An artificial neural network approach to recognise kinetic models from experimental data. (6th April 2020)

• Record Type: Journal Article
• Authors: Quaglio, Marco; Roberts, Louise; Bin Jaapar, Mohd Safarizal; Fraga, Eric S.; Dua, Vivek; Galvanin, Federico
• Is Part Of: Computers & chemical engineering. Volume 135(2020)
• Publication Date: 2020-04-06
• Subjects: Model selection -- Model discrimination -- Identifiability -- Machine learning -- Design of experiment; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.106759
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: The quantitative description of the dynamic behaviour of reacting systems requires the identification of an appropriate set of kinetic model equations. The selection of the correct model may pose substantial challenges as there may be a large number of candidate kinetic model structures. In this work, a model selection approach is presented where an Artificial Neural Network classifier is trained for recognising appropriate kinetic model structures given the available experimental evidence. The method does not require the fitting of kinetic parameters and it is well suited when there is a high number of candidate kinetic mechanisms. The approach is demonstrated on a simulated case study on the selection of a kinetic model for describing the dynamics of a three-component reacting system in a batch reactor. The sensitivity of the approach to a change in the experimental design and to a change in the system noise is assessed.