Intelligent Optimal Control Schemes Applied to Catalytic Reformer


D.Manamalli                    P.Kanagasabapathy                   K.Dhivya

Department of Instrumentation engineering,MIT Campus

Anna university,Chennai-44


Catalytic naphtha reforming is an important process carried out in refineries for upgrading low octane naphtha to high-octane gasoline. A reformer can meet many product demands through its wide range of design and flexibility in operation. This work deals with optimisation of catalytic reformer using Artificial Neural Networks (ANN) and fuzzy logic. This optimisation requires accurate process model, which is valid over wide range of operating conditions. In this work, a simple kinetic model has been developed. This model gives the temperature and concentration profiles of three important hydrocarbons (Naphthenes, Paraffins, Aromatics) across the reactors. An optimal control scheme using artificial neural networks has been developed to maximise the aromatics yield, subject to constraints in inlet temperature of the reactors. Two neural networks, one in forward path and other in feedback path are trained to give set points for temperature control loops of the three reactors. Similarly fuzzy logic based optimal control scheme has been developed in order to maximise the aromatics yield. Finally the results are compared with conventional aromatics yield and the best performance of intelligent control schemes have been proved.

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