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Process Systems Engineering
110th Anniversary: Bridging the Time and Length Scales of Process Systems with Data Calvin Tsay, and Michael Baldea Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b02282 • Publication Date (Web): 10 Aug 2019 Downloaded from pubs.acs.org on August 16, 2019
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Industrial & Engineering Chemistry Research
Bridging the Time and Length Scales of Process Systems with Data 110th Anniversary:
Calvin Tsay and Michael Baldea∗ McKetta Department of Chemical Engineering The University of Texas at Austin, Austin, TX 78712 E-mail:
[email protected] Abstract In this paper, we review the role of data as a bridge connecting the dierent time/length scales of chemical processes in mathematical modeling and multi-scale, integrated decision making.
We argue that this is a tting 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 data 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 multi-scale 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.
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Introduction Over the past decade or so, advances in computer hardware and communication have led to a data revolution, whereby increasingly large amounts of data are captured, stored, and processed; formats run the gamut from still and video images, to sound recordings, to commercial transactions, and to freeform text. Data science focuses on turning these vast troves of data into actionable, useful information, and has led to remarkable recent advances in the transactional, social, and interactive realms. Chemical plants are themselves sources of large-scale data sets. At most production sites, databases known as process historians store sensor readings and control actions (often recorded at one-minute or faster frequencies), and, in many cases, such records go back in time a decade or more. As such, it would be natural to conclude that data science could have a signi cant impact on analyzing and improving the operation of chemical processes. However, the impact of these methods in process systems engineering has been more limited. This can to a great extent be traced to a fundamental dierence between chemical processes and the systems/domains mentioned above: chemical processes are complicated, but are deterministic and governed by physical and chemical laws that are, in general, well understood. 1 It is thus to be expected that such systems will react in the same way (within some experimental noise) every time the same inputs are applied or the same operations are made. This is indeed the case unless the structure of the system changes unexpectedly (e.g, due to a fault). Such unexpected structural changes are in general di cult to detect mechanistically, and one of the most successful data science applications to date in the realm of chemical processing has been the detection and identi cation of faults. 2,3 By contrast, social and transactional systems are complex, in the sense that they can exhibit dierent behaviors and reactions to nominally similar circumstances, e.g., by self-organizing or learning. 4 These behaviors are (at least at present) less well understood from a mechanistic perspective and, as such, data-driven techniques can provide valuable and often unexpected insights. In this paper, we posit that data can provide another (class of) valuable insights, besides 2
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fault detection, in the operation of chemical processes. Namely, we will argue that data represent the bridge between the dierent time and length scales of chemical processes, particularly as it pertains to making decisions across multiple scales. Our main focus will be on the time scale aspect, but we will review progress thus far and introduce ideas for future research in both realms. The paper is organized as follows: in the next section, we provide the necessary background and motivate the multi-scale nature of the behavior of chemical processes, and review the associated challenges. Next, we discuss techniques for simultaneously dealing with multiple time horizons, followed by a section dedicated to integrating length scales. Subsequently, we describe several common data-driven methodologies employed in the reviewed areas. Finally, we discuss the remaining white areas of the map, as well as some challenges that may arise in exploring them.
Background Chemical process systems span a wide range of time scales and length scales, as illustrated in Figure 1. At the lowest level (smallest length scale/fastest time scale), lie the atomistic phenomena that determine the nature of chemical reactions and the properties of the resulting materials. Further in this continuum, we nd the structure of materials. Moving now to the visible range, we identify the construction and evolution of process units. These are then integrated into physically larger processing plants that are part of (even larger) enterprises. Generally speaking, as the physical size of the systems considered increases, so does their response time, and the time horizons considered in their analysis tend to increase as well. The optimization of process design and operations, as well as optimal control, are key activities of the discipline generally referred to as process systems engineering. They all rely on a mathematical model of the process itself, and as a consequence have to deal with the curse of dimensionality associated with capturing the continuum of time and length scales of process systems. Multi-scale models that attempt to describe process behavior across
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