Intelligent Chemical Purification Technique Based on Machine Learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916205042860032 |
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| author | Wu, Wenchao Xu, Hao Zhang, Dongxiao Mo, Fanyang |
| author_facet | Wu, Wenchao Xu, Hao Zhang, Dongxiao Mo, Fanyang |
| contents | We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise data acquisition and employing advanced machine learning algorithms, we constructed predictive models to forecast key separation parameters, thereby enhancing the efficiency and quality of chromatographic processes. The application of transfer learning allows the model to adapt across various column specifications, broadening its utility. A novel metric, separation probability ($S_p$), quantifies the likelihood of effective compound separation, validated through experimental verification. This study signifies a significant step forward int the application of AI in chemical research, offering a scalable solution to traditional chromatography challenges and providing a foundation for future technological advancements in chemical analysis and purification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligent Chemical Purification Technique Based on Machine Learning Wu, Wenchao Xu, Hao Zhang, Dongxiao Mo, Fanyang Machine Learning Artificial Intelligence Chemical Physics We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise data acquisition and employing advanced machine learning algorithms, we constructed predictive models to forecast key separation parameters, thereby enhancing the efficiency and quality of chromatographic processes. The application of transfer learning allows the model to adapt across various column specifications, broadening its utility. A novel metric, separation probability ($S_p$), quantifies the likelihood of effective compound separation, validated through experimental verification. This study signifies a significant step forward int the application of AI in chemical research, offering a scalable solution to traditional chromatography challenges and providing a foundation for future technological advancements in chemical analysis and purification. |
| title | Intelligent Chemical Purification Technique Based on Machine Learning |
| topic | Machine Learning Artificial Intelligence Chemical Physics |
| url | https://arxiv.org/abs/2404.09114 |