Abstract

Leaching is a complex solid–liquid reaction which has an important influence on the recovery efficiency of the spent lithium-ion batteries (LIBs). Therefore, it is of significant importance to utilize an appropriate technique to predict the effect of operating parameters on the optimized recovery rate. In the present study, a combined method of the artificial neural network (ANN) and particle swarm optimization algorithm (PSO) was used as a model to predict the leaching efficiency of cobalt from spent LIBs. To find the dependency of the leached percentage of cobalt on the operational parameters as model inputs, 42 repeatable numerous experiments are performed using H₂SO₄ in the presence of H₂O₂. It was found that the proposed model can be a useful technique in the demonstration of the nonlinear relationship between the leaching efficiency and the process parameters. The performance of PSO–ANN models was validated by statistical thresholds and compared with those of common ANN technique. Moreover, it was found that the pulp density of the leaching solution and the concentration of sulfuric acid were the most important reaction parameters of the spent LIBs recovery, respectively.

中文翻译：

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基于PSO-ANN的废弃锂离子电池钴浸出率预测

摘要

浸出是一种复杂的固液反应，对废锂离子电池（LIB）的回收效率具有重要影响。因此，利用适当的技术来预测操作参数对优化回收率的影响非常重要。在本研究中，使用人工神经网络（ANN）和粒子群优化算法（PSO）的组合方法作为模型来预测废LIB中钴的浸出效率。为了找到作为模型输入的钴的浸出百分比对操作参数的依赖性，在H ₂ O ₂存在下使用H ₂ SO ₄进行了42次可重复的大量实验。发现所提出的模型可以作为证明浸出效率与工艺参数之间非线性关系的有用技术。通过统计阈值验证了PSO-ANN模型的性能，并将其与普通ANN技术的性能进行了比较。此外，发现浸出溶液的纸浆密度和硫酸的浓度分别是回收废LIB最重要的反应参数。