Data-driven identification of a continuous type bioreactor

The aim of this paper is to provide a data-driven approach for modeling of a continuous type bioreactor. The data sets used for identification are gathered in the presence of various types of noises such as white and colored ones which reflects the practicality of the problem. Our purpose is generally to identify the bioreactor, in the presence of such noises, in which several model structures are employed, and then the best structure for each case is determined based on a performance index. The main originality of the paper is presenting the best model structure with optimum convergence rate and optimum orders (as low as possible) in the estimation algorithm of parameters. In this regard, for every proposed model structure, the maximum fitness indices have been selected so that for BJ, OE, ARMAX, ARX the maximum fitness are 98.14%, 64.85%, 97.29%, 96.26%, respectively. In particular, since the bioreactor is a multi-model system due to the different operating phases, by use of a forgetting factor, the identification is successfully carried out in the change of phases (e.g., from growth to the stationary) which depicts the effectiveness of the proposed techniques. All these results are supported by illustrative numerical simulations.