The advanced design of bioleaching process for metal recovery: A machine learning approach

Intelligent leaching rare earth elements from waste fluorescent lamps

Rare earth elements (REEs), one of the global key strategic resources, are widely applied in electronic information and national defense, etc. The sharply increasing demand for REEs leads to their overexploitation and environmental pollution. Recycling REEs from their second resources such as waste fluorescent lamps (WFLs) is a win-win strategy for REEs resource utilization and environmental production. Pyrometallurgy pretreatment combined with acid leaching is proven as an efficient approach to recycling REEs from WFLs. Unfortunately, due to the uncontrollable components of wastes, many trials were required to obtain the optimal parameters, leading to a high cost of recovery and new environmental risks. This study applied machine learning (ML) to build models for assisting the leaching of six REEs (Tb, Y, Eu, La, and Gd) from WFLs, only needing the measurement of particle size and composition of the waste feed. The feature importance analysis of 40 input features demonstrated that the particle size, Mg, Al, Fe, Sr, Ca, Ba, and Sb content in the waste feed, the pyrometallurgical and leaching parameters have important effects on REEs leaching. Furthermore, their influence rules on different REEs leaching were revealed. Finally, some verification experiments were also conducted to demonstrate the reliability and practicality of the model. This study can quickly get the optimal parameters and leaching efficiency for REEs without extensive optimization experiments, which significantly reduces the recovery cost and environmental risks. Our work carves a path for the intelligent recycling of strategic REEs from waste.

Recycling technologies, policies, prospects, and challenges for spent batteries

The recycling of spent batteries is an important concern in resource conservation and environmental protection, while it is facing challenges such as insufficient recycling channels, high costs, and technical difficulties. To address these issues, a review of the recycling of spent batteries, emphasizing the importance and potential value of recycling is conducted. Besides, the recycling policies and strategies implemented in representative countries are summarized, providing legal and policy support for the recycling industry. Moreover, a comprehensive classification and comparison of recycling technologies identify the characteristics and current status of different approaches. The integrated recycling technology provides a better recycling performance with zero-pollution recycling of spent battery. Biorecycling technology is expected to gain a broad development prospect in the future owing to the superiority of energy-saving and environmental protection, high recycling efficiency, via microbial degradation, enzymatic degradation, etc. Consequently, as for the existing recycling challenges of waste batteries, developing new recycling technology and perfecting its recycling system is an indispensable guarantee for the sustainable development of waste battery. Meanwhile, theoretical support is offered for the recycling of spent batteries.

Recovery of valuable metals from spent lithium-ion batteries using microbial agents for bioleaching: a review

Spent lithium-ion batteries (LIBs) are increasingly generated due to their widespread use for various energy-related applications. Spent LIBs contain several valuable metals including cobalt (Co) and lithium (Li) whose supply cannot be sustained in the long-term in view of their increased demand. To avoid environmental pollution and recover valuable metals, recycling of spent LIBs is widely explored using different methods. Bioleaching (biohydrometallurgy), an environmentally benign process, is receiving increased attention in recent years since it utilizes suitable microorganisms for selective leaching of Co and Li from spent LIBs and is cost-effective. A comprehensive and critical analysis of recent studies on the performance of various microbial agents for the extraction of Co and Li from the solid matrix of spent LIBs would help for development of novel and practical strategies for effective extraction of precious metals from spent LIBs. Specifically, this review focuses on the current advancements in the application of microbial agents namely bacteria (e.g., Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans) and fungi (e.g., Aspergillus niger) for the recovery of Co and Li from spent LIBs. Both bacterial and fungal leaching are effective for metal dissolution from spent LIBs. Among the two valuable metals, the dissolution rate of Li is higher than Co. The key metabolites which drive the bacterial leaching include sulfuric acid, while citric acid, gluconic acid and oxalic acid are the dominant metabolites in fungal leaching. The bioleaching performance depends on both biotic (microbial agents) and abiotic factors (pH, pulp density, dissolved oxygen level and temperature). The major biochemical mechanisms which contribute to metal dissolution include acidolysis, redoxolysis and complexolysis. In most cases, the shrinking core model is suitable to describe the bioleaching kinetics. Biological-based methods (e.g., bioprecipitation) can be applied for metal recovery from the bioleaching solution. There are several potential operational challenges and knowledge gaps which should be addressed in future studies to scale-up the bioleaching process. Overall, this review is of importance from the perspective of development of highly efficient and sustainable bioleaching processes for optimum resource recovery of Co and Li from spent LIBs, and conservation of natural resources to achieve circular economy.

Bioleaching Modeling-A Review

The leaching of minerals is one of the main unit operations in the metal dissolution process, and in turn it is a process that generates fewer environmental liabilities compared to pyrometallurgical processes. As an alternative to conventional leaching methods, the use of microorganisms in mineral treatment processes has become widespread in recent decades, due to advantages such as the non-production of emissions or pollution, energy savings, low process costs, products compatible with the environment, and increases in the benefit of low-grade mining deposits. The purpose of this work is to introduce the theoretical foundations associated with modeling the process of bioleaching, mainly the modeling of mineral recovery rates. The different models are collected from models based on conventional leaching dynamics modeling, based on the shrinking core model, where the oxidation process is controlled by diffusion, chemically, or by film diffusion until bioleaching models based on statistical analysis are presented, such as the surface response methodology or the application of machine learning algorithms. Although bioleaching modeling (independent of modeling techniques) of industrial (or large-scale mined) minerals is a fairly developed area, bioleaching modeling applied to rare earth elements is a field with great growth potential in the coming years, as in general bioleaching has the potential to be a more sustainable and environmentally friendly mining method than traditional mining methods.

Application of Machine Learning in a Mineral Leaching Process-Taking Pyrolusite Leaching as an Example

In this study, several machine learning models were used to analyze the process variables of electric-field-enhanced pyrolusite leaching and predict the leaching rate of manganese, and the applicability of those models in the leaching process of hydrometallurgy was compared. It showed that there was no correlation between the six leaching conditions; in addition to the leaching time, the concentrations of sulfuric acid and ferrous sulfate had great influences on the leaching of pyrolusite. The results of the prediction models showed that the support vector regression model has the best prediction performance, with regression index (R ²) = 0.92 and mean square error = 25.04, followed by the gradient boosting regression model (R ² > 0.85). In this research, machine learning models were applied to the optimization of the manganese leaching process, and the research process and methods were also applicable to other hydrometallurgical processes for majorization and result prediction.