Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is...

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Autores principales:	Rocio Camarena-Martinez, Rocio A. Lizarraga-Morales, Roberto Baeza-Serrato
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence artificial neural network biodigester geomembrane quality raw material Technology T
Acceso en línea:	https://doaj.org/article/d7ac56c6b60a44efb7062bb1acaea41a
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spelling	oai:doaj.org-article:d7ac56c6b60a44efb7062bb1acaea41a2021-11-11T16:04:01ZClassification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network10.3390/en142173451996-1073https://doaj.org/article/d7ac56c6b60a44efb7062bb1acaea41a2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7345https://doaj.org/toc/1996-1073Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is the coat that covers the biodigester used to control the migration of fluids. Therefore, the selection of the proper geomembrane, in terms of thickness, resistance, flexibility, etc., is fundamental. Unfortunately, there are no studies for the selection of geomembranes, and usually, it is an empirical process performed by workers based on their own experience. Such empirical selection might be inaccurate, limited, inconvenient, and even dangerous. In order to assist workers during the building process of a biodigester, this study proposes the use of an Artificial Neural Network (ANN) to classify a geomembrane as appropriate or not appropriate for the manufacture of a biodigester. The ANN is trained with a database built from qualitative and quantitative evaluations of different characteristics of geomembranes. The results indicate that the proposed ANN classifies the most suitable geomembranes with a 99.9% success rate. The proposed ANN becomes a reliable tool that contributes to the quality and safety of a biodigester.Rocio Camarena-MartinezRocio A. Lizarraga-MoralesRoberto Baeza-SerratoMDPI AGarticleartificial intelligenceartificial neural networkbiodigestergeomembranequalityraw materialTechnologyTENEnergies, Vol 14, Iss 7345, p 7345 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence artificial neural network biodigester geomembrane quality raw material Technology T
spellingShingle	artificial intelligence artificial neural network biodigester geomembrane quality raw material Technology T Rocio Camarena-Martinez Rocio A. Lizarraga-Morales Roberto Baeza-Serrato Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network
description	Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is the coat that covers the biodigester used to control the migration of fluids. Therefore, the selection of the proper geomembrane, in terms of thickness, resistance, flexibility, etc., is fundamental. Unfortunately, there are no studies for the selection of geomembranes, and usually, it is an empirical process performed by workers based on their own experience. Such empirical selection might be inaccurate, limited, inconvenient, and even dangerous. In order to assist workers during the building process of a biodigester, this study proposes the use of an Artificial Neural Network (ANN) to classify a geomembrane as appropriate or not appropriate for the manufacture of a biodigester. The ANN is trained with a database built from qualitative and quantitative evaluations of different characteristics of geomembranes. The results indicate that the proposed ANN classifies the most suitable geomembranes with a 99.9% success rate. The proposed ANN becomes a reliable tool that contributes to the quality and safety of a biodigester.
format	article
author	Rocio Camarena-Martinez Rocio A. Lizarraga-Morales Roberto Baeza-Serrato
author_facet	Rocio Camarena-Martinez Rocio A. Lizarraga-Morales Roberto Baeza-Serrato
author_sort	Rocio Camarena-Martinez
title	Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network
title_short	Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network
title_full	Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network
title_fullStr	Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network
title_full_unstemmed	Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network
title_sort	classification of geomembranes as raw material for defects reduction in the manufacture of biodigesters using an artificial neuronal network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d7ac56c6b60a44efb7062bb1acaea41a
work_keys_str_mv	AT rociocamarenamartinez classificationofgeomembranesasrawmaterialfordefectsreductioninthemanufactureofbiodigestersusinganartificialneuronalnetwork AT rocioalizarragamorales classificationofgeomembranesasrawmaterialfordefectsreductioninthemanufactureofbiodigestersusinganartificialneuronalnetwork AT robertobaezaserrato classificationofgeomembranesasrawmaterialfordefectsreductioninthemanufactureofbiodigestersusinganartificialneuronalnetwork
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Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

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