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Estimation of Overall Heat Transfer Coefficient (OHTC) of Coal-Water Slurry Based on Regression and Artificial Neural Network

In this study, an artificial neural network (ANN) and a regression model were developed to predict the overall heat transfer coefficient (OHTC) of coal slurry in an agitated vessel used in coal gasification. The intensity of heat transfer during mixing of fluids like coal slurry depends on the parameters of the experiments, including coal-water ratio, stirring speed, type of the stirrer, the design of the vessel, and conditions of the process. The vessel is heated through a jacketed vessel up to 100°C so that the steam will sustain the gasifier efficiency. Before entering into the gasifier as an input, the overall heat transfer coefficient has to be analyzed. However, to prepare an experimental setup is a very expensive and time-consuming procedure because of the high trial numbers. Because of these difficulties, the modeling and then testing of the system used numerical analysis such as regression and artificial neural network. At the end of study, both the ANN and regression analysis results were compared with experimental data.