Life cycle assessment (LCA) score prediction using deep learning on detergent plastic packing products

Life cycle assessment (LCA) is a systematic method for quantitatively analyzing the environmental impact of a product throughout the product life cycle. However, the obstacle in doing LCA is that it takes quite a lot of time to identify and register products to obtain an LCA score for the entire lif...

Full description

Saved in:
Bibliographic Details
Main Authors: Fatriansyah, Jaka Fajar, Putri, Anisa Rahmania, Hartoyo, Fernanda, Pradana, Agrin Febrian, Fauzi, Andrian
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Impact analysis
Life cycle assessment
Packaging
Product life cycle
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue 1
container_start_page
container_title
container_volume 2901
creator Fatriansyah, Jaka Fajar
Putri, Anisa Rahmania
Hartoyo, Fernanda
Pradana, Agrin Febrian
Fauzi, Andrian
description Life cycle assessment (LCA) is a systematic method for quantitatively analyzing the environmental impact of a product throughout the product life cycle. However, the obstacle in doing LCA is that it takes quite a lot of time to identify and register products to obtain an LCA score for the entire life of a product. One solution to overcome these limitations is to build a deep learning model to predict LCA scores on detergent plastic packaging products. The result of the research is an LCA score prediction program for detergent plastic packaging products using the eco-indicator 99 method using a total of 240 datasets consisting of 8% real/actual data, 75% dummy data, and 17% hybrid data. Other model parameters include test size 0.2, random state 6, batch size 64, with three hidden layers and density 64,32,16, epoch 1000, and learning rate 0.01. The program produces an optimum accuracy of 99.39% resulting from regression metrics using the R2 score.
doi_str_mv 10.1063/5.0179523
format conference_proceeding
fullrecord <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_scitation_primary_10_1063_5_0179523</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2902131621</sourcerecordid><originalsourceid>FETCH-LOGICAL-p133t-20b058b99e1c646763055ff4eb69e29f3bd0248610ace6dfd8d8d01324531a973</originalsourceid><addsrcrecordid>eNotUE1LxDAQDaLgunrwHwS8qNB1kjRpc1yW9QMKXhS8lTSdLF27bU3Sw_57W3YZmOG9-XjMI-SewYqBEi9yBSzTkosLsmBSsiRTTF2SBYBOE56Kn2tyE8IegOssyxfEFY1Dao-2RWpCwBAO2EX6WGzWTzTY3iMdPNaNjU3f0TE03Y7WiANt0fhuRhNdY0S_m_eG1oTYWDoY-zs3B9_Xo43hllw50wa8O9cl-X7dfm3ek-Lz7WOzLpKBCRETDhXIvNIamVWpypQAKZ1LsVIauXaiqoGnuWJgLKra1fkUwARPpWBGZ2JJHk53J-G_EUMs9_3ou0my5Bo4E0xNaUmeT1PBNtHMn5WDbw7GH0sG5exjKcuzj-IfmXlkmQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2902131621</pqid></control><display><type>conference_proceeding</type><title>Life cycle assessment (LCA) score prediction using deep learning on detergent plastic packing products</title><source>American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)</source><creator>Fatriansyah, Jaka Fajar ; Putri, Anisa Rahmania ; Hartoyo, Fernanda ; Pradana, Agrin Febrian ; Fauzi, Andrian</creator><contributor>P, Thangaraj ; H, Shankar ; K, Mohana Sundaram</contributor><creatorcontrib>Fatriansyah, Jaka Fajar ; Putri, Anisa Rahmania ; Hartoyo, Fernanda ; Pradana, Agrin Febrian ; Fauzi, Andrian ; P, Thangaraj ; H, Shankar ; K, Mohana Sundaram</creatorcontrib><description>Life cycle assessment (LCA) is a systematic method for quantitatively analyzing the environmental impact of a product throughout the product life cycle. However, the obstacle in doing LCA is that it takes quite a lot of time to identify and register products to obtain an LCA score for the entire life of a product. One solution to overcome these limitations is to build a deep learning model to predict LCA scores on detergent plastic packaging products. The result of the research is an LCA score prediction program for detergent plastic packaging products using the eco-indicator 99 method using a total of 240 datasets consisting of 8% real/actual data, 75% dummy data, and 17% hybrid data. Other model parameters include test size 0.2, random state 6, batch size 64, with three hidden layers and density 64,32,16, epoch 1000, and learning rate 0.01. The program produces an optimum accuracy of 99.39% resulting from regression metrics using the R2 score.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0179523</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Deep learning ; Impact analysis ; Life cycle assessment ; Packaging ; Product life cycle</subject><ispartof>AIP conference proceedings, 2023, Vol.2901 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,789,790,23930,23931,25140,27924,27925</link.rule.ids></links><search><contributor>P, Thangaraj</contributor><contributor>H, Shankar</contributor><contributor>K, Mohana Sundaram</contributor><creatorcontrib>Fatriansyah, Jaka Fajar</creatorcontrib><creatorcontrib>Putri, Anisa Rahmania</creatorcontrib><creatorcontrib>Hartoyo, Fernanda</creatorcontrib><creatorcontrib>Pradana, Agrin Febrian</creatorcontrib><creatorcontrib>Fauzi, Andrian</creatorcontrib><title>Life cycle assessment (LCA) score prediction using deep learning on detergent plastic packing products</title><title>AIP conference proceedings</title><description>Life cycle assessment (LCA) is a systematic method for quantitatively analyzing the environmental impact