{"data":{"id":"10.6084/m9.figshare.7941410","type":"dois","attributes":{"doi":"10.6084/m9.figshare.7941410","prefix":"10.6084","suffix":"m9.figshare.7941410","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"F. C. De Menezes","affiliation":[],"nameIdentifiers":[]},{"name":"R. M. Fontes","affiliation":[],"nameIdentifiers":[]},{"name":"K. P. Oliveira-Esquerre","affiliation":[],"nameIdentifiers":[]},{"name":"R. Kalid","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"APPLICATION OF UNCERTAINTY ANALYSIS OF ARTIFICIAL NEURAL NETWORKSFOR PREDICTING COAGULANT AND ALKALIZER DOSAGES IN A WATER TREATMENT PROCESS"}],"publisher":"SciELO journals","container":{},"publicationYear":2019,"subjects":[{"subject":"90499 Chemical Engineering not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Chemical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Chemical engineering","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2019-04-03","dateType":"Created"},{"date":"2019-04-16","dateType":"Updated"},{"date":"2019","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1590/0104-6632.20180354s20170039","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["3936313 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"ABSTRACT Artificial neural networks (ANNs) were built to predict coagulant (Model I) and alkalizer (Model II) dosages given raw and treated water parameters from a water clarifying process. Different ANN architectures were tested and optimal results were obtained with [10-10-10-01] and [08-12-12-01] nodes of input, hidden and output layers for Models I and II, respectively. Two algorithms based on GUM-S1weredevelopedto evaluate the artificial neural network parameter uncertainty and the coverage interval of model outputs. The results show that these algorithms can provide a better set of parameters for the ANN compared with the traditional training method. The present research provides a unique unifying view that considers neural networks and uncertainty analysis in a well-documented industrial case study.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://scielo.figshare.com/articles/APPLICATION_OF_UNCERTAINTY_ANALYSIS_OF_ARTIFICIAL_NEURAL_NETWORKSFOR_PREDICTING_COAGULANT_AND_ALKALIZER_DOSAGES_IN_A_WATER_TREATMENT_PROCESS/7941410","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":1,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2019-04-03T02:43:15.000Z","registered":"2019-04-03T02:43:16.000Z","published":"2019","updated":"2020-08-27T14:20:56.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.7941410","type":"media"}},"references":{"data":[{"id":"10.1590/0104-6632.20180354s20170039","type":"dois"}]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}