{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T05:07:23Z","timestamp":1739941643914,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Basic Scientific Research Project of Institution of Higher Learning of Liaoning Province","award":["LJKZ0293"],"award-info":[{"award-number":["LJKZ0293"]}]},{"name":"Postgraduate Education Reform Project of Liaoning Province","award":["LNYJG2022137"],"award-info":[{"award-number":["LNYJG2022137"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s00500-025-10444-y","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T12:58:01Z","timestamp":1738933081000},"page":"485-507","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bidirectional online sequence extreme learning machine and switching strategy for soft-sensor model of SMB chromatography separation process"],"prefix":"10.1007","volume":"29","author":[{"given":"Yong-Cheng","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8853-1927","authenticated-orcid":false,"given":"Jie-Sheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Yi-Peng","family":"Shang-Guan","sequence":"additional","affiliation":[]},{"given":"Xiao-Tian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Song-Bo","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,7]]},"reference":[{"issue":"16","key":"10444_CR1","doi-asserted-by":"publisher","first-page":"3574","DOI":"10.3390\/math11163574","volume":"11","author":"W Chai","year":"2023","unstructured":"Chai W, Zheng Y, Tian L et al (2023) GA-KELM: Genetic-algorithm-improved kernel extreme learning machine for traffic flow forecasting. Mathematics 11(16):3574","journal-title":"Mathematics"},{"key":"10444_CR2","doi-asserted-by":"crossref","unstructured":"Chernev V P, Santos L O, Wouwer A V, et al. Model Predictive Control of Simulated Moving Bed Chromatographic Processes Using Conservation Element\/Solution Element Method[C]\/\/2022 26th International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2022: 355\u2013361","DOI":"10.1109\/ICSTCC55426.2022.9931774"},{"key":"10444_CR3","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s10462-011-9208-z","volume":"36","author":"S Ding","year":"2011","unstructured":"Ding S, Su C, Yu J (2011) An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36:153\u2013162","journal-title":"Artif Intell Rev"},{"issue":"7","key":"10444_CR4","doi-asserted-by":"publisher","first-page":"2290","DOI":"10.1021\/ie990820o","volume":"39","author":"G D\u00fcnnebier","year":"2000","unstructured":"D\u00fcnnebier G, Fricke J, Klatt KU (2000) Optimal design and operation of simulated moving bed chromatographic reactors. Ind Eng Chem Res 39(7):2290\u20132304","journal-title":"Ind Eng Chem Res"},{"key":"10444_CR5","doi-asserted-by":"crossref","unstructured":"Frandsen J, Huusom J K, Gernaey K V, et al. (2023) Shortcut design method for multicomponent gradient simulated moving beds. AIChE J","DOI":"10.1002\/aic.18304"},{"key":"10444_CR6","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.cjph.2023.10.051","volume":"86","author":"XY Gao","year":"2023","unstructured":"Gao XY (2023) Considering the wave processes in oceanography, acoustics and hydrodynamics by means of an extended coupled (2+ 1)-dimensional Burgers system. Chin J Phys 86:572\u2013577","journal-title":"Chin J Phys"},{"key":"10444_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.aml.2024.109018","volume":"152","author":"XY Gao","year":"2024","unstructured":"Gao XY (2024a) Two-layer-liquid and lattice considerations through a (3+ 1)-dimensional generalized Yu-Toda-Sasa-Fukuyama system. Appl Math Lett 152:109018","journal-title":"Appl Math Lett"},{"issue":"4","key":"10444_CR8","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s12346-024-01034-8","volume":"23","author":"XY Gao","year":"2024","unstructured":"Gao XY (2024b) Auto-B\u00e4cklund transformation with the solitons and similarity reductions for a generalized nonlinear shallow water wave equation. Qualit Theory Dynam Syst 23(4):181","journal-title":"Qualit Theory Dynam Syst"},{"issue":"3","key":"10444_CR9","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.ifacol.2021.08.266","volume":"54","author":"S Gerlich","year":"2021","unstructured":"Gerlich S, Arab H, Buchholz M et al (2021) Experimental application of individual column state and parameter estimation in SMB processes to an amino acid separation. IFAC-PapersOnLine 54(3):348\u2013353","journal-title":"IFAC-PapersOnLine"},{"key":"10444_CR10","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/s12559-017-9474-4","volume":"9","author":"T Guo","year":"2017","unstructured":"Guo T, Zhang L, Tan X (2017) Neuron pruning-based discriminative extreme learning machine for pattern classification. Cogn Comput 9:581\u2013595","journal-title":"Cogn Comput"},{"issue":"1\u20133","key":"10444_CR11","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133):489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"10444_CR12","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2011","unstructured":"Huang