of a product throughout the product life cycle. However, the obstacle in doing LCA is that it takes quite a lot of time to identify and register products to obtain an LCA score for the entire life of a product. One solution to overcome these limitations is to build a deep learning model to predict LCA scores on detergent plastic packaging products. The result of the research is an LCA score prediction program for detergent plastic packaging products using the eco-indicator 99 method using a total of 240 datasets consisting of 8% real/actual data, 75% dummy data, and 17% hybrid data. Other model parameters include test size 0.2, random state 6, batch size 64, with three hidden layers and density 64,32,16, epoch 1000, and learning rate 0.01. The program produces an optimum accuracy of 99.39% resulting from regression metrics using the R2 score.</description><subject>Deep learning</subject><subject>Impact analysis</subject><subject>Life cycle assessment</subject><subject>Packaging</subject><subject>Product life cycle</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotUE1LxDAQDaLgunrwHwS8qNB1kjRpc1yW9QMKXhS8lTSdLF27bU3Sw_57W3YZmOG9-XjMI-SewYqBEi9yBSzTkosLsmBSsiRTTF2SBYBOE56Kn2tyE8IegOssyxfEFY1Dao-2RWpCwBAO2EX6WGzWTzTY3iMdPNaNjU3f0TE03Y7WiANt0fhuRhNdY0S_m_eG1oTYWDoY-zs3B9_Xo43hllw50wa8O9cl-X7dfm3ek-Lz7WOzLpKBCRETDhXIvNIamVWpypQAKZ1LsVIauXaiqoGnuWJgLKra1fkUwARPpWBGZ2JJHk53J-G_EUMs9_3ou0my5Bo4E0xNaUmeT1PBNtHMn5WDbw7GH0sG5exjKcuzj-IfmXlkmQ</recordid><startdate>20231215</startdate><enddate>20231215</enddate><creator>Fatriansyah, Jaka Fajar</creator><creator>Putri, Anisa Rahmania</creator><creator>Hartoyo, Fernanda</creator><creator>Pradana, Agrin Febrian</creator><creator>Fauzi, Andrian</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20231215</creationdate><title>Life cycle assessment (LCA) score prediction using deep learning on detergent plastic packing products</title><author>Fatriansyah, Jaka Fajar ; Putri, Anisa Rahmania ; Hartoyo, Fernanda ; Pradana, Agrin Febrian ; Fauzi, Andrian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p133t-20b058b99e1c646763055ff4eb69e29f3bd0248610ace6dfd8d8d01324531a973</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Deep learning</topic><topic>Impact analysis</topic><topic>Life cycle assessment</topic><topic>Packaging</topic><topic>Product life cycle</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fatriansyah, Jaka Fajar</creatorcontrib><creatorcontrib>Putri, Anisa Rahmania</creatorcontrib><creatorcontrib>Hartoyo, Fernanda</creatorcontrib><creatorcontrib>Pradana, Agrin Febrian</creatorcontrib><creatorcontrib>Fauzi, Andrian</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fatriansyah, Jaka Fajar</au><au>Putri, Anisa Rahmania</au><au>Hartoyo, Fernanda</au><au>Pradana, Agrin Febrian</au><au>Fauzi, Andrian</au><au>P, Thangaraj</au><au>H, Shankar</au><au>K, Mohana Sundaram</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Life cycle assessment (LCA) score prediction using deep learning on detergent plastic packing products</atitle><btitle>AIP conference proceedings</btitle><date>2023-12-15</date><risdate>2023</risdate><volume>2901</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Life cycle assessment (LCA) is a systematic method for quantitatively analyzing the environmental impact of a product throughout the product life cycle. However, the obstacle in doing LCA is that it takes quite a lot of time to identify and register products to obtain an LCA score for the entire life of a product. One solution to overcome these limitations is to build a deep learning model to predict LCA scores on detergent plastic packaging products. The result of the research is an LCA score prediction program for detergent plastic packaging products using the eco-indicator 99 method using a total of 240 datasets consisting of 8% real/actual data, 75% dummy data, and 17% hybrid data. Other model parameters include test size 0.2, random state 6, batch size 64, with three hidden layers and density 64,32,16, epoch 1000, and learning rate 0.01. The program produces an optimum accuracy of 99.39% resulting from regression metrics using the R2 score.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0179523</doi><tpages>6</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0094-243X
ispartof AIP conference proceedings, 2023, Vol.2901 (1)
issn 0094-243X
1551-7616
language eng
recordid cdi_scitation_primary_10_1063_5_0179523
source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Deep learning
Impact analysis
Life cycle assessment
Packaging
Product life cycle
title Life cycle assessment (LCA) score prediction using deep learning on detergent plastic packing products
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T22%3A18%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Life%20cycle%20assessment%20(LCA)%20score%20prediction%20using%20deep%20learning%20on%20detergent%20plastic%20packing%20products&rft.btitle=AIP%20conference%20proceedings&rft.au=Fatriansyah,%20Jaka%20Fajar&rft.date=2023-12-15&rft.volume=2901&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/5.0179523&rft_dat=%3Cproquest_scita%3E2902131621%3C/proquest_scita%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p133t-20b058b99e1c646763055ff4eb69e29f3bd0248610ace6dfd8d8d01324531a973%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2902131621&rft_id=info:pmid/&rfr_iscdi=true