Recursive robust kinetics estimation by using a mechanistic short-cut technique and a pattern-recognition procedure

Abstract

In the simulation of complex reactions, the use of mechanistic based kinetic models ( KM), although requiring extensive experimental and computational effort, presents the advantage of increased prediction reliability and physically meaningful estimated parameters. Because rapid off/on-line process simulation and optimisation usually require reduced KM-s, repeated parameter and model structure adaptations are necessary. Recently, Maria and Rippin (1995a,b) and Maria (1995) proposed a reliable short-cut technique ( MIP, the Modified Integral transformation Procedure) for rapid model identification and approximate parameter estimation. The MIP only implies rapid algebraic manipulations and does not present any convergence problems. Supplementary elements of reaction path recognition (similarity analysis, problem decomposition, alternative path discrimination, transfer of information rules), and model term-by-term sensitivity and estimate analysis, make the MIP solution more robust and of considerable improved quality compared with the classical direct estimation procedures. The procedure is very suitable for non-linear and ill-conditioned cases, being less sensitive to the noise level, outlier presence, or data and model degeneracy. The MIP, integrated in an expert system for kinetic modelling, allows a rapid kinetic data-bank check for suitable KM selection and adaptation to the new considered data. The MIP could also be used as a recursive parameter estimator by transferring previous information about the current process, without use of tuning factors or model linearizations during the identification rule. In this paper some completions to the MIP method are presented in order to improve the initial step when little prior information about the process is available: i) fast identification of a similar KM structure in the data-bank and discrimination among extended or reduced reaction path schema; ii) initial use of other direct estimation techniques and few data from the process to generate rough prior KM estimates; iii) initial use of non-linear regression ( NLS) steps and few data from the process to initiate the MIP recursive estimation.