{"id":"https://openalex.org/W7127077925","doi":"https://doi.org/10.1051/e3sconf/202564702001/pdf","title":"Multivariate analysis and soft computing-based prediction of energy potential in heterogenous waste streams","display_name":"Multivariate analysis and soft computing-based prediction of energy potential in heterogenous waste streams","publication_year":2025,"publication_date":"2025-08-29","ids":{"openalex":"https://openalex.org/W7127077925","doi":"https://doi.org/10.1051/e3sconf/202564702001/pdf"},"language":"en","primary_location":{"id":"pmh:oai:edpsciences.org:dkey/10.1051/e3sconf/202564702001","doi":"https://doi.org/10.1051/e3sconf/202564702001/pdf","is_oa":true,"landing_page_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202564702001/pdf","pdf_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202564702001/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/e3sconf/202564702001","raw_type":"Text"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202564702001/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077506090","display_name":"Oluwatobi Adeleke","orcid":"https://orcid.org/0000-0001-9306-6903","observed_orcids":["https://orcid.org/0000-0001-9306-6903"]},"institutions":[{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Oluwatobi Adeleke","raw_affiliation_strings":["Mechanical Engineering Sciences , University of Johannesburg , Johannesburg , South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Engineering Sciences , University of Johannesburg , Johannesburg , South Africa","institution_ids":["https://openalex.org/I24027795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122870115","display_name":"Tien-Chien Jen","orcid":"https://orcid.org/0000-0003-1743-4668","observed_orcids":["https://orcid.org/0000-0003-1743-4668"]},"institutions":[{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Tien-Chien Jen","raw_affiliation_strings":["Mechanical Engineering Sciences , University of Johannesburg , Johannesburg , South Africa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Engineering Sciences , University of Johannesburg , Johannesburg , South Africa","institution_ids":["https://openalex.org/I24027795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24027795"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.49408012,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11108","display_name":"Municipal Solid Waste Management","score":0.8389000296592712,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Waste Management and Disposal"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11108","display_name":"Municipal Solid Waste Management","score":0.8389000296592712,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Waste Management and Disposal"},"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/T10088","display_name":"Thermochemical Biomass Conversion Processes","score":0.09989999979734421,"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"}},{"id":"https://openalex.org/T10507","display_name":"Biodiesel Production and Applications","score":0.007199999876320362,"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/waste-to-energy","display_name":"waste-to-energy","score":0.8920000195503235},{"id":"https://openalex.org/keywords/waste-classification","display_name":"waste classification","score":0.7459999918937683},{"id":"https://openalex.org/keywords/elemental-composition","display_name":"elemental composition","score":0.7419999837875366},{"id":"https://openalex.org/keywords/proximate-analysis","display_name":"proximate analysis","score":0.8389999866485596},{"id":"https://openalex.org/keywords/feature-importance-analysis","display_name":"feature importance analysis","score":0.7599999904632568},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7160000205039978},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.8500000238418579},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.8999999761581421},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.984000027179718}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.829200029373169},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6105999946594238},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.583299994468689},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.574999988079071},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.43689998984336853},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43309998512268066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4275999963283539},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C115540264","wikidata":"https://www.wikidata.org/wiki/Q2945560","display_name":"Biomass (ecology)","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35920000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3573000133037567},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C196213036","wikidata":"https://www.wikidata.org/wiki/Q2915573","display_name":"Waste-to-energy","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.28790000081062317},{"id":"https://openalex.org/C2780692498","wikidata":"https://www.wikidata.org/wiki/Q16950721","display_name":"Component analysis","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C74887250","wikidata":"https://www.wikidata.org/wiki/Q3455892","display_name":"Principal component regression","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27950000762939453},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2741999924182892},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C75779659","wikidata":"https://www.wikidata.org/wiki/Q18538","display_name":"Municipal solid waste","level":2,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:edpsciences.org:dkey/10.1051/e3sconf/202564702001","doi":"https://doi.org/10.1051/e3sconf/202564702001/pdf","is_oa":true,"landing_page_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202564702001/pdf","pdf_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202564702001/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/e3sconf/202564702001","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:edpsciences.org:dkey/10.1051/e3sconf/202564702001","doi":"https://doi.org/10.1051/e3sconf/202564702001/pdf","is_oa":true,"landing_page_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202564702001/pdf","pdf_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202564702001/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/e3sconf/202564702001","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/12","display_name":"Responsible consumption and production","score":1.0},{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.9090999960899353},{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.4399999976158142}],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/12","display_name":"Responsible consumption and production","score":0.49340033531188965}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323959","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7127077925.pdf","grobid_xml":"https://content.openalex.org/works/W7127077925.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,151],"study":[1],"presents":[2],"a":[3,14,160],"data-driven":[4],"framework":[5],"for":[6,10,64,93,163],"characterizing":[7],"waste-derived":[8],"biomass":[9],"energy":[11],"recovery.":[12],"Utilizing":[13],"dataset":[15],"comprising":[16],"higher":[17],"heating":[18],"value":[19],"(HHV),":[20],"elemental":[21],"composition,":[22],"and":[23,32,53,54,84,141,148,156,166,176],"proximate":[24],"properties":[25],"of":[26,66,91,105,117,144,153],"diverse":[27],"waste":[28,79,92,164,174],"streams,":[29],"correlation":[30],"analysis":[31,35],"feature":[33],"importance":[34,41],"(FIA)":[36],"using":[37],"Random":[38],"Forest":[39],"(RF)\u2019s":[40],"metrics":[42],"were":[43,56],"conducted":[44],"to":[45,77],"identify":[46],"key":[47],"parameters":[48],"influencing":[49],"HHV":[50,167],"prediction.":[51],"Carbon":[52],"Hydrogen":[55],"identified":[57],"as":[58],"the":[59,67,89,109],"most":[60],"significant":[61],"contributors,":[62],"accounting":[63],"75\u201380%":[65],"model\u2019s":[68],"predictive":[69,115],"strength.":[70],"Principal":[71],"Component":[72],"Analysis":[73],"(PCA)":[74],"was":[75,99],"applied":[76],"cluster":[78],"types":[80],"based":[81],"on":[82],"compositional":[83],"energetic":[85],"similarities,":[86],"aiding":[87],"in":[88,108,172],"classification":[90,165],"optimized":[94],"waste-to-energy":[95],"(WtE)":[96],"strategies.":[97],"Dimensionality":[98],"effectively":[100],"reduced":[101],"with":[102,137],"over":[103],"90-95%":[104],"variance":[106],"captured":[107],"first":[110],"four":[111],"principal":[112],"components.":[113],"The":[114,129],"performance":[116,134],"three":[118],"machine":[119,157],"learning":[120,158],"models\u2014Artificial":[121],"Neural":[122],"Network":[123],"(ANN),":[124],"Support":[125],"Vector":[126],"Machine":[127],"(SVM).":[128],"RF":[130],"model":[131],"demonstrated":[132],"superior":[133],"during":[135],"training":[136],"RMSE,":[138],"MAE,":[139],"MAD,":[140],"rMBE":[142],"values":[143],"0.8606,":[145],"0.5945,":[146],"0.3864,":[147],"0.0895,":[149],"respectively.":[150],"integration":[152],"statistical":[154],"techniques":[155],"provides":[159],"robust":[161],"tool":[162],"estimation,":[168],"promoting":[169],"data-informed":[170],"decisions":[171],"sustainable":[173],"management":[175],"energy.":[177]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2026-02-03T00:00:00"}