Causality validation of multilevel flow modelling

Emil Krabbe Nielsen, Akio Gofuku, Xinxin Zhang, Ole Ravn, Morten Lind

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Multilevel Flow Modeling is a methodology for inferring causes or effects of process system anomalies. A procedure for validating model causality is proposed, as interest has increased from industry in applications to safety-critical systems. A series of controlled experiments are conducted as simulations in K-Spice, a dynamic process simulator, by manipulating actuators to analyse the response of process variables. The system causality is analysed stochastically under a defined range of randomly sampled process conditions. The causal influence of an actuator on a process variable is defined as a probability of a qualitative and discrete causal state. By testing an MFM model, and interpreting the propagation paths produced by MFM, the results from MFM are compared to the stochastic causality analysis to determine the model accuracy. The method has been applied to a produced water treatment system for separation of liquid and gas, to revise the causal relations of the model.

Original languageEnglish
Article number106944
JournalComputers and Chemical Engineering
Publication statusPublished - Sept 2 2020


  • Causal inference
  • Causality
  • Multilevel flow modelling
  • Validation

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications


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