In
this paper, we review the role of data as a “bridge”
connecting the different time/length scales of chemical processes
in mathematical modeling and multiscale, integrated decision making.
We argue 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 analysis 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 process systems. Existing research challenges
and potential directions for future work are discussed.