The DOI matches its Crossref record; 87 of the 88 references it cites were found in a registry or an index.
Advances and opportunities in machine learning for process data analytics
Abstract
Abstract In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory over the last 20 years and commercial successes by leading big data companies. Then we discuss the characteristics of process manufacturing systems and briefly review the data analytics research and development in the last three decades. We give three attributes for process data analytics to make machine learning techniques applicable in the process industries. Next we provide a perspective on the currently active topics in machine learning that could be opportunities for process data analytics research and development. Finally we address the importance of a data analytics culture. Issues discussed range from technology development to workforce education and from government initiatives to curriculum enhancement.
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“artificial intelligence is proving to be an effective tool for predicting with great precision the possibility of an event occurring [60]”
From Burnout, Resilience, and COVID-19 among Teachers: Predictive Capacity of an Artificial Neural Network (2021) · cites this paper as [60]
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Perspectives on the integration between first-principles and data-driven modeling
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Metadata
- Authors
- Qin, S. J., & Chiang, L. H.
- Affiliations
- University of Southern California (US)
- Dow Chemical (United States) (US)
- Corresponding author
- Qin, S. Joe
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 126
- Pages
- 465-473
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W2941089296
- Semantic Scholar
- 36cfd68da9e0c521c8b09ecf9ca48e0dbf24cc19
- Cited by
- 351
- References
- 88
- Citations by year
- 2026: 21 · 2025: 39 · 2024: 52 · 2023: 75 · 2022: 58 · 2021: 53 · 2020: 39 · 2019: 14
- FWCI
- 22.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Chemical Engineering (publishedVersion)
- Funders
- University of Southern California
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- machine learning, process manufacturing, statistical learning theory, big data, workforce education, curriculum development, government initiatives, chemical process industry
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