Bridging the time and length scales of process systems with data

This report reviews the role of data as a “bridge” connecting the different time/length scales of chemical processes in mathematical modeling and multiscale, integrated decision making. This report further argues that this is a fitting role of “big data” in the chemical industry, an area that comprises complicated, yet deterministic, physical systems. Such systems can be described using physical and chemical laws that are generally well-understood. As such, data and their analyses are less likely to provide the unexpected and/or surprising insights that they have generated in other sectors (e.g., the transactional economy, social sciences). Nevertheless, historical operating data—which are often plentiful and available at little cost—can be converted to very useful information for multiscale mathematical modeling of chemical processes. Several examples of integration are provided, mapped on the continuum of time and length scales of chemical process systems. Existing research challenges and potential directions for future work are discussed.

Name thesis:degree_name

Master of Science in Engineering

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

The University of Texas at Austin

Year dc:date.issued

2019

Author dc:creator

• Tsay, Calvin

Advisor dc:contributor.advisor

• Baldea, Michael

• Process systems engineering
• Data-driven modeling

Identifier URI

http://dx.doi.org/10.26153/tsw/11643

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/84671