Lead is the primary toxic element found in jarosite residue; it is necessary to synthesize simulated lead-containing jarosite residue (SLJS) to investigate its lead release behavior and predict the slag’s stability and potential for secondary environmental pollution. This study explores the ion release behavior, leaching toxicity, and stability of SLJS during freeze–thaw cycles with EDTA (E-FTC). Experimental results demonstrate that the release of lead, iron, and sulfate from SLJS under E-FTC is contingent upon multiple factors, including solution pH, EDTA concentration, freeze–thaw cycles, freezing temperature, and freeze–thaw mode. Specifically, employing an EDTA concentration of 200 mM, a pH of 6, a freezing temperature of −20 °C, and 12 freeze–thaw cycles, the lead release reaches 15.1 mM, accounting for 94.9% of the total lead content, while iron is negligibly released, thus enabling effective separation of lead from iron. Subsequent to E-FTC, the exchangeable lead content exhibits a substantial reduction, accompanied by a marked increase in residual lead, resulting in a remarkable 98% reduction in leaching toxicity. Moreover, the equilibrium concentration of lead in the continuous stable leaching solution is 0.13 mg/L, significantly below the lead toxicity threshold (5 mg/L). Therefore, environmental stability can be greatly enhanced. This study presents a novel approach for the safe disposal of jarosite residue under mild conditions and at low temperatures, contributing to the broader field of environmentally sustainable waste management.
The safe disposal of hazardous waste from zinc hydrometallurgy, such as jarosite residue, is crucial for the sustainable development of the industry. The chemical, structural and morphological properties of jarosite residue from zinc smelting were studied by a combination of various characterizations, and environmental stability was evaluated using the toxicity characteristic leaching procedure (TCLP), Chinese standard leaching tests (CSLT) and long-term leaching experiments (LTLE). Phase composition analysis revealed that zinc ferrite and sodium jarosite were the main phases present in the jarosite residue. TCLP and CSLT analyses indicated that the Zn and Pb contents exceeded their respective toxicity identification standards by more than 30 times and 8 times, respectively, exceeding the threshold values of the standard. The LTLE results demonstrated that Pb concentrations continued to exceed the standard limits, even after long contact times. This study has paramount significance in the prediction of jarosite residue stability and the evaluation of its potential for secondary environmental pollution.
Driving style recognition plays a key role in ensuring driving safety and improving vehicle traffic efficiency. With the development of sensing technology, data-driven methods are more widely uesd to recognize driving style. However, adequately labeling data is difficult for supervised learning methods, while the classification accuracy is not sufficiently approved for unsupervised learning methods. This paper proposes a new driving style recognition method based on Tri-CatBoost, which takes CatBoost as base classifier and effectively utilizes the semi-supervised learning mechanism to reduce the dependency on data labels and improve the recognition ability. First, statistical features were extracted from the velocity, acceleration and jerk signals to fully characterize the driving style. The kernel principal component analysis was used to perform nonlinear feature dimension reduction to eliminate feature coupling. CatBoost is an ensemble of symmetric decision trees whose symmetry structure endows it fewer parameters, faster training and testing, and a higher accuracy. Then, a Tri-Training strategy is employed to integrate the base CatBoost classifiers and fully exploit the unlabeled data to generate pseudo-labels, by which the base CatBoost classifiers are optimized. To verify the effectiveness of the proposed method, a large number of experiments are performed on the UAH DriveSet. When the labeling ratio is 50%, the macro precision of Tri-CatBoost is 0.721, which is 15.7% higher than that of unsupervised K-means, 1.6% higher than that of supervised GBDT, 3.7% higher than that of Self-Training, 0.7% higher than that of Co-training, 1.5% higher than that of random forest, 6.7% higher than that of decision tree, and 4.0% higher than that of multilayer perceptron. The macro recall of Tri-CatBoost is 0.744, which is also higher than other methods. The experimental results fully demonstrate the superiority of this work in reducing label dependency and improving recognition performance, which indicates that the proposed method has broad application prospects.
Recent years have seen significant advances in supercapacitor-based applications in portable electronics, where the switching resistor circuit acts as a common cell balancing circuit when charging the supercapacitors. However, existing charging control methods suffer from low energy efficiency, leading to considerable energy loss, and thermal heating. In this paper, we propose a state-of-health (SoH)-aware energy-efficient charging method to maximize the energy efficiency of supercapacitors during the charging process. First, we provide a sufficient and necessary condition to maximize the energy efficiency. Then, an online SoH estimation algorithm is designed to estimate capacitance and balancing resistance in real time. Thereafter, an SoH-aware energy-efficient charging algorithm is further proposed to be implemented in microcontrollers. A charger prototype has been built to verify the effectiveness of the proposed charging algorithm. Extensive simulation and experiment results show that the energy efficiency of the proposed design is improved considerably when compared with existing methods.
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