Optimization of process conditions for maximum metal recovery from spent zinc‐manganese Batteries: Illustration of Statistical based Automated Neural Network approach

Ruhatiya, Chaitanya, Shaosen, Su, Wang, Chin‐Tsan, Jishnu, A. K. and Bhalerao, Yogesh ORCID: https://orcid.org/0000-0002-0743-8633 (2020) Optimization of process conditions for maximum metal recovery from spent zinc‐manganese Batteries: Illustration of Statistical based Automated Neural Network approach. Energy Storage, 2 (3). ISSN 2578-4862

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Abstract

Recovery of the vital metals from spent batteries using bioleaching is one of the commonly used method for recycling of spent batteries. In this study, a Statistical based Automated Neural Network approach is proposed for determination of optimum input parameters values in bioleaching of zinc‐manganese batteries. Experiments are performed to measure the recovery of zinc and manganese based on the input parameters such as energy substrates concentration, pH control of bioleaching media, incubating temperature and pulp density. It was found that the proposed model based metal extraction models precisely estimated the yields of zinc and manganese with higher values of coefficient of determination of 0.94. Based on global sensitivity analysis, it was found that for the extraction of zinc, the most contributing parameters are pulp density and pH while for extraction of Mn the most contributing parameters are pulp density and incubating temperature. The optimum parameter values for maximum recovery of zinc and maximum recovery of manganese are determined using optimization method of simulated annealing. The optimum parameter values obtained for maximum recovery of Zn metal are as substrates concentration 32 g/L, pH 1.9‐2.0, incubating temperature 30 °C, pulp density 10% and substrates concentration 32 g/L, pH 2.0, incubating temperature 35 °C, pulp density 8% for maximum recovery of Mn.

Item Type:	Article
Additional Information:	Special Issue: Renewable Energy and Energy Storage Systems
Faculty \ School:	Faculty of Science > School of Engineering (former - to 2024)
UEA Research Groups:	Faculty of Science > Research Groups > Materials, Manufacturing & Process Modelling
Related URLs:	https://onlinelibrary.wiley.com/doi/abs/...
Depositing User:	LivePure Connector
Date Deposited:	05 Feb 2020 04:50
Last Modified:	07 Nov 2024 12:42
URI:	https://ueaeprints.uea.ac.uk/id/eprint/73977
DOI:	10.1002/est2.111

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