Identification of a fluidized catalytic cracking unit: an orthonormal basis function approach

Multivariable system identification of a model IV fluidized catalytic cracking unit is performed using a linear time invariant model parametrization based on orthonormal basis functions. This model structure is a linear regression structure which results in a simple convex optimization problem for least squares prediction error identification. Unknown initial conditions are estimated simultaneously with the system dynamics to account for the slow drift of the measured output from the given initial condition to a stationary working point. The model accuracy for low frequencies is improved by a steady-state constraint on the estimated model and incorporation of prior knowledge of the large time constants in the model structure. The model accuracy is furthermore improved by an iteration over identification of a high order model and model reduction. First a high order model is estimated using an orthonormal basis. This model is reduced and used to generate a new orthonormal basis which is used in the following iteration step for high order estimation. With the approach followed accurate models over a large frequency range are estimated with only a limited amount of data