Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach

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@Article{YUN:2019:JCP,

• author = "Liu Yun and Wei Li and Akhil Garg and Sivasriprasanna Maddila and Liang Gao and Zhun Fan and P. Buragohain and Chin-Tsan Wang",
• title = "Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach",
• journal = "Journal of Cleaner Production",
• volume = "218",
• pages = "130--140",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Recycling, Copper recovery, Waste printed circuit boards, Waste management",
• ISSN = "0959-6526",
• DOI = "doi:10.1016/j.jclepro.2018.11.226",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652618336291",
• abstract = "There are a number of government directives and regulations as well as many public schemes on the recycling of batteries, in spite of this; the quantity of batteries that are actually recycled is still very low. Current production capacity cannot meet projected demand of Lithium-ion batteries. To counter this, the reclamation and repurposing of metals like cadmium, Lithium and Zinc from used or spent batteries is the only viable scheme. This is both environmentally friendly and economically feasible. An alternative is the selective chemical leaching in the presence of Sulfuric acid and Sodium metabisulfite. In this paper, the effect of these chemicals as well as the solid-to-liquid ratio and time of retention is comprehensively studied. Experiments are designed for the recovery of Zinc and cadmium from the spend Lithium-ion batteries mix. To maximize the recovery of Zinc and cadmium, the combined genetic programming and simulated annealing approach is proposed. Genetic programming is used for the formulation of functional relationship between recovered metals Zinc and cadmium and the inputs (Solid/Liquid ratio, concentration of Sulfuric acid, mass of Sodium metabisulfite and retention time). The optimal input conditions determined using the simulated annealing algorithm includes Solid/Liquid ratio of 11.7percent, 0.86a M Sulfuric acid, 0.56a g/g of Sodium metabisulfite and 45a min of retention time. Three dimensions surface analysis reveals that a lower value of Solid/Liquid ratio favours a better yield. The optimal conditions are validated using experiments. This confirms the efficacy of simulated annealing aided genetic programming techniques as well as the optimal conditions of the metal extraction",

}

Genetic Programming entries for Liu Yun Wei Li Akhil Garg Sivasriprasanna Maddila Liang Gao Zhun Fan P Buragohain Chin-Tsan Wang

Citations