Petroleum Engineering
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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 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.Item Modeling of tool wear parameters in high-pressure coolant assisted turning of Titanium Alloy Ti-6Al-4V using artificial neural networks(Akamai University, 2009) Fadare, D. A.; Ezugwu, E. O.; Bonney, J .Titanium alloy (Ti-6Al-4V) can be economically machined with high-pressure coolant (HPC) supply. In this study, an artificial neural network (ANN) model was developed for the analysis and prediction of tool wear parameters when machining Ti-6Al-4V alloy with conventional flow and high-pressure coolant flow, up to 203 bar. Machining trials were conducted at different cutting conditions for both rough and finish turning operations with uncoated carbide (K10 grade) and double TiAIN/TiN, PVD coated carbide (K10 substrate) inserts. The cutting parameters (cutting speed, feed rate, depth of cut, coolant pressure, and tool type) and the process parameters (cutting forces, feed force, machined surface roughness, and circularity) were used as input data set to train the three-layered feed-forward, back-propagation artificial neural networks. The networks were trained to predict tool life and wear rate separately. The results show that the correlation coefficients between the neural network predictions and experimental values of tool life, tool wear and wear rate were 0.996 and 0.999, respectively, suggesting the reliability of the neural network model for analysis and optimization of cutting process.Item Intelligent tool condition monitoring in high-speed turning of Titanium Ti-6Al-4V alloy(Kwame Nkrumah University of Science and Technology (KNUST), 2009) Fadare, D. A.; Ezugwu, E. O.; Bonney, J.Intelligent Tool Condition Monitoring (TCM) is an essential requirement in the drive towards automated machining operations. In this paper, a Multi-Layered Perceptron (MLP) neural network model has been developed for on-line condition monitoring of tool wear in high-speed turning of Titanium-based alloy (Ti-6Al-4V). Machining trials were conducted for typical rough and finish turning operations with cutting speed (90 – 120 m/min), feed rate (0.15 – 0.2 mm/rev), and depth of cut (0.5 -2.0 mm) using uncoated cemented carbide (K10 grade) inserts with Inter-national Standard Organization (ISO) designation “CNMG 120412”. The tool maximum flank wear (VBmax), cutting forces (feed force, Fx, and tangential force, Fz), and spindle motor power were measured during each machining operation. The cutting parameters (cutting speed, feed rate, and depth of cut), and cutting force and spindle power were used in isolation or in combination as input dataset in training the neural network to predict wear land on cutting tool at different stages of wear propagation (light, medium and heavy). The neural network model was designed using Matlab® neural toolbox. Accuracy of model for the prediction of tool wear at different wear stages were evaluated based on the Percentage Error (PE) for both roughing and finishing operations. Results showed that, the heavy wear stage (PE = ±5%) was predicted more accurately compared to the light (PE = +5 to -10%) and medium (PE = +25 to -30%) wear stages. The combination of the force, power signals and cutting parameters improved performance of the model.Item Energy analysis for production of powdered and pelletised organic fertilizer in Nigeria(Asian Research Publishing Network, 2006-06) Fadare, D. A.; Bamiro, O. A.; Oni, A. O.Energy study was conducted in an organic fertilizer plant in Ibadan, Nigeria, to determine the energy requirement for production of both powdered and pelletised organic fertilizer. The energy consumption patterns of the unit operations were evaluated for production of 9,000 kg of the finished products. The analysis revealed that eight and nine defined unit operations were required for the production of powder and pellets, respectively. The electrical and manual energy required for the production of powdered fertilizer were 94.45 and 5.55% of the total energy, respectively, with corresponding 93.9 and 5.07% for the production of pelletised fertilizer. The respective average energy intensities were estimated to be 0.28 and 0.35 MJ/kg for powder and pellets. The most energy intensive operation was identified as the pulverizing unit with energy intensity of 0.09 MJ/kg, accounting for respective proportions of 33.4 and 27.0% of the total energy for production of powder and pellets. Optimisation of the pulverizing process is suggested to make the system energy efficient.Item Energy analysis of an organic fertilizer plant in Ibadan, Nigeria(Asian Research Publishing Network, 2009) Fadare, D. A.; Bamiro, O. A.; Oni, A. O.Energy study was conducted in an organic fertilizer plan in Ibadan, Nigeria, to determine the energy requirement for production of both powdered and pelletised fertilizer. The energy consumption patterns of the unit operations were evaluated for production of 9,000 kg of the finished products. The analysis revealed that eight and nine defined unit operations were required production of powder and pellets, respectively. The electrical and manual energy required for the production of powder were 94.45 and 5.55% of the total energy, respectively, with corresponding 93.9 and 5.07% for the production of pelletised fertilizer. The respective average energy intensities were estimated to be 0.28 and 0.35 MJ/kg for powder and pellets. The most energy intensive operation was identified as the pulverizing unit with energy intensity of 0.09 MJ/kg, accounting for respective proportions of 33. 4 and 27.0% of the total energy for production of powder and pellets. Optimisation of the pulverizing process is suggested to make the system energy efficient.Item Effects of cutting parameters on surface roughness during high-speed turning of Ti-6AI-4V Alloy(INSInet Publication, 2009) Fadare, D. A.; Sales, W. F.; Ezugwu, E. O.; Bonney, J.; Oni, A. O.Surface roughness constitutes one of the most critical constraints for the selection of machine tools and cutting parameters in metal cutting operations. In this study, the steepest descent method was used to study the effects of cutting parameters (cutting speed, feed rate and depth of cut) on surface roughness of machined Ti-6AI-4V alloy workpiece at high-speed conditions. Machining trials were conducted at different cutting conditions using uncoated carbide inserts with ISO designation CNMG 120412 under conventional coolant supply, while a stylus type instrument was used to measure the centerline average surface roughness (Ra). The results revealed that, surface roughness was more sensitive to variation in feed rate followed by cutting speed and depth of cut. The study is of importance to machinist in the selection of appropriate combinations of machining parameters for high-speed turning of Ti-6AI-4V alloy workpiece.