The accuracy and efficiency of GA and PSO optimization schemes on estimating reaction kinetic parameters of biomass pyrolysis
Reaction kinetic parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are applied to estimate the kinetic parameters of three-...
Saved in:
| Published in: | Energy (Oxford) Vol. 176; pp. 582 - 588 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Oxford
Elsevier Ltd
01.06.2019
Elsevier BV |
| Subjects: | |
| ISSN: | 0360-5442, 1873-6785 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Reaction kinetic parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are applied to estimate the kinetic parameters of three-component parallel reaction mechanism based on the thermogravimetric experiment in wide heating rates. The accuracy and efficiency of GA and PSO algorithms are compared with each other under the identical optimization conditions. The results indicate the better optimization abilities of PSO with the closer convergence solution to the global optimum and quicker convergence to the solution than GA based on the three-component parallel reaction mechanism of biomass pyrolysis. Especially, the improvement of best fitting value of PSO reaches up to 30% compared with that of GA. Furthermore, 14 estimated kinetic parameters of best fitting value are obtained and the mass loss rate predicted results including three separate components (hemicellulose, cellulose and lignin) are compared with experimental data.
•PSO and GA are compared in the application of biomass pyrolysis.•PSO represents better optimization abilities with the improvement of 30% than GA.•The estimated kinetic parameters of best fitting value are obtained.•Predicted mass loss rates of GA fail in the accuracy of shoulder region. |
|---|---|
| AbstractList | Reaction kinetic parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are applied to estimate the kinetic parameters of three-component parallel reaction mechanism based on the thermogravimetric experiment in wide heating rates. The accuracy and efficiency of GA and PSO algorithms are compared with each other under the identical optimization conditions. The results indicate the better optimization abilities of PSO with the closer convergence solution to the global optimum and quicker convergence to the solution than GA based on the three-component parallel reaction mechanism of biomass pyrolysis. Especially, the improvement of best fitting value of PSO reaches up to 30% compared with that of GA. Furthermore, 14 estimated kinetic parameters of best fitting value are obtained and the mass loss rate predicted results including three separate components (hemicellulose, cellulose and lignin) are compared with experimental data. Reaction kinetic parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are applied to estimate the kinetic parameters of three-component parallel reaction mechanism based on the thermogravimetric experiment in wide heating rates. The accuracy and efficiency of GA and PSO algorithms are compared with each other under the identical optimization conditions. The results indicate the better optimization abilities of PSO with the closer convergence solution to the global optimum and quicker convergence to the solution than GA based on the three-component parallel reaction mechanism of biomass pyrolysis. Especially, the improvement of best fitting value of PSO reaches up to 30% compared with that of GA. Furthermore, 14 estimated kinetic parameters of best fitting value are obtained and the mass loss rate predicted results including three separate components (hemicellulose, cellulose and lignin) are compared with experimental data. •PSO and GA are compared in the application of biomass pyrolysis.•PSO represents better optimization abilities with the improvement of 30% than GA.•The estimated kinetic parameters of best fitting value are obtained.•Predicted mass loss rates of GA fail in the accuracy of shoulder region. |
| Author | Zhang, Wenlong Yu, Lei Lu, Kaihua Ding, Yanming |
| Author_xml | – sequence: 1 givenname: Yanming surname: Ding fullname: Ding, Yanming email: dingym@cug.edu.cn organization: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China – sequence: 2 givenname: Wenlong surname: Zhang fullname: Zhang, Wenlong organization: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China – sequence: 3 givenname: Lei surname: Yu fullname: Yu, Lei email: yulei2018@zzu.edu.cn organization: School of Water Conservancy & Environment, Zhengzhou University, Zhengzhou 450001, China – sequence: 4 givenname: Kaihua surname: Lu fullname: Lu, Kaihua organization: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China |
