College of Engineering

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Start date: 2018End date: 2019

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    Industrial- and automotive-used lubricating oils recycling cum acidic sludge treatment
    (Springer, 2019) Oladimeji, Temitayo E; Oguntuashe, Kehinde M.; Emetere, Moses E.; Efeovbokhan, Vincent E.; Odunlami, Olayemi A.; Obanla, Oyinlola R.
    Increased rise of industries and car usage in Nigeria and urban development is exponentially on the increase giving rise to multiple waste generation. Evaluation of the different recycling processes showed that acid-clay process has the highest environmental risk as well as the lowest cost; hence, this work added a treatment method for the slurry produced after treatment with acid-clay method, thereby reducing the environmental concern caused by acid and acid sludge formed in the process. The acid ratio was varied between 0 and 20% and adsorbent ratio between 15 and 25%. Automotive-used lubricating oil and industrial-used lubricating oil were treated using two different samples, acid and adsorbent. An increase in acid concentration showed a significant difference over the properties of oil such as density, viscosity, flash point, and other physiochemical properties nevertheless increasing the amount of acid over the optimum point made on significant change. Varying of adsorbent ratio showed little significant effect to density and flash point, while yield and viscosity were unaffected. Optimum point being at 10% acid and 25% adsorbent gave optimal result. All metal contaminants are substantially removed; total base number was improved, while increase in flash point suggested the method effectiveness. Treatment of used industrial oil was found to be easier to re-refine due to less contamination.
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    FAULT IDENTIFICATION SYSTEM FOR ELECTRIC POWER TRANSMISSION LINES USING ARTIFICIAL NEURAL NETWORKS
    (International Journal of Scientific & Engineering Research Volume 9, Issue 2, 2018-02) Mbamaluikem Peter O.,; Aderemi Oluwaseun S.,; Awelewa Ayokunle A.
    Electric power transmission line faults hinder the continuity of electric power supplied and increase the system downtime thereby increasing the loss of electric power transmitted. Early fault detection and classification leads to prompt clearance of faults with an attendant effect of improved reliability and efficiency of the power system network. In view of this, this paper develops an arti-ficial neural network (ANN)-based detector and classifier to indicate and classify respectively a fault on Nigeria 33-kV electric power transmission lines. The transmission lines are modeled in Simulink using SimPowerSystems toolbox in MATLAB. Fault simulations are carried out, and the resulting instantaneous values of voltages and currents are used to develop the proposed fault identification sys-tem using multilayer perceptron feedforward artificial neural networks with backpropagation algorithm. Results are presented to vali-date the effectiveness and efficiency of the developed identification system for detecting and classifying faults. The Mean Square Error (MSE), linear regression and the confusion matrix are used as performance evaluators for the system. The ANN-based identification system achieved MSE of 4.77399e-10 and an accuracy of 100% for fault detection. This indicates that the performance of the developed ANN-based identification system is highly satisfactory and may be practically implemented on the Nigeria transmission lines.