The Role of Big Data in Industrial (Bio)Chemical Process Operations

Isuru A. Udugama; Carina L. Gargalo; Yoshiyuki Yamashita; Michael A. Taube; Ahmet Palazoglu; Brent R. Young; Krist V. Gernaey; Murat Kulahci; Christoph Bayer

doi:10.1021/acs.iecr.0c01872

The Role of Big Data in Industrial (Bio)Chemical Process Operations

Isuru A. Udugama, Carina L. Gargalo, Yoshiyuki Yamashita, Michael A. Taube, Ahmet Palazoglu, Brent R. Young, Krist V. Gernaey, Murat Kulahci, Christoph Bayer^*

^*Corresponding author for this work

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

310 Downloads (Pure)

Abstract

With the emergence of Industry 4.0 and Big Data initiatives there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this manuscript is to provide a perspective of the current status of Big Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this manuscript provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of Statistical Process Monitoring and (Engineering) Process Control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored and a path forward is proposed.

Original language	English
Journal	Industrial and Engineering Chemistry Research
Volume	59
Issue number	34
Pages (from-to)	15283–15297
ISSN	0888-5885
DOIs	https://doi.org/10.1021/acs.iecr.0c01872
Publication status	Published - 2020

Keywords

Big Data applications
Chemical engineering
Closed-loop systems
Control engineering
Production management
Statistical analysis

Access to Document

10.1021/acs.iecr.0c01872

FulltextAccepted author manuscript, 417 KBLicence: CC BY-NC-ND

OpenUrl availability

Full text

Cite this

@article{61b1fa5f2de9451185e423e2fd64fe13,

title = "The Role of Big Data in Industrial (Bio)Chemical Process Operations",

abstract = "With the emergence of Industry 4.0 and Big Data initiatives there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this manuscript is to provide a perspective of the current status of Big Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this manuscript provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of Statistical Process Monitoring and (Engineering) Process Control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored and a path forward is proposed.",

keywords = "Big Data applications, Chemical engineering, Closed-loop systems, Control engineering, Production management, Statistical analysis",

author = "{A. Udugama}, Isuru and Gargalo, {Carina L.} and Yoshiyuki Yamashita and Taube, {Michael A.} and Ahmet Palazoglu and Young, {Brent R.} and Gernaey, {Krist V.} and Murat Kulahci and Christoph Bayer",

year = "2020",

doi = "10.1021/acs.iecr.0c01872",

language = "English",

volume = "59",

pages = "15283–15297",

journal = "Industrial and Engineering Chemistry Research",

issn = "0888-5885",

publisher = "American Chemical Society",

number = "34",

}

TY - JOUR

T1 - The Role of Big Data in Industrial (Bio)Chemical Process Operations

AU - A. Udugama, Isuru

AU - Gargalo, Carina L.

AU - Yamashita, Yoshiyuki

AU - Taube, Michael A.

AU - Palazoglu, Ahmet

AU - Young, Brent R.

AU - Gernaey, Krist V.

AU - Kulahci, Murat

AU - Bayer, Christoph

PY - 2020

Y1 - 2020

N2 - With the emergence of Industry 4.0 and Big Data initiatives there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this manuscript is to provide a perspective of the current status of Big Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this manuscript provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of Statistical Process Monitoring and (Engineering) Process Control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored and a path forward is proposed.

AB - With the emergence of Industry 4.0 and Big Data initiatives there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this manuscript is to provide a perspective of the current status of Big Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this manuscript provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of Statistical Process Monitoring and (Engineering) Process Control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored and a path forward is proposed.

KW - Big Data applications

KW - Chemical engineering

KW - Closed-loop systems

KW - Control engineering

KW - Production management

KW - Statistical analysis

U2 - 10.1021/acs.iecr.0c01872

DO - 10.1021/acs.iecr.0c01872

M3 - Journal article

SN - 0888-5885

VL - 59

SP - 15283

EP - 15297

JO - Industrial and Engineering Chemistry Research

JF - Industrial and Engineering Chemistry Research

IS - 34

ER -

The Role of Big Data in Industrial (Bio)Chemical Process Operations

Abstract

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this