Quality classification of uranium dioxide pellets for PWR reactor using ANFIS

Dede Sutarya; Benyamin Kusumo Putro

doi:10.1109/TENCON.2011.6129075

Quality classification of uranium dioxide pellets for PWR reactor using ANFIS

Dede Sutarya, Benyamin Kusumo Putro

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

An ANFIS networks was used for classifying quality of green pellets for PWR reactor. It applied several physical indicators for classification; height; volume; weight and density. A total of 200 data sets of observation data was collected from one lot final compacting process of uranium dioxide pellets for PWR type reactors in the laboratory of experimental fuel element installation (IEBE) BATAN and used for training and testing the model. The performance criterion selected for the comparison between the actual and the estimated data are the root mean square error (RMSE), maximum relative error (MRE), and goodness of fit (R ²). Up to 90% of the data could be correctly classified using this model. It is applicable in evaluation and classification of pellets quality.

Original language	English
Title of host publication	TENCON 2011 - 2011 IEEE Region 10 Conference
Subtitle of host publication	Trends and Development in Converging Technology Towards 2020
Pages	118-122
Number of pages	5
DOIs	https://doi.org/10.1109/TENCON.2011.6129075
Publication status	Published - 1 Dec 2011
Event	2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020, TENCON 2011 - Bali, Indonesia Duration: 21 Nov 2011 → 24 Nov 2011

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON

Conference

Conference	2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020, TENCON 2011
Country	Indonesia
City	Bali
Period	21/11/11 → 24/11/11

Keywords

adaptive neuro fuzzy inference system
classification
uranium dioxide pellets quality

Access to Document

10.1109/TENCON.2011.6129075

Link to publication in Scopus

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Sutarya, D., & Putro, B. K. (2011). Quality classification of uranium dioxide pellets for PWR reactor using ANFIS. In TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020 (pp. 118-122). [6129075] (IEEE Region 10 Annual International Conference, Proceedings/TENCON). https://doi.org/10.1109/TENCON.2011.6129075

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title = "Quality classification of uranium dioxide pellets for PWR reactor using ANFIS",

abstract = "An ANFIS networks was used for classifying quality of green pellets for PWR reactor. It applied several physical indicators for classification; height; volume; weight and density. A total of 200 data sets of observation data was collected from one lot final compacting process of uranium dioxide pellets for PWR type reactors in the laboratory of experimental fuel element installation (IEBE) BATAN and used for training and testing the model. The performance criterion selected for the comparison between the actual and the estimated data are the root mean square error (RMSE), maximum relative error (MRE), and goodness of fit (R 2). Up to 90% of the data could be correctly classified using this model. It is applicable in evaluation and classification of pellets quality.",

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author = "Dede Sutarya and Putro, {Benyamin Kusumo}",

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Sutarya, D & Putro, BK 2011, Quality classification of uranium dioxide pellets for PWR reactor using ANFIS. in TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020., 6129075, IEEE Region 10 Annual International Conference, Proceedings/TENCON, pp. 118-122, 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020, TENCON 2011, Bali, Indonesia, 21/11/11. https://doi.org/10.1109/TENCON.2011.6129075

Quality classification of uranium dioxide pellets for PWR reactor using ANFIS. / Sutarya, Dede; Putro, Benyamin Kusumo.

TENCON 2011 - 2011 IEEE Region 10 Conference: Trends and Development in Converging Technology Towards 2020. 2011. p. 118-122 6129075 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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