On case-based control of dynamic industrial processes with the use of fuzzy representation
Model-based diagnostics and control of industrial processes has become even more popular nowadays. To this end, a model of the given (dynamic) process is needed. However, there are several objects involved in processes, which may both cause severe problems when they are subject to a model, thus making a model-based approach inapplicable. An example of such an object is an electric furnace for reduction of copper from slag. Research focused on creating an analytical model of this object was unsuccessful. Therefore, the authors proposed a new approach based on case-based reasoning (CBR). Knowledge among the system is carried by selected realizations of the dynamic non-stationary process. Signals and attributes measured in initial state of the process, during its course and at some control points, are represented using a special process-oriented method that allows reducing unnecessary information. A special fuzzy similarity measure was introduced for efficient search of resembling examples. Properties of both the system of data representation and similarity measure are controlled by means of multiple parameters. To assure sufficient performance of the CBR system, an evolutionary algorithm was efficiently used. The paper ends with some exemplary results obtained by means of software, which implements the developed methodology.
industrial processes fuzzy representation case-based reasoning fuzzy similarity measure open-loop control evolutionary tuning