| BookMark | eNqFkc1q3DAUhUVJoJOkb9CFoJtu7ErWn91FIYQ2LQQSSLoWsnyVaGpLruQJuNB3r2amqyzalaSr7xwu55yhkxADIPSWkpoSKj9sawiQHte6IbSrCa8JI6_QhraKVVK14gRtCJOkEpw3r9FZzltCiGi7boN-PzwBNtbukrErNmHA4Jy3HkJ5RoevLw_Du_tbHOfFT_6XWXwMONsnmCDjcoVc5mUaHnECYw_fP3yAxVs8m2QmWCDlvVnv42RyxvOa4rhmny_QqTNjhjd_z3P0_cvnh6uv1c3t9bery5vKcsKXig6GOgpGWOrAMtZZ5lTHrWzlwBnpheulEv0gCiUVlx1IYQau6NCbhkpg5-j90XdO8eeuLKwnny2MowkQd1k3jSphScbagr57gW7jLoWyXaF4o5Tkghbq45GyKeacwGnrl0MySzJ-1JTofTN6q4_N6H0zmnBdmili_kI8p5JgWv8n-3SUQUnq2UPS-dATDD6BXfQQ_b8N_gA4fK5O |
| CitedBy_id | crossref_primary_10_1016_j_jaap_2025_106988 crossref_primary_10_3390_polym11122080 crossref_primary_10_1016_j_polymdegradstab_2021_109739 crossref_primary_10_1016_j_renene_2020_11_122 crossref_primary_10_1016_j_psep_2020_01_012 crossref_primary_10_1016_j_tca_2020_178686 crossref_primary_10_1016_j_jclepro_2020_125042 crossref_primary_10_1109_ACCESS_2023_3290316 crossref_primary_10_1177_0734904120982887 crossref_primary_10_3390_polym12020421 crossref_primary_10_1016_j_psep_2019_12_041 crossref_primary_10_1016_j_indcrop_2020_112085 crossref_primary_10_1016_j_energy_2019_116015 crossref_primary_10_1109_ACCESS_2021_3072993 crossref_primary_10_4103_ijehe_ijehe_52_20 crossref_primary_10_1061__ASCE_AS_1943_5525_0001398 crossref_primary_10_1016_j_joei_2025_102180 crossref_primary_10_1080_15567036_2023_2247359 crossref_primary_10_1007_s00034_021_01842_2 crossref_primary_10_1016_j_jaap_2022_105802 crossref_primary_10_1016_j_jenvman_2024_120055 crossref_primary_10_1016_j_tca_2024_179802 crossref_primary_10_1016_j_envres_2023_116978 crossref_primary_10_1016_j_energy_2022_125936 crossref_primary_10_1007_s11227_024_06099_5 crossref_primary_10_1016_j_jaap_2024_106875 crossref_primary_10_1016_j_energy_2025_135238 crossref_primary_10_1016_j_icheatmasstransfer_2022_105896 crossref_primary_10_1016_j_jpowsour_2021_229512 crossref_primary_10_1007_s10570_020_03183_w crossref_primary_10_1016_j_enganabound_2024_106001 crossref_primary_10_3390_math11143095 crossref_primary_10_1007_s13399_024_06187_9 crossref_primary_10_1016_j_energy_2019_05_021 crossref_primary_10_1109_ACCESS_2021_3107802 crossref_primary_10_1016_j_bej_2020_107685 crossref_primary_10_1016_j_biortech_2020_123020 crossref_primary_10_1016_j_energy_2019_05_148 crossref_primary_10_1016_j_jocs_2021_101390 crossref_primary_10_1016_j_energy_2020_117010 crossref_primary_10_1007_s10694_022_01339_7 crossref_primary_10_1016_j_fuel_2022_123717 crossref_primary_10_1016_j_firesaf_2023_103917 crossref_primary_10_1016_j_powtec_2019_08_048 crossref_primary_10_1016_j_fuel_2024_133474 crossref_primary_10_1016_j_tca_2020_178708 crossref_primary_10_1155_2019_1480392 crossref_primary_10_1063_5_0268372 crossref_primary_10_3390_su13041929 crossref_primary_10_1016_j_chaos_2022_112660 crossref_primary_10_1016_j_ces_2024_121047 crossref_primary_10_3390_w15101931 crossref_primary_10_1007_s00289_022_04352_6 crossref_primary_10_1016_j_chemosphere_2019_124486 crossref_primary_10_1016_j_energy_2021_123012 crossref_primary_10_1016_j_energy_2022_124863 crossref_primary_10_3390_w13121670 crossref_primary_10_1016_j_energy_2019_116636 crossref_primary_10_1016_j_jmsy_2021_03_023 crossref_primary_10_1109_TSTE_2022_3175662 crossref_primary_10_3390_ma16072683 crossref_primary_10_1016_j_combustflame_2019_10_016 crossref_primary_10_3390_ma13245595 crossref_primary_10_1016_j_biortech_2019_122079 crossref_primary_10_1016_j_solener_2022_01_043 crossref_primary_10_1016_j_eswa_2023_119580 crossref_primary_10_1016_j_energy_2020_117624 crossref_primary_10_1016_j_techfore_2022_121858 crossref_primary_10_1177_0954406219897089 crossref_primary_10_3390_en16145302 crossref_primary_10_1016_j_seppur_2021_120130 crossref_primary_10_3390_pr10081431 crossref_primary_10_1016_j_jclepro_2020_121997 crossref_primary_10_1016_j_jenvman_2024_122752 crossref_primary_10_1016_j_ijheatmasstransfer_2022_123072 crossref_primary_10_1177_0020294019878872 crossref_primary_10_1016_j_ijheatfluidflow_2025_110065 crossref_primary_10_1007_s11144_024_02727_6 crossref_primary_10_1016_j_jece_2024_113435 crossref_primary_10_1016_j_undsp_2023_01_004 crossref_primary_10_1016_j_fuel_2022_124344 crossref_primary_10_1016_j_indcrop_2022_115501 crossref_primary_10_3390_polym13244359 crossref_primary_10_1080_17445302_2024_2397281 crossref_primary_10_1080_00102202_2024_2329303 crossref_primary_10_3390_su17083677 crossref_primary_10_33889_IJMEMS_2024_9_1_002 crossref_primary_10_1007_s10694_021_01169_z crossref_primary_10_1016_j_energy_2020_117831 crossref_primary_10_1016_j_biortech_2022_128481 crossref_primary_10_1016_j_jclepro_2023_139133 crossref_primary_10_1155_2020_1365195 crossref_primary_10_1016_j_applthermaleng_2021_116651 crossref_primary_10_1016_j_energy_2020_118404 crossref_primary_10_1007_s11705_022_2230_7 