Browsing by Author "Fadare, D. A."

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The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria
(Elsevier Limited, 2010) Fadare, D. A.
This paper presents the application of Artificial Neural Network (ANN) in modeling the heat transfer coefficient of a staggered multi-row, multi-column, cross-flow, tube-type heat exchanger. Heat transfer data were obtained experimentally for air flowing over a bank of copper tubes arranged in staggered configuration with 5 rows and 4 columns at different air flow rates with throttle valve openings at 10 - 100%. The Reynolds number and the row number were used as input parameters, while the Nusselt number was used as output parameter in training and testing of the multi-layered, feed-forward, back-propagation neural networks. The network used in this study was designed using the MATLAB® Neural Network Toolbox. The results show that the accuracy between the neural networks predictions and experimental values was achieved with Mean Absolute Relative Error (MRE) less than 1 and 4% for the training and testing data sets respectively, suggesting the reliability of the networks as a modeling tool for engineers in preliminary design of heat exchangers.
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.
An artificial neural network model for forecasting daily global solar radiation in Ibadan, Nigeria
(2009) Fadare, D. A.; Olugasa, T. T.
Solar radiation, the primary driver for many physical, chemical and biological processes on the earth's surface is considered the most indispensable parameter in the performance prediction of solar power systems. In this study, an artificial neural network (ANN) model was developed for predicting missing solar radiation data for Ibadan (Lat. 7.43°N; Long. 3.9°E; Alt. 227.2m), Nigeria. This study utilized daily solar radiation data for the period of 1984 to 2007 (24 years) from a meteorological station in Ibadan. The ANN model was designed using the Matlab® Neural Network Toolbox and five different structures of the model were investigated. Structure 1 utilized solar radiation data for 5 days to predict the next 25 days expected data; structure 2 utilized data for 10 days to predict the next 20 days; structure 3 used data for 15 days to predict succeeding 15 days; structure 4 used data for 25 days to predict next 5 days data; structure 5 used data for 5 days to predict the next 1 day solar radiation. The different structures were trained by using solar radiation data for 22 years and one year and the prediction accuracies were evaluated using the solar radiation values for year 2007. Results showed that structure 5 with correlation coefficient of 0.73 and 0.79 when trained with 22 years and 1 year, respectively gave the best prediction performance. Thus, indicating the suitability of structure 5 for prediction of solar radiation missing data.
Artificial neural network model for prediction of friction factor in pipe flow
(INSInet Publication, 2009) Fadare, D. A.; Ofidhe, U. I.
Determination of friction factor is an essential prerequisite in pipe flow calculations. The Darcy-Weisbach equation and other analytical models have been developed for the estimation of friction factor. But these developed models are complex and involve iterative schemes which are time consuming. In this study, a suitable model based on artificial neural network (ANN) technique was proposed for estimation of factor to friction in pipe flow. Multilayered perceptron (MLP) neural networks with feed-forward back-propagation training algorithms were designed using the neural network toolbox for MATLAB®. The input parameters of the networks were pipe relative roughness and Reynold’s number of the flow, while the friction factor was used as the output parameter. The performance of the networks was determined based the mean on absolute percentage error (MAPE), mean squared error (MSE), sum of squared errors (SSE), and correlation coefficient (R-value). Results have shown that the network with 2-20-31-1 configuration trained with the Levenberg-Marquardt 'trainlm' function had the best performance with R-value (0.999), MAPE (0.68%), MSE (5.335xI0-7), and SSE (3.414x10-4). A graphic user interface (GUI) with plotting capabilities was developed for easy application of the model. The proposed model is suitable for modeling and prediction of friction to factor in pipe flow for on-line computer-based computations.
Artificial neural network modeling of heat transfer in a staggered cross-flow tube type heat exchanger
(Akamai University, 2008-11) Fadare, D. A.; Fatona, A. S.
This paper presents the application of Artificial Neural Network (ANN) in modeling the heat transfer coefficient of a staggered multi-row, multi-column, cross-flow, tube-type heat exchanger. Heat transfer data were obtained experimentally for air flowing over a bank of copper tubes arranged in staggered configuration with 5 rows and 4 columns at different air flow rates with throttle valve openings at 10 - 100%. The Reynolds number and the row number were used as input parameters, while the Nusselt number was used as output parameter in training and testing of the multi-layered, feed-forward, back-propagation neural networks. The network used in this study was designed using the MATLAB® Neural Network Toolbox. The results show that the accuracy between the neural networks predictions and experimental values was achieved with Mean Absolute Relative Error (MRE) less than 1 and 4% for the training and testing data sets respectively, suggesting the reliability of the networks as a modeling tool for engineers in preliminary design of heat exchangers.
