Application and comparison of different identification schemes under industrial conditions

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Abstract
Two new identification procedures based on different strategies are presented to determine a mathematical model for the crude iron quality of a blast furnace process. The first algorithm estimates the parameters of a common linear difference equation model based on identification results obtained from separate series of data with given model structure. Prior to complete estimation of the parameters, the second method automatically detects the significant terms of a general model to optimally characterize a linear or non-linear system. The process is described by a general discrete polynomial expansion of past and present input signals and past output signals. This algorithm is based on orthogonalization and application of several information criteria. A comparison of the prediction accuracy of the linear model obtained from the first method with the linear and non-linear model resulting from the second algorithm and with models developed by other identification procedures is presented and some experiences from this application are discussed.

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