Browsing by Author "Asere, Abraham A."

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    Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner
    (Energy by Elsevier, 2013-09) Adewole, Bamiji Z.; Abidakun, Olatunde A.; Asere, Abraham A.
    This study deals with ANN (artificial neural network) modeling of a swirl burner. The model was used to predict the flame temperature and pollutant emissions (CO (carbon monoxide) and NOx (nitrogen oxide)) from combustion of LPG (liquefied petroleum gas) in the swirl burner. The data for the training and testing of the proposed ANN was obtained by combusting LPG at various equivalent ratios (LPG/air ratios) and swirler’s vane angles in a low swirl burner. Vane angles of 35e60 in steps of 5 and equivalent ratios of 0.94, 0.90, 0.85, 0.80, 0.75, 0.71, 0.66 and 0.61 were considered. An ANN model based on standard back-propagation algorithms for the swirl burner was developed using some of the experimental data for training and validation. The performance of the ANN was tested by comparing the predicted outputs with the experimental values that were not used in training the network. R values of 0.94 were obtained for CO and NOx and 0.99 for flame temperature. These results show that very strong correlation exists between the ANN predicted values and the experimental results. Therefore, this study demonstrates that the performance and emissions of swirl burner can be accurately predicted using ANN approach.
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    Characteristics of CO and NOx emissions from combustion of transmethylated palm kernel oil-based biodiesel blends in a compression ignition engine
    (Journal of King Saud University – Engineering Sciences, 2018-02) Shote, Adeola S.; Betiku, Eriola; Asere, Abraham A.
    This study assessed hazardous emissions from transesterified Palm Kernel Oil-based (PKO-based) biodiesel blends in a Compression Ignition Engine (CIE). Automotive Gas Oil (AGO) was blended with the PKO-methyl esters in the ratios 1:9; 2:8; 3:7; . . .; 9:1. The various blends were thereafter fired in a CIE. Besides, 100% AGO and 100% PKO-methyl esters were also burnt in the CIE. Results showed that as the concentration of the PKO biodiesel increased in the blends, carbon monoxide (CO) emissions reduced. There was about 35% significant reduction in the lethal CO emissions as the concentration of methyl esters increased in the blends at 99.9% confidence (p 0.001). At 90% confidence, there were no significant changes in NOx emissions as a result of change in blend ratios (p > 0.01). There exists a degree of association between NOx and gas temperature in agreement with Zeldovich mechanism.
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    Emission and Combustion Characteristics of Lafia-Obi Coal in Fluidized Bed Combustor
    (Advanced Materials Research, 2013) Popoola, Olubunmi Tolulope; Asere, Abraham A.
    The technology of fluidized bed coal combustion (FBC) and its advantages over conventional coal burning systems is now well established and is extensively reported in the literature. There is also some emphasis in literature about the suitability of Lafia-Obi coal in FBC. However, there is little quantitative or qualitative information on theperformance of Lafia-Obi in FBC. This paper reports a study of the combustion of monosized coal fractions fed continuously to the bed via an overbed feeder. Using appropriate ASTM standards, proximate and ultimate analyses of samples of Lafia-Obi coal were carried out and the coal was then combusted in a fluidized bed. Results showed that Lafia-Obi coal has low moisture, high volatile matter and very high fixed carbon content. The volatile matter content places Lafia-Obi in the medium volatile bituminous rank. The data obtained is useful in application of fluidized bed combustion for energy production using Lafia-Obi Coal
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    ENVIRONMENTAL IMPLICATION OF COMBUSTION: THE MODELING OF COAL COMBUSTIONa
    (National Mathematical Centre Abuja, 2015) Asere, Abraham A.
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    Modelling of synthesis of waste cooking oil methyl esters by artificial neural network and response surface methodology
    (International Journal of Ambient Energy, 2018-01) Soji-Adekunle, Ayowumi R.; Asere, Abraham A.; Ishola, Niyi B.; Oloko- Oba, Idris M.; Betiku, Eriola
    This present study was carried out to investigate the application of artificial neural network (ANN) and response surface methodology (RSM) as modelling tools for predicting the waste cooking oil methyl esters (WCOME) yield obtained from alkali-catalysed methanolysis of waste cooking oil (WCO). The impact of process parameters involved was studied by a central composite rotatable design. A comparison of the two developed models for the methanolysis process was carried out based on pertinent statistical parameters. The calculated values of coefficient of determination (R2) of 0.9950 and the average absolute deviation (AAD) of 0.4930 for the ANN model compared with R2 of 0.9843 and AAD of 0.9376 for the RSM model demonstrated that the ANN model was more accurate than the RSM model. The actual maximum WCOME yield of 94 wt% was obtained at a reaction temperature of 55°C, a catalyst amount of 1 w/v, a reaction time of 70 min and a methanol-to-oil ratio of 6:1.