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Pareto optimization in oil refinery. / Kostenko, Dmitri; Arseniev, Dmitriy; Shkodyrev, Vyacheslav; Onufriev, Vadim.

Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings. ред. / Ying Tan; Yuhui Shi; Milan Tuba. Springer Nature, 2020. стр. 26-33 (Communications in Computer and Information Science; Том 1234 CCIS).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Kostenko, D, Arseniev, D, Shkodyrev, V & Onufriev, V 2020, Pareto optimization in oil refinery. в Y Tan, Y Shi & M Tuba (ред.), Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings. Communications in Computer and Information Science, Том. 1234 CCIS, Springer Nature, стр. 26-33, 5th International Conference on Data Mining and Big Data, DMBD 2020, Belgrade, Сербия, 14/07/20. https://doi.org/10.1007/978-981-15-7205-0_3

APA

Kostenko, D., Arseniev, D., Shkodyrev, V., & Onufriev, V. (2020). Pareto optimization in oil refinery. в Y. Tan, Y. Shi, & M. Tuba (Ред.), Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings (стр. 26-33). (Communications in Computer and Information Science; Том 1234 CCIS). Springer Nature. https://doi.org/10.1007/978-981-15-7205-0_3

Vancouver

Kostenko D, Arseniev D, Shkodyrev V, Onufriev V. Pareto optimization in oil refinery. в Tan Y, Shi Y, Tuba M, Редакторы, Data Mining and Big Data - 5th International Conference, DMBD 2020, Proceedings. Springer Nature. 2020. стр. 26-33. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-15-7205-0_3

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. Редактор / Ying Tan ; Yuhui Shi ; Milan Tuba. Springer Nature, 2020. стр. 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 -

ID: 77973602