crossref_primary_10_1016_j_matpr_2022_05_313 crossref_primary_10_1016_j_proci_2022_08_057 crossref_primary_10_1061__ASCE_IS_1943_555X_0000647 crossref_primary_10_1080_00102202_2025_2540313 crossref_primary_10_1016_j_enconman_2019_111923 crossref_primary_10_1016_j_ins_2023_119606 crossref_primary_10_1016_j_renene_2025_123158 crossref_primary_10_1016_j_camwa_2024_05_026 crossref_primary_10_3390_polym12081744 crossref_primary_10_1016_j_wasman_2020_05_039 crossref_primary_10_1002_bbb_2589 crossref_primary_10_1016_j_energy_2021_120568 crossref_primary_10_1002_er_6742 crossref_primary_10_1016_j_measurement_2019_107028 crossref_primary_10_1088_1402_4896_acd305 crossref_primary_10_36306_konjes_1083352 crossref_primary_10_1007_s12010_019_03089_9 crossref_primary_10_1016_j_combustflame_2025_114148 crossref_primary_10_1016_j_fuel_2021_121724 crossref_primary_10_1016_j_fuel_2024_132469 crossref_primary_10_1002_celc_202200229 crossref_primary_10_1002_fam_2769 crossref_primary_10_1109_ACCESS_2020_2972826 crossref_primary_10_1007_s12010_020_03300_2 crossref_primary_10_1016_j_fuel_2022_126622 crossref_primary_10_1016_j_jenvman_2023_119080 crossref_primary_10_1007_s00024_025_03815_x crossref_primary_10_1016_j_energy_2021_120791 crossref_primary_10_1016_j_fuel_2023_129836 crossref_primary_10_1016_j_fuproc_2020_106509 crossref_primary_10_1002_vnl_21841 crossref_primary_10_1016_j_indcrop_2020_112337 crossref_primary_10_1007_s00466_022_02188_5 crossref_primary_10_3390_su12052086 crossref_primary_10_1038_s41598_021_84729_1 crossref_primary_10_1016_j_jaap_2024_106669 crossref_primary_10_1007_s00500_020_04830_x crossref_primary_10_3390_act12030097 crossref_primary_10_1016_j_firesaf_2023_103762 crossref_primary_10_3390_biomimetics7030084 crossref_primary_10_3390_en15186571 crossref_primary_10_1002_smtd_202500372 crossref_primary_10_1155_2022_4952215 crossref_primary_10_1007_s40430_021_02928_3 crossref_primary_10_1016_j_fuel_2022_127123 crossref_primary_10_1155_2022_3741370 crossref_primary_10_3390_electronics10040466 crossref_primary_10_1007_s11270_023_06795_7 crossref_primary_10_5004_dwt_2022_28817 crossref_primary_10_1016_j_fuel_2023_130338 crossref_primary_10_1017_jmo_2022_62 crossref_primary_10_1177_10775463221105698 crossref_primary_10_1016_j_jqsrt_2019_106600 crossref_primary_10_1016_j_enganabound_2022_04_001 crossref_primary_10_1080_10106049_2025_2480306 crossref_primary_10_1016_j_fuel_2021_121308 crossref_primary_10_1016_j_firesaf_2021_103333 crossref_primary_10_1016_j_jaap_2023_106279 crossref_primary_10_1016_j_asoc_2020_106900 crossref_primary_10_1016_j_jaap_2023_106030 crossref_primary_10_1186_s13068_022_02203_0 crossref_primary_10_3390_ma13010158 crossref_primary_10_1016_j_fuel_2021_120696 crossref_primary_10_1080_21681015_2023_2184426 crossref_primary_10_1016_j_chemosphere_2020_126673 crossref_primary_10_1016_j_eqs_2024_04_004 crossref_primary_10_1093_ce_zkad081 crossref_primary_10_1016_j_energy_2022_123622 crossref_primary_10_1016_j_fuel_2019_116251 crossref_primary_10_1016_j_tsep_2024_103026 crossref_primary_10_1016_j_energy_2021_122112 crossref_primary_10_1016_j_aei_2024_102381 crossref_primary_10_1016_j_pnucene_2021_104108 crossref_primary_10_1016_j_renene_2020_08_024 crossref_primary_10_1016_j_energy_2019_116144 crossref_primary_10_3390_app13095584 |
| Cites_doi | 10.1016/j.applthermaleng.2018.03.045 10.1016/j.buildenv.2018.08.061 10.1016/j.energy.2018.03.075 10.1016/j.fuel.2018.02.143 10.1016/j.pecs.2006.12.001 10.1016/j.energy.2018.12.174 10.1016/j.fuel.2018.11.030 10.1021/ie0201157 10.1016/j.jclepro.2018.10.006 10.1016/j.enconman.2016.04.104 10.1016/j.firesaf.2005.12.004 10.1016/j.energy.2018.09.133 10.2478/s11696-009-0109-4 10.1016/j.enconman.2016.11.016 10.1007/s10973-017-6212-9 10.1016/j.knosys.2015.12.020 10.1016/j.pecs.2017.05.004 10.1021/ef501380c 10.1016/j.fuel.2017.11.113 10.1016/j.jclepro.2017.10.216 10.1016/j.cej.2007.05.024 10.3801/IAFSS.FSS.10-751 10.1016/j.fuel.2018.05.140 10.1016/j.enconman.2017.05.020 10.1016/j.biortech.2016.05.091 10.1016/j.biortech.2015.10.082 10.1016/j.tca.2011.03.034 10.1016/j.biortech.2018.07.134 10.1016/j.ces.2009.05.028 10.1016/j.fuproc.2015.05.001 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier Ltd Copyright Elsevier BV Jun 1, 2019 |
| Copyright_xml | – notice: 2019 Elsevier Ltd – notice: Copyright Elsevier BV Jun 1, 2019 |
| DBID | AAYXX CITATION 7SP 7ST 7TB 8FD C1K F28 FR3 KR7 L7M SOI 7S9 L.6 |
| DOI | 10.1016/j.energy.2019.04.030 |
