Selective Flotation of Elemental Sulfur from Pressure Acid Leaching Residue of Zinc Sulfide

An efficient flotation process was developed to selectively recover elemental sulfur from a high-sulfur pressure acid leaching residue of zinc sulfide concentrate. The process mineralogy analysis showed that the sulfur content reached 46.21%, and 81.97% of the sulfur existed as elemental sulfur which was the major mineral in the residue and primarily existed as pellet aggregate and biconical euhedral crystal. An elemental sulfur concentrate product with 99.9% of recovery and 83.46% of purity was obtained using the flotation process of one-time blank rougher, two-time agent-added roughers, and two-time cleaners with Z-200 as collector and Na2S + ZnSO4 + Na2SO3 as depressant. The flotation experiment using return water indicated that the cycle use of return water had no adverse effect on the flotation performance of elemental sulfur. The process mineralogy analysis manifested that main minerals in the residue directionally went into the flotation products. Most of elemental sulfur entered the concentrate while other minerals almost completely went into the tailing. Main valuable elements lead, zinc, and silver entered the tailing with sulfides and could be recovered by lead smelting. The proposed process can realize the comprehensive recovery of valuable components in the high-sulfur residue and thus it has wide industrial application prospect

Selective Flotation of Elemental Sulfur from Pressure Acid Leaching Residue of Zinc Sulfide

An efficient flotation process was developed to selectively recover elemental sulfur from a high-sulfur pressure acid leaching residue of zinc sulfide concentrate. The process mineralogy analysis showed that the sulfur content reached 46.21%, and 81.97% of the sulfur existed as elemental sulfur which was the major mineral in the residue and primarily existed as pellet aggregate and biconical euhedral crystal. An elemental sulfur concentrate product with 99.9% of recovery and 83.46% of purity was obtained using the flotation process of one-time blank rougher, two-time agent-added roughers, and two-time cleaners with Z-200 as collector and Na2S + ZnSO4 + Na2SO3 as depressant. The flotation experiment using return water indicated that the cycle use of return water had no adverse effect on the flotation performance of elemental sulfur. The process mineralogy analysis manifested that main minerals in the residue directionally went into the flotation products. Most of elemental sulfur entered the concentrate while other minerals almost completely went into the tailing. Main valuable elements lead, zinc, and silver entered the tailing with sulfides and could be recovered by lead smelting. The proposed process can realize the comprehensive recovery of valuable components in the high-sulfur residue and thus it has wide industrial application prospect

A Review of Recovery of Palladium from the Spent Automobile Catalysts

The spent automobile catalysts (SAC) is the major secondary source of palladium and the production of SAC is increasing rapidly over years. The price of palladium keeps rising over the years, which demonstrates its preciousness and urgent industrial demand. Recovering palladium from the spent automobile catalysts benefits a lot from economic and environmental protection aspects. This review aims to provide some new considerations of recovering palladium from the spent automotive catalysts by summarizing and discussing both hydrometallurgical and pyrometallurgical methods. The processes of pretreatment, leaching/extraction, and separation/recovery of palladium from the spent catalysts are introduced, and related reaction mechanisms and process flows are given, especially detailed for hydrometallurgical methods. Hydrometallurgical methods such as chloride leaching with oxidants possess a high selectivity of palladium and low consumption of energy, and are cost-effective and flexible for different volume feeds compared with pyrometallurgical methods. The recovery ratios of palladium and other platinum-group metals should be the focus of competition since their prices have been rapidly increased over the years, and hence more efficient extractants with high selectivity of palladium even in the complexed leachate should be proposed in the future

A Review of Recovery of Palladium from the Spent Automobile Catalysts

The spent automobile catalysts (SAC) is the major secondary source of palladium and the production of SAC is increasing rapidly over years. The price of palladium keeps rising over the years, which demonstrates its preciousness and urgent industrial demand. Recovering palladium from the spent automobile catalysts benefits a lot from economic and environmental protection aspects. This review aims to provide some new considerations of recovering palladium from the spent automotive catalysts by summarizing and discussing both hydrometallurgical and pyrometallurgical methods. The processes of pretreatment, leaching/extraction, and separation/recovery of palladium from the spent catalysts are introduced, and related reaction mechanisms and process flows are given, especially detailed for hydrometallurgical methods. Hydrometallurgical methods such as chloride leaching with oxidants possess a high selectivity of palladium and low consumption of energy, and are cost-effective and flexible for different volume feeds compared with pyrometallurgical methods. The recovery ratios of palladium and other platinum-group metals should be the focus of competition since their prices have been rapidly increased over the years, and hence more efficient extractants with high selectivity of palladium even in the complexed leachate should be proposed in the future

An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Background: Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective: This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods: An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions: Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients' age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms