Intelligent optimization of process conditions for maximum metal recovery from spent zinc-manganese batteries

Abstract

By 2025, 2 million metric tons of batteries must be recycled. Among these batteries, the spent Zinc-Manganese batteries poses a serious threat to environment due to toxic heavy metals. This metals are toxic but at same time vital for various industrial applications. This metals are generally recovered by physical-chemical process which are highly energy intensive and polluting. An eco-friendly recycling process has to be explored to tackle such issue. The bioleaching is one such eco-friendly recycling method. The objective of this work is to optimize the process parameters of bioleaching method, so as to make this process commercially viable. The optimization of this process is done through statistical based automated neural network intelligent optimization approach. The formulated models were inline with the complex behaviour of bioleaching process. The training and validation performance of the models were near to 1. The parametric, global sensitivity and interaction analysis was undertaken for understanding the relationship between different parameters and its affect on the metal yield. The optimum values of process parameters were determined for maximizing the metal yield.