Artificial neural network predictive modeling of uncoated carbide tool wear when turning NST 37.2 steel
(Asian Research Publishing Network, 2012-04) Asafa, T. B.; Fadare, D. A.
We report the development of a predictive model based on Artificial Neural Network (ANN) for the estimation of flank and nose wear of uncoated carbide inserts during orthogonal turning of NST (Nigerian steel) 37.2. Turning experiments were conducted at different cutting conditions on a M300 Harrison lathe using Sandvic Coromant uncoated carbide inserts with ISO designations SNMA 120406 using full factorial design. Cutting speed (v), feed rate (f), depth of cut (d), spindle power (W), and length of cut (l) were the input parameters to both the machining experiments as well as the ANN prediction model while the flank wear (VB) and nose wear (NC) were the output variables. Nine different structures of multi-layer perceptron neural networks with feed-forward and back-propagation learning algorithms were designed using the MATLAB Neural Network Toolbox. An optimal ANN architecture of 5-12-4-2 with the Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained using Taguchi method of experimental design. The results of ANN prediction show that the model generalized well with root mean square errors (RMSE) of 3.6% and 4.7% for flank and nose wear, respectively. With the optimized ANN architecture, parametric study was conducted to relate the effect of each turning parameters on the tool wear. The ANN predictive model captures the dynamic behaviour of the tool wear and can be deployed effectively for online monitoring process.
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.
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.
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. E
This 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.
Development and application of a machine vision system for measurement of surface roughness
(Asian Research Publishing Network, 2009-07) Fadare, D. A.; Oni, A. O.
Monioring of surface roughness is an essential component in planning of machining processes as it affects the surface quality and dimensional accuracy of machined components. In this study, the development and application of a machine vision system suitable for on-line measurement of surface roughness of machined components using artificial neural network (ANN) is described. The system, which was based on digital image processing of the machined surface, consisted of a CCD camera, PC, Microsoft Windows Video Maker, frame grabber, Video to USB cable, digital image processing software (Photoshop, and MATLAB digital image processing toolbox), and two light sources. The images of the machined surface were captured; analyzed and optical roughness features were estimated using the 2-D fast Fourier transform (FFT) algorithm. A multilayer perceptron (MLP) neural network was used to model and predict the optical roughness values. Tool wear index and five features extracted from the surface images were used as input dataset in training and testing the ANN model. The results showed that the ANN predicted optical roughness values were found to be in close agreement with the calculated values (R2-value = 0.9529). Thus, indicating that the proposed machine vision system and ANN model are adequate for online monitoring and control of surface roughness in machining environment.
Development and application of a machine vision system for measurement of tool wear
(Asian Research Publishing Network, 2009-06) Fadare, D. A.; Oni, A. O.
Tool wear measurement is of great concern in machining industry, as it affects the surface qualities, dimensional accuracy and production costs of the machined components. The orthodox methods of measuring tool wear are time consuming and limited in accuracy and application. In this study, machine vision system based on digital image processing was developed for measurement of tool wear. The basic components of the system are: a charge coupled device (CCD) camera, PC, Microsoft Windows Video Maker, frame grabber, Video to USB cable, digital image processing software (Photoshop and digital image processing toolbox for MATLAB), multi-directional insert fixture, and light source. Tool wear images were captured and ten different wear features: length, width, area, equivalent diameter, centroid, major axis length, minor axis length, solidity, eccentricity and orientation were extracted from the images. The pixels dimension of the system was found to be Px = 0.03306 and Py = 0.03333. The accuracy of the system compared to SANDVIK Coromant hand-held microscopic lens was found to have an absolute error less than 3.13%. The system has been applied in the analysis of tool wear of uncoated cemented carbide inserts used for turning of NST 37.2 steel. A tool wear index (TWI) was proposed as a potential indicator for tool wear monitoring. A graphical user interface (GUI) was designed for easy application of system
Development of a computer aided software for power transmission shaft design with multiple criteria
(Obafemi Awolowo University, Ife, 2010) Fadare, D. A.; Akanbi, O.Y.
Power transmission shafts, such as the crankshaft, impeller shaft, propeller-shafts, camshafts etc, are essential machine elements with wide application in mechanical systems. The manual shaft design procedure is known to be tedious and complex becajuse of large numbers of formulas, many computations and iteration procedures involved in the design. However, the use of Computer Aided Software (CAS) offers improved accuracy, high speed computations, reduced rigorousness and cumbersomeness involved in manual design procedure. This paper discusses the development of a Computer Aided Design and Drafuing (CADD) of power transmission shafts with multiple design criteria (strength, torsional rigidity, critical speed, Soderberg and lateral rigidity). Visual Basic 2008 was used for the implementation of the algorithm, while Fireworks was used for the development of the Graphical User Interface (GUI). The developed CADD is called UI-CADSHAFT. The software was applied for a typical case study and validated with manual design. Accurate results with increased productivity about twenty folds over the manual design are obtained. The software is fully interactive, user-friendly and runs stand-alone on Microsoft Windows.
