FUZZY ACTIVATED NEURAL MODELS FOR PRODUCT QUALITY MONITORING IN REFINERIES

Abstract In the paper the problem of estimating the octane number of powerformed gasoline produced in a refinery is addressed. The model is designed in order to replace the existing measurement device during maintenance operation guaranteeing the continuity of product quality monitoring and control. Linear and nonlinear Moving Average models based on MLP neural networks have been designed to take into account the two different working points of the process and different strategies are compared. The models obtained are presently implemented on line in the refinery to be tested over a long period.

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