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Alexandridis, A., Sarimveis, H., Bafas, G. and Retsina T. (2002) A Neural Network Approach for Modeling and Control of Continuous Digesters. TAPPI Fall Technical Conference, San Diego.

has been cited by the following article:

• TITLE: Prediction of Kappa Number in Eucalyptus Kraft Pulp Continuous Digester Using the Box & Jenkins Methodology

  AUTHORS: Flávio M. Correia, José V. Hallak d’Angelo, Roger J. Zemp, Sueli A. Mingoti

  KEYWORDS: Continuous Digester, Kappa Number Prediction, Time Series, Box & Jenkins

  JOURNAL NAME: Advances in Chemical Engineering and Science, Vol.4 No.4, October 28, 2014

  ABSTRACT: The quality of the resulting pulping continuous digesters is monitored by measuring the Kappa number, which is a reference of residual lignin. The control of the kappa number is carried out mainly in the top of the digester, therefore it is important to get some indication of this analysis beforehand. In this context, the aim of this work was to obtain a prediction model of the kappa number in advance to the laboratory results. This paper proposes a new approach using the Box & Jenkins methodology to develop a dynamic model for predicting the kappa number from a Kamyr continuous digester from an eucalyptus Kraft pulp mill in Brazil. With a database of 1500 observations over a period of 30 days of operation, some ARMA models were studied, leading to the choice of ARMA (1, 2) as the best forecasting model. After fitting the model, we performed validation with a new set of data from 30 days of operation, achieving a model of 2.7% mean absolute percent error.