{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:02:49Z","timestamp":1784530969212,"version":"3.55.0"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014438","name":"Business Finland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014438","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Chemical Engineering"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.compchemeng.2026.109808","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T23:43:01Z","timestamp":1783467781000},"page":"109808","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Feature extraction strategies for multi-rate process data: Applications to CMC manufacturing and Tennessee Eastman simulation"],"prefix":"10.1016","volume":"214","author":[{"given":"Markku","family":"Ohenoja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tero","family":"Vuolio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1096-0094","authenticated-orcid":false,"given":"Mika","family":"Ruusunen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.compchemeng.2026.109808_bib0001","doi-asserted-by":"crossref","first-page":"307","DOI":"10.12732\/ijpam.v94i3.2","article-title":"Cutoff threshold of variable importance in projection for variable selection","volume":"94","author":"Akarachantachote","year":"2014","journal-title":"Int. J. Pure Appl. Math."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.106900","article-title":"Mechanism discovery and model identification using genetic feature extraction and statistical testing","volume":"140","author":"Chakraborty","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107470","article-title":"AI-DARWIN: a first principles-based model discovery engine using machine learning","volume":"154","author":"Chakraborty","year":"2021","journal-title":"Comput. Chem. Eng."},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109808_bib0005","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TE.1969.4320436","article-title":"The fast Fourier transform and its applications","volume":"12","author":"Cooley","year":"1969","journal-title":"IEEE Trans. Educ."},{"issue":"02","key":"10.1016\/j.compchemeng.2026.109808_bib0006","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1142\/S0219720005001004","article-title":"Minimum redundancy feature selection from microarray gene expression data","volume":"3","author":"Ding","year":"2003","journal-title":"J. Bioinform. Comput. Biol."},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109808_bib0007","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","article-title":"A plant-wide industrial process control problem","volume":"17","author":"Downs","year":"1993","journal-title":"Comput. Chem. Eng."},{"issue":"10","key":"10.1016\/j.compchemeng.2026.109808_bib0008","doi-asserted-by":"crossref","DOI":"10.1109\/JSEN.2024.3384275","article-title":"Anomaly detection based on data super-resolution in industrial cyber\u2013physical systems with multirate sampling","volume":"24","author":"Du","year":"2024","journal-title":"IEEE Sens. J."},{"issue":"2260","key":"10.1016\/j.compchemeng.2026.109808_bib0009","doi-asserted-by":"crossref","DOI":"10.1098\/rspa.2021.0904","article-title":"Ensemble-SINDy: robust sparse model discovery in the low-data, high-noise limit, with active learning and control","volume":"478","author":"Fasel","year":"2022","journal-title":"Proc. R. Soc. A"},{"key":"10.1016\/j.compchemeng.2026.109808_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106461","article-title":"FLIRT: a feature generation toolkit for wearable data","volume":"212","author":"F\u00f6ll","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0011","first-page":"1157","article-title":"An Introduction to Variable and Feature Selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109808_bib0012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0013","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.jprocont.2015.11.004","article-title":"A novel process monitoring and fault detection approach based on statistics locality preserving projections","volume":"37","author":"He","year":"2016","journal-title":"J. Process Control"},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109808_bib0014","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1002\/aic.12247","article-title":"Statistics pattern analysis: a new process monitoring framework and its application to semiconductor batch processes","volume":"57","author":"He","year":"2011","journal-title":"AIChE J."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0015","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.compchemeng.2019.04.010","article-title":"Feature space monitoring for smart manufacturing via statistics pattern analysis","volume":"126","author":"He","year":"2019","journal-title":"Comput. Chem. Eng."},{"issue":"2","key":"10.1016\/j.compchemeng.2026.109808_bib0016","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/S0169-7439(01)00154-X","article-title":"Some theoretical aspects of partial least squares regression","volume":"58","author":"Helland","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2024.108600","article-title":"Orthogonal projection based statistical feature extraction for continuous process monitoring","volume":"183","author":"Ji","year":"2024","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0018","series-title":"Proceedings The 7th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies","article-title":"Generalised lp norms in vibration analysis of process equipments","author":"Lahdelma","year":"2010"},{"key":"10.1016\/j.compchemeng.2026.109808_bib0019","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/9658350","article-title":"Content-based image retrieval and feature extraction: a comprehensive review","volume":"2019","author":"Latif","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0020","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.compchemeng.2026.109808_bib0021","doi-asserted-by":"crossref","DOI":"10.3389\/fceng.2022.1064221","article-title":"Feature-based statistical process monitoring for pressure swing adsorption processes","author":"Lee","year":"2022","journal-title":"Front. