Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation
Assessing the sustainable development of green hydrogen and assessing its potential environmental impacts using the Life Cycle Assessment is crucial. Challenges in LCA, like missing environmental data, are often addressed using machine learning, such as artificial neural networks. However, to find a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/6/4/122 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850240923296858112 |
|---|---|
| author | Yajing Chen Urs Liebau Shreyas Mysore Guruprasad Iaroslav Trofimenko Christine Minke |
| author_facet | Yajing Chen Urs Liebau Shreyas Mysore Guruprasad Iaroslav Trofimenko Christine Minke |
| author_sort | Yajing Chen |
| collection | DOAJ |
| description | Assessing the sustainable development of green hydrogen and assessing its potential environmental impacts using the Life Cycle Assessment is crucial. Challenges in LCA, like missing environmental data, are often addressed using machine learning, such as artificial neural networks. However, to find an ML solution, researchers need to read extensive literature or consult experts. This research demonstrates how customised LLMs, trained with domain-specific papers, can help researchers overcome these challenges. By starting small by consolidating papers focused on the LCA of proton exchange membrane water electrolysis, which produces green hydrogen, and ML applications in LCA. These papers are uploaded to OpenAI to create the LlamaIndex, enabling future queries. Using the LangChain framework, researchers query the customised model (GPT-3.5-turbo), receiving tailored responses. The results demonstrate that customised LLMs can assist researchers in providing suitable ML solutions to address data inaccuracies and gaps. The ability to quickly query an LLM and receive an integrated response across relevant sources presents an improvement over manually retrieving and reading individual papers. This shows that leveraging fine-tuned LLMs can empower researchers to conduct LCAs more efficiently and effectively. |
| format | Article |
| id | doaj-art-9dbc6b28d7684bdcab499e966954489f |
| institution | OA Journals |
| issn | 2504-4990 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-9dbc6b28d7684bdcab499e966954489f2025-08-20T02:00:43ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-11-01642494251410.3390/make6040122Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models AugmentationYajing Chen0Urs Liebau1Shreyas Mysore Guruprasad2Iaroslav Trofimenko3Christine Minke4Institute of Mineral and Waste Processing, Recycling and Circular Economy Systems, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, GermanyCenter for Digital Green Tech, August-Wilhelm-Scheer Institute, 38678 Clausthal-Zellerfeld, GermanyCenter for Digital Green Tech, August-Wilhelm-Scheer Institute, 38678 Clausthal-Zellerfeld, GermanyCenter for Digital Green Tech, August-Wilhelm-Scheer Institute, 38678 Clausthal-Zellerfeld, GermanyInstitute of Mineral and Waste Processing, Recycling and Circular Economy Systems, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, GermanyAssessing the sustainable development of green hydrogen and assessing its potential environmental impacts using the Life Cycle Assessment is crucial. Challenges in LCA, like missing environmental data, are often addressed using machine learning, such as artificial neural networks. However, to find an ML solution, researchers need to read extensive literature or consult experts. This research demonstrates how customised LLMs, trained with domain-specific papers, can help researchers overcome these challenges. By starting small by consolidating papers focused on the LCA of proton exchange membrane water electrolysis, which produces green hydrogen, and ML applications in LCA. These papers are uploaded to OpenAI to create the LlamaIndex, enabling future queries. Using the LangChain framework, researchers query the customised model (GPT-3.5-turbo), receiving tailored responses. The results demonstrate that customised LLMs can assist researchers in providing suitable ML solutions to address data inaccuracies and gaps. The ability to quickly query an LLM and receive an integrated response across relevant sources presents an improvement over manually retrieving and reading individual papers. This shows that leveraging fine-tuned LLMs can empower researchers to conduct LCAs more efficiently and effectively.https://www.mdpi.com/2504-4990/6/4/122life cycle assessmentgreen hydrogenmachine learningcustomised large language model |
| spellingShingle | Yajing Chen Urs Liebau Shreyas Mysore Guruprasad Iaroslav Trofimenko Christine Minke Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation Machine Learning and Knowledge Extraction life cycle assessment green hydrogen machine learning customised large language model |
| title | Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation |
| title_full | Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation |
| title_fullStr | Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation |
| title_full_unstemmed | Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation |
| title_short | Advancing Life Cycle Assessment of Sustainable Green Hydrogen Production Using Domain-Specific Fine-Tuning by Large Language Models Augmentation |
| title_sort | advancing life cycle assessment of sustainable green hydrogen production using domain specific fine tuning by large language models augmentation |
| topic | life cycle assessment green hydrogen machine learning customised large language model |
| url | https://www.mdpi.com/2504-4990/6/4/122 |
| work_keys_str_mv | AT yajingchen advancinglifecycleassessmentofsustainablegreenhydrogenproductionusingdomainspecificfinetuningbylargelanguagemodelsaugmentation AT ursliebau advancinglifecycleassessmentofsustainablegreenhydrogenproductionusingdomainspecificfinetuningbylargelanguagemodelsaugmentation AT shreyasmysoreguruprasad advancinglifecycleassessmentofsustainablegreenhydrogenproductionusingdomainspecificfinetuningbylargelanguagemodelsaugmentation AT iaroslavtrofimenko advancinglifecycleassessmentofsustainablegreenhydrogenproductionusingdomainspecificfinetuningbylargelanguagemodelsaugmentation AT christineminke advancinglifecycleassessmentofsustainablegreenhydrogenproductionusingdomainspecificfinetuningbylargelanguagemodelsaugmentation |