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Neural network modelling and prediction of the flotation deinking behaviour of complex recycled paper mixes.

In the absence of any significant legislation, paper recycling in South Africa has grown
to a respectable recovery rate of 43% in 2008, driven mainly by the major paper
manufacturers. Recently introduced legislation will further boost the recovery rate of
recycled paper. Domestic household waste represents the major remaining source of
recycled paper. This source will introduce greater variability into the paper streams
entering the recycling mills, which will result in greater process variability and operating
difficulties. This process variability manifests itself as lower average brightness or
increased bleaching costs. Deinking plants will require new techniques to adapt to the
increasingly uncertain composition of incoming recycled paper streams. As a
developing country, South Africa is still showing growth in the publication paper and
hygiene paper markets, for which recycled fibre is an important source of raw material.
General deinking conditions pertaining to the South African tissue and newsprint
deinking industry were obtained through field surveys of the local industry and
assessment of the current and future requirements for deinking of differing quality
materials.
A large number of operating parameters ranging from waste mixes, process variables
and process chemical additions, typically affect the recycled paper deinking process.
In this study, typical newsprint and fine paper deinking processes were investigated
using the techniques of experimental design to determine the relative effects of
process chemical additions, pH, pulping and flotation times, pulping and flotation
consistencies and pulping and flotation temperatures on the final deinked pulp
properties.
Samples of recycled newsprint, magazines and fine papers were pulped and deinked
by flotation in the laboratory. Handsheets were formed and the brightness, residual ink
concentration and the yield were measured. It was determined that the type of
recycled paper had the greatest influence on final brightness, followed by bleaching
conditions, flotation cell residence time and flotation consistency. The residual ink
concentration and yield were largely determined by residence time and consistency in
the flotation cell.
The laboratory data generated was used to train artificial neural networks which
described the laboratory data as a multi-dimensional mathematical model. It was found
that regressions of approximately 0.95, 0.84 and 0.72 were obtained for brightness,
residual ink concentration and yield respectively.
Actual process data from three different deinking plants manufacturing seven different
grades of recycled pulp was gathered. The data was aligned to the laboratory
conditions to take into account the different process layouts and efficiencies and to
compensate for the differences between laboratory and plant performance. This data
was used to validate the neural networks and select the models which best described
the overall deinking performances across all of the plants. It was found that the
brightness and residual ink concentration could be predicted in a commercial operation
with correlations in excess of 0.9. Lower correlations of ca. 0.5 were obtained for yield.
It is intended to use the data and models to develop a predictive model to facilitate the
management and optimization of a commercial flotation deinking processes with
respect to waste input and process conditions. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2011.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/8465
Date January 2011
CreatorsPauck, W. J.
ContributorsPocock, Jon., Venditti, Richard.
Source SetsSouth African National ETD Portal
Languageen_ZA
Detected LanguageEnglish
TypeThesis

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