Semantic Scholar Open Access 2022 92 sitasi

Maximizing information from chemical engineering data sets: Applications to machine learning

Alexander Thebelt Johannes Wiebe Jan Kronqvist Calvin Tsay R. Misener

Lihat Sumber DOI

Abstrak

It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for many chemical engineering applications. This review discusses how challenging data characteristics arise in chemical engineering applications. We identify four characteristics of data arising in chemical engineering applications that make applying classical artificial intelligence approaches difficult: (1) high variance, low volume data, (2) low variance, high volume data, (3) noisy/corrupt/missing data, and (4) restricted data with physics-based limitations. For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges. Finally, we identify several challenges for future research.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (5)

Alexander Thebelt

Johannes Wiebe

Jan Kronqvist

Calvin Tsay

R. Misener

Format Sitasi

APA MLA BibTeX

Thebelt, A., Wiebe, J., Kronqvist, J., Tsay, C., Misener, R. (2022). Maximizing information from chemical engineering data sets: Applications to machine learning. https://doi.org/10.1016/j.ces.2022.117469

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.ces.2022.117469

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 92×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.ces.2022.117469
Akses: Open Access ✓