| DatabaseName | CrossRef Electronics & Communications Abstracts Environment Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Environmental Sciences and Pollution Management ANTE: Abstracts in New Technology & Engineering Engineering Research Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace Environment Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts Engineering Research Database Environment Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Environmental Sciences and Pollution Management AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Civil Engineering Abstracts AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics Environmental Sciences |
| EISSN | 1873-6785 |
| EndPage | 588 |
| ExternalDocumentID | 10_1016_j_energy_2019_04_030 S036054421930653X |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AARJD AAXUO ABJNI ABMAC ABYKQ ACDAQ ACGFS ACIWK ACRLP ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHIDL AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE KOM LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL RPZ SDF SDG SES SPC SPCBC SSR SSZ T5K TN5 XPP ZMT ~02 ~G- 29G 6TJ 9DU AAHBH AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABFNM ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO ADXHL AEIPS AEUPX AFJKZ AFPUW AGQPQ AHHHB AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB G-2 HVGLF HZ~ R2- SAC SEW WUQ ~HD 7SP 7ST 7TB 8FD AGCQF C1K F28 FR3 KR7 L7M SOI 7S9 L.6 |
| ID | FETCH-LOGICAL-c404t-1da1f1ea5c1fec339c3f794c686d430b5fb675bd51f167469e65ad471dba216e3 |
| ISICitedReferencesCount | 191 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000470939500047&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0360-5442 |
| IngestDate | Sun Sep 28 10:16:02 EDT 2025 Wed Aug 13 08:36:35 EDT 2025 Sat Nov 29 01:40:49 EST 2025 Tue Nov 18 22:18:58 EST 2025 Fri Feb 23 02:23:59 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | GA Kinetic parameters PSO Optimization scheme Biomass pyrolysis |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c404t-1da1f1ea5c1fec339c3f794c686d430b5fb675bd51f167469e65ad471dba216e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 2242776451 |
| PQPubID | 2045484 |
| PageCount | 7 |
| ParticipantIDs | proquest_miscellaneous_2271876338 proquest_journals_2242776451 crossref_citationtrail_10_1016_j_energy_2019_04_030 crossref_primary_10_1016_j_energy_2019_04_030 elsevier_sciencedirect_doi_10_1016_j_energy_2019_04_030 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-06-01 |
| PublicationDateYYYYMMDD | 2019-06-01 |
| PublicationDate_xml | – month: 06 year: 2019 text: 2019-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Energy (Oxford) |
| PublicationYear | 2019 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Di Blasi (bib27) 2008; 34 Nzihou, Stanmore, Lyczko, Minh (bib7) 2019; 170 Prata, Schwaab, Lima, Pinto (bib16) 2009; 64 Grønli, Várhegyi, Di Blasi (bib30) 2002; 41 Song (bib9) 2011 Ding, Ezekoye, Zhang, Wang, Lu (bib11) 2018; 232 Navarro, López, Veses, Callén, García (bib6) 2018; 165 Kennedy, Eberhart (bib23) 1995 Ding, Zhou, Wang, Lu, Lu (bib12) 2018; 268 Guo, Zhao, Sun, Li, Lu (bib4) 2018; 215 Lautenberger, Rein, Fernandez-Pello (bib21) 2006; 41 Lautenberger, Fernandez-Pello (bib26) 2011; 10 Jiang, Xiao, He, Sun, Gong, Sun (bib29) 2015; 138 Abdelouahed, Leveneur, Vernieres-Hassimi, Balland, Bechara (bib17) 2017; 128 Yu, Wang, Wang (bib10) 2016; 96 Liu, Nie, Hua, Peng, Liu, Wei (bib14) 2019; 147 Li, Huang, Fleischmann, Rein, Ji (bib18) 2014; 28 Xu, Jiang, Wang (bib19) 2017; 146 Vyazovkin, Burnham, Criado, Pérez-Maqueda, Popescu, Sbirrazzuoli (bib15) 2011; 520 Gašparovič, Koreňová, Jelemenský (bib31) 2010; 64 Yu, Li (bib2) 2019; 207 Ding, Ezekoye, Lu, Wang, Zhou (bib32) 2017; 132 Cai, Nie, Chen, Yang, Liu (bib13) 2019; 239 Chen, Xu, Zhang, Lo, Lu (bib20) 2018; 136 Sharma, Sheth (bib1) 2018; 151 Wu, Huang, Xin, Chen (bib3) 2018; 172 Ding, Wang, Chaos, Chen, Lu (bib25) 2016; 200 Saha, Reddy, Ghoshal (bib22) 2008; 138 Ferreiro, Rabaçal, Costa (bib5) 2016; 125 Jiang, Zhang, Li, He, Gao, Zhou (bib8) 2018; 222 Buyukada (bib24) 2016; 216 Wang, Dai, Yang, Luo (bib28) 2017; 62 Liu (10.1016/j.energy.2019.04.030_bib14) 2019; 147 Ding (10.1016/j.energy.2019.04.030_bib11) 2018; 232 Prata (10.1016/j.energy.2019.04.030_bib16) 2009; 64 Chen (10.1016/j.energy.2019.04.030_bib20) 2018; 136 Ding (10.1016/j.energy.2019.04.030_bib25) 2016; 200 Ding (10.1016/j.energy.2019.04.030_bib32) 2017; 132 Di Blasi (10.1016/j.energy.2019.04.030_bib27) 2008; 34 Jiang (10.1016/j.energy.2019.04.030_bib8) 2018; 222 Lautenberger (10.1016/j.energy.2019.04.030_bib21) 2006; 41 Grønli (10.1016/j.energy.2019.04.030_bib30) 2002; 41 Gašparovič (10.1016/j.energy.2019.04.030_bib31) 2010; 64 Yu (10.1016/j.energy.2019.04.030_bib2) 2019; 207 Sharma (10.1016/j.energy.2019.04.030_bib1) 2018; 151 Abdelouahed (10.1016/j.energy.2019.04.030_bib17) 2017; 128 Cai (10.1016/j.energy.2019.04.030_bib13) 2019; 239 Wu (10.1016/j.energy.2019.04.030_bib3) 2018; 172 Li (10.1016/j.energy.2019.04.030_bib18) 