NFLUENCING PROCESS VARIABLES AND PREDICTIVE MODELS FOR OPACITY USING REAL DATA OF MWPI

Authors

Abstract

The impact of a number of variables involved in pulp processing on the opacity fluctuation of newsprint produced by Mazandaran Wood and Paper Industries (MWPI) from hardwood chemi-mechanical pulp was studied. Using real datafrom MWPI paper plant, datasets were prepared and the variables that had the greatest influence on paper opacity were
found using correlation and mutual information. The se included stock pressure in the third group clean ers, the amount of fibres retained on 48 mesh screen, rush to drug
ratio, output of second fan pump, and head box slice opening. Then, appropriate neural network predictive models were developed and tested with a suitable dataset to better control the opacity of newsprint produced at MWPI. The models were successfully validated using new real data from the mill, demonstrating the generalization capacity of the neural network models

Keywords

data-mining, mutual information, neural networks, newsprint, predictive models, opacity

Subject

data mining

Journal

Cellulose Chemistry and Technology, Vol. 50, #1, pp. 93-100, Roumanian Academy of Sciences , January 2016

DOI