Alkali charge is one of the most relevant variables in the continuous kraft cooking process. The white liquor mass flow rate can be determined by analyzing the chip bulk density fed to the process. At the mills, the total time for this analysis usually is greater than the residence time in the digester. This can lead to an increasing error in the mass of white liquor added relative to the specified alkali charge. This paper proposes a new approach using the Box-Jenkins methodology to develop a dynamic model for predicting chip bulk density. Industrial data were gathered on 1948
observations over a period of 12 months from a Kamyr continuous digester at a bleached eucalyptus kraft pulp mill in Brazil. Autoregressive integrated moving average (ARIMA) models were evaluated according to different statistical decision criteria, leading to the choice of ARIMA (2,0,2) as the best forecasting model, which was validated against a new dataset gathered during 2 months of operations. A combination of predictors has shown more accurate results compared to those obtained
by laboratory analysis, allowing a reduction of around 25% of the chip bulk density error to the alkali addition amount.
Statistical decision methods in the presence of linear nuisance parameters and despite imaging system heteroscedastic noise: Application to wheel surface inspection
