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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Processes

ISSN

2227-9717

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

000804291600001

EID výsledku v databázi Scopus

2-s2.0-85131037283