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.
Effect of heat treatment on mechanical properties and microstructure of NST 37-2 steel
(Scientific Research, 2011) Fadare, D. A.; Fadara, T. G.; Akanbi, O. Y.
Engineering materials, mostly steel, are heat treated under controlled sequence of heating and cooling to alter their physical and mechanical properties to meet desired engineering applications. In this study, the effect of heat treatment (annealing, normalising, hardening, and tempering) on the microstructure and some selected mechanical properties of NST 37-2 steel were studied. Sample of steel was purchased from local market and the spectrometry analysis was carried out. The steel samples were heat treated in an electric furnace at different temperature levels and holding times; and then cooled in different media. The mechanical properties (tensile yield strength, ultimate tensile strength, Young’s modulus, percentage reduction, percentage elongation, toughness and hardness) of the treated and untreated samples were determined using standard methods and the microstructure of the samples was examined using metallographic microscope equipped with camera. Results showed that the mechanical properties of NST 37-2 steel can be changed and improved by various heat treatments for a particular application. It was also found that the annealed samples with mainly ferrite structure gave the lowest tensile strength and hardness value and highest ductility and toughness value while hardened sample which comprise martensite gave the highest tensile strength and hardness value and lowest ductility and toughness value.
Effect of moisture content on cracking characteristics of African walnut (Tetracarpidium Conophorum)
(2009) Fadare, D. A.; Aransiola, S. O.
The influence of moisture content (9.6 - 30.8% wet basis) on cracking characteristics: cracking force, specific deformation and energy requirement for cracking of African walnuts (Tetracarpidium conophorum) was investigated. The nuts were loaded along the major, intermediate, and minor diameter and the data was subjected to ANOVA. Results showed that cracking force decreased significantly (p<0.05) with increased moisture content, while there was no significant difference in specific deformation and energy requirement for cracking in the three loading directions. The average cracking forces required along the major, intermediate and minor diameter were 118.60, 122.75, and 138.80 N, respectively.
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.
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.
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.
Energy and cost analysis of organic fertilizer production in Nigeria
(Elsevier Limited, 2010) Fadare, D. A.; Bamiro, O. A.|.; Oni, A. O
Energy study was conducted in an organic fertilizer production plant in Nigeria to determine the energy consumption patterns and the associated costs for the production of both powdered and pelletised fertilizer. Analysis was conducted for a daily production of 9000 kg of the finished products. Eight and nine defined unit operations were required for production of powder and pellets, respectively. The electrical and manual energy required for the production of powder were 94.5 and 5.6% of the total energy, respectively, with corresponding 93.9 and 5.1% for the production of pellets. The respective average energy intensities were estimated as 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. The energy cost per unit production for powdered and pelletised fertilizer using generator were evaluated as #2.92 ($0.021) and #3.87 ($0.028), respectively, with corresponding values of #1.65 ($0.012) and #2.00 ($0.014) when electrical energy from the national grid was used. The energy intensities for the production of organic fertilizers were significantly lower than that of inorganic fertilizers.
Energy and exergy analyses of malt drink production in Nigeria
(Elsevier Limited, 2010-12) Fadare, D. A.; Nkpubre, D. O.|.; Oni, A. O; Falana, A.; Waheed, M. A.; Bamiro, O. A
Energy requirements and exergy inefficiencies for processing of malt drink were estimated for a Nigerian brewery. The process was divided into twenty-one basic unit operations and grouped into four main group operations: silo house, brew house, filter room and packaging house. The energy intensity for processing a batch of 9.8 tonnes brew grains to 562 hl of malt drink was estimated as 261.63 MJ/hl consisting of electrical (41.01%), thermal (58.81%) and manual (0.19%) of the total energy. The most energy intensive group operation was the Packaging House operation, followed by the Brew House operation with energy intensities of 223.19 and 35.94 MJ/hl respectively. The exergy analysis revealed that the packaging house operation was responsible for most of the inefficiency (92.16%) followed by brew house operation (7.17%) and the silo house and filter room operations with less than 1%of the total exergy lost. The most exergy loss took place in the pasteurizer which accounted for 59.75% of the overall system inefficiency. Modification in the pasteurizer and use of spent grains as alternate source of energy in the steam boiler were recommended to improve the energy efficiency of the system.