000170432 001__ 170432
000170432 005__ 20260420103355.0
000170432 0247_ $$2doi$$a10.1016/j.seppur.2026.137628
000170432 0248_ $$2sideral$$a148902
000170432 037__ $$aART-2026-148902
000170432 041__ $$aeng
000170432 100__ $$aOtarola, Luis Lillo
000170432 245__ $$aPredicting filtration clogging behavior using machine learning and physicochemical parameters in final wine filtration processes
000170432 260__ $$c2026
000170432 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170432 5203_ $$aThe 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.
000170432 540__ $$9info:eu-repo/semantics/embargoedAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000170432 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000170432 700__ $$ade la Fuente-Mella, Hanns
000170432 700__ $$aDomarchi, Alonso Peña
000170432 700__ $$0(orcid)0000-0002-9628-5738$$aMarzo Navarro, Mercedes$$uUniversidad de Zaragoza
000170432 700__ $$aCeroni, José
000170432 7102_ $$14011$$2095$$aUniversidad de Zaragoza$$bDpto. Direc.Mark.Inves.Mercad.$$cÁrea Comerci.Investig.Mercados
000170432 773__ $$g394 (2026), 137628 [13 pp.]$$pSep. Purif. Technol.$$tSeparation and Purification Technology$$x1383-5866
000170432 8564_ $$s961594$$uhttps://zaguan.unizar.es/record/170432/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2028-03-18
000170432 8564_ $$s941342$$uhttps://zaguan.unizar.es/record/170432/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2028-03-18
000170432 909CO $$ooai:zaguan.unizar.es:170432$$particulos$$pdriver
000170432 951__ $$a2026-04-18-10:49:27
000170432 980__ $$aARTICLE