GB, Zhou H, Ding X et al (2011) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cyber Part B (Cybernetics) 42(2):513\u2013529","journal-title":"IEEE Trans Syst Man Cyber Part B (Cybernetics)"},{"key":"10444_CR13","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.1016\/j.neucom.2017.09.090","volume":"275","author":"FK Inaba","year":"2018","unstructured":"Inaba FK, Salles EOT, Perron S et al (2018) DGR-ELM\u2013distributed generalized regularized ELM for classification. Neurocomputing 275:1522\u20131530","journal-title":"Neurocomputing"},{"key":"10444_CR14","doi-asserted-by":"crossref","unstructured":"Jiabi D, Ling L, Decheng Y. Determination of adsorption isotherm of column of simulated moving bed[C]\/\/2017 36th Chinese Control Conference (CCC). IEEE, 2017: 2773\u20132777","DOI":"10.23919\/ChiCC.2017.8027785"},{"key":"10444_CR15","doi-asserted-by":"crossref","unstructured":"Jo C Y, Lee K, Lee C G, et al. (2023) Development of a stepwise solvent-gradient SMB process for continuous-mode purification of methoxyethyl nucleoside phosphoramidites to be used as the key building blocks of oligonucleotide drugs. Chem Eng J 143990","DOI":"10.1016\/j.cej.2023.143990"},{"key":"10444_CR16","doi-asserted-by":"crossref","unstructured":"Jo C Y, Mo H K, Lee K B, et al. (2023) Optimal design of a simulated-moving-bed separation process for economical production of xylitol from bamboo-hydrolysis byproducts. Separ Purif Technol 125828","DOI":"10.1016\/j.seppur.2023.125828"},{"key":"10444_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2023.144884","volume":"472","author":"HJ Kang","year":"2023","unstructured":"Kang HJ, Jo CY, Mun S (2023) Improving the economical efficiency of a simulated-moving-bed process for biofuel production from agarose in red algae. Chem Eng J 472:144884","journal-title":"Chem Eng J"},{"key":"10444_CR18","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.neucom.2005.03.002","volume":"68","author":"MB Li","year":"2005","unstructured":"Li MB, Huang GB, Saratchandran P et al (2005) Fully complex extreme learning machine. Neurocomputing 68:306\u2013314","journal-title":"Neurocomputing"},{"key":"10444_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.128776","volume":"282","author":"Y Li","year":"2023","unstructured":"Li Y, Wang S, Chen L et al (2023) Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries. Energy 282:128776","journal-title":"Energy"},{"issue":"6","key":"10444_CR20","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1109\/TNN.2006.880583","volume":"17","author":"NY Liang","year":"2006","unstructured":"Liang NY, Huang GB, Saratchandran P et al (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Networks 17(6):1411\u20131423","journal-title":"IEEE Trans Neural Networks"},{"issue":"10","key":"10444_CR21","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1016\/j.ces.2003.12.026","volume":"59","author":"YI Lim","year":"2004","unstructured":"Lim YI, Jorgensen SB (2004) A fast and accurate numerical method for solving simulated moving bed (SMB) chromatographic separation problems. Chem Eng Sci 59(10):1931\u20131947","journal-title":"Chem Eng Sci"},{"key":"10444_CR22","first-page":"89","volume":"5S","author":"Li Ling","year":"2006","unstructured":"Ling Li, Yuanwei J, Decheng Y (2006) Soft-sensor technology in SMB chromatogram separation process. Microcomp Inform 5S:89\u201391","journal-title":"Microcomp Inform"},{"issue":"11","key":"10444_CR23","first-page":"1298","volume":"31","author":"Li Ling","year":"2014","unstructured":"Ling Li, Decheng Y, Yuanwei J (2014) Soft-simulation modeling and simulation research on simulating moving bed processes. Comp Appl Chem 31(11):1298\u20131302","journal-title":"Comp Appl Chem"},{"key":"10444_CR24","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s12559-017-9473-5","volume":"9","author":"Y Liu","year":"2017","unstructured":"Liu Y, Zhang L, Deng P et al (2017) Common subspace learning via cross-domain extreme learning machine. Cogn Comput 9:555\u2013563","journal-title":"Cogn Comput"},{"key":"10444_CR25","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1023\/A:1007661119649","volume":"41","author":"MA Maloof","year":"2000","unstructured":"Maloof MA, Michalski RS (2000) Selecting examples for partial memory learning. Mach Learn 41:27\u201352","journal-title":"Mach Learn"},{"issue":"3","key":"10444_CR26","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1016\/j.ifacol.2021.08.299","volume":"54","author":"J Matos","year":"2021","unstructured":"Matos J, Faria RPV, Loureiro JM et al (2021) Optimal design of smb units: a novel strategy based on particles swarm optimization. IFAC-PapersOnLine 54(3):548\u2013553","journal-title":"IFAC-PapersOnLine"},{"issue":"1","key":"10444_CR27","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0021-9673(97)00048-4","volume":"769","author":"M Mazzotti","year":"1997","unstructured":"Mazzotti M, Storti