Modeling and prediction of flotation performance using support vector regression

Vladimir Despotović; Maja S. Trumić; Milan Ž. Trumić

doi:10.5937/ror1701031D

Authors

Vladimir Despotović University of Belgrade, Technical Faculty, Bor, Serbia Author https://orcid.org/0000-0002-8950-4111
Maja S. Trumić University of Belgrade, Technical Faculty, Bor, Serbia Author https://orcid.org/0000-0001-9361-4412
Milan Ž. Trumić University of Belgrade, Technical Faculty, Bor, Serbia Author https://orcid.org/0000-0002-4321-5218

DOI:

https://doi.org/10.5937/ror1701031D

Keywords:

deinking, flotation, paper recycling, machine learning, support vector regression

Abstract

Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR), is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

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