142 citations · 277 across the 11 of their papers we have counts for
12 papers
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…
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…
When is gray-box modeling advantageous for virtual flow metering?
M. Hotvedt, B. Grimstad, D. Ljungquist +1
Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predict…
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes
E. Bradford, L. Imsland, M. Reble +1
Nonlinear model predictive control (NMPC) is an efficient approach for the control of nonlinear multivariable dynamic systems with constraints, which however requires an accurate p…
On gray-box modeling for virtual flow metering
Mathilde Hotvedt, Bjarne Grimstad, Dag Ljungquist +1
A virtual flow meter (VFM) enables continuous prediction of flow rates in petroleum production systems. The predicted flow rates may aid the daily control and optimization of a pet…
Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study
Bjarne Grimstad, Mathilde Hotvedt, Anders T. Sandnes +2
Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous…
Identifiability and physical interpretability of hybrid, gray-box models -- a case study
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Model identifiability concerns the uniqueness of uncertain model parameters to be estimated from available process data and is often thought of as a prerequisite for the physical i…
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data
Leif Erik Andersson, Bart Doekemeijer, Daan van der Hoek +2
This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization i…
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization. Mechanistic models based o…
Stochastic data-driven model predictive control using Gaussian processes
E. Bradford, L. Imsland, D. Zhang +1
Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear controlsystems with constraints. Gaussian processes (GPs) presen…
Stochastic Nonlinear Model Predictive Control with State Estimation by Incorporation of the Unscented Kalman Filter
Eric Bradford, Lars Imsland
Nonlinear model predictive control has become a popular approach to deal with highly nonlinear and unsteady state systems, the performance of which can however deteriorate due to u…
Optimal Boundary Control of 2x2 Linear Hyperbolic PDEs
Agus Hasan, Lars Imsland, Ivan Ivanov +2
The present paper develops an optimal linear quadratic boundary controller for linear hyperbolic partial differential equations (PDEs) with actuation on only one end of…