Prediction of power consumption from real process data of an industrial wood chip refining plant

The energy issue leads to improve the efficiency of production processes, which plays a fundamental role in minimizing their environmental impact and energy consumption. The pulp and paper industry is an excellent representation of an energy-intensive manufacturing process that urgently needs to become more efficient. In particular, the primary focus should be placed on the refining phase, which constitutes most of the energy usage. This characteristic is shared by all process that includes wood fiber extraction, such as the engineered wood panel industry. Within this frame, our work deals with the identification of a wood chips refining process operating in closed loop, committed to the production of Medium Density Fiberboard (MDF), in order to provide a long-term prediction of energy consumption. We perform the identification via multi-batch Simulation Error Minimization (SEM) from a real process data set related to a large-scale production plant. In conclusion, we will propose the application of the refiner motor current estimation and of the refiner disc gap drift to model the wear effect that characterizes the process.