G, Morbidelli M (1997) Optimal operation of simulated moving bed units for nonlinear chromatographic separations. J Chromatogr A 769(1):3\u201324","journal-title":"J Chromatogr A"},{"key":"10444_CR28","doi-asserted-by":"crossref","unstructured":"Oh T H, Oh S K, Lee J M. Modeling and optimization of simulated moving bed chromatography with side streams[C]\/\/2017 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP). IEEE, 2017: 624\u2013629","DOI":"10.1109\/ADCONIP.2017.7983852"},{"issue":"3","key":"10444_CR29","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1002\/aic.690390310","volume":"39","author":"G Storti","year":"1993","unstructured":"Storti G, Mazzotti M, Morbidelli M et al (1993) Robust design of binary countercurrent adsorption separation processes. AIChE J 39(3):471\u2013492","journal-title":"AIChE J"},{"issue":"2","key":"10444_CR30","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.jprocont.2013.11.001","volume":"24","author":"P Suvarov","year":"2014","unstructured":"Suvarov P, Kienle A, Nobre C et al (2014) Cycle to cycle adaptive control of simulated moving bed chromatographic separation processes. J Process Control 24(2):357\u2013367","journal-title":"J Process Control"},{"key":"10444_CR31","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.neucom.2015.04.018","volume":"166","author":"LD Tavares","year":"2015","unstructured":"Tavares LD, Saldanha RR, Vieira DAG (2015) Extreme learning machine with parallel layer perceptrons. Neurocomputing 166:164\u2013171","journal-title":"Neurocomputing"},{"key":"10444_CR32","first-page":"1","volume":"2019","author":"D Wang","year":"2019","unstructured":"Wang D, Wang JS, Wang SY et al (2019) Soft sensing modeling of the SMB chromatographic separation process based on the adaptive neural fuzzy inference system. J Sens 2019:1\u201316","journal-title":"J Sens"},{"key":"10444_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106411","volume":"123","author":"K Wu","year":"2023","unstructured":"Wu K, Xu C, Yan J et al (2023) Error-distribution-free kernel extreme learning machine for traffic flow forecasting. Eng Appl Artif Intell 123:106411","journal-title":"Eng Appl Artif Intell"},{"key":"10444_CR34","doi-asserted-by":"crossref","unstructured":"Xie C F, Xu L X, Wang R, et al. Concentration Control of SMB System Based on Hierarchical Fuzzy Controller[C]\/\/Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15\u201318 October 2019, Arad, Romania. Springer Singapore, 2022: 201\u2013212","DOI":"10.1007\/978-981-16-5036-9_22"},{"issue":"9","key":"10444_CR35","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1109\/TNNLS.2012.2202289","volume":"23","author":"Y Yang","year":"2012","unstructured":"Yang Y, Wang Y, Yuan X (2012) Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Transact Neur Networks Learn Syst 23(9):1498\u20131505","journal-title":"IEEE Transact Neur Networks Learn Syst"},{"issue":"10","key":"10444_CR36","doi-asserted-by":"publisher","first-page":"2504","DOI":"10.1021\/ie010832l","volume":"41","author":"Y Zang","year":"2002","unstructured":"Zang Y, Wankat PC (2002) SMB operation strategy\u2212 partial feed. Ind Eng Chem Res 41(10):2504\u20132511","journal-title":"Ind Eng Chem Res"},{"issue":"2","key":"10444_CR37","doi-asserted-by":"publisher","first-page":"508","DOI":"10.3390\/pr11020508","volume":"11","author":"X Zhang","year":"2023","unstructured":"Zhang X, Liu J, Ray AK et al (2023a) Research progress on the typical variants of simulated moving bed: from the established processes to the advanced technologies. Processes 11(2):508","journal-title":"Processes"},{"key":"10444_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110817","volume":"278","author":"Q Zhang","year":"2023","unstructured":"Zhang Q, Tsang ECC, He Q et al (2023b) Ensemble of kernel extreme learning machine based elimination optimization for multi-label classification. Knowl-Based Syst 278:110817","journal-title":"Knowl-Based Syst"},{"issue":"18","key":"10444_CR39","doi-asserted-by":"publisher","first-page":"4307","DOI":"10.1016\/0009-2509(96)00262-X","volume":"51","author":"G Zhong","year":"1996","unstructured":"Zhong G, Guiochon G (1996) Analytical solution for the linear ideal model of simulated moving bed chromatography. Chem Eng Sci 51(18):4307\u20134319","journal-title":"Chem Eng Sci"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10444-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10444-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10444-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T07:03:08Z","timestamp":1739862188000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10444-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10444"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10444-y","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"8 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"There are no Ethical problem in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}