FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3013623430> ?p ?o ?g. }

• W3013623430 endingPage "114427" @default.
• W3013623430 startingPage "114427" @default.
• W3013623430 abstract "Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered neural network model is developed to predict the permeate flux in forward osmosis. The developed model is tested for its generalization capability by including lab-scale experimental data from several published studies. Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and the draw solution and temperature of the feed solution and the draw solution. The development of optimum network architecture is supported by studying the impact of the number of neurons and hidden layers on the neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of the model is tested against untrained published data. The performance of the ANN model is compared with a transport-based model in the literature. A simple machine learning technique such as a multiple linear regression (MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its ability to form a complex relationship between inputs and output better than MLR." @default.
• W3013623430 created "2020-04-03" @default.
• W3013623430 creator A5077115642 @default.
• W3013623430 creator A5083246810 @default.
• W3013623430 creator A5089593241 @default.
• W3013623430 date "2020-06-01" @default.
• W3013623430 modified "2023-09-29" @default.
• W3013623430 title "Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux" @default.
• W3013623430 cites W1872089867 @default.
• W3013623430 cites W1965766173 @default.
• W3013623430 cites W1971347939 @default.
• W3013623430 cites W1972315390 @default.
• W3013623430 cites W1986260005 @default.
• W3013623430 cites W2001892959 @default.
• W3013623430 cites W2004847971 @default.
• W3013623430 cites W2005704027 @default.
• W3013623430 cites W2013888518 @default.
• W3013623430 cites W2016055197 @default.
• W3013623430 cites W2016710655 @default.
• W3013623430 cites W2018198710 @default.
• W3013623430 cites W2026132179 @default.
• W3013623430 cites W2027843998 @default.
• W3013623430 cites W2031296447 @default.
• W3013623430 cites W2054727583 @default.
• W3013623430 cites W2056808208 @default.
• W3013623430 cites W2058126399 @default.
• W3013623430 cites W2059172262 @default.
• W3013623430 cites W2060391650 @default.
• W3013623430 cites W2066718448 @default.
• W3013623430 cites W2071975690 @default.
• W3013623430 cites W2072880242 @default.
• W3013623430 cites W2073001825 @default.
• W3013623430 cites W2074517302 @default.
• W3013623430 cites W2082619287 @default.
• W3013623430 cites W2083503146 @default.
• W3013623430 cites W2131740321 @default.
• W3013623430 cites W2141374763 @default.
• W3013623430 cites W2159488176 @default.
• W3013623430 cites W2164121299 @default.
• W3013623430 cites W2254168642 @default.
• W3013623430 cites W2474494086 @default.
• W3013623430 cites W2512286372 @default.
• W3013623430 cites W2665043980 @default.
• W3013623430 cites W2766370639 @default.
• W3013623430 cites W2794167267 @default.
• W3013623430 cites W2921691021 @default.
• W3013623430 cites W2953709523 @default.
• W3013623430 cites W2965166832 @default.
• W3013623430 doi "https://doi.org/10.1016/j.desal.2020.114427" @default.
• W3013623430 hasPublicationYear "2020" @default.
• W3013623430 type Work @default.
• W3013623430 sameAs 3013623430 @default.
• W3013623430 citedByCount "54" @default.
• W3013623430 countsByYear W30136234302020 @default.
• W3013623430 countsByYear W30136234302021 @default.
• W3013623430 countsByYear W30136234302022 @default.
• W3013623430 countsByYear W30136234302023 @default.
• W3013623430 crossrefType "journal-article" @default.
• W3013623430 hasAuthorship W3013623430A5077115642 @default.
• W3013623430 hasAuthorship W3013623430A5083246810 @default.
• W3013623430 hasAuthorship W3013623430A5089593241 @default.
• W3013623430 hasConcept C111919701 @default.
• W3013623430 hasConcept C127413603 @default.
• W3013623430 hasConcept C130797344 @default.
• W3013623430 hasConcept C134306372 @default.
• W3013623430 hasConcept C154945302 @default.
• W3013623430 hasConcept C177148314 @default.
• W3013623430 hasConcept C185592680 @default.
• W3013623430 hasConcept C186060115 @default.
• W3013623430 hasConcept C33923547 @default.
• W3013623430 hasConcept C41008148 @default.
• W3013623430 hasConcept C41625074 @default.
• W3013623430 hasConcept C50644808 @default.
• W3013623430 hasConcept C55493867 @default.
• W3013623430 hasConcept C81842627 @default.
• W3013623430 hasConcept C86803240 @default.
• W3013623430 hasConcept C98045186 @default.
• W3013623430 hasConceptScore W3013623430C111919701 @default.
• W3013623430 hasConceptScore W3013623430C127413603 @default.
• W3013623430 hasConceptScore W3013623430C130797344 @default.
• W3013623430 hasConceptScore W3013623430C134306372 @default.
• W3013623430 hasConceptScore W3013623430C154945302 @default.
• W3013623430 hasConceptScore W3013623430C177148314 @default.
• W3013623430 hasConceptScore W3013623430C185592680 @default.
• W3013623430 hasConceptScore W3013623430C186060115 @default.
• W3013623430 hasConceptScore W3013623430C33923547 @default.
• W3013623430 hasConceptScore W3013623430C41008148 @default.
• W3013623430 hasConceptScore W3013623430C41625074 @default.
• W3013623430 hasConceptScore W3013623430C50644808 @default.
• W3013623430 hasConceptScore W3013623430C55493867 @default.
• W3013623430 hasConceptScore W3013623430C81842627 @default.
• W3013623430 hasConceptScore W3013623430C86803240 @default.
• W3013623430 hasConceptScore W3013623430C98045186 @default.
• W3013623430 hasFunder F4320322472 @default.
• W3013623430 hasFunder F4320332753 @default.
• W3013623430 hasLocation W30136234301 @default.
• W3013623430 hasOpenAccess W3013623430 @default.
• W3013623430 hasPrimaryLocation W30136234301 @default.

• next