{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:22:39Z","timestamp":1780420959052,"version":"3.54.1"},"reference-count":89,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["PB\/BDE\/128562\/2017"],"award-info":[{"award-number":["PB\/BDE\/128562\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["UID\/EQU\/00102\/2020"],"award-info":[{"award-number":["UID\/EQU\/00102\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Chemical Process Engineering and Forest Products Research Centre (CIEPQPF)","award":["PB\/BDE\/128562\/2017"],"award-info":[{"award-number":["PB\/BDE\/128562\/2017"]}]},{"name":"the Chemical Process Engineering and Forest Products Research Centre (CIEPQPF)","award":["UID\/EQU\/00102\/2020"],"award-info":[{"award-number":["UID\/EQU\/00102\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions.<\/jats:p>","DOI":"10.3390\/s22103734","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"3734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling"],"prefix":"10.3390","volume":"22","author":[{"given":"Tiago","family":"Dias","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, University of Coimbra, CIEPQPF, Rua S\u00edlvio Lima, P\u00f3lo II\u2014Pinhal de Marrocos, 3030-790 Coimbra, Portugal"},{"name":"Petrogal, S.A., 4451-852 Le\u00e7a da Palmeira, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodolfo","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Petrogal, S.A., 4451-852 Le\u00e7a da Palmeira, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro M.","family":"Saraiva","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, University of Coimbra, CIEPQPF, Rua S\u00edlvio Lima, P\u00f3lo II\u2014Pinhal de Marrocos, 3030-790 Coimbra, Portugal"},{"name":"Dean of NOVA IMS, Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4997-8865","authenticated-orcid":false,"given":"Marco S.","family":"Reis","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, University of Coimbra, CIEPQPF, Rua S\u00edlvio Lima, P\u00f3lo II\u2014Pinhal de Marrocos, 3030-790 Coimbra, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1002\/aic.14523","article-title":"Process Data Analytics in the Era of Big Data","volume":"60","author":"Qin","year":"2014","journal-title":"AIChE J."},{"key":"ref_2","unstructured":"Reis, M.S., Braatz, R.D., and Chiang, L.H. 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