Pareto Optimization in Oil Refinery

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 t...

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Autores principales: Kostenko, Dmitri, Arseniev, Dmitriy, Shkodyrev, Vyacheslav, Onufriev, Vadim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351681/
http://dx.doi.org/10.1007/978-981-15-7205-0_3
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author Kostenko, Dmitri
Arseniev, Dmitriy
Shkodyrev, Vyacheslav
Onufriev, Vadim
author_facet Kostenko, Dmitri
Arseniev, Dmitriy
Shkodyrev, Vyacheslav
Onufriev, Vadim
author_sort Kostenko, Dmitri
collection PubMed
description 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.
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spelling pubmed-73516812020-07-13 Pareto Optimization in Oil Refinery Kostenko, Dmitri Arseniev, Dmitriy Shkodyrev, Vyacheslav Onufriev, Vadim Data Mining and Big Data Article 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. 2020-07-11 /pmc/articles/PMC7351681/ http://dx.doi.org/10.1007/978-981-15-7205-0_3 Text en © Springer Nature Singapore Pte Ltd. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Kostenko, Dmitri
Arseniev, Dmitriy
Shkodyrev, Vyacheslav
Onufriev, Vadim
Pareto Optimization in Oil Refinery
title Pareto Optimization in Oil Refinery
title_full Pareto Optimization in Oil Refinery
title_fullStr Pareto Optimization in Oil Refinery
title_full_unstemmed Pareto Optimization in Oil Refinery
title_short Pareto Optimization in Oil Refinery
title_sort pareto optimization in oil refinery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7351681/
http://dx.doi.org/10.1007/978-981-15-7205-0_3
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AT arsenievdmitriy paretooptimizationinoilrefinery
AT shkodyrevvyacheslav paretooptimizationinoilrefinery
AT onufrievvadim paretooptimizationinoilrefinery

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