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0022","series-title":"Feature Extraction, Construction and Selection: a Data Mining Perspective","author":"Liu","year":"1998"},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109808_bib0023","doi-asserted-by":"crossref","DOI":"10.1063\/1.5011683","article-title":"Geometric k-nearest neighbor estimation of entropy and mutual information","volume":"28","author":"Lord","year":"2018","journal-title":"Chaos"},{"key":"10.1016\/j.compchemeng.2026.109808_bib0024","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.chemolab.2012.07.010","article-title":"A review of variable selection methods in partial least squares regression","volume":"118","author":"Mehmood","year":"2012","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109808_bib0025","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3390\/machines8030043","article-title":"Roller leveler monitoring using acceleration measurements and models for steel strip properties","volume":"8","author":"Nikula","year":"2020","journal-title":"Machines"},{"key":"10.1016\/j.compchemeng.2026.109808_bib0026","series-title":"The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society, SIMS 2021, September 21-23, Virtual Conference","article-title":"Development of a surrogate-model based energy efficiency estimator for a multi-step chemical process","author":"Ohenoja","year":"2022"},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109808_bib0027","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1002\/asmb.2477","article-title":"Predicting ships' CO2 emissions using feature-oriented methods","volume":"36","author":"Reis","year":"2020","journal-title":"Appl. Stoch. Models Bus. Ind."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compchemeng.2019.01.014","article-title":"Data-driven methods for batch data analysis\u2013a critical overview and mapping on the complexity scale","volume":"124","author":"Rendall","year":"2019","journal-title":"Comput. Chem. Eng."},{"issue":"1\u20132","key":"10.1016\/j.compchemeng.2026.109808_bib0029","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1002\/apj.5500120118","article-title":"Production variance in purified carboxymethyl cellulose (CMC) manufacture","volume":"12","author":"Stigsson","year":"2004","journal-title":"Dev. Chem. Eng. Miner. Process."},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109808_bib0030","doi-asserted-by":"crossref","DOI":"10.1002\/bimj.202200104","article-title":"Against the \u201cone method fits all data sets\u201d philosophy for comparison studies in methodological research","volume":"66","author":"Strobl","year":"2022","journal-title":"Biometr. J."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2021.107445","article-title":"Multiclass moisture classification in woodchips using IIoT Wi-Fi and machine learning techniques","volume":"154","author":"Suthar","year":"2021","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0032","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compchemeng.2019.05.016","article-title":"Next-generation virtual metrology for semiconductor manufacturing: a feature-based framework","volume":"127","author":"Suthar","year":"2019","journal-title":"Comput. Chem. Eng."},{"issue":"4","key":"10.1016\/j.compchemeng.2026.109808_bib0033","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s13042-020-01096-5","article-title":"Recent advances in deep learning","volume":"11","author":"Wang","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"10","key":"10.1016\/j.compchemeng.2026.109808_bib0034","doi-asserted-by":"crossref","DOI":"10.3390\/s19102240","article-title":"Monitoring chemical processes using judicious fusion of multi-rate sensor data","volume":"19","author":"Wang","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.compchemeng.2026.109808_bib0035","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1016\/j.compchemeng.2017.02.010","article-title":"The ALAMO approach to machine learning","volume":"106","author":"Wilson","year":"2017","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0036","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isatra.2018.07.038","article-title":"Performance monitoring method based on balanced partial least square and statistics pattern analysis","volume":"81","author":"Yang","year":"2018","journal-title":"ISA Trans."},{"key":"10.1016\/j.compchemeng.2026.109808_bib0037","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.neucom.2018.02.091","article-title":"Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis","volume":"293","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2026.109808_bib0038","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.neucom.2019.09.075","article-title":"Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection","volume":"376","author":"Zhou","year":"2019","journal-title":"Neurocomputing"}],"container-title":["Computers & Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426002607?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426002607?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:19:11Z","timestamp":1784528351000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135426002607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":37,"alternative-id":["S0098135426002607"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109808","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Feature extraction strategies for multi-rate process data: Applications to CMC manufacturing and Tennessee Eastman simulation","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109808","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109808"}}