{"id":"https://openalex.org/W4229486284","doi":"https://doi.org/10.1109/access.2021.3063231","title":"Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on SSAE-KELM","display_name":"Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on SSAE-KELM","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W4229486284","doi":"https://doi.org/10.1109/access.2021.3063231"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3063231","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3063231","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2021.3063231","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039821809","display_name":"Yuhong Wang","orcid":"https://orcid.org/0000-0001-8881-1067"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhong Wang","raw_affiliation_strings":["College of Control Science and Engineering, China University of Petroleum, Qingdao, China"],"raw_orcid":"https://orcid.org/0000-0001-8881-1067","affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, China University of Petroleum, Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009974893","display_name":"Shengkun Wang","orcid":"https://orcid.org/0000-0002-4416-7035"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengkun Wang","raw_affiliation_strings":["College of Control Science and Engineering, China University of Petroleum, Qingdao, China"],"raw_orcid":"https://orcid.org/0000-0002-4416-7035","affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, China University of Petroleum, Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210162190"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.0473,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.89163243,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"9","issue":null,"first_page":"36466","last_page":"36474"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9811000227928162,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anaerobic-digestion","display_name":"Anaerobic digestion","score":0.7467479705810547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6780343055725098},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.6366472244262695},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6262683868408203},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.531602144241333},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5053809285163879},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4727150797843933},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.41622093319892883},{"id":"https://openalex.org/keywords/process-engineering","display_name":"Process engineering","score":0.3623112440109253},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.32545626163482666},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.16902431845664978},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15280458331108093},{"id":"https://openalex.org/keywords/methane","display_name":"Methane","score":0.1494937539100647}],"concepts":[{"id":"https://openalex.org/C499616599","wikidata":"https://www.wikidata.org/wiki/Q555853","display_name":"Anaerobic digestion","level":3,"score":0.7467479705810547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6780343055725098},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.6366472244262695},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6262683868408203},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.531602144241333},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5053809285163879},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4727150797843933},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.41622093319892883},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.3623112440109253},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32545626163482666},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.16902431845664978},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15280458331108093},{"id":"https://openalex.org/C516920438","wikidata":"https://www.wikidata.org/wiki/Q37129","display_name":"Methane","level":2,"score":0.1494937539100647},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3063231","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3063231","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:bc5f03f1e6a348b48286954ddc9f2fa5","is_oa":true,"landing_page_url":"https://doaj.org/article/bc5f03f1e6a348b48286954ddc9f2fa5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 36466-36474 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3063231","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3063231","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1974874858","https://openalex.org/W2006170764","https://openalex.org/W2040506628","https://openalex.org/W2059830677","https://openalex.org/W2105722058","https://openalex.org/W2477325593","https://openalex.org/W2606776959","https://openalex.org/W2750690746","https://openalex.org/W2765226309","https://openalex.org/W2884168847","https://openalex.org/W2895264248","https://openalex.org/W2905389288","https://openalex.org/W2934768696","https://openalex.org/W2938631860","https://openalex.org/W2944589016","https://openalex.org/W2990705538","https://openalex.org/W2999433230","https://openalex.org/W3004054259","https://openalex.org/W3111210357","https://openalex.org/W6743504269"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2567582497","https://openalex.org/W2897701482","https://openalex.org/W2372520823","https://openalex.org/W2305850980","https://openalex.org/W4312950481","https://openalex.org/W2989872913"],"abstract_inverted_index":{"Anaerobic":[0],"digestion":[1,26,48,77],"technology":[2],"is":[3,17,50,98,136,148],"the":[4,24,46,66,75,80,92,112,115,121,125,128,131,161,167],"most":[5],"environmentally":[6],"friendly":[7],"approach":[8],"to":[9,64,100,119,138,150],"treat":[10],"kitchen":[11,30],"waste.":[12,31],"Volatile":[13],"fatty":[14,41,171],"acid":[15,42],"(VFA)":[16,43,173],"an":[18],"essential":[19],"quality":[20],"monitoring":[21,68],"indicator":[22],"in":[23,45],"anaerobic":[25,47,76],"process":[27,49,108],"of":[28,39,74,82,91,107,114,127,169],"treating":[29],"In":[32],"this":[33],"paper,":[34],"a":[35,94,143],"soft":[36,162],"measurement":[37],"method":[38],"volatile":[40,170],"concentration":[44,168],"established":[51],"based":[52],"on":[53],"stacked":[54],"supervised":[55,96],"auto-encoder":[56],"combine":[57],"kernel":[58,132],"extreme":[59,116,133],"learning":[60,117,134],"machine":[61,118,135],"algorithm":[62,147],"(SSAE-KELM)":[63],"improve":[65],"real-time":[67],"level":[69],"and":[70,86,89,103,176],"resource":[71],"conversion":[72],"efficiency":[73,90,126],"process.":[78],"Given":[79],"problems":[81],"poor":[83],"feature":[84,105,145],"extraction":[85,106],"low":[87],"accuracy":[88],"model,":[93],"stack":[95],"autoencoder":[97],"proposed":[99],"realize":[101,139],"nonlinear":[102],"deep":[104],"data.":[109],"Simultaneously,":[110],"using":[111],"idea":[113],"train":[120],"network":[122],"significantly":[123],"improves":[124],"model.":[129],"Then,":[130],"used":[137],"regression":[140],"modelling.":[141],"Besides,":[142],"combined":[144],"selection":[146],"presented":[149],"select":[151],"auxiliary":[152],"variables":[153],"more":[154,174],"accurately.":[155,177],"The":[156],"simulation":[157],"results":[158],"demonstrate":[159],"that":[160],"sensor":[163],"model":[164],"can":[165],"predict":[166],"acids":[172],"efficiently":[175]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}