Monitoring and control of polymer production line based on machine learning

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Monitoring and control of polymer production line based on machine learning. / Abdurakipov, S.

In: Journal of Physics: Conference Series, Vol. 2119, No. 1, 012159, 15.12.2021.

Research output: Contribution to journal › Conference article › peer-review

Harvard

Abdurakipov, S 2021, 'Monitoring and control of polymer production line based on machine learning', Journal of Physics: Conference Series, vol. 2119, no. 1, 012159. https://doi.org/10.1088/1742-6596/2119/1/012159

APA

Abdurakipov, S. (2021). Monitoring and control of polymer production line based on machine learning. Journal of Physics: Conference Series, 2119(1), [012159]. https://doi.org/10.1088/1742-6596/2119/1/012159

Vancouver

Abdurakipov S. Monitoring and control of polymer production line based on machine learning. Journal of Physics: Conference Series. 2021 Dec 15;2119(1):012159. doi: 10.1088/1742-6596/2119/1/012159

Author

Abdurakipov, S. / Monitoring and control of polymer production line based on machine learning. In: Journal of Physics: Conference Series. 2021 ; Vol. 2119, No. 1.

BibTeX

@article{75e958440dd44221aa6a71907ff0a2e0,

title = "Monitoring and control of polymer production line based on machine learning",

abstract = "The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.",

author = "S. Abdurakipov",

note = "Publisher Copyright: {\textcopyright} 2021 Institute of Physics Publishing. All rights reserved.; 37th Siberian Thermophysical Seminar, STS 2021 ; Conference date: 14-09-2021 Through 16-09-2021",

year = "2021",

month = dec,

day = "15",

doi = "10.1088/1742-6596/2119/1/012159",

language = "English",

volume = "2119",

journal = "Journal of Physics: Conference Series",

issn = "1742-6588",

publisher = "IOP Publishing Ltd.",

number = "1",

}

RIS

TY - JOUR

T1 - Monitoring and control of polymer production line based on machine learning

AU - Abdurakipov, S.

PY - 2021/12/15

Y1 - 2021/12/15

N2 - The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.

AB - The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.

UR - http://www.scopus.com/inward/record.url?scp=85123594558&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/2119/1/012159

DO - 10.1088/1742-6596/2119/1/012159

M3 - Conference article

AN - SCOPUS:85123594558

VL - 2119

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012159

T2 - 37th Siberian Thermophysical Seminar, STS 2021

Y2 - 14 September 2021 through 16 September 2021

ER -

ID: 35609947