John Bagterp Jørgensen

Technical University of Denmark

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Henrik Madsen

Technical University of Denmark

Niels Kjølstad Poulsen

Technical University of Denmark

Dimitri Boiroux

Novo Nordisk (Denmark)

Tobias Ritschel

University College London

Jakob Kjøbsted Huusom

Technical University of Denmark

Kirsten Nørgaard

Steno Diabetes Centers

Sten Bay Jørgensen

Technical University of Denmark

Signe Schmidt

Steno Diabetes Centers

Gianluca Frison

University of Freiburg

Leo Emil Sokoler

Technical University of Denmark

Cooperative Institutions

Technical University of Denmark

143

University of Copenhagen

Norwegian University of Science and Technology

Novo Nordisk (Denmark)

Danish Geotechnical Society

Ørsted (Denmark)

Office of Advanced Scientific Computing Research

Aalborg University

Dynamic Systems (United States)

Environmental and Water Resources Engineering

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Large-scale Virtual Clinical Trials of Closed-loop Treatments for People with Type 1 Diabetes

arXiv (Cornell University) (2022)

Tobias Ritschel Asbjørn Thode Reenberg John Bagterp Jørgensen

We propose a virtual clinical trial for assessing the safety and efficacy of closed-loop diabetes treatments prior to an actual clinical trial. Such virtual trials enable rapid and risk-free pretrial testing of algorithms, and they can be used to compare different treatment variations for large and diverse populations. The participants are represented by multiple mathematical models, consisting of stochastic differential equations, and we use Monte Carlo closed-loop simulations to compute detailed statistics of the closed-loop treatments. We implement the virtual clinical trial using high-performance software and hardware, and we present an example trial with two mathematical models of one~million participants over 52~weeks (i.e., two~million simulations), which can be completed in 2~h 9~min.

10.48550/arxiv.2205.01332

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Citations (0)

Thermal storage power balancing with model predictive control

2022 European Control Conference (ECC) (2013)

Rasmus Halvgaard Niels Kjølstad Poulsen Henrik Madsen John Bagterp Jørgensen

The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination. The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates that the method allows for the integration of flexible thermal loads in a smart energy system in which consumption follows the changing production.

Model Predictive Control

Power demand

Dynamic demand

10.23919/ecc.2013.6669197

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Citations (9)

A Tuning Approach for Offset-free MPC with Conditional Reference Adaptation

IFAC Proceedings Volumes (2014)

Harald Waschl John Bagterp Jørgensen Jakob Kjøbsted Huusom Luigi del Re

Model Predictive Control

Reference model

Feedback loop

Internal model

Robustness

10.3182/20140824-6-za-1003.02369

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Citations (7)

Environmental tools in product development

Proceedings of the ... IEEE International Symposium on Electronics and the Environment (2002)

Henrik Wenzel Michael Zwicky Hauschild John Bagterp Jørgensen Leo Alting

A precondition for design of environmentally friendly products is that the design team has access to methods and tools supporting the introduction of environmental criteria in product development. A large Danish program, EDIP, is being carried out by the Institute for Product Development, Technical University of Denmark, in cooperation with 5 major Danish companies aiming at the development and testing of such tools. These tools are presented in this paper.< >

10.1109/isee.1994.337295

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Citations (17)

Waterflooding optimization in uncertain geological scenarios

Computational Geosciences (2013)

Andrea Capolei Eka Suwartadi Bjarne Foss John Bagterp Jørgensen

Robust Optimization

10.1007/s10596-013-9371-1

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Citations (34)

A Dynamical Simulation Model of a Cement Clinker Rotary Kiln

2022 European Control Conference (ECC) (2024)

Jan Lorenz Svensen Wilson Ricardo Leal da Silva Javier Pigazo Merino Dinesh Sampath John Bagterp Jørgensen

Rotary kiln

10.23919/ecc64448.2024.10590953

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Electrification of Clay Calcination: A First Look into Dynamic Modeling and Energy Management for Integration with Sustainable Power Grids

arXiv (Cornell University) (2024)

Bruno Laurini Nicola Cantisani Wilson R. Leal da Silva Yi Zong John Bagterp Jørgensen

This article explores the electrification in clay calcination, proposing a dynamic model and energy management strategy for the integration of electrified calcination plants into sustainable power grids. A theoretical dynamic modeling of the electrified calcination process is introduced, aiming at outlining temperature profiles and energy usage - thus exploring the feasibility of electrification. The model serves as a tool for optimizing parameters, estimating system behavior, and enabling model-based process control. An innovative energy management model is also presented, ensuring efficient assimilation of electrified calcination plants into the power grid. It encapsulates demand-supply balancing and optimizes renewable energy usage. In essence, we provide an insightful pathway to a more sustainable cement production, underlining the value of renewable energy sources and effective energy management in the context of clay calcination.

Sustainable Energy

10.48550/arxiv.2404.12695

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Economic Model Predictive Control for Energy Systems in Smart Homes

2021 IEEE Conference on Control Technology and Applications (CCTA) (2019)

Hjørdis Schlüter Dimitri Boiroux Niels Kjølstad Poulsen Henrik Madsen John Bagterp Jørgensen

In this paper we present simulations of Economic Model Predictive Control (EMPC) for control of the energy system in a smart home. The energy system in a smart home consists of a stationary battery, photo voltaic solar cells on the roof, a heat pump for heating, and an electrical vehicle battery that is charged. Using weather forecasts, thermal comfort and driving profiles, the EMPC coordinates this energy system and its interaction with the external energy system by purchasing and selling electricity at prices that are announced in advance. The EMPC is a linear program with soft constraints for the output constraints and an objective function that represents the cost of energy. In contrast to existing methods, the key novelties in the present paper is the use of multi-level soft constraints and an objective function that accounts not only for the cost of energy used during the prediction horizon but also for the value of energy stored at the end of the prediction horizon. We demonstrate by simulation, that these novelties are important for well-behaved closed-loop performance of the EMPC.

Model Predictive Control

10.1109/ccta.2019.8920663

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Citations (4)

Model Identification Using Stochastic Differential Equation Grey-Box Models in Diabetes

Journal of Diabetes Science and Technology (2013)

Anne Katrine Duun‐Henriksen Signe Schmidt Rikke Meldgaard Røge Jonas Bech Møller Kirsten Nørgaard

Background: The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. Methods: An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the “time to peak of meal response” parameter. Results: We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the “peak time of meal absorption” parameter showed that the absorption rate varied according to meal type. Conclusion: This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development.

Identification

10.1177/193229681300700220

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Citations (58)

Economic nonlinear MPC for a population of thermostatically controlled loads

Computer Science - Research and Development (2017)

Nikita Zemtsov Jaroslav Hlava G.A. Frantsuzova Henrik Madsen John Bagterp Jørgensen

10.1007/s00450-017-0354-7

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Citations (3)