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Cognition, Technology & Work

, Volume 21, Issue 1, pp 113–131 | Cite as

Cognitive challenges of changeability: adjustment to system changes and transfer of knowledge in modular chemical plants

  • Romy MüllerEmail author
Original Article
  • 93 Downloads

Abstract

In the chemical industry, highly changeable modular plants allow for system reconfigurations on shortest timescales: a number of processing units can be combined to optimize the plant setup for current demands. As a consequence, human operators are frequently confronted with newly assembled systems that differ from previous ones in some ways while not differing in others. This partial overlap creates a number of challenges with regard to operator performance and learning. Both differentiation and generalization of knowledge are needed, which leads to a goal conflict: on the one hand, operators have to know the specifics of the current system, update their understanding of functional relations between system parameters, and use operation procedures that are tailored to the requirements of the current situation. On the other hand, they need to apply the knowledge they have acquired in previous plant setups to solve problems in a new one. While unwanted carryover must be avoided, appropriate transfer is essential. The present article provides an overview of the challenges and potentials of learning and transfer in changing environments as discussed in the cognitive science and situated cognition literatures. This overview is of prescriptive nature: it presents results and theories that should be considered when analyzing operator performance and designing interfaces or training in modular plants. To this end, the article considers how learning is adapted to the volatility of the environment, how mental representations are updated, how conceptual and procedural knowledge is transferred to new situations, and how learning is shaped by interactions with the environment.

Keywords

Modular plants Cyber–physical systems Operator tasks Stability–flexibility balance Learning Transfer 

Notes

Acknowledgements

Parts of this work were supported by a grant of the German Research Foundation (PA 1232/6-1). I am especially thankful to Leon Urbas for providing lots of inspiration over the last years, for many valuable discussions about modular plants and the associated operator requirements, and for support in constructing some of the modular plant examples used in this article. Moreover, I want to thank two anonymous reviewers for their valuable feedback on an earlier version of the manuscript.

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Authors and Affiliations

  1. 1.Chair of Engineering Psychology and Applied Cognitive ResearchTechnische Universität DresdenDresdenGermany

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