In order to explore the reduction pathways of zinc oxide in LiCl molten salt and the optimal process,experiments were conducted in an alumina crucible using metallic lithium as the reducing agent and lithium chloride ...In order to explore the reduction pathways of zinc oxide in LiCl molten salt and the optimal process,experiments were conducted in an alumina crucible using metallic lithium as the reducing agent and lithium chloride molten salt as the reaction medium at 923 K.The study assessed the effects of lithium thermochemical reduction and electrolytic reduction of ZnO.The volatilization behavior of metal oxides in molten salts,the equivalent of a reducing agent,reduction time,amount of molten salt,stirring time,and the method of reduction feed were investigated for their impacts on the reduction yield and product composition.X-ray powder diffraction(XRD)analysis of the products showed that lithium reduction of ZnO not only produced metallic Zn but also formed a LiZn alloy.Electrolytic reduction can be used to obtain the metallic Zn product by controlling the potential below-2.2 V(vs Ag/Ag^(+)).Moreover,sintered oxides and higher electrode potentials could enhance the efficiency of electrolysis.Under the optimal reaction conditions determined experimentally,the lithium reduction experiment achieved a yield of 77.2%after a 12-h test,and the electrolytic reduction reached a yield of 85.4%after a 6-h test.展开更多
This study addresses the challenge of predicting zinc(Zn)recovery from carbonate ores via sodium hydroxide(NaOH)leaching.This complex process influenced by variable ore composition,surface passivation effects,and nonl...This study addresses the challenge of predicting zinc(Zn)recovery from carbonate ores via sodium hydroxide(NaOH)leaching.This complex process influenced by variable ore composition,surface passivation effects,and nonlinear reaction dynamics,which complicate reagent optimization and process control in hydrometallurgical operations.To tackle this,a dataset containing 422 experimental observations was compiled from previous studies,incorporating ore composition and process parameters,such as NaOH concentration,leaching time,temperature,and solid-to-liquid ratio.Four regression models(decision tree,neural network,generalized additive model,and random forest)were trained and evaluated using performance metrics,such as coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and symmetrical mean absolute percentage error(SMAPE).Among these,the random forest model achieved the best predictive accuracy,with R^(2)value of 0.8541 on the test set and the lowest error rates,demonstrating its effectiveness in capturing the complex relationships between input variables and Zn recovery.Explainable artificial intelligence,particularly SHapley additive exPlanations(SHAP)analysis,revealed that NaOH concentration,leaching time,and solid-to-liquid ratio had the most positive influence on Zn recovery,whereas elements such as Ca,Fe,and Pb had inhibitory effects.These findings align with known geochemical behavior and provide valuable insights for reagent optimization and process effi-ciency in leaching processes.This study demonstrates the practical potential of machine learning in mineral processing,offering a scalable framework for optimizing Zn recovery from non-sulfide ores and a data-driven approach to enhance decision-making in hydrometallurgical applications.展开更多
文摘In order to explore the reduction pathways of zinc oxide in LiCl molten salt and the optimal process,experiments were conducted in an alumina crucible using metallic lithium as the reducing agent and lithium chloride molten salt as the reaction medium at 923 K.The study assessed the effects of lithium thermochemical reduction and electrolytic reduction of ZnO.The volatilization behavior of metal oxides in molten salts,the equivalent of a reducing agent,reduction time,amount of molten salt,stirring time,and the method of reduction feed were investigated for their impacts on the reduction yield and product composition.X-ray powder diffraction(XRD)analysis of the products showed that lithium reduction of ZnO not only produced metallic Zn but also formed a LiZn alloy.Electrolytic reduction can be used to obtain the metallic Zn product by controlling the potential below-2.2 V(vs Ag/Ag^(+)).Moreover,sintered oxides and higher electrode potentials could enhance the efficiency of electrolysis.Under the optimal reaction conditions determined experimentally,the lithium reduction experiment achieved a yield of 77.2%after a 12-h test,and the electrolytic reduction reached a yield of 85.4%after a 6-h test.
文摘This study addresses the challenge of predicting zinc(Zn)recovery from carbonate ores via sodium hydroxide(NaOH)leaching.This complex process influenced by variable ore composition,surface passivation effects,and nonlinear reaction dynamics,which complicate reagent optimization and process control in hydrometallurgical operations.To tackle this,a dataset containing 422 experimental observations was compiled from previous studies,incorporating ore composition and process parameters,such as NaOH concentration,leaching time,temperature,and solid-to-liquid ratio.Four regression models(decision tree,neural network,generalized additive model,and random forest)were trained and evaluated using performance metrics,such as coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and symmetrical mean absolute percentage error(SMAPE).Among these,the random forest model achieved the best predictive accuracy,with R^(2)value of 0.8541 on the test set and the lowest error rates,demonstrating its effectiveness in capturing the complex relationships between input variables and Zn recovery.Explainable artificial intelligence,particularly SHapley additive exPlanations(SHAP)analysis,revealed that NaOH concentration,leaching time,and solid-to-liquid ratio had the most positive influence on Zn recovery,whereas elements such as Ca,Fe,and Pb had inhibitory effects.These findings align with known geochemical behavior and provide valuable insights for reagent optimization and process effi-ciency in leaching processes.This study demonstrates the practical potential of machine learning in mineral processing,offering a scalable framework for optimizing Zn recovery from non-sulfide ores and a data-driven approach to enhance decision-making in hydrometallurgical applications.
