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

Lorenzo Tuissi, Daniele Ravasio, S. Spinelli, A. Ballarino
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Abstract

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.
中密度纤维板精炼电机负荷的神经网络建模
在本研究中,采用人工神经网络对中密度纤维板热机械制浆过程中精炼厂的能耗进行多步预测。通过这种方式,可以将得到的模型集成到基于模型的控制中。精炼过程具有大量变量的特点,人工神经网络是一种成熟的多变量数据处理方法,能够识别被监测变量之间的非线性隐藏关系。具有稳定性保证的长短期记忆网络和变压器网络都是由于它们能够模拟动态系统的演化而实现的。仿真结果证明了两种模型的多步预测能力。
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