Prediction of cellulose sheet cutting using Machine Learning
Authors: Troncoso Espinosa Fredy Humberto , Avello Betancur Yamil Gerard , Martinez Flores Luis Andres 
Published: August 2021

Cellulose is the main raw material for the production of paper. Companies that produce it present in their production line the cutting of the cellulose sheet. This failure is sporadic and has a high economic impact since it paralyzes the production line for several hours, incurring unproductive hours and a large deployment of human and financial resources. In this research, the use of Data Mining is proposed to define a machine learning algorithm that allows predicting the cutting of the cellulose sheet in a production line of a cellulose plant in Chile. The results show that by applying this technique it is possible to predict the cutting of the cellulose sheet well in advance to take corrective actions to avoid cutting and thus minimize the economic impact associated with the failure.

Keywords: Data Mining, machine learning, cellulose, productivity.

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