An artificial neural network approach to recognise kinetic models from experimental data 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 threecomponent 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.

Datadriven modeling of semibatch manufacturing: a rubber compounding test case The continuously growing amount of available data from manufacturing processes supports the development of datadriven models. The typical target application of these models is optimal control and continuous quality management within an objective of zerodefect manufacturing. However, data obtained from batch processes are characterized by its high dimensionality that exceeds the computational capabilities of online applications and datadriven modelu0027s reliability must be guaranteed for proper industrial implementation. We explore two approaches to reduce problemu0027s size: feature extraction and feature selection; several multivariate regression methods are also compared regarding it precision and robustness. We base our analysis on an industrial rubber compounding process where natural rubber is blended in a semibatch mixer with several additives, then it is further mixed up using cylinders and it is conditioned in bands for storing. For this process, real production data is collected and stored in the manufacturing execution system of the company. The objective of the analysis is to predict mechanical properties of the rubber at the end of the processes. Based on the provided data, several datadriven models are built and tested. From the comparison among them it is concluded: models based on feature extraction and artificial neural networks yield the highest accuracy, while featureselected models provide better physical interpretability and increased robustness regarding industrial deployment.