A new method for dynamic modelling of bread dough kneading based on artificial neural network

This paper presents a dynamic model of the kneading process based on artificial neural networks. This dynamic neuronal model allows predicting the bread dough temperature and the delivered power necessary to carry out mechanical work. This neuronal technique offers the advantage of very short computational times and the ability to describe nonlinear relationships, sometimes causal, explicit or implicit, between the input and output of a system. We used the recurrent neural networks to capture the dynamic of the process. The type and the number of inputs of the neural networks, as well as the nature of the learning set, the architecture and the parameter learning technique have been studied. The comparison of the results with experimental data shows the possibility to predict the temperature and the power delivered to the dough for various operating conditions.

► We develop a dynamic model of the kneading process based on artificial neural networks. ► This model predicts the bread dough temperature and the delivered power necessary to carry out mechanical work. ► We study the model performance on a validation set. ► We show the effectiveness and reliability of the neural approach for this type of complex operation.