Nonlinear observers with applications in the steel industry

Abstract: Access to measurements is a necessity in most technical applications, in order to detect faults, monitor performance, or exercise control. In some cases, however, installing measurement equipment is very expensive or even impossible. In such a case, estimates can be produced instead. In an observer, this is done by combining process knowledge, in the form of an analytical process model, with information, in the form of indirect measurements. If the process model is in the form of a system of linear differential equations, then the problem of constructing an observer is essentially solved by the Kalman filter and the Luenberger observer. For a system of nonlinear differential equations, however, there is no generic solution, which is the reason for extensive research in this area for the past decades. This thesis treats the development and analysis of nonlinear observers for three applications in the steel industry. The first application is the detection of gas leakages in a pulverized coal injection plant. An observer whose residual is sensitive to the gas leakage flow, has been designed for a nonlinear process model. A Generalized Likelihood Ratio test was applied to the residual to distinguish between different types of leakages. The method has been implemented in the plant and tested successfully with actual leakages. Furthermore, a Laguerre spectrum representation of the residual was utilized, to reduce disturbances and computational effort. The second application is the detection of clogging in pulverized coal injection lines. An observer, with a state variable that represents clogging, has been designed for a time-varying process model. An adaptive threshold for the estimated clogging variable was calculated. In experiments with data from the plant, the method was shown to detect clogging successfully, without producing false alarms. The third application is the estimation of metal analysis in the steel converter process. A nonlinear, physical process model was utilized and an observer was proposed, whose feedback is weighted by the sensitivity of the output with respect to the state. Experiments with data from a converter plant show that this strategy provides accurate estimates of the carbon content in the converter. Furthermore, a generalization of the proposed observer structure has been analyzed in terms of asymptotic stability and region of attraction.