2014; 28 Buyukada (10.1016/j.energy.2019.04.030_bib24) 2016; 216 Wang (10.1016/j.energy.2019.04.030_bib28) 2017; 62 Jiang (10.1016/j.energy.2019.04.030_bib29) 2015; 138 Yu (10.1016/j.energy.2019.04.030_bib10) 2016; 96 Guo (10.1016/j.energy.2019.04.030_bib4) 2018; 215 Navarro (10.1016/j.energy.2019.04.030_bib6) 2018; 165 Nzihou (10.1016/j.energy.2019.04.030_bib7) 2019; 170 Ding (10.1016/j.energy.2019.04.030_bib12) 2018; 268 Lautenberger (10.1016/j.energy.2019.04.030_bib26) 2011; 10 Xu (10.1016/j.energy.2019.04.030_bib19) 2017; 146 Kennedy (10.1016/j.energy.2019.04.030_bib23) 1995 Ferreiro (10.1016/j.energy.2019.04.030_bib5) 2016; 125 Vyazovkin (10.1016/j.energy.2019.04.030_bib15) 2011; 520 Song (10.1016/j.energy.2019.04.030_bib9) 2011 Saha (10.1016/j.energy.2019.04.030_bib22) 2008; 138 |
| References_xml | – volume: 216 start-page: 280 year: 2016 end-page: 286 ident: bib24 article-title: Co-combustion of peanut hull and coal blends: artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation publication-title: Bioresour Technol – volume: 136 start-page: 484 year: 2018 end-page: 491 ident: bib20 article-title: Kinetic study on pyrolysis of waste phenolic fibre-reinforced plastic publication-title: Appl Therm Eng – volume: 34 start-page: 47 year: 2008 end-page: 90 ident: bib27 article-title: Modeling chemical and physical processes of wood and biomass pyrolysis publication-title: Prog Energy Combust Sci – volume: 520 start-page: 1 year: 2011 end-page: 19 ident: bib15 article-title: ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data publication-title: Thermochim Acta – volume: 146 start-page: 124 year: 2017 end-page: 133 ident: bib19 article-title: Thermal decomposition of rape straw: pyrolysis modeling and kinetic study via particle swarm optimization publication-title: Energy Convers Manag – volume: 215 start-page: 735 year: 2018 end-page: 743 ident: bib4 article-title: Facile synthesis of silica aerogel supported K2CO3 sorbents with enhanced CO2 capture capacity for ultra-dilute flue gas treatment publication-title: Fuel – volume: 132 start-page: 102 year: 2017 end-page: 109 ident: bib32 article-title: Comparative pyrolysis behaviors and reaction mechanisms of hardwood and softwood publication-title: Energy Convers Manag – volume: 147 start-page: 444 year: 2019 end-page: 460 ident: bib14 article-title: Research on tunnel ventilation systems: dust diffusion and pollution behaviour by air curtains based on CFD technology and field measurement publication-title: Build Environ – volume: 172 start-page: 466 year: 2018 end-page: 474 ident: bib3 article-title: Scenario analysis of carbon emissions’ anti-driving effect on Qingdao’s energy structure adjustment with an optimization model, Part Ⅰ: carbon emissions peak value prediction publication-title: J Clean Prod – volume: 232 start-page: 147 year: 2018 end-page: 153 ident: bib11 article-title: The effect of chemical reaction kinetic parameters on the bench-scale pyrolysis of lignocellulosic biomass publication-title: Fuel – volume: 268 start-page: 77 year: 2018 end-page: 80 ident: bib12 article-title: Modeling and analysis of bench-scale pyrolysis of lignocellulosic biomass based on merge thickness publication-title: Bioresour Technol – volume: 138 start-page: 20 year: 2008 end-page: 29 ident: bib22 article-title: Hybrid genetic algorithm to find the best model and the globally optimized overall kinetics parameters for thermal decomposition of plastics publication-title: Chem Eng J – volume: 200 start-page: 658 year: 2016 end-page: 665 ident: bib25 article-title: Estimation of beech pyrolysis kinetic parameters by shuffled complex evolution publication-title: Bioresour Technol – start-page: 1942 year: 1995 end-page: 1948 ident: bib23 article-title: Particle swarm optimization publication-title: Proceedings of IEEE international conference on neural networks – volume: 170 start-page: 326 year: 2019 end-page: 337 ident: bib7 article-title: The catalytic effect of inherent and adsorbed metals on the fast/flash pyrolysis of biomass: a review publication-title: Energy – volume: 207 start-page: 772 year: 2019 end-page: 787 ident: bib2 article-title: A flexible-possibilistic stochastic programming method for planning municipal-scale energy system through introducing renewable energies and electric vehicles publication-title: J Clean Prod – volume: 128 start-page: 1201 year: 2017 end-page: 1213 ident: bib17 article-title: Comparative investigation for the determination of kinetic parameters for biomass pyrolysis by thermogravimetric analysis publication-title: J Therm Anal Calorim – volume: 28 start-page: 6130 year: 2014 end-page: 6139 ident: bib18 article-title: Pyrolysis of medium-density