Recovery of toxic and vital metal from spent Li‐ion batteries is a vital problem in the recycling industry. The recycling processes such as bioleaching are much simpler and environment friendly but lack the required efficiency for metal recovery to prove the commercial feasibility of the model. This work focuses on increasing the efficiency of the bioleaching process by targeting its intermediate processes for maximum vital metal recovery. The intermediate process of biomass generation from Aspergillus niger fungus is targeted. The data from experimental design is modelled using support vector regression with v‐fold cross‐validation. The bioleaching process is optimized such that maximum biomass concentration is obtained for efficient and commercially viable metal recovery. The results are divided into four sections, each addressing an important issue of the recycling process mechanism. The generated model is found to have good stability and accurate process mechanism predictability. Global sensitivity and interaction analysis is employed for efficient weighted optimization. The model generated trends and optimization results are verified through the profiling curve as well as past literature experimental data. This work reports the maximum biomass concentration of 25 g/L. The model employed is stable and efficient, reaching a stable optimized value under 300 iterations. The optimized input parameters values obtained are 144.39 g/L, 1.29% v/v, 6.70, 1513.05 ppm, 4989.79 ppm, 2094.22 ppm, 347.57 ppm and 2.37 for sucrose concentration, inoculum size, initial pH, oxalic acid, gluconic acid, malic acid, citric acid concentration and final pH, respectively.

中文翻译：

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废锂离子电池生物浸出过程的智能优化：支持向量回归方法的应用

从废旧锂离子电池中回收有毒和重要金属是回收行业中的重要问题。诸如生物浸出之类的再循环过程非常简单且对环境友好，但缺乏金属回收所需的效率以证明该模型的商业可行性。这项工作的重点是通过将生物浸出过程的中间过程作为目标，以实现最大的活泼金属回收，从而提高其效率。黑曲霉产生生物质的中间过程真菌是有针对性的。来自实验设计的数据使用支持向量回归与v折交叉验证进行建模。优化了生物浸出工艺，以便获得最大的生物质浓度，以实现有效的商业上可行的金属回收。结果分为四个部分，每个部分都涉及回收过程机制的重要问题。发现所生成的模型具有良好的稳定性和准确的过程机制可预测性。全局敏感性和交互分析用于有效的加权优化。通过轮廓曲线以及过去的文献实验数据验证了模型生成的趋势和优化结果。这项工作报告最大生物量浓度为25 g / L。使用的模型稳定且有效，在300次迭代中达到稳定的优化值。对于蔗糖浓度，接种量，初始pH，草酸，葡萄糖酸，苹果酸，获得的最佳输入参数值为144.39 g / L，1.29％v / v，6.70、1513.05 ppm，4989.79 ppm，2094.22 ppm，347.57 ppm和2.37酸，柠檬酸浓度和最终pH值。