The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Machine learning: Overview of the recent progresses and implications for the process systems engineering field
Abstract
Abstract Machine learning (ML) has recently gained in popularity, spurred by well-publicized advances like deep learning and widespread commercial interest in big data analytics. Despite the enthusiasm, some renowned experts of the field have expressed skepticism, which is justifiable given the disappointment with the previous wave of neural networks and other AI techniques. On the other hand, new fundamental advances like the ability to train neural networks with a large number of layers for hierarchical feature learning may present significant new technological and commercial opportunities. This paper critically examines the main advances in deep learning. In addition, connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed. Implications of these advances for the fields of process and energy systems engineering are also discussed.
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Cites
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Cited by
Xia, K., Sacco, C., Kirkpatrick, M., Saidy, C., Nguyen, L., Kircaliali, A., & Harik, R. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.012 · Cited by 406
Forecasting yield by integrating agrarian factors and machine learning models: A survey
Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., & Srinivasan, K. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.10.024 · Cited by 346
Deep reinforcement learning in production systems: a systematic literature review
Panzer, M., & Bender, B. (2022) · International Journal of Production Research · doi:10.1080/00207543.2021.1973138 · Cited by 282
Perspectives on the integration between first-principles and data-driven modeling
Bradley, W., Kim, J., Kilwein, Z., Blakely, L., Eydenberg, M., Jalvin, J., Laird, C., & Boukouvala, F. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107898 · Cited by 231
Machine learning for biochemical engineering: A review
Mowbray, M., Savage, T., Wu, C., Song, Z., Cho, B. A., Del Rio-Chanona, E. A., & Zhang, D. (2021) · Biochemical Engineering Journal · doi:10.1016/j.bej.2021.108054 · Cited by 188
Metadata
- Authors
- Lee, J. H., Shin, J., & Realff, M. J.
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 114
- Pages
- 111-121
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W2764120290
- Cited by
- 387
- References
- 15
- FWCI
- 16.07
- Type
- journal article
- Language
- en
- Open access
- closed
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