Publication detail

Deep Learning Approach for Industrial Process Improvement

TENG, S. MÁŠA, V. STEHLÍK, P., LAM, H.

Original Title

Deep Learning Approach for Industrial Process Improvement

Type

journal article in Scopus

Language

English

Original Abstract

The full operation of an industrial processing facility with artificial intelligence has been the holy grail of The full operation of an industrial processing facility with artificial intelligence has been the holy grail of Industry 4.0. One of the inherent difficulties is the enumerate and complex nature of processing information within an industrial plant. Hence, such data should be processed efficiently. This paper demonstrates the effectiveness of a deep auto-encoder neural network for the dimensionality reduction of industrial processing data. The deep auto-encoder neural network functions to intake all possible processing data from the processing system by sending it into an encoder neural network. Subsequently, the encoder condenses the data into highly compressed encoded variables. The network is trained in an unsupervised manner, where a decoder neural network simultaneously attempts to revert the encoded variables to their original form. Such a deep learning approach allows data to be highly compressed into lower dimensions. The coded variables retain critical information of the processing system, allowing reconstruction of the full process data. Auto-encoder neural networks are also able to provide noise removal for encoded data. For application, the encoded variable can be utilized as an effective dimension-reduced variable that can be used for plant-wide optimization. This paper also discusses the further applications of encoded variables for industrial process improvements using the Industrial Internet of Things (IIoT) technologies.

Keywords

Deep Learning, Indutry 4.0, Artificial Intelligence, Energy Efficiency, Process Improvement, Autoencoder

Authors

TENG, S.; MÁŠA, V.; STEHLÍK, P., LAM, H.

Released

30. 10. 2019

Publisher

AIDIC Servizi S.r.l.

Location

Milan, Italy

ISBN

2283-9216

Periodical

Chemical Engineering Transactions

Year of study

76

Number

1

State

Republic of Italy

Pages from

487

Pages to

492

Pages count

6

URL

https://www.aidic.it/cet/19/76/082.pdf

BibTex

@article{BUT160580,
  author="Sin Yong {Teng} and Vítězslav {Máša} and Petr {Stehlík}",
  title="Deep Learning Approach for Industrial Process Improvement",
  journal="Chemical Engineering Transactions",
  year="2019",
  volume="76",
  number="1",
  pages="487--492",
  doi="10.3303/CET1976082",
  issn="2283-9216",
  url="https://www.aidic.it/cet/19/76/082.pdf"
}