000070055 001__ 70055
000070055 005__ 20190709135702.0
000070055 0247_ $$2doi$$a10.3390/w10030310
000070055 0248_ $$2sideral$$a105459
000070055 037__ $$aART-2018-105459
000070055 041__ $$aeng
000070055 100__ $$aAcevedo, L.
000070055 245__ $$aImproving the distillate prediction of a membrane distillation unit in a trigeneration scheme by using artificial neural networks
000070055 260__ $$c2018
000070055 5060_ $$aAccess copy available to the general public$$fUnrestricted
000070055 5203_ $$aAn Artificial Neural Network (ANN) has been developed to predict the distillate produced in a permeate gap membrane distillation (PGMD) module with process operating conditions (temperatures at the condenser and evaporator inlets, and feed seawater flow). Real data obtained from experimental tests were used for the ANN training and further validation and testing. This PGMD module constitutes part of an isolated trigeneration pilot unit fully supplied by solar and wind energy, which also provides power and sanitary hot water (SHW) for a typical single family home. PGMD production was previously estimated with published data from the MD module manufacturer by means of a new type in the framework of Trnsys® simulation within the design of the complete trigeneration scheme. The performance of the ANN model was studied and improved through a parametric study varying the number of neurons in the hidden layer, the number of experimental datasets and by using different activation functions. The ANN obtained can be easily exported to be used in simulation, control or process analysis and optimization. Here, the ANN was finally used to implement a new type to estimate the PGMD production of the unit by using the inlet parameters obtained by the complete simulation model of the trigeneration unit based on Renewable Energy Sources (RES).
000070055 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/ENE2014-59947-R
000070055 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000070055 590__ $$a2.524$$b2018
000070055 591__ $$aWATER RESOURCES$$b29 / 91 = 0.319$$c2018$$dQ2$$eT1
000070055 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000070055 700__ $$0(orcid)0000-0003-4408-6881$$aUche, J.$$uUniversidad de Zaragoza
000070055 700__ $$aDel-Amo, A.
000070055 7102_ $$15004$$2590$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Máquinas y Motores Térmi.
000070055 773__ $$g10, 3 (2018), 310 [21 pp]$$pWater (Basel)$$tWater (Basel)$$x2073-4441
000070055 8564_ $$s812562$$uhttp://zaguan.unizar.es/record/70055/files/texto_completo.pdf$$yVersión publicada
000070055 8564_ $$s112319$$uhttp://zaguan.unizar.es/record/70055/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000070055 909CO $$ooai:zaguan.unizar.es:70055$$particulos$$pdriver
000070055 951__ $$a2019-07-09-12:50:04
000070055 980__ $$aARTICLE