fiberboard: optimized search for kinetics scheme and parameters via a genetic algorithm driven by Kissinger’s method publication-title: Energy Fuel – volume: 41 start-page: 204 year: 2006 end-page: 214 ident: bib21 article-title: The application of a genetic algorithm to estimate material properties for fire modeling from bench-scale fire test data publication-title: Fire Saf J – volume: 96 start-page: 156 year: 2016 end-page: 170 ident: bib10 article-title: Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization publication-title: Knowl Base Syst – volume: 165 start-page: 731 year: 2018 end-page: 742 ident: bib6 article-title: Kinetic study for the co-pyrolysis of lignocellulosic biomass and plastics using the distributed activation energy model publication-title: Energy – volume: 62 start-page: 33 year: 2017 end-page: 86 ident: bib28 article-title: Lignocellulosic biomass pyrolysis mechanism: a state-of-the-art review publication-title: Prog Energy Combust Sci – volume: 64 start-page: 174 year: 2010 end-page: 181 ident: bib31 article-title: Kinetic study of wood chips decomposition by TGA publication-title: Chem Pap – start-page: 2354 year: 2011 end-page: 2357 ident: bib9 article-title: Parameter estimation of the pyrolysis model for fir based on particle swarm algorithm publication-title: Conference parameter estimation of the pyrolysis model for fir based on particle swarm algorithm. 2011 second international conference on mechanic automation and control engineering – volume: 10 start-page: 751 year: 2011 end-page: 764 ident: bib26 article-title: Optimization algorithms for material pyrolysis property estimation publication-title: Fire Saf Sci – volume: 239 start-page: 623 year: 2019 end-page: 635 ident: bib13 article-title: Effect of air flowrate on pollutant dispersion pattern of coal dust particles at fully mechanized mining face based on numerical simulation publication-title: Fuel – volume: 138 start-page: 48 year: 2015 end-page: 55 ident: bib29 article-title: Application of genetic algorithm to pyrolysis of typical polymers publication-title: Fuel Process Technol – volume: 151 start-page: 1007 year: 2018 end-page: 1017 ident: bib1 article-title: Multi reaction apparent kinetic scheme for the pyrolysis of large size biomass particles using macro-TGA publication-title: Energy – volume: 64 start-page: 3953 year: 2009 end-page: 3967 ident: bib16 article-title: Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: application for an industrial polypropylene reactor publication-title: Chem Eng Sci – volume: 41 start-page: 4201 year: 2002 end-page: 4208 ident: bib30 article-title: Thermogravimetric analysis and devolatilization kinetics of wood publication-title: Ind Eng Chem Res – volume: 222 start-page: 11 year: 2018 end-page: 20 ident: bib8 article-title: Pyrolytic behavior of waste extruded polystyrene and rigid polyurethane by multi kinetics methods and Py-GC/MS publication-title: Fuel – volume: 125 start-page: 290 year: 2016 end-page: 300 ident: bib5 article-title: A combined genetic algorithm and least squares fitting procedure for the estimation of the kinetic parameters of the pyrolysis of agricultural residues publication-title: Energy Convers Manag – volume: 136 start-page: 484 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib20 article-title: Kinetic study on pyrolysis of waste phenolic fibre-reinforced plastic publication-title: Appl Therm Eng doi: 10.1016/j.applthermaleng.2018.03.045 – volume: 147 start-page: 444 year: 2019 ident: 10.1016/j.energy.2019.04.030_bib14 article-title: Research on tunnel ventilation systems: dust diffusion and pollution behaviour by air curtains based on CFD technology and field measurement publication-title: Build Environ doi: 10.1016/j.buildenv.2018.08.061 – volume: 151 start-page: 1007 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib1 article-title: Multi reaction apparent kinetic scheme for the pyrolysis of large size biomass particles using macro-TGA publication-title: Energy doi: 10.1016/j.energy.2018.03.075 – volume: 222 start-page: 11 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib8 article-title: Pyrolytic behavior of waste extruded polystyrene and rigid polyurethane by multi kinetics methods and Py-GC/MS publication-title: Fuel doi: 10.1016/j.fuel.2018.02.143 – volume: 34 start-page: 47 issue: 1 year: 2008 ident: 10.1016/j.energy.2019.04.030_bib27 article-title: Modeling chemical and physical processes of wood and biomass pyrolysis publication-title: Prog Energy Combust Sci doi: 10.1016/j.pecs.2006.12.001 – volume: 170 start-page: 326 year: 2019 ident: 10.1016/j.energy.2019.04.030_bib7 article-title: The catalytic effect of inherent and adsorbed metals on the fast/flash