PREDICTING KAPPA NUMBER IN A KRAFT PULP CONTINUOUS DIGESTER: A COMPARISON OF FORECASTING METHODS

Abstract This paper discusses kappa number prediction models using Single Exponential Smoothing, Multiple Linear Regression Analysis, the Time Series Method of Box-Jenkins (ARIMA) and Artificial Neural Networks. Applying a database of an industrial eucalyptus Kraft pulp continuous digester, these fo...

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Bibliographic Details
Published in:Brazilian journal of chemical engineering 2018-09, Vol.35 (3), p.1081-1094
Main Authors: Correia, Flávio Marcelo, d'Angelo, José Vicente Hallak, Almeida, Gustavo Matheus, Mingoti, Sueli Aparecida
Format: Article
Language:English
Subjects:
ENGINEERING, CHEMICAL
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Summary:Abstract This paper discusses kappa number prediction models using Single Exponential Smoothing, Multiple Linear Regression Analysis, the Time Series Method of Box-Jenkins (ARIMA) and Artificial Neural Networks. Applying a database of an industrial eucalyptus Kraft pulp continuous digester, these four different methods were evaluated according to different statistical decision criteria. After fitting the parameters of the models, validations were performed using a new dataset. Results, advantages and limitations of the four methods were compared. Some statistical forecasting indexes indicate that the ARIMA model showed more accurate estimation results, achieving a MAPE lower than 3 % and over 90% of the prediction data deviations lower than one kappa unit.
ISSN:0104-6632
1678-4383
1678-4383
DOI:10.1590/0104-6632.20180353s20160678