FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366812669> ?p ?o ?g. }

• W4366812669 endingPage "597" @default.
• W4366812669 startingPage "583" @default.
• W4366812669 abstract "Abstract While the uncertainty brought about by a varying feed mineralogy was taken into consideration, the paper investigated the modeling and prediction of the leaching behavior of complex copper-cobalt bearing ores, using an artificial neural network (ANN) with a backforward algorithm. The process optimization is further conducted using the response surface methodology (RSM) employing the Box-Behnken design (BBD). Seven (7) parameters were considered in a multiple linear regression according to the L 12 screening plan (2 7 ) of Plackett–Burman. From the seven parameters, four including solid percentage (15, 27.5, 40%), time (45, 90, 135 min), particle size passing (53, 75, 105 µm), and Fe 2+ ion concentration (2, 4, 6 g/L) are modeled with L 27 (3 4 ) BBD. With a composite desirability of 0.94, leaching yields of 93.46% Cu and 89.43% Co were obtained. The neural network algorithm used is the BFGS (Broyden, Fletcher, Goldfarb and Shanno) algorithm multilayer perceptron with the hyperbolic tangent activation function for the hidden layer and a linear activation function for the neural output. The Multilayer perceptron {4–7-1} structure was chosen as a suitable arrangement for Cu leaching. Comparing the predicted values and those obtained experimentally resulted with a correlation coefficient of 0.9552 for the data trained in the artificial neural network and 0.8742 for the data obtained with the response surface methodology. The synergy of these 2 techniques shows that the prediction can be achieved by means of the ANN giving the values of the root mean square errors (RMSE) of 0.0115, 0.00624, 0.0229, respectively, for the training, testing and validation sets for copper recovery while the correlational study between variables could be done through the RSM. The above includes only the 95% confidence interval while the remaining 5% would be uncertain. The above results and conclusion are accompanied by the relative uncertainty as the ore mineralogy varies. The combination of the synergistic use of ANN and RSM with the sensitivity analysis has approached the process to the physics of the Multi-criteria decision-making. Graphical Abstract" @default.
• W4366812669 created "2023-04-25" @default.
• W4366812669 creator A5010101903 @default.
• W4366812669 creator A5037790216 @default.
• W4366812669 date "2023-03-04" @default.
• W4366812669 modified "2023-09-30" @default.
• W4366812669 title "Predicting Optimized Dissolution of Selected African Copperbelt Copper-cobalt-bearing Ores by Means of Neural Network Prediction and Response Surface Methodology Modeling" @default.
• W4366812669 cites W1749747644 @default.
• W4366812669 cites W1965449287 @default.
• W4366812669 cites W1989850594 @default.
• W4366812669 cites W2026943567 @default.
• W4366812669 cites W2053874495 @default.
• W4366812669 cites W2063323546 @default.
• W4366812669 cites W2075184363 @default.
• W4366812669 cites W2082174168 @default.
• W4366812669 cites W2093919938 @default.
• W4366812669 cites W2324040587 @default.
• W4366812669 cites W2577163572 @default.
• W4366812669 cites W2773035868 @default.
• W4366812669 cites W2886607156 @default.
• W4366812669 cites W2899332429 @default.
• W4366812669 cites W2910944167 @default.
• W4366812669 cites W2922248590 @default.
• W4366812669 cites W2925344597 @default.
• W4366812669 cites W2970541029 @default.
• W4366812669 cites W2973753844 @default.
• W4366812669 cites W3005159548 @default.
• W4366812669 cites W3037628290 @default.
• W4366812669 cites W3039075666 @default.
• W4366812669 cites W3136524480 @default.
• W4366812669 cites W3146639793 @default.
• W4366812669 cites W3168164890 @default.
• W4366812669 cites W3176181112 @default.
• W4366812669 cites W3212930033 @default.
• W4366812669 cites W4205236922 @default.
• W4366812669 cites W4252205200 @default.
• W4366812669 cites W4309822032 @default.
• W4366812669 doi "https://doi.org/10.1007/s41660-023-00312-3" @default.
• W4366812669 hasPublicationYear "2023" @default.
• W4366812669 type Work @default.
• W4366812669 citedByCount "2" @default.
• W4366812669 countsByYear W43668126692023 @default.
• W4366812669 crossrefType "journal-article" @default.
• W4366812669 hasAuthorship W4366812669A5010101903 @default.
• W4366812669 hasAuthorship W4366812669A5037790216 @default.
• W4366812669 hasBestOaLocation W43668126691 @default.
• W4366812669 hasConcept C105795698 @default.
• W4366812669 hasConcept C11413529 @default.
• W4366812669 hasConcept C128990827 @default.
• W4366812669 hasConcept C132721684 @default.
• W4366812669 hasConcept C150077022 @default.
• W4366812669 hasConcept C151319957 @default.
• W4366812669 hasConcept C154945302 @default.
• W4366812669 hasConcept C179717631 @default.
• W4366812669 hasConcept C186060115 @default.
• W4366812669 hasConcept C191897082 @default.
• W4366812669 hasConcept C192562407 @default.
• W4366812669 hasConcept C31258907 @default.
• W4366812669 hasConcept C33923547 @default.
• W4366812669 hasConcept C38365724 @default.
• W4366812669 hasConcept C41008148 @default.
• W4366812669 hasConcept C48921125 @default.
• W4366812669 hasConcept C50644808 @default.
• W4366812669 hasConcept C544778455 @default.
• W4366812669 hasConcept C60908668 @default.
• W4366812669 hasConcept C86803240 @default.
• W4366812669 hasConceptScore W4366812669C105795698 @default.
• W4366812669 hasConceptScore W4366812669C11413529 @default.
• W4366812669 hasConceptScore W4366812669C128990827 @default.
• W4366812669 hasConceptScore W4366812669C132721684 @default.
• W4366812669 hasConceptScore W4366812669C150077022 @default.
• W4366812669 hasConceptScore W4366812669C151319957 @default.
• W4366812669 hasConceptScore W4366812669C154945302 @default.
• W4366812669 hasConceptScore W4366812669C179717631 @default.
• W4366812669 hasConceptScore W4366812669C186060115 @default.
• W4366812669 hasConceptScore W4366812669C191897082 @default.
• W4366812669 hasConceptScore W4366812669C192562407 @default.
• W4366812669 hasConceptScore W4366812669C31258907 @default.
• W4366812669 hasConceptScore W4366812669C33923547 @default.
• W4366812669 hasConceptScore W4366812669C38365724 @default.
• W4366812669 hasConceptScore W4366812669C41008148 @default.
• W4366812669 hasConceptScore W4366812669C48921125 @default.
• W4366812669 hasConceptScore W4366812669C50644808 @default.
• W4366812669 hasConceptScore W4366812669C544778455 @default.
• W4366812669 hasConceptScore W4366812669C60908668 @default.
• W4366812669 hasConceptScore W4366812669C86803240 @default.
• W4366812669 hasFunder F4320323959 @default.
• W4366812669 hasIssue "3" @default.
• W4366812669 hasLocation W43668126691 @default.
• W4366812669 hasOpenAccess W4366812669 @default.
• W4366812669 hasPrimaryLocation W43668126691 @default.
• W4366812669 hasRelatedWork W1987886632 @default.
• W4366812669 hasRelatedWork W2004910322 @default.
• W4366812669 hasRelatedWork W2161649813 @default.
• W4366812669 hasRelatedWork W2331701639 @default.
• W4366812669 hasRelatedWork W2797282764 @default.
• W4366812669 hasRelatedWork W2890929759 @default.
• W4366812669 hasRelatedWork W2998088892 @default.

• next