A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides

Despite being recognized as a key component towards reducing global GHG emissions, many challenges remain throughout the hydrogen value chain. Hydrogen storage is one critical aspect, since proper storage is essential for its wide scale deployment. One storage option is the chemisorption of hydrogen...

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Autores principales:	K Anthea Rana, John Andrei Acantilado, Jared Ethan Santos, Raymond R. Tan, Kathleen B. Aviso
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/68b9ef69da14401b9194913382f18ad3
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spelling	oai:doaj.org-article:68b9ef69da14401b9194913382f18ad32021-11-15T21:47:59ZA Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides10.3303/CET21881062283-9216https://doaj.org/article/68b9ef69da14401b9194913382f18ad32021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11899https://doaj.org/toc/2283-9216Despite being recognized as a key component towards reducing global GHG emissions, many challenges remain throughout the hydrogen value chain. Hydrogen storage is one critical aspect, since proper storage is essential for its wide scale deployment. One storage option is the chemisorption of hydrogen in metals and intermetallic alloys (i.e., metal hydrides). This approach can potentially address issues on safety, infrastructure, and cost which currently hinder the transition toward a hydrogen economy. The only suitable method for determining appropriate hydrogen storage materials is through experimentation which is resource intensive. There are many parameters that can affect the hydrogen storage capacity of a material; databases on previously investigated materials can be used to narrow down future investigation to the most promising candidates. Machine learning (ML) techniques can be employed to determine how different properties predict for hydrogen storage capacity. ML can use previously compiled data to generate a classification model that can be utilized for determining and predicting a material's viability for hydrogen storage. This paper uses the hyperbox ML technique to generate interpretable decision rules to predict if a metal hydride is a good candidate for hydrogen storage. A case study which specifically focuses on complex and magnesium hydrides is used to demonstrate this approach. The generated decision model had a false positive rate of 22.0 % and false negative rate of 36.8 %.K Anthea RanaJohn Andrei AcantiladoJared Ethan SantosRaymond R. TanKathleen B. AvisoAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 K Anthea Rana John Andrei Acantilado Jared Ethan Santos Raymond R. Tan Kathleen B. Aviso A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides
description	Despite being recognized as a key component towards reducing global GHG emissions, many challenges remain throughout the hydrogen value chain. Hydrogen storage is one critical aspect, since proper storage is essential for its wide scale deployment. One storage option is the chemisorption of hydrogen in metals and intermetallic alloys (i.e., metal hydrides). This approach can potentially address issues on safety, infrastructure, and cost which currently hinder the transition toward a hydrogen economy. The only suitable method for determining appropriate hydrogen storage materials is through experimentation which is resource intensive. There are many parameters that can affect the hydrogen storage capacity of a material; databases on previously investigated materials can be used to narrow down future investigation to the most promising candidates. Machine learning (ML) techniques can be employed to determine how different properties predict for hydrogen storage capacity. ML can use previously compiled data to generate a classification model that can be utilized for determining and predicting a material's viability for hydrogen storage. This paper uses the hyperbox ML technique to generate interpretable decision rules to predict if a metal hydride is a good candidate for hydrogen storage. A case study which specifically focuses on complex and magnesium hydrides is used to demonstrate this approach. The generated decision model had a false positive rate of 22.0 % and false negative rate of 36.8 %.
format	article
author	K Anthea Rana John Andrei Acantilado Jared Ethan Santos Raymond R. Tan Kathleen B. Aviso
author_facet	K Anthea Rana John Andrei Acantilado Jared Ethan Santos Raymond R. Tan Kathleen B. Aviso
author_sort	K Anthea Rana
title	A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides
title_short	A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides
title_full	A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides
title_fullStr	A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides
title_full_unstemmed	A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides
title_sort	binary hyperbox classifier model for hydrogen storage in metal hydrides
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/68b9ef69da14401b9194913382f18ad3
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A Binary Hyperbox Classifier Model for Hydrogen Storage in Metal Hydrides

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