{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2060"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2060","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Integrated optimization approach for plasma-based radioactive waste treatment process","abstract":"This research presents an integrated optimization framework for inductively coupled plasma (ICP) torch for low- and intermediate-level radioactive waste treatment purposes. Traditional segregated optimization approaches fail to capture the complex multiphysical interactions between fluid flow, plasma behavior, and waste processing. The proposed framework couples computational fluid dynamics (ANSYS), plasma simulations (COMSOL Multiphysics), and process modeling (Aspen Plus) into a unified workflow, enabling automated data exchange and coordinated parameter updates. Optimization is performed using genetic algorithm, with candidate solutions evaluated across all domains. Comparative analysis of segregated and integrated optimization approaches demonstrated the advantages of the proposed method in achieving superior performance and efficiency. The integrated optimization framework demonstrated clear improvements over the segregated approach. Specifically, plasma efficiency increased by 9%, energy yield (LHV) by 5%, and overall fitness by 24%, indicating enhanced overall system performance.","abstract_html":"This research presents an integrated optimization framework for inductively coupled plasma (ICP) torch for low- and intermediate-level radioactive waste treatment purposes. Traditional segregated optimization approaches fail to capture the complex multiphysical interactions between fluid flow, plasma behavior, and waste processing. The proposed framework couples computational fluid dynamics (ANSYS), plasma simulations (COMSOL Multiphysics), and process modeling (Aspen Plus) into a unified workflow, enabling automated data exchange and coordinated parameter updates. Optimization is performed using genetic algorithm, with candidate solutions evaluated across all domains. Comparative analysis of segregated and integrated optimization approaches demonstrated the advantages of the proposed method in achieving superior performance and efficiency. The integrated optimization framework demonstrated clear improvements over the segregated approach. Specifically, plasma efficiency increased by 9%, energy yield (LHV) by 5%, and overall fitness by 24%, indicating enhanced overall system performance.","abstract_has_math":false,"creators":["Stetsiuk, Roman"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Gaber, Hossam"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-11-01","date_published":"2025-11-01","updated_at":"2026-07-24T05:35:43Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2060","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gaber, Hossam"]},{"key":"dc:creator","label":"Author","values":["Stetsiuk, Roman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-20T21:26:05Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-11-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/2060"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research presents an integrated optimization framework for inductively coupled plasma (ICP) torch for low- and intermediate-level radioactive waste treatment purposes. Traditional segregated optimization approaches fail to capture the complex multiphysical interactions between fluid flow, plasma behavior, and waste processing. The proposed framework couples computational fluid dynamics (ANSYS), plasma simulations (COMSOL Multiphysics), and process modeling (Aspen Plus) into a unified workflow, enabling automated data exchange and coordinated parameter updates. Optimization is performed using genetic algorithm, with candidate solutions evaluated across all domains. Comparative analysis of segregated and integrated optimization approaches demonstrated the advantages of the proposed method in achieving superior performance and efficiency. The integrated optimization framework demonstrated clear improvements over the segregated approach. Specifically, plasma efficiency increased by 9%, energy yield (LHV) by 5%, and overall fitness by 24%, indicating enhanced overall system performance."]},{"key":"dc:title","label":"Title","values":["Integrated optimization approach for plasma-based radioactive waste treatment process"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gaber, Hossam"],"dc:creator":["Stetsiuk, Roman"],"dc:date.accessioned":["2026-01-20T21:26:05Z"],"dc:date.issued":["2025-11-01"],"dc:description.abstract":["This research presents an integrated optimization framework for inductively coupled plasma (ICP) torch for low- and intermediate-level radioactive waste treatment purposes. Traditional segregated optimization approaches fail to capture the complex multiphysical interactions between fluid flow, plasma behavior, and waste processing. The proposed framework couples computational fluid dynamics (ANSYS), plasma simulations (COMSOL Multiphysics), and process modeling (Aspen Plus) into a unified workflow, enabling automated data exchange and coordinated parameter updates. Optimization is performed using genetic algorithm, with candidate solutions evaluated across all domains. Comparative analysis of segregated and integrated optimization approaches demonstrated the advantages of the proposed method in achieving superior performance and efficiency. The integrated optimization framework demonstrated clear improvements over the segregated approach. Specifically, plasma efficiency increased by 9%, energy yield (LHV) by 5%, and overall fitness by 24%, indicating enhanced overall system performance."],"dc:identifier.uri":["https://hdl.handle.net/10155/2060"],"dc:language.iso":["en"],"dc:title":["Integrated optimization approach for plasma-based radioactive waste treatment process"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:43Z"}