A comparative study on machine learning regression algorithms aplied to modeling gas centrifuge
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2022
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Brazilian Journal of Development
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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.
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ANDRADE, DELVONEI A. de; MESQUITA, ROBERTO N. de; NASCIMENTO, NATAN P. A comparative study on machine learning regression algorithms aplied to modeling gas centrifuge. Brazilian Journal of Development, v. 8, n. 7, p. 52669-52681, 2022. DOI: 10.34117/bjdv8n7-265. Disponível em: http://repositorio.ipen.br/handle/123456789/33380. Acesso em: 16 Mar 2025.
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