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Assessment of flotation kinetics modeling using information criteria; case studies of elevated-pyritic copper sulfide and high-grade carbonaceous sedimentary apatite ores

机译:利用信息标准评估浮选动力学建模; 升高 - 硫化硫化铜和高级碳质沉积磷灰石矿石案例研究

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Despite flotation kinetic modeling is well discussed in the literature, its evaluation from overfitting, the number of model parameters and model complexities have not been adequately addressed. Flotation kinetic behavior of two deposits including an elevated-pyritic (Cu/S = 0.21) complex copper sulfide ore and a high-grade carbonaceous sedimentary apatite (P2O5 >= 25%) ore were investigated. The flotation kinetic experiments were carried out in a mechanically agitated batch flotation cell. Different flotation kinetic models including seven common empirical and initially four mathematical models were applied to the experimental data. In addition to assessment of the goodness of fit (GOF) for each model, a factor of model complexity was considered using advanced statistical techniques (i.e. Bayesian information (BIC), low of iterated logarithm (LILC) and Akaike information (AIC) indices). The results confirmed that flotation kinetic modeling significantly depends on the feed type. The empirical models were found more sensitive than the mathematical ones to the ore properties and the mineral types. Furthermore, the mathematical models demonstrated relatively favorable results than the practical models concerning the variation of ore properties due to the consideration of more parameters in the modeling. Finally, it was concluded that the IC indices must be applied to the process of model selection owing to consideration of GOF, the complexity of a model and model consistency. The IC was introduced as a more reliable indicator than the common regression approach for evaluating, sequential ordering and selecting the suitable flotation kinetic models. Further studies are required for model's generalizability from a statistical point of view.
机译:尽管浮选动力学建模在文献中进行了很好的讨论,但其从过度装备的评估,模型参数和模型复杂性的数量没有得到充分解决。研究了包括升高的 - 脱脂(Cu / S = 0.21)复合硫化铜矿石和高级碳质沉积磷灰石(P2O5> = 25%)矿石的两种沉积物的浮选动力学。浮选动力学实验在机械搅拌的批量浮选细胞中进行。不同的浮选动力学模型,包括七个常见的经验和最初四种数学模型应用于实验数据。除了评估每个模型的拟合良好(GOF)之外,使用先进的统计技术(即贝叶斯信息(BIC),迭代对数(LILC)和Akaike信息(AIC)指数的思考统计技术(即,迭代信息(AIC)指数而考虑模型复杂性因素) 。结果证实浮选动力学建模显着取决于进料类型。发现经验模型比数学型材更敏感到矿石属性和矿物类型。此外,数学模型的结果比考虑到更多参数的矿石特性的变化而表现出相对有利的结果。最后,得出结论是,由于考虑到GOF,模型和模型一致性的复杂性,必须将IC指数应用于模型选择的过程。作为评估,顺序排序和选择合适的浮选动力学模型的常见回归方法,IC被引入更可靠的指标。从统计观点来看,需要进一步研究。

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