{"id":{"repo_id":"poli-torino","oai_identifier":"oai:iris.polito.it:11583/3004942"},"canonical_url":"https://search.dev.ndltd.org/etd/poli-torino/oai:iris.polito.it:11583/3004942","repository":{"repo_id":"poli-torino","name":"Politecnico di Torino","base_url":"https://iris.polito.it/oai/request"},"display":{"title":"Modeling and Statistical Analysis for Low-Carbon Transition Pathways - Hydrogen Storage Optimization and Data-Driven Methane Leak Mitigation Strategies","abstract":"L'abstract è presente nell'allegato / the abstract is in the attachment","abstract_html":"L&#x27;abstract è presente nell&#x27;allegato / the abstract is in the attachment","abstract_has_math":false,"creators":["ROZZI, ELENA"],"institution":"Politecnico di Torino","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["SANTARELLI, MASSIMO","LANZINI, ANDREA"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-10-15","date_published":"2025-10-15","updated_at":"2026-07-24T03:50:45Z","subjects":["Hydrogen","Solid-State Hydrogen Storage","Energy System Optimization","Methane Emissions Mitigation","Bayesian Regression Model","Machine Learning for Energy Systems","Settore IIND-07/A - Fisica tecnica industriale"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://dx.doi.org/10.13121/polito/porto/3004942","10.13121/polito/porto/3004942"],"render_values":[{"text":"http://dx.doi.org/10.13121/polito/porto/3004942","href":"http://dx.doi.org/10.13121/polito/porto/3004942","code":true},{"text":"10.13121/polito/porto/3004942","href":"https://doi.org/10.13121/polito/porto/3004942","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/11583/3004942","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rozzi, Elena","SANTARELLI, MASSIMO","LANZINI, ANDREA"]},{"key":"dc:creator","label":"Author","values":["ROZZI, ELENA"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-10-15"]},{"key":"dc:publisher","label":"Institution","values":["Politecnico di Torino","country:Italy"]},{"key":"dc:relation","label":"Dc Relation","values":["firstpage:1","lastpage:378","numberofpages:378"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Hydrogen","Solid-State Hydrogen Storage","Energy System Optimization","Methane Emissions Mitigation","Bayesian Regression Model","Machine Learning for Energy Systems","Settore IIND-07/A - Fisica tecnica industriale"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/11583/3004942","http://dx.doi.org/10.13121/polito/porto/3004942","10.13121/polito/porto/3004942"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["L'abstract è presente nell'allegato / the abstract is in the attachment"]},{"key":"dc:title","label":"Title","values":["Modeling and Statistical Analysis for Low-Carbon Transition Pathways - Hydrogen Storage Optimization and Data-Driven Methane Leak Mitigation Strategies"]}]}],"canonical_facts":{"dc:contributor":["Rozzi, Elena","SANTARELLI, MASSIMO","LANZINI, ANDREA"],"dc:creator":["ROZZI, ELENA"],"dc:date":["2025-10-15"],"dc:description":["L'abstract è presente nell'allegato / the abstract is in the attachment"],"dc:identifier":["https://hdl.handle.net/11583/3004942","http://dx.doi.org/10.13121/polito/porto/3004942","10.13121/polito/porto/3004942"],"dc:language":["eng"],"dc:publisher":["Politecnico di Torino","country:Italy"],"dc:relation":["firstpage:1","lastpage:378","numberofpages:378"],"dc:rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Hydrogen","Solid-State Hydrogen Storage","Energy System Optimization","Methane Emissions Mitigation","Bayesian Regression Model","Machine Learning for Energy Systems","Settore IIND-07/A - Fisica tecnica industriale"],"dc:title":["Modeling and Statistical Analysis for Low-Carbon Transition Pathways - Hydrogen Storage Optimization and Data-Driven Methane Leak Mitigation Strategies"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T03:50:45Z"}