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eess.SY2022
Adjustment formulas for learning causal steady-state models from closed-loop operational data
Kristian Løvland, Bjarne Grimstad, Lars Struen Imsland
Steady-state models which have been learned from historical operational data may be unfit for model-based optimization unless correlations in the training data which are introduced…
eess.SY2022
Passive learning to address nonstationarity in virtual flow metering applications
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Steady-state process models are common in virtual flow meter applications due to low computational complexity, and low model development and maintenance cost. Nevertheless, the pre…