Modeling of bubble surface area flux in an industrial rougher column using artificial neural network and statistical techniques

• Published in: Minerals engineering 2010, Vol.23 (2), p.83-90
• Main Authors: Massinaei, M., Doostmohammadi, R.
• Format: Article
• Language: English
• Subjects: Flotation machines; Froth flotation; Modeling; Neural networks
• ISSN: 0892-6875; 1872-9444
• DOI: 10.1016/j.mineng.2009.10.005

Previous studies in mechanical and column flotation cells have shown that bubble surface area flux (S b) is an appropriate indicator of gas dispersion in a flotation cell which has a relatively strong correlation with flotation rate constant. In the present investigation, based on extensive tests conducted in an industrial Metso Minerals CISA flotation column (4 m in diameter and 12 m in height) in a rougher circuit, S b as a function of the most significant operating variables which affect gas dispersion in a flotation column (i.e. superficial gas velocity, slurry density (solids%) and frother dosage/type) was modeled using artificial neural network (ANN) and statistical (non-linear regression) techniques. The models were developed taking into consideration a data set consisting of 82 experimental tests conducted in an industrial rougher column (at a copper concentrator in Iran) operating under a variety of experimental conditions. This paper outlines the development of the models and validation using a number of randomly selected datasets. Limitations of the present models are discussed and comments and recommendations on further investigations are given.