Comparison between neural networks and multiple regression methods in metallurgical performance modeling of flotation column

Artificial neural networks are relatively new computational tools which their inherent ability to learn and recognize highly non-linear and complex relationships makes them ideally suited in solving a wide range of complex real-world problems. In this research, different techniques (Linear regression, Non-linear regression, Back propagation neural network, Radial Basis Function for the estimation of Cu grade and recovery values in flotation column concentrate are studied. Modeling is performed based on 90 datasets at different operating conditions at Sarcheshmeh pilot plant, a copper concentrator in Iran, which include chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu grade in the rougher feed and flotation column feed, column tail and final concentrate streams. The results of models were also expressed and analyzed by intuitive graphics. The results indicated that a four-layer BP network gave the most accurate metallurgical performance prediction and all of the neural network models outperformed non-linear regression in the estimation process for the same set of data.