TY - JOUR
T1 - Causality validation of multilevel flow modelling
AU - Nielsen, Emil Krabbe
AU - Gofuku, Akio
AU - Zhang, Xinxin
AU - Ravn, Ole
AU - Lind, Morten
N1 - Funding Information:
This work was supported by the Danish Hydrocarbon Research and Technology Centre and Otto M?nsteds Fond.
Funding Information:
This work was supported by the Danish Hydrocarbon Research and Technology Centre and Otto Mønsteds Fond.
Publisher Copyright:
© 2020
PY - 2020/9/2
Y1 - 2020/9/2
N2 - 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.
AB - 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.
KW - Causal inference
KW - Causality
KW - Multilevel flow modelling
KW - Validation
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U2 - 10.1016/j.compchemeng.2020.106944
DO - 10.1016/j.compchemeng.2020.106944
M3 - Article
AN - SCOPUS:85086391935
SN - 0098-1354
VL - 140
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 106944
ER -