Use of Radial Basis Function Network to Predict Optimum Calcium and Magnesium Levels in Seawater and Application of Pretreated Seawater by Biomineralization as Crucial Tools to Improve Copper Tailings Flocculation

The combined use of the Radial Basis Function Network (RBFN) model with pretreated seawater by biomineralization (BSw) was investigated as an approach to improve copper tailings flocculation for mining purposes. The RBFN was used to set the optimal ranges of Ca(2+)and Mg(2+)concentration at different Ph in artificial seawater to optimize the performance of the mine tailings sedimentation process. The RBFN was developed by considering Ca(2+)and Mg(2+)concentration as well as pH as input variables, and mine tailings settling rate (Sr) and residual water turbidity (T) as output variables. The optimal ranges of Ca(2+)and Mg(2+)concentration were found, respectively: (i) 169-338 and 0-130 mg center dot L(-1)at pH 9.3; (ii) 0-21 and 400-741 mg center dot L(-1)at pH 10.5; (iii) 377-418 and 703-849 mg center dot L(-1)at pH 11.5. The settling performance predicted by the RBFN was compared with that measured in raw seawater (Sw), chemically pretreated seawater (CHSw), BSw, and tap water (Tw). The results highlighted that the RBFN model is greatly useful to predict the settling performance in CHSw. On the other hand, the highest Sr values (i.e., 5.4, 5.7, and 5.4 m center dot h(-1)) were reached independently of pH when BSw was used as a separation medium for the sedimentation process.