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.