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 Ca²⁺ and Mg²⁺ concentration at different Ph in artificial seawater to optimize the performance of the mine tailings sedimentation process. The RBFN was developed by considering Ca²⁺ and Mg²⁺ 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 Ca²⁺ and Mg²⁺ concentration were found, respectively: (i) 169–338 and 0–130 mg·L⁻¹ at pH 9.3; (ii) 0–21 and 400–741 mg·L^–1 at pH 10.5; (iii) 377–418 and 703–849 mg·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·h^–1) were reached independently of pH when BSw was used as a separation medium for the sedimentation process.

中文翻译：

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利用径向基函数网络预测海水中的最佳钙和镁含量，以及利用生物矿化法将预处理的海水作为改善铜尾矿絮凝的关键工具

研究了将径向基函数网络（RBFN）模型与生物矿化（BSw）预处理的海水组合使用，作为改善采矿用铜尾矿絮凝的一种方法。RBFN用于设定人造海水中不同pH下Ca ²⁺和Mg ²⁺的最佳浓度范围，以优化矿山尾矿沉降过程的性能。通过将Ca ²⁺和Mg ^2+的浓度以及pH值作为输入变量，并将矿山尾矿沉降速率（Sr）和残留水浊度（T）作为输出变量来开发RBFN 。分别找到了Ca ²⁺和Mg ²⁺浓度的最佳范围：（i）169–338和0–130 mg·L在pH 9.3下为^-1；（ii）pH 10.5时为0-21和400-741 mg·L ^–1；（iii）pH 11.5时为377–418和703–849 mg·L ^-1。将RBFN预测的沉降性能与在原海水（Sw），化学预处理海水（CHSw），BSw和自来水（Tw）中测得的性能进行了比较。结果表明，RBFN模型对于预测CHSw的沉降性能非常有用。另一方面，当BSw用作沉淀过程的分离介质时，与pH无关，达到了最高的Sr值（即5.4、5.7和5.4 m·h ^–1）。