Contributo in atti di convegno, 2023, ENG, 10.1109/iccae56788.2023.10111131

Neural Network Modeling of the Refining Motor Load for Medium-Density Fibreboard Production

Lorenzo Tuissi, Daniele Ravasio, Stefano Spinelli, Andrea Ballarino

Consiglio Nazionale delle Ricerche

In this study, artificial neural networks are adopted to perform multi-step predictions of the power consumed by the refiner of a thermo-mechanical pulping process specialized in medium-density fiberboard production. In this way, the obtained model can be integrated within a model-based control. The refining process is characterized by a large number of variables, and artificial neural networks are a well-established methodology for multivariate data processing, able to identify the non-linear hidden relationship between monitored variables. Both a Long Short-Term Memory network, with stability guarantees, and a Transformer one are implemented due to their ability to model the evolution of dynamical systems. Simulation results prove both models' multi-step prediction capabilities.

2023 the 15th International Conference on Computer and Automation Engineering, pp. 519–523, 03-05/03/2023

Keywords

thermo-mechanical pulping, Transformer neural networks, LSTM neural networks, refining energy prediction, system identification

CNR authors

Tuissi Lorenzo, Ravasio Daniele, Ballarino Andrea, Spinelli Stefano

CNR institutes