The applicability of artificial neural network (ANN) to predict the bioleaching of metals using from computer printed circuit boards (CPCB) and the influence of process parameters were studied. The influence of process parameters initial pH (1.6‐2.4), pulp density (2%‐13%), and the initial volume of Inoculum (5%‐25%) were investigated on the rate of bioleaching of metals from CPCB. Network inputs were fed as initial pH, pulp density, and inoculum volume and with the extraction of Cu, Ag, and Au as output. The ANN was developed using the Levenberg‐Marquardt algorithm and trained for modeling and prediction. The most fitting architectures for Cu, Ag, and Au were [4‐5‐5‐2‐1], [4‐7‐5‐2‐1], [4‐7‐1‐1‐1] trained with Levenberg‐Marquardt algorithm, respectively. The R values were observed to be 0.996, 0.997, and 0.993 for Cu, Ag, and Au extraction predictions, respectively. The genetic algorithm model defined by ANN was used to achieve maximum extraction rates for Cu, Au, and Ag. The predicted data showed that there is a great capability of using ANN for the prediction of Cu, Ag, and Au extraction from CPCB through bioleaching process. Hence, the ANN model can be used to control the operational conditions for improved metals extraction through bioleaching.

中文翻译：

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使用 Levenberg-Marquardt 算法对废计算机印刷电路板中金属的生物浸出进行神经网络预测

研究了人工神经网络 (ANN) 使用计算机印刷电路板 (CPCB) 预测金属生物浸出的适用性以及工艺参数的影响。研究了工艺参数初始 pH (1.6-2.4)、纸浆密度 (2%-13%) 和初始接种量 (5%-25%) 对 CPCB 中金属生物浸出率的影响。网络输入作为初始 pH 值、纸浆密度和接种量输入，并以 Cu、Ag 和 Au 的提取作为输出。人工神经网络是使用 Levenberg-Marquardt 算法开发的，并经过建模和预测训练。与 Levenberg 一起训练的 Cu、Ag 和 Au 最合适的架构是 [4-5-5-2-1]、[4-7-5-2-1]、[4-7-1-1-1] -Marquardt 算法，分别。观察到 Cu、Ag 的 R 值为 0.996、0.997 和 0.993，和 Au 提取预测，分别。ANN 定义的遗传算法模型用于实现 Cu、Au 和 Ag 的最大提取率。预测数据表明，使用人工神经网络预测通过生物浸出工艺从 CPCB 中提取 Cu、Ag 和 Au 的能力很强。因此，ANN 模型可用于控制操作条件，以通过生物浸出改进金属提取。