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Big Data Analytics in Chemical Engineering.

Leo Chiang1, Bo Lu1, Ivan Castillo1

  • 1The Dow Chemical Company, Freeport, Texas 77541;

Annual Review of Chemical and Biomolecular Engineering
|March 17, 2017
PubMed
Summary
This summary is machine-generated.

Big data analytics transforms chemical engineering data into actionable insights. Addressing challenges in volume, variety, and velocity requires collaborative innovation and workforce development for enterprise-level success.

Keywords:
Industry 4.0Internet of thingsbig data analyticsdata-driven modelingmachine learning

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Area of Science:

  • Chemical Engineering
  • Data Science
  • Industrial Analytics

Background:

  • The chemical engineering field generates vast amounts of data (volume) from diverse sources (variety).
  • Effective utilization of this data for real-time decision-making (velocity) presents significant analytical challenges.
  • Existing big data tools and strategies may not fully address the complexities of chemical process data.

Purpose of the Study:

  • To review recent advancements in big data analytics across key industries relevant to chemical engineering.
  • To identify and discuss the technical, platform, and cultural challenges hindering big data adoption in these sectors.
  • To propose a collaborative approach for overcoming these challenges and achieving enterprise-level success.

Main Methods:

  • Literature review of big data applications in chemicals, energy, semiconductors, pharmaceuticals, and food industries.
  • Analysis of common technical hurdles, including data integration, quality, and processing.
  • Examination of platform requirements, such as scalable infrastructure and appropriate analytical tools.
  • Discussion of cultural barriers, including data literacy and organizational readiness.

Main Results:

  • Big data analytics are increasingly applied across diverse industries, offering significant potential for optimization.
  • Key challenges include data integration, ensuring data quality, and selecting the right analytical tools.
  • Platform scalability and the development of user-friendly interfaces are critical for effective implementation.
  • Cultural shifts towards data-driven decision-making are essential for realizing the full benefits of big data.

Conclusions:

  • Collaborative efforts between government, academia, and industry are crucial for advancing big data analytics in chemical engineering.
  • Focusing on workforce development and fostering innovation is necessary to overcome current challenges.
  • Successful enterprise-level big data implementation requires a holistic approach addressing technology, platforms, and people.