Predictive modelling of Fe(III) precipitation in iron removal process for bioleaching circuits.
In this study, the applicability of three modelling approaches was determined in an effort to describe complex relationships between process parameters and to predict the performance of an integrated process, which consisted of a fluidized bed bioreactor for Fe(3+) regeneration and a gravity settler for precipitative iron removal. Self-organizing maps were used to visually evaluate the associations between variables prior to the comparison of two different modelling methods, the multiple regression modelling and artificial neural network (ANN) modelling, for predicting Fe(III) precipitation. With the ANN model, an excellent match between the predicted and measured data was obtained (R (2) = 0.97). The best-fitting regression model also gave a good fit (R (2) = 0.87). This study demonstrates that ANNs and regression models are robust tools for predicting iron precipitation in the integrated process and can thus be used in the management of such systems.
Nurmi
Pauliina
P
Department of Chemistry and Bioengineering, Tampere University of Technology, Finland. pauliina.nurmi@inbox.com
Ozkaya
Bestamin
B
Kaksonen
Anna H
AH
Tuovinen
Olli H
OH
Puhakka
Jaakko A
JA
D000465:Algorithms; D057005:Bioengineering; D019149:Bioreactors; D011232:Chemical Precipitation; D007501:Iron / Q000737:chemistry / Q000302:isolation & purification*; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012044:Regression Analysis
D007501:Iron