170432 20260420103355.0 doi 10.1016/j.seppur.2026.137628 sideral 148902 ART-2026-148902 eng Otarola, Luis Lillo Predicting filtration clogging behavior using machine learning and physicochemical parameters in final wine filtration processes 2026 The wine bottling process includes a mechanical filtration to remove unwanted particles such as microorganisms and crystalline precipitates, thus ensuring product quality. This step involves high operational costs due to the variability in the quantity and type of particles, which obstruct the filtering medium, reducing flow and increasing pressure differential, negatively impacting processing and bottling time. Traditionally, clogging power could only be measured through laboratory analysis once the wine was ready for bottling, limiting efficient production planning. This study analyzes the variability of the filtration speed index through analytical and statistical methods and examines how certain physicochemical parameters influence these measurements. Using data from 689 records from a renowned Chilean vineyard, machine learning techniques are applied to evaluate different predictive models. The results indicate that the Gradient Boosting Machine model predicts the filtration speed index with an average absolute error of 10.66%, significantly lower than the analytical limit of 20%, thus facilitating timely and effective operational decision-making. Access copy available to the general public Unrestricted info:eu-repo/semantics/embargoedAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion de la Fuente-Mella, Hanns Domarchi, Alonso Peña Marzo Navarro, Mercedes Universidad de Zaragoza (orcid)0000-0002-9628-5738 Ceroni, José 4011 095 Universidad de Zaragoza Dpto. Direc.Mark.Inves.Mercad. Área Comerci.Investig.Mercados 394 (2026), 137628 [13 pp.] Sep. Purif. Technol. Separation and Purification Technology 1383-5866 961594 http://zaguan.unizar.es/record/170432/files/texto_completo.pdf Postprint info:eu-repo/date/embargoEnd/2028-03-18 941342 http://zaguan.unizar.es/record/170432/files/texto_completo.jpg?subformat=icon icon Postprint info:eu-repo/date/embargoEnd/2028-03-18 oai:zaguan.unizar.es:170432 articulos driver 2026-04-18-10:49:27 ARTICLE