An integrated approach to intelligent modeling of industrial plants

In this paper an integrated hierarchical soft computing methodology for modeling of industrial plants by aggregating models of different types is presented. The problem of designing adequate and reliable models for non-linear plants with large uncertainties in under consideration here. The proposed approach has the ability to model system behavior under different circumstances and it is especially efficient for complex industrial systems with immeasurable process variables and large uncertainties. A fuzzy cognitive map (FCM) is used to aggregate multiple models and to create a hybrid model, which makes a selection between the different models, according to the current operational conditions of the industrial process. The proposed methodology is considered as a promising way to cope with the modeling of a real industrial plant.