{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T03:51:24Z","timestamp":1768708284521,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T00:00:00Z","timestamp":1623196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-of-the-art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators\u2019 decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.<\/jats:p>","DOI":"10.3390\/s21123991","type":"journal-article","created":{"date-parts":[[2021,6,9]],"date-time":"2021-06-09T14:16:04Z","timestamp":1623248164000},"page":"3991","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column"],"prefix":"10.3390","volume":"21","author":[{"given":"Iratxe","family":"Ni\u00f1o-Adan","sequence":"first","affiliation":[{"name":"TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain"},{"name":"Department of Automatic Control and Systems Engineering, Faculty of Engineering, University of the Basque Country UPV\/EHU, 48013 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itziar","family":"Landa-Torres","sequence":"additional","affiliation":[{"name":"Petronor Innovaci\u00f3n S.L, 48550 Muskiz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5656-9042","authenticated-orcid":false,"given":"Diana","family":"Manjarres","sequence":"additional","affiliation":[{"name":"TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eva","family":"Portillo","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Systems Engineering, Faculty of Engineering, University of the Basque Country UPV\/EHU, 48013 Bilbao, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc\u00eda","family":"Orbe","sequence":"additional","affiliation":[{"name":"Petronor Innovaci\u00f3n S.L, 48550 Muskiz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TII.2018.2815717","article-title":"Design and Implementation of hybrid modeling and PFC for oxygen content regulation in a coke furnace","volume":"14","author":"Zhang","year":"2018","journal-title":"IEEE Trans. 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