Abstract
The circulating fluidized bed (CFB) is one of the complex nonlinear systems which has gained acceptance in a wide variety of fields like catalytic cracking, power generation and mineral processing. Compared with conventional fluidized beds, CFB have many advantages including better interfacial contacting and reduced back mixing. CFB is a relatively new method of forcing chemical reactions to occur in the chemical and petroleum industries. In the absence of conventional means to derive a reliable model, we have devised a model of the circulating fluidized bed using neural networks, which have the ability to characterize such complex systems through their non-linear mapping. The main objective is to develop a real time NN model to simulate and control the CFB. It has been shown that the attempt has been a successful and the results are presented.
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