Please use this identifier to cite or link to this item: http://hdl.handle.net/10321/2377
Title: Neural network modelling and prediction of the flotation deinking behaviour of industrial paper recycling processes
Authors: Pauck, Walter James 
Venditti, Richard 
Pocock, Jon 
Andrew, Jerome Edward 
Keywords: Deinking;Flotation;Magazines;Modelling;Neural networks;Newsprint;Office waste;Process control
Issue Date: 2014
Publisher: Nordic Pulp & Paper Research Journal
Source: Pauck, W. J.; Venditti, R.; Pocock, J. and Andrew, J. 2014. Neural network modelling and prediction of the flotation deinking behaviour of industrial paper recycling processes. Nordic Pulp & Paper Research Journal. 29(3): 521-532.
Abstract: The removal of ink from recovered papers by flotation deinking is considered to be the “heart” of the paper recycling process. Attempts to model the deinking flotation process from first principles has resulted in complex and not readily usable models. Artificial neural networks are adept at modelling complex and poorly understood phenomena. Based on data generated in a laboratory, artificial neural network models were developed for the flotation deinking process. Representative samples of recycled newsprint, magazines and fine papers were pulped and deinked by flotation in the laboratory, under a wide variety of practical conditions. The brightness, residual ink concentration and the yield were measured and used to train artificial neural networks. Regressions of approximately 0.95, 0.85 and 0.79 respectively were obtained. These models were validated using actual plant data from three different deinking plants manufacturing seven different grades of recycled pulp. It was found that the brightness and residual ink concentration could be predicted with correlations in excess of 0.9. Lower correlations of ca. 0.43 were obtained for the flotation yield. It is intended to use the data to develop predictive models to facilitate the management and optimization of commercial flotation deinking processes with respect to recycled paper inputs and process conditions.
URI: http://hdl.handle.net/10321/2377
ISSN: 2000-0669
Appears in Collections:Research Publications (Engineering and Built Environment)

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