distribution function(Akamai University, 2008-06) Fadare, D. A.Modeling of wind speed variation is an essential requirement in the estimation of the wind energy potential for a typical site. In this paper, the wind energy potential in Ibadan (Lat. 7.43°N; Long. 3.9°E; Alt. 227.2m) is statistically analyzed using daily wind speed data for 10 years (1995-2004) obtained from the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. The daily, monthly, seasonal, and yearly wind speed probability density distributions are modeled using Weibull Distribution Function. The measured annual mean wind speed in Ibadan is 2.75 ms-1, while mean wind speed and the power density predicted by the Weibull probability density function are 2.947 m/s and 15.484 Wm-2, respectively. Ibadan can be classified as a low wind energy region. The coefficient of determination (R2) between the actual wind speeds and the Weibull predicted values ranged between 0.475 - 0.792. The Weibull distribution function can be used with acceptable accuracy for prediction of wind energy output required for preliminary design and assessment of wind power plants.Item Optimization of turning NST 37.2 steel with uncoated carbide cutting tools(Nigerian Institution of Mechanical Engineers, 2010) Fadare, D. A.; Asafa, T. B.Selection of optimimum machining parameters is an essential factor in process planning for efficient metal cutting operations. In this study, an artificial neural network based tool wear predictive model and a genetic algorithm-based optimization model were developed to determine the optimum cutting parameters for turning NST 37.2 steel with uncoated carbide cutting inserts. Multi-layer, feed-forare, back -propagation network was used in predictive model, while maximum metal removal rate (MRR) was used as the objective function and tool wear as samples NST 37.2 steel bars with 25mm diameter and 400mm length s workspiece and Sandvice Coromant® uncoated carbide inserts with International Standard Organization (ISO) designation SNMA 12406. Dry machining at different cutting conditions with cutting speed (v), feed rate (f) and depth of cut (d) ranging from 20.42-42.42 mm/min, 1.0-2.2 mm/rev and 0.2-0.8mm, respectively were carried out. Eight passes of 50mm length of cut were machined at each conediiton, the spindle power and tool wear (flank and nose) were measured during each cutting operation. Results have shown that the predictive model had acceptable accurancy and optimum cutting parameters obtained were: v=42.32mm/min, f= 2.19 mm/rev and d = 0.8mm.Item Modelling of solar energy potential in Nigeria using an artificial neural network model(Elsevier Limited, 2009) Fadare, D. A.In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4–14°N, log. 2–15°E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983–1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01–5.62 to 5.43–3.54 kW h/m2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.Item Modelling the association between in vitro gas production and chemical composition of some lesser known tropical browse forages using artificial neural network(2007) Fadare, D. A.; |Babayemi, O. J.In vitro gas production of four different browse plants (Azadirachta indica, Terminalia catappa, Mangifera indica and Vernonia amygdalina) was investigated under different extractions. The relationship between the forage composition parameters (dry matter, organic matter, crude protein, acid detergent fibre, neutral detergent fibre and acid detergent lignin), process parameters (extraction mode and incubation time), and volume of gas production were modelled with artificial neural network (ANN). The ANN model consisted of simple, multi-layered, back-propagation networks with eight input neurons consisting of the composition and process parameters and one output neuron for the gas volume. The networks were trained with different algorithms and varying number of layer and neuron in the hidden layer to determine the optimum network architecture. The network with single hidden layer having 45 ‘tangent sigmoid’ neurons trained with Livenberg-Marquard algorithm combined with ‘early stopping’ technique was found to be the optimum network for the model with R-value: mean = 0.9504; max. = 0.9618; min. = 0.9343; and std. = 0.0059. The influence of each chemical composition and processing parameters on gas production was simulated. The developed ANN model offers a more cost and time efficient strategy in feed evaluation for ruminant animals.Item Modeling and forecasting of short-term half-hourly electric load at the University of Ibadan, Nigeria(Akamai University, 2009-11) Fadare, D. A.; Dahunsi, O. A.In this study, the short-term load pattern for the University of Ibadan was investigated and a multi-layered feed-forward artificial neural networks (ANN) model was developed to forecast the time series half-hourly load pattern of the system using the load data for a period of 5 years (2000 to 2004). The study showed that the mean half-hourly load for the period of study ranged between 1.3 and 2.2 MW, and the coefficient of determination (R2-values) of the ANN predicted and the measured half-hourly load for test dataset decreased from 0.6832 to 0.4835 with increase in the lead time from 0.5 to 10.0 hours.