{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/154957"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/154957","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"SIMULATION AND OPTIMIZATION OF COMBINED CYCLE GAS TURBINE POWER PLANTS","abstract":"Combined cycle gas turbine (CCGT) power plants normally run under off-design (namely part-load) conditions during their lifetime. Off-design operation decreases plant thermal efficiency and increases carbon emissions. Hence, there are strong incentives to study and improve CCGT performance under off-design conditions. In view of this, this thesis aims to (1) develop a generic simulation model with tailored solution strategy to capture and study the full off-design characteristics of CCGT plants; (2) propose a new operating strategy that allows CCGT plants to set their acceptable gas turbine temperature limits for efficient off-design operation; (3) develop a novel inlet air cooling system to improve CCGT performance during hot seasons; (4) develop a machine learning-based method to predict gas turbine performance and reproduce correction curves from real operational data.","abstract_html":"Combined cycle gas turbine (CCGT) power plants normally run under off-design (namely part-load) conditions during their lifetime. Off-design operation decreases plant thermal efficiency and increases carbon emissions. Hence, there are strong incentives to study and improve CCGT performance under off-design conditions. In view of this, this thesis aims to (1) develop a generic simulation model with tailored solution strategy to capture and study the full off-design characteristics of CCGT plants; (2) propose a new operating strategy that allows CCGT plants to set their acceptable gas turbine temperature limits for efficient off-design operation; (3) develop a novel inlet air cooling system to improve CCGT performance during hot seasons; (4) develop a machine learning-based method to predict gas turbine performance and reproduce correction curves from real operational data.","abstract_has_math":false,"creators":["LIU ZUMING"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-15","date_published":"2019-01-15","updated_at":"2026-07-24T03:33:34Z","subjects":["Gas turbine, Combined cycle, Off-design performance, Simulation, Optimization"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["LIU ZUMING"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2019-01-15"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://scholarbank.nus.edu.sg/handle/10635/154957"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gas turbine, Combined cycle, Off-design performance, Simulation, Optimization"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarbank.nus.edu.sg/bitstreams/1c5399cd-4574-4188-9c65-19afa2102d68/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Combined cycle gas turbine (CCGT) power plants normally run under off-design (namely part-load) conditions during their lifetime. Off-design operation decreases plant thermal efficiency and increases carbon emissions. Hence, there are strong incentives to study and improve CCGT performance under off-design conditions. In view of this, this thesis aims to (1) develop a generic simulation model with tailored solution strategy to capture and study the full off-design characteristics of CCGT plants; (2) propose a new operating strategy that allows CCGT plants to set their acceptable gas turbine temperature limits for efficient off-design operation; (3) develop a novel inlet air cooling system to improve CCGT performance during hot seasons; (4) develop a machine learning-based method to predict gas turbine performance and reproduce correction curves from real operational data."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["22a9b81f3de9071b73c278c17f1369ce","a62ff510078d1ba115dc6a6764f162b3"]},{"key":"dc:title","label":"Title","values":["SIMULATION AND OPTIMIZATION OF COMBINED CYCLE GAS TURBINE POWER PLANTS"]}]}],"canonical_facts":{"dc:creator":["LIU ZUMING"],"dc:date.issued":["2019-01-15"],"dc:description.abstract":["Combined cycle gas turbine (CCGT) power plants normally run under off-design (namely part-load) conditions during their lifetime. Off-design operation decreases plant thermal efficiency and increases carbon emissions. Hence, there are strong incentives to study and improve CCGT performance under off-design conditions. In view of this, this thesis aims to (1) develop a generic simulation model with tailored solution strategy to capture and study the full off-design characteristics of CCGT plants; (2) propose a new operating strategy that allows CCGT plants to set their acceptable gas turbine temperature limits for efficient off-design operation; (3) develop a novel inlet air cooling system to improve CCGT performance during hot seasons; (4) develop a machine learning-based method to predict gas turbine performance and reproduce correction curves from real operational data."],"dc:format.checksum.md5":["22a9b81f3de9071b73c278c17f1369ce","a62ff510078d1ba115dc6a6764f162b3"],"dc:identifier.uri":["https://scholarbank.nus.edu.sg/bitstreams/1c5399cd-4574-4188-9c65-19afa2102d68/download"],"dc:relation.isreferencedby":["https://scholarbank.nus.edu.sg/handle/10635/154957"],"dc:subject":["Gas turbine, Combined cycle, Off-design performance, Simulation, Optimization"],"dc:title":["SIMULATION AND OPTIMIZATION OF COMBINED CYCLE GAS TURBINE POWER PLANTS"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:33:34Z"}