Modified integral procedure (MIP) as a reliable short-cut method for kinetic model estimation: isothermal, non-isothermal and (semi-)batch process cases

Abstract In both small and large-scale investigations, a reliable short-cut procedure to estimate the approximate parameters is very useful for the successive rapid checking of different Kinetic Model ( KM ) structures for their adaptation to current process data. An improved quality of the initial parameter guess also improves the reliability and the convergence rate for a subsequent exact Nonlinear Least Squares ( NLS ) regression technique applied for fitting the final model. The recently proposed Modified Integral transformation Procedure ( MIP ) short-cut estimation method of Maria and Rippin (1995) [ Computers and Chemical Engineering 19 (Supplement), S709–S714 (1995)] adds supplementary elements of similarity analysis and prior information about similar model structures to the classical Integral transformation Procedure ( IP ) for kinetic parameter estimation. By exploiting the model structure and the interactive use of information stored in a kinetic databank, the MIP makes rapid adaptation of a KM and parameters, describing an already studied process, to a similar process under study with only the product distribution known. The problem decomposition and the term-by-term sensitivity and estimation analysis of the model for various portions of experimental data sets result in a very effective MIP . The generated initial parameter estimate is more reliable and of better quality compared with the classical direct techniques, especially for non-linear and ill-conditioned cases. Algebraic transfer of information functions are developed in interaction with the kinetic databank, leading to a rapid check of different kinetics, or the same kinetic model for different data sets, without time-consuming intermediate NLS steps. The MIP was integrated in an expert system for kinetic identification and coupled with statistical data/estimate analysis (Maria, 1993 [ Computers and Chemical Engineering 17 (Supplement), S435–S440 (1993)]; Maria and Rippin, 1996 [ Computers and Chemical Engineering 20 (Supplement), S587–S592 (1996)]). MIP implies any iterative search, it has no convergence problems and requires no tuning factor. The basic MIP , developed for isothermal data treatment, is also shown to be suitable for on-line kinetics identification in (semi-) batch processes. The interaction with the prior information allows on-line adaptations of the model structure and parameters, comparable with extended Kalman Filter ( EKF )-based recursive estimators. In the present work these results are also extrapolated for linear kinetics estimation by using non-isothermal data.