Standard
Pareto optimization in oil refinery. / Kostenko, Dmitri; Arseniev, Dmitriy; Shkodyrev, Vyacheslav; Onufriev, Vadim.
Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings. ed. / Ying Tan; Yuhui Shi; Milan Tuba. Springer Nature, 2020. p. 26-33 (Communications in Computer and Information Science; Vol. 1234 CCIS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Kostenko, D
, Arseniev, D, Shkodyrev, V & Onufriev, V 2020,
Pareto optimization in oil refinery. in Y Tan, Y Shi & M Tuba (eds),
Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings. Communications in Computer and Information Science, vol. 1234 CCIS, Springer Nature, pp. 26-33, 5th International Conference on Data Mining and Big Data, DMBD 2020, Belgrade, Serbia,
14/07/20.
https://doi.org/10.1007/978-981-15-7205-0_3APA
Kostenko, D.
, Arseniev, D., Shkodyrev, V., & Onufriev, V. (2020).
Pareto optimization in oil refinery. In Y. Tan, Y. Shi, & M. Tuba (Eds.),
Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings (pp. 26-33). (Communications in Computer and Information Science; Vol. 1234 CCIS). Springer Nature.
https://doi.org/10.1007/978-981-15-7205-0_3Vancouver
Author
Kostenko, Dmitri
; Arseniev, Dmitriy ; Shkodyrev, Vyacheslav ; Onufriev, Vadim. /
Pareto optimization in oil refinery. Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings. editor / Ying Tan ; Yuhui Shi ; Milan Tuba. Springer Nature, 2020. pp. 26-33 (Communications in Computer and Information Science).
BibTeX
@inproceedings{b8bf145d7e3f499194c8f327bd1cb3b5,
title = "Pareto optimization in oil refinery",
abstract = "This article describes the process of multicriteria optimization of a complex industrial control object using Pareto efficiency. The object is being decomposed and viewed as a hierarchy of embedded orgraphs. Performance indicators and controlling factors lists are created based on the orgraphs and technical specifications of an object, thus allowing to systematize sources of influence. Using statistical data archives to train, the neural network approximates key sensors data to identify the model of the controllable object and optimize it.",
keywords = "Decomposition, Identification, Multicriteria optimization, Neural network, Pareto efficiency, SPEA2",
author = "Dmitri Kostenko and Dmitriy Arseniev and Vyacheslav Shkodyrev and Vadim Onufriev",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 5th International Conference on Data Mining and Big Data, DMBD 2020 ; Conference date: 14-07-2020 Through 20-07-2020",
year = "2020",
doi = "10.1007/978-981-15-7205-0_3",
language = "English",
isbn = "9789811572043",
series = "Communications in Computer and Information Science",
publisher = "Springer Nature",
pages = "26--33",
editor = "Ying Tan and Yuhui Shi and Milan Tuba",
booktitle = "Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - Pareto optimization in oil refinery
AU - Kostenko, Dmitri
AU - Arseniev, Dmitriy
AU - Shkodyrev, Vyacheslav
AU - Onufriev, Vadim
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - This article describes the process of multicriteria optimization of a complex industrial control object using Pareto efficiency. The object is being decomposed and viewed as a hierarchy of embedded orgraphs. Performance indicators and controlling factors lists are created based on the orgraphs and technical specifications of an object, thus allowing to systematize sources of influence. Using statistical data archives to train, the neural network approximates key sensors data to identify the model of the controllable object and optimize it.
AB - This article describes the process of multicriteria optimization of a complex industrial control object using Pareto efficiency. The object is being decomposed and viewed as a hierarchy of embedded orgraphs. Performance indicators and controlling factors lists are created based on the orgraphs and technical specifications of an object, thus allowing to systematize sources of influence. Using statistical data archives to train, the neural network approximates key sensors data to identify the model of the controllable object and optimize it.
KW - Decomposition
KW - Identification
KW - Multicriteria optimization
KW - Neural network
KW - Pareto efficiency
KW - SPEA2
UR - http://www.scopus.com/inward/record.url?scp=85088751715&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-7205-0_3
DO - 10.1007/978-981-15-7205-0_3
M3 - Conference contribution
AN - SCOPUS:85088751715
SN - 9789811572043
T3 - Communications in Computer and Information Science
SP - 26
EP - 33
BT - Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Tuba, Milan
PB - Springer Nature
T2 - 5th International Conference on Data Mining and Big Data, DMBD 2020
Y2 - 14 July 2020 through 20 July 2020
ER -
