A comparative study on machine learning regression algorithms aplied to modeling gas centrifuge
dc.contributor.author | ANDRADE, DELVONEI A. de | pt_BR |
dc.contributor.author | MESQUITA, ROBERTO N. de | pt_BR |
dc.contributor.author | NASCIMENTO, NATAN P. | pt_BR |
dc.coverage | Nacional | pt_BR |
dc.date.accessioned | 2022-12-01T18:04:49Z | |
dc.date.available | 2022-12-01T18:04:49Z | |
dc.date.issued | 2022 | pt_BR |
dc.description.abstract | The gas Centrifuge is a very hard equipment to model, because it involves a gas dynamic with many complications, such as hypersonic waves and rarefied regions combined with continuous flow areas. Therefore, data analysis regressions remain currently a very important technique to understand and describe the problem in a practical way. This paper intends to apply and compare several regression techniques using machine learning, to obtain a hydraulic and a separative power model of gas centrifuge used in enrichment plants. For this purpose, a set of normalized data composed of 134 experimental lines was used, observing the variables of interest, the separation power (dU), and the waste pressure (Pw), through the following explanatory variables: feed flow (F), cut (q), and product pressure (Pp). The comparisons were presented between the results obtained for the models generated by the following: algorithms, multivariate regression, multivariate adaptive regression splines – MARS, bootstrap aggregating multivariate adaptive regression splines – Bagging MARS, artificial neural network – ANN, extreme gradient boosting – XGBoost, support vector regression– Poly SVR, radial basis Function support vector regression – RBF SVR, K-nearest neighbors – KNN and Stacked Ensemble. That way, to avoid overfitting and provide insights about generalization of the models in unseen data, during the training phase, the k-fold cross validation approach was used. Subsequently, the residuals were analyzed, and the models were compared by the following metrics: Root mean square error – RMSE; Mean squared error – MSE; Mean absolute error – MAE; and Coefficient of determination – R2. | pt_BR |
dc.format.extent | 52669-52681 | pt_BR |
dc.identifier.citation | ANDRADE, DELVONEI A. de; MESQUITA, ROBERTO N. de; NASCIMENTO, NATAN P. A comparative study on machine learning regression algorithms aplied to modeling gas centrifuge. <b>Brazilian Journal of Development</b>, v. 8, n. 7, p. 52669-52681, 2022. DOI: <a href="https://dx.doi.org/10.34117/bjdv8n7-265">10.34117/bjdv8n7-265</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/33380. | |
dc.identifier.doi | 10.34117/bjdv8n7-265 | pt_BR |
dc.identifier.fasciculo | 7 | pt_BR |
dc.identifier.issn | 2525-8761 | pt_BR |
dc.identifier.orcid | 0000-0002-5355-0925 | pt_BR |
dc.identifier.orcid | 0000-0002-6689-3011 | pt_BR |
dc.identifier.orcid | https://orcid.org/0000-0002-5355-0925 | |
dc.identifier.orcid | https://orcid.org/0000-0002-6689-3011 | |
dc.identifier.percentilfi | Sem Percentil | pt_BR |
dc.identifier.percentilfiCiteScore | Sem Percentil CiteScore | pt_BR |
dc.identifier.uri | http://repositorio.ipen.br/handle/123456789/33380 | |
dc.identifier.vol | 8 | pt_BR |
dc.relation.ispartof | Brazilian Journal of Development | pt_BR |
dc.rights | openAccess | pt_BR |
dc.subject | isotope separation | |
dc.subject | gas centrifuges | |
dc.subject | machine learning | |
dc.subject | multivariate analysis | |
dc.subject | neural networks | |
dc.subject | algorithms | |
dc.subject | automation | |
dc.title | A comparative study on machine learning regression algorithms aplied to modeling gas centrifuge | pt_BR |
dc.title.alternative | Um estudo comparativo sobre algoritmos de regressão de aprendizagem de máquinas aplicado à modelagem de centrífugas a gás | pt_BR |
dc.type | Artigo de periódico | pt_BR |
dspace.entity.type | Publication | |
ipen.autor | ROBERTO NAVARRO DE MESQUITA | |
ipen.autor | DELVONEI ALVES DE ANDRADE | |
ipen.codigoautor | 1375 | |
ipen.codigoautor | 1258 | |
ipen.contributor.ipenauthor | ROBERTO NAVARRO DE MESQUITA | |
ipen.contributor.ipenauthor | DELVONEI ALVES DE ANDRADE | |
ipen.date.recebimento | 22-12 | |
ipen.identifier.fi | Sem F.I. | pt_BR |
ipen.identifier.fiCiteScore | Sem CiteScore | pt_BR |
ipen.identifier.ipendoc | 29028 | pt_BR |
ipen.type.genre | Artigo | |
relation.isAuthorOfPublication | 1975aa9b-be26-4f48-8196-96eeb4c2c0c3 | |
relation.isAuthorOfPublication | 0eeb4436-68e5-4573-a603-35f5fc912178 | |
relation.isAuthorOfPublication.latestForDiscovery | 0eeb4436-68e5-4573-a603-35f5fc912178 | |
sigepi.autor.atividade | MESQUITA, ROBERTO N. de:1375:420:N | pt_BR |
sigepi.autor.atividade | ANDRADE, DELVONEI A. de:1258:420:S | pt_BR |