“…Demergasso et al [ 117 ] developed a decision support system for the bioleaching process in heaps (using the automatic learning algorithms of K means and decision trees) where a user could match the operating conditions with the historical set of data, obtaining the expected performance, such as mineral recovery, leaching agent consumption, or microbial activity. Other applications of machine learning to the mineral bioleaching process include the estimation of the recovery rate in the bioleaching process using a machine learning approach, as in the work developed by Mokarian et al [ 118 ], where 40 regression-based machine learning algorithms were evaluated, the random forest regression being the algorithm that presented the highest performance (77% accuracy). The variables used by Mokarian et al [ 118 ] consider the type of bacteria, temperature, pulp density, initial pH, the method used, particle size distribution, and density and type of resources, concluding that the resources, the size distribution and density of the particles, the temperature, and the type of microorganisms—bacteria and/or fungi—were the most influential variables for the estimation of the mineral recovery rate.…”
Section: Modeling Of Mineral Bioleachingmentioning
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.
“…Demergasso et al [ 117 ] developed a decision support system for the bioleaching process in heaps (using the automatic learning algorithms of K means and decision trees) where a user could match the operating conditions with the historical set of data, obtaining the expected performance, such as mineral recovery, leaching agent consumption, or microbial activity. Other applications of machine learning to the mineral bioleaching process include the estimation of the recovery rate in the bioleaching process using a machine learning approach, as in the work developed by Mokarian et al [ 118 ], where 40 regression-based machine learning algorithms were evaluated, the random forest regression being the algorithm that presented the highest performance (77% accuracy). The variables used by Mokarian et al [ 118 ] consider the type of bacteria, temperature, pulp density, initial pH, the method used, particle size distribution, and density and type of resources, concluding that the resources, the size distribution and density of the particles, the temperature, and the type of microorganisms—bacteria and/or fungi—were the most influential variables for the estimation of the mineral recovery rate.…”
Section: Modeling Of Mineral Bioleachingmentioning
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.
“… 68 Furthermore, biorecycling technologies have the potential to offer lower environmental impact and energy consumption compared to conventional physical and chemical methods. 110 Figure 8 Bio-recycling technology (A) Possible multi-phase reactions between bio-organic acids and lithium battery particles. Copyright 2018, Elsevier, Reproduced with permission.…”
“…The major advantages and disadvantages of various types of recycling methods are given in Table 2 . Among the various types of recycling methods, bleaching is cost-effective, environmentally friendly, simple in operation and less energy intensive ( Golmohammadzadeh et al, 2022 ; Mokarian et al, 2022 ). As of 2018, the recycling rate of LIBs was only 8.86% ( Mao et al, 2022 ), and the global rate of Li recycling is even lower (i.e., < 1%) ( Swain, 2017 ).…”
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.
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