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Processes 2019, 7(3), 151; https://doi.org/10.3390/pr7030151

Data-Mining for Processes in Chemistry, Materials, and Engineering

1,*,†
,
2,*
and
3
1
College of Chemistry, Sichuan University, Chengdu 610064, China
2
William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 West Woodruff Avenue, Columbus, OH 43210, USA
3
School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Current Address: Department of Chemistry and the Institute for Computational and Engineering Sciences, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA.
Received: 11 February 2019 / Revised: 2 March 2019 / Accepted: 4 March 2019 / Published: 11 March 2019
(This article belongs to the Special Issue Process Modelling and Simulation)
Full-Text   |   PDF [3784 KB, uploaded 11 March 2019]   |  

Abstract

With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although the research and application targets are various, many important common points still exist in their data-mining. We then propose a generalized strategy based on the philosophy of data-mining, which should be applicable for the design and optimization targets for processes in various fields with both scientific and industrial purposes. View Full-Text
Keywords: data-mining; machine learning; neural networks; chemistry; materials; engineering; energy data-mining; machine learning; neural networks; chemistry; materials; engineering; energy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, H.; Zhang, Z.; Zhao, Z.-Z. Data-Mining for Processes in Chemistry, Materials, and Engineering. Processes 2019, 7, 151.

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