Machine Learning Based Framework for Biorefinery Environmental Assessment
The transformation of actual processes into sustainable processes is a major study subject over recent years, particularly through the circular economy. However, the environmental assessments require a huge quantity of data and many of these data are heterogeneous. Environmental evaluation tools wou...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2022-11-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/12934 |
| _version_ | 1798015947168022528 |
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| author | Nancy Prioux Rachid Ouaret Jean-Pierre Belaud |
| author_facet | Nancy Prioux Rachid Ouaret Jean-Pierre Belaud |
| author_sort | Nancy Prioux |
| collection | DOAJ |
| description | The transformation of actual processes into sustainable processes is a major study subject over recent years, particularly through the circular economy. However, the environmental assessments require a huge quantity of data and many of these data are heterogeneous. Environmental evaluation tools would clearly benefit from Data Science approaches in the Big Data context. This paper focuses on developing a framework for decision-making in Process System Engineering by coupling Machine Learning techniques and environmental assessment. Five-steps framework have been deployed in a framework and tested on the comparison of biomass pretreatment processes for glucose production. Some scientific articles have been selected thanks to specific keywords in Science Direct and Web of Science. The data architecture and in particular the data analysis allows us to bring data to higher quality such as a material balance check. The approach gives access to a process-impact matrix which is analyzed through Dimensional Reduction methods in order to highlight similar impacts and/or processes. |
| first_indexed | 2024-04-11T15:42:50Z |
| format | Article |
| id | doaj.art-ab6716d77e1f44f8b96eef6bb5eba0d3 |
| institution | Directory Open Access Journal |
| issn | 2283-9216 |
| language | English |
| last_indexed | 2024-04-11T15:42:50Z |
| publishDate | 2022-11-01 |
| publisher | AIDIC Servizi S.r.l. |
| record_format | Article |
| series | Chemical Engineering Transactions |
| spelling | doaj.art-ab6716d77e1f44f8b96eef6bb5eba0d32022-12-22T04:15:44ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162022-11-019610.3303/CET2296087Machine Learning Based Framework for Biorefinery Environmental AssessmentNancy PriouxRachid OuaretJean-Pierre BelaudThe transformation of actual processes into sustainable processes is a major study subject over recent years, particularly through the circular economy. However, the environmental assessments require a huge quantity of data and many of these data are heterogeneous. Environmental evaluation tools would clearly benefit from Data Science approaches in the Big Data context. This paper focuses on developing a framework for decision-making in Process System Engineering by coupling Machine Learning techniques and environmental assessment. Five-steps framework have been deployed in a framework and tested on the comparison of biomass pretreatment processes for glucose production. Some scientific articles have been selected thanks to specific keywords in Science Direct and Web of Science. The data architecture and in particular the data analysis allows us to bring data to higher quality such as a material balance check. The approach gives access to a process-impact matrix which is analyzed through Dimensional Reduction methods in order to highlight similar impacts and/or processes.https://www.cetjournal.it/index.php/cet/article/view/12934 |
| spellingShingle | Nancy Prioux Rachid Ouaret Jean-Pierre Belaud Machine Learning Based Framework for Biorefinery Environmental Assessment Chemical Engineering Transactions |
| title | Machine Learning Based Framework for Biorefinery Environmental Assessment |
| title_full | Machine Learning Based Framework for Biorefinery Environmental Assessment |
| title_fullStr | Machine Learning Based Framework for Biorefinery Environmental Assessment |
| title_full_unstemmed | Machine Learning Based Framework for Biorefinery Environmental Assessment |
| title_short | Machine Learning Based Framework for Biorefinery Environmental Assessment |
| title_sort | machine learning based framework for biorefinery environmental assessment |
| url | https://www.cetjournal.it/index.php/cet/article/view/12934 |
| work_keys_str_mv | AT nancyprioux machinelearningbasedframeworkforbiorefineryenvironmentalassessment AT rachidouaret machinelearningbasedframeworkforbiorefineryenvironmentalassessment AT jeanpierrebelaud machinelearningbasedframeworkforbiorefineryenvironmentalassessment |