pyrolysis of biomass: a review publication-title: Energy doi: 10.1016/j.energy.2018.12.174 – volume: 239 start-page: 623 year: 2019 ident: 10.1016/j.energy.2019.04.030_bib13 article-title: Effect of air flowrate on pollutant dispersion pattern of coal dust particles at fully mechanized mining face based on numerical simulation publication-title: Fuel doi: 10.1016/j.fuel.2018.11.030 – volume: 41 start-page: 4201 issue: 17 year: 2002 ident: 10.1016/j.energy.2019.04.030_bib30 article-title: Thermogravimetric analysis and devolatilization kinetics of wood publication-title: Ind Eng Chem Res doi: 10.1021/ie0201157 – volume: 207 start-page: 772 year: 2019 ident: 10.1016/j.energy.2019.04.030_bib2 article-title: A flexible-possibilistic stochastic programming method for planning municipal-scale energy system through introducing renewable energies and electric vehicles publication-title: J Clean Prod doi: 10.1016/j.jclepro.2018.10.006 – volume: 125 start-page: 290 year: 2016 ident: 10.1016/j.energy.2019.04.030_bib5 article-title: A combined genetic algorithm and least squares fitting procedure for the estimation of the kinetic parameters of the pyrolysis of agricultural residues publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.04.104 – volume: 41 start-page: 204 issue: 3 year: 2006 ident: 10.1016/j.energy.2019.04.030_bib21 article-title: The application of a genetic algorithm to estimate material properties for fire modeling from bench-scale fire test data publication-title: Fire Saf J doi: 10.1016/j.firesaf.2005.12.004 – volume: 165 start-page: 731 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib6 article-title: Kinetic study for the co-pyrolysis of lignocellulosic biomass and plastics using the distributed activation energy model publication-title: Energy doi: 10.1016/j.energy.2018.09.133 – volume: 64 start-page: 174 issue: 2 year: 2010 ident: 10.1016/j.energy.2019.04.030_bib31 article-title: Kinetic study of wood chips decomposition by TGA publication-title: Chem Pap doi: 10.2478/s11696-009-0109-4 – volume: 132 start-page: 102 year: 2017 ident: 10.1016/j.energy.2019.04.030_bib32 article-title: Comparative pyrolysis behaviors and reaction mechanisms of hardwood and softwood publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.11.016 – volume: 128 start-page: 1201 issue: 2 year: 2017 ident: 10.1016/j.energy.2019.04.030_bib17 article-title: Comparative investigation for the determination of kinetic parameters for biomass pyrolysis by thermogravimetric analysis publication-title: J Therm Anal Calorim doi: 10.1007/s10973-017-6212-9 – volume: 96 start-page: 156 year: 2016 ident: 10.1016/j.energy.2019.04.030_bib10 article-title: Multiple learning particle swarm optimization with space transformation perturbation and its application in ethylene cracking furnace optimization publication-title: Knowl Base Syst doi: 10.1016/j.knosys.2015.12.020 – volume: 62 start-page: 33 year: 2017 ident: 10.1016/j.energy.2019.04.030_bib28 article-title: Lignocellulosic biomass pyrolysis mechanism: a state-of-the-art review publication-title: Prog Energy Combust Sci doi: 10.1016/j.pecs.2017.05.004 – volume: 28 start-page: 6130 issue: 9 year: 2014 ident: 10.1016/j.energy.2019.04.030_bib18 article-title: Pyrolysis of medium-density fiberboard: optimized search for kinetics scheme and parameters via a genetic algorithm driven by Kissinger’s method publication-title: Energy Fuel doi: 10.1021/ef501380c – volume: 215 start-page: 735 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib4 article-title: Facile synthesis of silica aerogel supported K2CO3 sorbents with enhanced CO2 capture capacity for ultra-dilute flue gas treatment publication-title: Fuel doi: 10.1016/j.fuel.2017.11.113 – volume: 172 start-page: 466 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib3 article-title: Scenario analysis of carbon emissions’ anti-driving effect on Qingdao’s energy structure adjustment with an optimization model, Part Ⅰ: carbon emissions peak value prediction publication-title: J Clean Prod doi: 10.1016/j.jclepro.2017.10.216 – volume: 138 start-page: 20 issue: 1–3 year: 2008 ident: 10.1016/j.energy.2019.04.030_bib22 article-title: Hybrid genetic algorithm to find the best model and the globally optimized overall kinetics parameters for thermal decomposition of plastics publication-title: Chem Eng J doi: 10.1016/j.cej.2007.05.024 – volume: 10 start-page: 751 year: 2011 ident: 10.1016/j.energy.2019.04.030_bib26 article-title: Optimization algorithms for material pyrolysis property estimation publication-title: Fire Saf Sci doi: 10.3801/IAFSS.FSS.10-751 – volume: 232 start-page: 147 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib11 article-title: The effect of