{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T07:59:55Z","timestamp":1778572795825,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T00:00:00Z","timestamp":1567468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873241"],"award-info":[{"award-number":["61873241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY18F030024"],"award-info":[{"award-number":["LY18F030024"]}]},{"name":"the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China","award":["ICT1900330"],"award-info":[{"award-number":["ICT1900330"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework. With the online semi-supervised learning method, the valuable information hidden in unlabeled data can be explored and absorbed into the prediction model. The application results to an industrial blast furnace show that BLSM has better prediction performance compared with other supervised soft sensors.<\/jats:p>","DOI":"10.3390\/s19173814","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T08:28:13Z","timestamp":1567585693000},"page":"3814","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models"],"prefix":"10.3390","volume":"19","author":[{"given":"Xing","family":"He","sequence":"first","affiliation":[{"name":"Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5573-1781","authenticated-orcid":false,"given":"Kaixin","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2943-8332","authenticated-orcid":false,"given":"Zengliang","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4066-689X","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1002\/aic.690450314","article-title":"Dynamic behavior of iron forms in rapid reduction of carbon-coated iron ore","volume":"45","author":"Sugawara","year":"1999","journal-title":"AIChE J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/S0959-1524(00)00026-3","article-title":"Mathematical model for predictive control of the bell-less top charging system of a blast furnace","volume":"11","author":"Radhakrishnan","year":"2001","journal-title":"J. Process Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1016\/j.compchemeng.2005.05.024","article-title":"Multi-dimensional transient mathematical simulator of blast furnace process based on multi-fluid and kinetic theories","volume":"29","author":"Nogami","year":"2005","journal-title":"Comput. Chem. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"669","DOI":"10.2355\/isijinternational.45.669","article-title":"A three-dimensional mathematical modelling of drainage behavior in blast furnace hearth","volume":"45","author":"Nishioka","year":"2005","journal-title":"ISIJ Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"914","DOI":"10.2355\/isijinternational.50.914","article-title":"Recent progress and future perspective on mathematical modeling of blast furnace","volume":"50","author":"Ueda","year":"2010","journal-title":"ISIJ Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/S0959-1524(99)00052-9","article-title":"Neural networks for the identification and control of blast furnace hot metal quality","volume":"10","author":"Radhakrishnan","year":"2000","journal-title":"J. Process Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"573","DOI":"10.2355\/isijinternational.44.573","article-title":"Blast furnace hot metal temperature prediction through neural networks-based models","volume":"44","author":"Jimenez","year":"2007","journal-title":"ISIJ Int."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.asoc.2005.09.001","article-title":"A genetic algorithms based multi-objective neural net applied to noisy blast furnace data","volume":"7","author":"Pettersson","year":"2007","journal-title":"Appl. Soft Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9236","DOI":"10.1021\/ie200274q","article-title":"Nonlinear modeling method applied to prediction of hot metal silicon in the ironmaking blast furnace","volume":"50","author":"Nurkkala","year":"2011","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1002\/srin.200506080","article-title":"A blast furnace prediction model combining neural network with partial least square regression","volume":"76","author":"Hao","year":"2005","journal-title":"Steel Res. Int."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.2355\/isijinternational.45.1943","article-title":"Prediction of silicon content in blast furnace hot metal using partial least squares (PLS)","volume":"45","author":"Bhattacharya","year":"2005","journal-title":"ISIJ Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1179\/174328107X155358","article-title":"Hot metal temperature prediction in blast furnace using advanced model based on fuzzy logic tools","volume":"34","author":"Martin","year":"2007","journal-title":"Ironmak. Steelmak."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1021\/ie990504+","article-title":"On the development of predictive models with applications to a metallurgical process","volume":"39","author":"Waller","year":"2000","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1021\/ie070879s","article-title":"Assessing the predictability for blast furnace system through nonlinear time series analysis","volume":"47","author":"Gao","year":"2008","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"316","DOI":"10.2355\/isijinternational.42.316","article-title":"Application of nonlinear time series analysis to the prediction of silicon content of pig iron","volume":"42","author":"Waller","year":"2002","journal-title":"ISIJ Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0167-2789(99)00135-9","article-title":"Time series analysis and prediction on complex dynamical behavior observed in a blast furnace","volume":"135","author":"Miyano","year":"2000","journal-title":"Physica D"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/srin.201000082","article-title":"A sliding-window smooth support vector regression model for nonlinear blast furnace system","volume":"82","author":"Jian","year":"2011","journal-title":"Steel Res. Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1109\/TIE.2011.2159693","article-title":"Modeling of the thermal state change of blast furnace hearth with support vector machines","volume":"59","author":"Gao","year":"2012","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1002\/aic.11724","article-title":"A chaos-based iterated multistep predictor for blast furnace ironmaking process","volume":"55","author":"Gao","year":"2009","journal-title":"AIChE J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1002\/aic.14426","article-title":"Data-based multiscale modeling for blast furnace system","volume":"60","author":"Chu","year":"2014","journal-title":"AIChE J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.2355\/isijinternational.52.1764","article-title":"A study of blast furnace dynamics using multiple autoregressive vector models","volume":"52","author":"Nurkkala","year":"2012","journal-title":"ISIJ Int."},{"key":"ref_22","first-page":"2213","article-title":"Data-driven time discrete models for dynamic prediction of the hot metal silicon content in the blast furnace\u2014A review","volume":"9","author":"Gao","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107","DOI":"10.2355\/isijinternational.ISIJINT-2016-292","article-title":"Adaptive weighting just-in-time-learning quality prediction model for an industrial blast furnace","volume":"57","author":"Chen","year":"2017","journal-title":"ISIJ Int."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, K., Liang, Y., Gao, Z., and Liu, Y. (2017). Just-in-time correntropy soft sensor with noisy data for industrial silicon content prediction. Sensors, 17.","DOI":"10.3390\/s17081830"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.compchemeng.2007.07.005","article-title":"Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry","volume":"32","author":"Kano","year":"2008","journal-title":"Comput. Chem. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abonyi, J., Farsang, B., and Kulcsar, T. (2014, January 23\u201325). Data-driven development and maintenance of soft-sensors. Proceedings of the IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Herlany, Slovakia.","DOI":"10.1109\/SAMI.2014.6822414"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yang, C., Liu, K., Chen, B., and Yao, Y. (2019). Domain adaptation transfer learning soft sensor for product quality prediction. Chemom. Intell. Lab. Syst., 192.","DOI":"10.1016\/j.chemolab.2019.103813"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20590","DOI":"10.1109\/ACCESS.2017.2756872","article-title":"Data mining and analytics in the process industry: The role of machine learning","volume":"5","author":"Ge","year":"2017","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8776","DOI":"10.1021\/acs.energyfuels.7b00576","article-title":"Flame images for oxygen content prediction of combustion systems using DBN","volume":"31","author":"Liu","year":"2017","journal-title":"Energy Fuels"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6538","DOI":"10.1109\/TIE.2017.2784394","article-title":"Automatic pearl classification machine based on a multistream convolutional neural network","volume":"65","author":"Xuan","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8244","DOI":"10.1109\/TIE.2018.2885684","article-title":"Multiview generative adversarial network and its application in pearl classification","volume":"66","author":"Xuan","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.chemolab.2018.07.002","article-title":"Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers","volume":"180","author":"Zheng","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1002\/aic.14270","article-title":"Mixture semisupervised principal component regression model and soft sensor application","volume":"60","author":"Ge","year":"2014","journal-title":"AIChE J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.chemolab.2017.10.009","article-title":"Industrial Mooney viscosity prediction using fast semi-supervised empirical model","volume":"171","author":"Zheng","year":"2017","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.chemolab.2018.01.008","article-title":"Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes","volume":"174","author":"Liu","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1002\/aic.12351","article-title":"Applicability domains and accuracy of prediction of soft sensor models","volume":"57","author":"Kaneko","year":"2011","journal-title":"AIChE J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.jprocont.2013.03.008","article-title":"Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes","volume":"23","author":"Liu","year":"2013","journal-title":"J. Process Control"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","article-title":"An insight into extreme learning machines: Random neurons, random features and kernels","volume":"6","author":"Huang","year":"2014","journal-title":"Cogn. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2566","DOI":"10.1016\/j.neucom.2010.12.043","article-title":"SELM: Semi-supervised ELM with application in sparse calibrated location estimation","volume":"74","author":"Liu","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1016\/j.neucom.2008.09.002","article-title":"Bagging for Gaussian process regression","volume":"72","author":"Chen","year":"2009","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3814\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:16:32Z","timestamp":1760188592000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,3]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["s19173814"],"URL":"https:\/\/doi.org\/10.3390\/s19173814","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,3]]}}}