Machine-Learning-Assisted Prediction of Maximum Metal Recovery from Spent Zinc-Manganese Batteries

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10249913" target="_blank" >RIV/61989100:27230/22:10249913 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2227-9717/10/5/1034" target="_blank" >https://www.mdpi.com/2227-9717/10/5/1034</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr10051034" target="_blank" >10.3390/pr10051034</a>

Result language

angličtina

Original language name

Machine-Learning-Assisted Prediction of Maximum Metal Recovery from Spent Zinc-Manganese Batteries

Original language description

Spent zinc-manganese batteries contain heavy toxic metals that pose a serious threat to the environment. Recovering these metals is vital not only for industrial use but also for saving the environment. Recycling metal from spent batteries is a complex task. In this study, machine-learning-based predictive models are developed for predicting metal recovery from spent zinc-manganese batteries by studying the energy substrates concentration, pH control of bioleaching media, incubating temperature and pulp density. The main objective of this study is to make a detailed comparison among five machine learning models, namely, linear regression, random forest regression, AdaBoost regression, gradient boosting regression and XG boost regression. All the machine learning models are tuned for optimal hyperparameters. The results from each of the machine learning models are compared using several statistical metrics such as R-2, mean squared error (MSE), mean absolute error (MAE), maximum error and median error. The XG Boost regression model is observed to be the most effective among the tested algorithms.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Processes

ISSN

2227-9717

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

1-14

UT code for WoS article

000804291600001

EID of the result in the Scopus database

2-s2.0-85131037283