chemical reaction kinetic parameters on the bench-scale pyrolysis of lignocellulosic biomass publication-title: Fuel doi: 10.1016/j.fuel.2018.05.140 – volume: 146 start-page: 124 year: 2017 ident: 10.1016/j.energy.2019.04.030_bib19 article-title: Thermal decomposition of rape straw: pyrolysis modeling and kinetic study via particle swarm optimization publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2017.05.020 – start-page: 1942 year: 1995 ident: 10.1016/j.energy.2019.04.030_bib23 article-title: Particle swarm optimization – volume: 216 start-page: 280 year: 2016 ident: 10.1016/j.energy.2019.04.030_bib24 article-title: Co-combustion of peanut hull and coal blends: artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation publication-title: Bioresour Technol doi: 10.1016/j.biortech.2016.05.091 – volume: 200 start-page: 658 year: 2016 ident: 10.1016/j.energy.2019.04.030_bib25 article-title: Estimation of beech pyrolysis kinetic parameters by shuffled complex evolution publication-title: Bioresour Technol doi: 10.1016/j.biortech.2015.10.082 – volume: 520 start-page: 1 issue: 1–2 year: 2011 ident: 10.1016/j.energy.2019.04.030_bib15 article-title: ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data publication-title: Thermochim Acta doi: 10.1016/j.tca.2011.03.034 – volume: 268 start-page: 77 year: 2018 ident: 10.1016/j.energy.2019.04.030_bib12 article-title: Modeling and analysis of bench-scale pyrolysis of lignocellulosic biomass based on merge thickness publication-title: Bioresour Technol doi: 10.1016/j.biortech.2018.07.134 – volume: 64 start-page: 3953 issue: 18 year: 2009 ident: 10.1016/j.energy.2019.04.030_bib16 article-title: Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization: application for an industrial polypropylene reactor publication-title: Chem Eng Sci doi: 10.1016/j.ces.2009.05.028 – start-page: 2354 year: 2011 ident: 10.1016/j.energy.2019.04.030_bib9 article-title: Parameter estimation of the pyrolysis model for fir based on particle swarm algorithm – volume: 138 start-page: 48 year: 2015 ident: 10.1016/j.energy.2019.04.030_bib29 article-title: Application of genetic algorithm to pyrolysis of typical polymers publication-title: Fuel Process Technol doi: 10.1016/j.fuproc.2015.05.001 |
| SSID | ssj0005899 |
| Score | 2.6329365 |
| Snippet | Reaction kinetic parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 582 |
| SubjectTerms | Algorithms Biomass Biomass pyrolysis Cellulose Convergence Genetic algorithms Hemicellulose Kinetic parameters Lignin Optimization scheme Parameter estimation Particle swarm optimization PSO Pyrolysis Reaction mechanisms system optimization thermogravimetry |
| Title | The accuracy and efficiency of GA and PSO optimization schemes on estimating reaction kinetic parameters of biomass pyrolysis |
| URI | https://dx.doi.org/10.1016/j.energy.2019.04.030 https://www.proquest.com/docview/2242776451 https://www.proquest.com/docview/2271876338 |
| Volume | 176 |
| WOSCitedRecordID | wos000470939500047&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6785 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005899 issn: 0360-5442 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Pb9MwFLZKhwQXBIOJjoGMhLhUmZrEsZNjBd0AVR0SnehOlpM4oqNNuraZyoH_gT-ZZ8dOWia0ceASWY6dtH1f_T4_vx8IvQl8oNFZ6jvCp5FDvLTnhC6jjkhkDPqBebHUVUuGbDQKJ5Poc6v1y8bCXM9YnoebTbT4r6KGPhC2Cp39B3HXD4UOaIPQ4Qpih-udBS-SpFyqQu7KLC51lggdYgnM8LRfBQh8OesWsFzMTRxmF3a5UiVugqZKvKGIrKpCJE0t8e_ARlVuV5UqfK5caLQLiAreB_bdXfxYFjq3yY6hvworVPlMN5ULfW10eG8qqVyIfG5157b1-qvMZ0XTf1Fq84Gc2o5hWXmCTL-VYttuoUKlrH9VHa_VcwJCdtdiRruL4wBYf1BV-zPralBVKDIq2ty7sfpXhojLY6m_n_Lbi3QeW3Pys5Nse3TGT86HQz4eTMZvF1eOqkOmzutNUZZ7aM9jQRS20V7_42DyqXEbCnVN0vrT23BM7TN488V_ozt_KH7NZsaP0SOzDcH9Cj5PUEvm--iBjVJf7aODQRMBCQONClg9RT8BX9jiCwOUcIMvXGT4tK87AV94G1_Y4AtDs8EXtvjCBl-4wZd6mMEXrvH1DJ2fDMbvPjimhoeTkB5ZO24q3MyVIkjcTCa-HyV-BiogoSFNid-LgyyGLWucBjCKMkIjSQORAmNKY-G5VPoHqJ0XuXyOMGUyTIkHszNBaBgK5mbMz1x1VJxlxOsg3_7UPDEJ7lWdlRm3noyXvBIQVwLiPcJBQB3k1LMWVYKXW8YzK0VuSGpFPjmg8JaZR1bo3KwXKw4M2mOMksDtoNf1bVji1bmdyGVRqjFAIIEH-OHhHca8QA-bv9sRaq-XpXyJ7ifX6-lq-crA-TcNNcvR |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+accuracy+and+efficiency+of+GA+and+PSO+optimization+schemes+on+estimating+reaction+kinetic+parameters+of+biomass+pyrolysis&rft.jtitle=Energy+%28Oxford%29&rft.au=Ding%2C+Yanming&rft.au=Zhang%2C+Wenlong&rft.au=Yu%2C+Lei&rft.au=Lu%2C+Kaihua&rft.date=2019-06-01&rft.issn=0360-5442&rft.volume=176+p.582-588&rft.spage=582&rft.epage=588&rft_id=info:doi/10.1016%2Fj.energy.2019.04.030&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0360-5442&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0360-5442&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0360-5442&client=summon |