Sulfur oxidative coupling of methane process development and its modeling via machine learning
<p dir="ltr">Sulfur oxidative coupling of methane (SOCM) has seen a significant improvement in catalyst design and performances, but there is still a lack of development at process level. We propose an optimized SOCM process flow diagram, with integrated waste heat recovery system an...
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2022
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| _version_ | 1864513554013487104 |
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| author | Giovanni Scabbia (13751501) |
| author2 | Ahmed Abotaleb (9596108) Alessandro Sinopoli (4318555) |
| author2_role | author author |
| author_facet | Giovanni Scabbia (13751501) Ahmed Abotaleb (9596108) Alessandro Sinopoli (4318555) |
| author_role | author |
| dc.creator.none.fl_str_mv | Giovanni Scabbia (13751501) Ahmed Abotaleb (9596108) Alessandro Sinopoli (4318555) |
| dc.date.none.fl_str_mv | 2022-06-01T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1002/aic.17793 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Sulfur_oxidative_coupling_of_methane_process_development_and_its_modeling_via_machine_learning/22257481 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Chemical engineering Information and computing sciences Data management and data science Machine learning BTX heterogeneous catalysis machine learning process simulations sulfur oxidative coupling of methane |
| dc.title.none.fl_str_mv | Sulfur oxidative coupling of methane process development and its modeling via machine learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Sulfur oxidative coupling of methane (SOCM) has seen a significant improvement in catalyst design and performances, but there is still a lack of development at process level. We propose an optimized SOCM process flow diagram, with integrated waste heat recovery system and an efficient separation technique. The outcomes of the simulated process were used to design a data-driven modeling approach, based on machine learning methods, and to evaluate its interpolation accuracy. The simultaneous multi-input/multioutput relationship between the different parameters of the SOCM system were determined, revealing the optimum reaction conditions for the maximum benzene, toluene and xylene yield, at the minimum CH<sub>4</sub> and H<sub>2</sub>S recycling rate.</p><h2>Other Information</h2><p dir="ltr">Published in: AIChE Journal<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1002/aic.17793" target="_blank">http://dx.doi.org/10.1002/aic.17793</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ef6439c5c7d45af11f8f98b8bd77c475 |
| identifier_str_mv | 10.1002/aic.17793 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/22257481 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Sulfur oxidative coupling of methane process development and its modeling via machine learningGiovanni Scabbia (13751501)Ahmed Abotaleb (9596108)Alessandro Sinopoli (4318555)EngineeringChemical engineeringInformation and computing sciencesData management and data scienceMachine learningBTXheterogeneous catalysismachine learningprocess simulationssulfur oxidative coupling of methane<p dir="ltr">Sulfur oxidative coupling of methane (SOCM) has seen a significant improvement in catalyst design and performances, but there is still a lack of development at process level. We propose an optimized SOCM process flow diagram, with integrated waste heat recovery system and an efficient separation technique. The outcomes of the simulated process were used to design a data-driven modeling approach, based on machine learning methods, and to evaluate its interpolation accuracy. The simultaneous multi-input/multioutput relationship between the different parameters of the SOCM system were determined, revealing the optimum reaction conditions for the maximum benzene, toluene and xylene yield, at the minimum CH<sub>4</sub> and H<sub>2</sub>S recycling rate.</p><h2>Other Information</h2><p dir="ltr">Published in: AIChE Journal<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1002/aic.17793" target="_blank">http://dx.doi.org/10.1002/aic.17793</a></p>2022-06-01T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1002/aic.17793https://figshare.com/articles/journal_contribution/Sulfur_oxidative_coupling_of_methane_process_development_and_its_modeling_via_machine_learning/22257481CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/222574812022-06-01T12:00:00Z |
| spellingShingle | Sulfur oxidative coupling of methane process development and its modeling via machine learning Giovanni Scabbia (13751501) Engineering Chemical engineering Information and computing sciences Data management and data science Machine learning BTX heterogeneous catalysis machine learning process simulations sulfur oxidative coupling of methane |
| status_str | publishedVersion |
| title | Sulfur oxidative coupling of methane process development and its modeling via machine learning |
| title_full | Sulfur oxidative coupling of methane process development and its modeling via machine learning |
| title_fullStr | Sulfur oxidative coupling of methane process development and its modeling via machine learning |
| title_full_unstemmed | Sulfur oxidative coupling of methane process development and its modeling via machine learning |
| title_short | Sulfur oxidative coupling of methane process development and its modeling via machine learning |
| title_sort | Sulfur oxidative coupling of methane process development and its modeling via machine learning |
| topic | Engineering Chemical engineering Information and computing sciences Data management and data science Machine learning BTX heterogeneous catalysis machine learning process simulations sulfur oxidative coupling of methane |