Experimentally Driven Guaranteed Parameter Estimation: a Way to Speed up Model-Based Design of Experiments Techniques

Abstract Parameter estimation in modelling reaction kinetics is affected by the prior knowledge on the domain of variability of model parameters which can be very limited at the beginning of model building activities. In conventional parameter estimation approaches a reasonably wide domain of variability for kinetic parameters is initially assumed, but this uncertainty on domain definition might deeply affect the efficiency of model-based experimental design techniques for model validation. In this work, we propose the use of binary classification techniques to define a feasible parametric region of parameter variability satisfying a set of user-defined model-based constraints. The proposed approach is illustrated in a case study of consecutive reactions in a plug flow reactor.