OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION
This work is part of a scientific research program whose objective is to prevent pitting corrosion of an open cooling circuit of a nuclear installation. Various corrosion inhibitors have been studied. The performances of pitting corrosion inhibition were discussed and compared on the basis of severa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor's University
2018-05-01
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| Series: | Journal of Engineering Science and Technology |
| Subjects: | |
| Online Access: | http://jestec.taylors.edu.my/Vol%2013%20issue%205%20May%202018/13_5_2.pdf |
| _version_ | 1818136021127135232 |
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| author | Y. BOUKHARI M. N. BOUCHERIT M. ZAABAT S. AMZERT K. BRAHIMI |
| author_facet | Y. BOUKHARI M. N. BOUCHERIT M. ZAABAT S. AMZERT K. BRAHIMI |
| author_sort | Y. BOUKHARI |
| collection | DOAJ |
| description | This work is part of a scientific research program whose objective is to prevent pitting corrosion of an open cooling circuit of a nuclear installation. Various corrosion inhibitors have been studied. The performances of pitting corrosion inhibition were discussed and compared on the basis of several criteria. The
experimental data were collected in a large table and subjected to algorithms in order to construct models for predicting corrosion inhibition performance. We used four algorithms: Genetic Algorithm-Artificial Neural Network (GAANN); Least Squares-Support Vector Machine (LS-SVM), K Nearest Neighbors (KNN) and Regression Tree (RT). We optimized the data fraction
reserved for learning and we sought to optimize the parameters specific to each algorithm. The efficiency of pitting inhibition increases in the following order: HCO3- < H2PO4- < CO32- < PO4-2 < PO4 3- < SiO3 2- < MoO4 2- < WO4 2-. Our results showed that the order of performance of the algorithms is: RT < KNN < LS-SVM < GA-ANN. |
| first_indexed | 2024-12-11T09:33:47Z |
| format | Article |
| id | doaj.art-0c0fcb024d6a4656815a2a003bfe7bc7 |
| institution | Directory Open Access Journal |
| issn | 1823-4690 |
| language | English |
| last_indexed | 2024-12-11T09:33:47Z |
| publishDate | 2018-05-01 |
| publisher | Taylor's University |
| record_format | Article |
| series | Journal of Engineering Science and Technology |
| spelling | doaj.art-0c0fcb024d6a4656815a2a003bfe7bc72022-12-22T01:12:56ZengTaylor's UniversityJournal of Engineering Science and Technology1823-46902018-05-0113511531164OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSIONY. BOUKHARI0M. N. BOUCHERIT1M. ZAABAT2S. AMZERT3K. BRAHIMI4Oum El Bouaghi University, Oum El Bouaghi 04000, AlgeriaResearch & Development Unit in Nuclear Engineering - URDIN, Algiers - AlgeriaOum El Bouaghi University, Oum El Bouaghi 04000, AlgeriaNuclear Research Center of Birine - AlgeriaNuclear Research Center of Birine - AlgeriaThis work is part of a scientific research program whose objective is to prevent pitting corrosion of an open cooling circuit of a nuclear installation. Various corrosion inhibitors have been studied. The performances of pitting corrosion inhibition were discussed and compared on the basis of several criteria. The experimental data were collected in a large table and subjected to algorithms in order to construct models for predicting corrosion inhibition performance. We used four algorithms: Genetic Algorithm-Artificial Neural Network (GAANN); Least Squares-Support Vector Machine (LS-SVM), K Nearest Neighbors (KNN) and Regression Tree (RT). We optimized the data fraction reserved for learning and we sought to optimize the parameters specific to each algorithm. The efficiency of pitting inhibition increases in the following order: HCO3- < H2PO4- < CO32- < PO4-2 < PO4 3- < SiO3 2- < MoO4 2- < WO4 2-. Our results showed that the order of performance of the algorithms is: RT < KNN < LS-SVM < GA-ANN.http://jestec.taylors.edu.my/Vol%2013%20issue%205%20May%202018/13_5_2.pdfGenetic Algorithm-Artificial Neural Network (GA-ANN)Least squares support vector machine (LS-SVM)Regression tree (RT)K Nearest neighbor (KNN)Pitting potential (Epit)Inorganic inhibitors |
| spellingShingle | Y. BOUKHARI M. N. BOUCHERIT M. ZAABAT S. AMZERT K. BRAHIMI OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION Journal of Engineering Science and Technology Genetic Algorithm-Artificial Neural Network (GA-ANN) Least squares support vector machine (LS-SVM) Regression tree (RT) K Nearest neighbor (KNN) Pitting potential (Epit) Inorganic inhibitors |
| title | OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION |
| title_full | OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION |
| title_fullStr | OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION |
| title_full_unstemmed | OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION |
| title_short | OPTIMIZATION OF LEARNING ALGORITHMS IN THE PREDICTION OF PITTING CORROSION |
| title_sort | optimization of learning algorithms in the prediction of pitting corrosion |
| topic | Genetic Algorithm-Artificial Neural Network (GA-ANN) Least squares support vector machine (LS-SVM) Regression tree (RT) K Nearest neighbor (KNN) Pitting potential (Epit) Inorganic inhibitors |
| url | http://jestec.taylors.edu.my/Vol%2013%20issue%205%20May%202018/13_5_2.pdf |
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