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As the energy cost is rising steadily and the environmental pollution is becoming an important issue, the roles of process plant boilers like that of the sugar plants boilers need better understanding as they can be made to operate in either energy conserving or bio-mass conserving modes. However, practical models of the plants need to be developed before any effort at optimization and cogeneration is carried out. These boiler plants are difficult to model as they have non-linearities between input and output parameters and have large no. of parameters. Efforts have been reported in literature where techniques like that of Neural Networks have been used. A similar methodology has been evolved here successfully to model a 100 Ton water tube bagasse fired cogeneration boiler through back propagation trained Neural Networks.

MODELING COGENERATION POWER PLANTS USING NEURAL NETWORKS