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happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset\u2019s behaviour several days in advance.<\/jats:p>","DOI":"10.3390\/en15176308","type":"journal-article","created":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T21:01:31Z","timestamp":1661806891000},"page":"6308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8210-5468","authenticated-orcid":false,"given":"Jo\u00e3o Antunes","family":"Rodrigues","sequence":"first","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 6200-358 Covilh\u00e3, Portugal"},{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Universidade Lus\u00f3fona, Campo Grande 376, 1749-024 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra\u2014ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[{"name":"Polytechnic of Coimbra\u2014ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal"},{"name":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-194 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3630-6426","authenticated-orcid":false,"given":"Ricardo J. 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