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Licensed Unlicensed Requires Authentication Published online by De Gruyter December 27, 2021

Big data analytics opportunities for applications in process engineering

  • Mitra Sadat Lavasani , Nahid Raeisi Ardali , Rahmat Sotudeh-Gharebagh EMAIL logo , Reza Zarghami , János Abonyi and Navid Mostoufi

Abstract

Big data is an expression for massive data sets consisting of both structured and unstructured data that are particularly difficult to store, analyze and visualize. Big data analytics has the potential to help companies or organizations improve operations as well as disclose hidden patterns and secret correlations to make faster and intelligent decisions. This article provides useful information on this emerging and promising field for companies, industries, and researchers to gain a richer and deeper insight into advancements. Initially, an overview of big data content, key characteristics, and related topics are presented. The paper also highlights a systematic review of available big data techniques and analytics. The available big data analytics tools and platforms are categorized. Besides, this article discusses recent applications of big data in chemical industries to increase understanding and encourage its implementation in their engineering processes as much as possible. Finally, by emphasizing the adoption of big data analytics in various areas of process engineering, the aim is to provide a practical vision of big data.


Corresponding author: Rahmat Sotudeh-Gharebagh, Process Design and Simulation Research Center, School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The authors are sincerely grateful for the financial support provided by Iran’s National Elites Foundation for postdoc researchers through the Allameh Tabatabaei Award.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2020-09-08
Accepted: 2021-10-11
Published Online: 2021-12-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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