Feature-based model identification of nonlinear biotechnological processes

Abstract

Abstract Modelling ill-defined systems requires powerful tools to attain a quantitatively description of the studied systems. In this paper, a modelling concept is presented that tackles the problems inherent to processes for which the necessary a priori knowledge for deductive analysis is lacking. The approach can be summarised as follows. By means of relationship detectors, such as SAPS or fractional factorials, the existence of a causal structure can be deduced qualitatively. In a next step of the modelling task, the goal is to find the quantitative description of this relationship. This step can be subdivided in two phases, i.e. model structure characterisation and finally parameter estimation. In this paper new techniques are proposed (and validated on real-life experimental results) to achieve the latter steps. In order to separate the structure characterisation from the parameter estimation task, an approach is taken in which parameter-invariant features are extracted from the data. The properties of these features are chosen in such a way that a classifier can select a specific model description. The decomposition in Zernike features and the recurrent neural network classifier, introduced by Sudharsanan, constitute the implementation of this concept. To check the feasibility of this modelling approach, a biotechnological application is chosen as a test case. Due to the changing nature of the wastewater treatment process, reflected in a set of mathematical descriptions applicable at different time instances, the aforementioned methodology will give the possibility to develop more efficient adaptive controllers.