{"id":"https://openalex.org/W2765519060","doi":"https://doi.org/10.1109/whispers.2014.8077617","title":"High-fidelity forward model for determining combustion efficiency of industrial flares","display_name":"High-fidelity forward model for determining combustion efficiency of industrial flares","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2765519060","doi":"https://doi.org/10.1109/whispers.2014.8077617","mag":"2765519060"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2014.8077617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2014.8077617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020529630","display_name":"R. Panfili","orcid":"https://orcid.org/0000-0002-1605-0331"},"institutions":[{"id":"https://openalex.org/I178975111","display_name":"Spectral Sciences (United States)","ror":"https://ror.org/026jgjr74","country_code":"US","type":"company","lineage":["https://openalex.org/I178975111"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"R. Panfili","raw_affiliation_strings":["Spectral Sciences, Inc., Burlington, MA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Spectral Sciences, Inc., Burlington, MA","institution_ids":["https://openalex.org/I178975111"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5020529630"],"corresponding_institution_ids":["https://openalex.org/I178975111"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.32751092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"11","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11320","display_name":"Atmospheric Ozone and Climate","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11320","display_name":"Atmospheric Ozone and Climate","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11111","display_name":"Spectroscopy and Laser Applications","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7421166896820068},{"id":"https://openalex.org/keywords/combustion","display_name":"Combustion","score":0.5600324869155884},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5074037909507751},{"id":"https://openalex.org/keywords/process-engineering","display_name":"Process engineering","score":0.49299880862236023},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4615405201911926},{"id":"https://openalex.org/keywords/stack","display_name":"Stack (abstract data type)","score":0.43897777795791626},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3837461769580841},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.19983601570129395},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17245832085609436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.14421746134757996}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7421166896820068},{"id":"https://openalex.org/C105923489","wikidata":"https://www.wikidata.org/wiki/Q133235","display_name":"Combustion","level":2,"score":0.5600324869155884},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5074037909507751},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.49299880862236023},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4615405201911926},{"id":"https://openalex.org/C9395851","wikidata":"https://www.wikidata.org/wiki/Q177929","display_name":"Stack (abstract data type)","level":2,"score":0.43897777795791626},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3837461769580841},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.19983601570129395},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17245832085609436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14421746134757996},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2014.8077617","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2014.8077617","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338291","display_name":"Sandia National Laboratories","ror":"https://ror.org/01apwpt12"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1561142337","https://openalex.org/W1966523582","https://openalex.org/W1968351289","https://openalex.org/W2087070363","https://openalex.org/W2126858388","https://openalex.org/W2133151341","https://openalex.org/W2147891316","https://openalex.org/W2256578114","https://openalex.org/W2541192326","https://openalex.org/W4205182410","https://openalex.org/W4285719527","https://openalex.org/W6633443985"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2404757046","https://openalex.org/W2385371209","https://openalex.org/W4250051149","https://openalex.org/W2083270190"],"abstract_inverted_index":{"The":[0],"petroleum":[1],"refining":[2],"process":[3],"utilizes":[4],"hydrocarbon":[5],"combustion":[6],"by":[7,94],"industrial":[8],"flares":[9,17],"to":[10,40,91,126],"dispose":[11],"of":[12,21,44,59,65,86],"waste":[13],"gas":[14],"streams.":[15],"These":[16],"emit":[18],"substantial":[19],"quantities":[20],"greenhouse":[22],"gases":[23],"into":[24],"the":[25,42,84,109,127,132],"atmosphere.":[26],"As":[27],"a":[28,63,66,71,77,87],"result,":[29],"there":[30],"is":[31],"an":[32],"ongoing":[33],"need":[34],"for":[35],"monitoring":[36],"and":[37,54,70,104,107,122],"control":[38],"technology":[39],"minimize":[41],"products":[43,61],"incomplete":[45],"combustion,":[46],"including":[47],"ozone-forming":[48],"highly-reactive":[49],"volatile":[50],"organic":[51],"compounds":[52],"(HR-VOCs)":[53],"human":[55],"carcinogens.":[56],"Remote":[57],"sensing":[58],"emission":[60],"using":[62],"combination":[64],"hyperspectral":[67],"infrared":[68],"imager":[69],"high-performance":[72],"retrieval":[73,96,110],"algorithm":[74,111],"can":[75],"provide":[76],"robust,":[78],"low-cost":[79],"solution.":[80],"Here,":[81],"we":[82],"outline":[83],"construction":[85],"high-fidelity":[88],"forward":[89],"model":[90,99],"be":[92],"used":[93],"our":[95],"algorithm.":[97],"This":[98],"takes":[100],"user-supplied":[101],"layer":[102],"temperatures":[103],"molecular":[105],"concentrations":[106],"provides":[108],"with":[112,124],"bandpass-specific":[113],"spectral":[114],"radiances":[115],"(for":[116],"least-squares":[117],"fitting":[118],"against":[119],"measured":[120],"data)":[121],"derivatives":[123],"respect":[125],"dependent":[128],"variables":[129],"(to":[130],"adjust":[131],"next":[133],"iterative":[134],"step).":[135]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}