This, combined with isothermal nature of reactor, permits operation at the optimum temperature for a long reaction time. Stirred tank reactors, by virtue of their large volume, provide a long residence time. The stirred tank reactors are by their nature well suited to liquid-phase reactions. In continuous stirred tank reactor (CSTR), an agitator is deliberately introduced to disperse the reactants thoroughly into the reaction mixture immediately after they enter the tank. In batchwise mode, reactors are charged at the beginning of the reaction and products are removed at the end of the reaction. Depending on mode of operation, reactors are classified as batchwise or continuous. In size and appearance it may often seem to be one of the least impressive items of equipment, but its demand and performance are usually the most important factors in the design of whole plant. In any manufacturing process, where there is a chemical change taking place, a chemical reactor is at the heart of the plant. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Finally effects of different norms are tested along with variation in gamma memory filter. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. The standard back propagation algorithm with momentum term has been proposed in this model. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic.
A focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process.
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