activity
20172023
most citedA Family of ESDIRK Integration Methods

7 citations · 20 across the 23 of their papers we have counts for

collaborators

23 papers

eess.SY2023

Mathematical meal models for simulation of human metabolism

Tobias K. S. Ritschel, Asbjørn Thode Reenberg, Peter Emil Carstensen +2

We present and critically discuss five commonly used mathematical models of the meal glucose rate of appearance in humans. Such models are key to simulation of the metabolism in he…

q-bio.QM2023

Quantitative modeling and simulation of biochemical processes in the human body

Jacob Bendsen, Peter Emil Carstensen, Asbjørn Thode Reenberg +2

We present a whole-body model of human metabolism that utilizes a system of organs and blood vessels to simulate the enzymatic reactions. The model focuses on key organs, including…

math.OC2023★ 1 cited

Dynamic Optimization for Monoclonal Antibody Production

Morten Wahlgreen Kaysfeld, Deepak Kumar, Marcus Krogh Nielsen +1

This paper presents a dynamic optimization numerical case study for Monoclonal Antibody (mAb) production. The fermentation is conducted in a continuous perfusion reactor. We repres…

math.OC2023

Data Assimilation for Combined Parameter and State Estimation in Stochastic Continuous-Discrete Nonlinear Systems

Tarek Diaa-Eldeen, Marcus Krogh Nielsen, Carl Fredrik Berg +2

Data assimilation (DA) provides a general framework for estimation in dynamical systems based on the concepts of Bayesian inference. This constitutes a common basis for the differe…

eess.SY2023

Software principles and concepts applied in the implementation of cyber-physical systems for real-time advanced process control

Anders H. D. Andersen, Zhanhao Zhang, Steen Hørsholt +2

Cyber-physical systems (CPSs) for real-time advanced process control (RT-APC) are a class of control systems using network communication to control industrial processes. In this pa…

math.OC2022★ 1 cited

Modeling, scientific computing and optimal control for renewable energy systems with storage

Nicola Cantisani, Tobias K. S. Ritschel, Christian A. Thilker +2

This paper presents models for renewable energy systems with storage, and considers its optimal operation. We model and simulate wind and solar power production using stochastic di…

eess.SY2022★ 1 cited

Model-based control algorithms for the quadruple tank system: An experimental comparison

Anders H. D. Andersen, Tobias K. S. Ritschel, Steen Hørsholt +2

We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical s…

math.OC2022

Modelling and Economic Optimal Control for a Laboratory-scale Continuous Stirred Tank Reactor for Single-cell Protein Production

Marcus Krogh Nielsen, Jens Dynesen, Jess Dragheim +5

In this paper, we present a novel kinetic growth model for the micro-organism \textit{Methylococcus capsulatus} (Bath) that couples growth and pH. We apply growth kinetics in a mod…

eess.SY2022★ 1 cited

Model Predictive Control Tuning by Monte Carlo Simulation and Controller Matching

Morten Ryberg Wahlgreen, John Bagterp Jørgensen, Mario Zanon

This paper presents a systematic method for the selection of the Model Predictive Control (MPC) stage cost. We match the MPC feedback law to a proportional-integral (PI) controller…

math.OC2022★ 1 cited

State Estimation for Continuous-Discrete-Time Nonlinear Stochastic Systems

Marcus Krogh Nielsen, Tobias K. S. Ritschel, Ib Christensen +4

State estimation incorporates the feedback in optimization based advanced process control systems and is very important for the performance of model predictive control. We describe…

eess.SY2022

Performance Quantification of a Nonlinear Model Predictive Controller by Parallel Monte Carlo Simulations of a Closed-loop System

Morten Wahlgreen Kaysfeld, Mario Zanon, John Bagterp Jørgensen

This paper presents a parallel Monte Carlo simulation based performance quantification method for nonlinear model predictive control (NMPC) in closed-loop. The method provides dist…

eess.SY2022

Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetes

Sarah Ellinor Engell, Tinna Björk Aradóttir, Tobias K. S. Ritschel +2

In type 2 diabetes (T2D) treatment, finding a safe and effective basal insulin dose is a challenge. The dose-response is highly individual and to ensure safety, people with T2D tit…

math.OC2022

State Estimation Methods for Continuous-Discrete Nonlinear Systems involving Stochastic Differential Equations

Marcus Krogh Nielsen, Tobias K. S. Ritschel, Ib Christensen +4

In this work, we present methods for state estimation in continuous-discrete nonlinear systems involving stochastic differential equations. We present the extended Kalman filter, t…

q-bio.QM2022

A whole-body multi-scale mathematical model for dynamic simulation of the metabolism in man

Peter Emil Carstensen, Jacob Bendsen, Asbjørn Thode Reenberg +2

We propose a whole-body model of the metabolism in man as well as a generalized approach for modeling metabolic networks. Using this approach, we are able to write a large metaboli…

math.OC2022★ 1 cited

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

Tobias K. S. 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…

math.OC2022★ 1 cited

Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas

Asbjørn Thode Reenberg, Tobias K. S. Ritschel, Emilie B. Lindkvist +5

In this work, we present a switching nonlinear model predictive control (NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use maximum likelihood estimation (MLE)…

math.OC2022★ 1 cited

High-performance Uncertainty Quantification in Large-scale Virtual Clinical Trials of Closed-loop Diabetes Treatment

Asbjørn Thode Reenberg, Tobias K. S. Ritschel, Bernd Dammann +1

In this paper, we propose a virtual clinical trial for assessing the performance and identifying risks in closed-loop diabetes treatments. Virtual clinical trials enable fast and r…

math.OC2022

A one-size-fits-all artificial pancreas for people with type 1 diabetes based on physiological insight and feedback control

Tobias K. S. Ritschel, Asbjørn Thode Reenberg, Emilie B. Lindkvist +6

We propose a model-free artificial pancreas (AP) for people with type 1 diabetes. The algorithmic parameters are tuned to a virtual population of 1,000,000 individuals, and the AP…

math.OC2019

Filtering and Prediction of the Blood Glucose Concentration using an Android Smart Phone and a Continuous Glucose Monitor

Zeinab Mahmoudi, Dimitri Boiroux, Tobias K. S. Ritschel +1

In this paper we numerically assess the performance of Java linear algebra libraries for the implementation of nonlinear filters in an Android smart phone (Samsung A5 2017). We imp…

math.OC2018★ 3 cited

Dynamic Optimization of Thermodynamically Rigorous Models of Multiphase Flow in Porous Subsurface Oil Reservoirs

Tobias K. S. Ritschel, John Bagterp Jørgensen

In this paper, we consider dynamic optimization of thermal and isothermal oil recovery processes which involve multicomponent three-phase flow in porous media. We present thermodyn…

math.NA2018★ 7 cited

A Family of ESDIRK Integration Methods

John Bagterp Jørgensen, Morten Rode Kristensen, Per Grove Thomsen

In this paper we derive and analyze the properties of explicit singly diagonal implicit Runge-Kutta (ESDIRK) integration methods. We discuss the principles for construction of Rung…

math.OC2018

Risk minimization in life-cycle oil production optimization

Andrea Capolei, Lasse Hjuler Christiansen, John Bagterp Jørgensen

The geology of oil reservoirs is largely unknown. Consequently, the reservoir models used for production optimization are subject to significant uncertainty. To minimize the associ…

math.OC2017★ 2 cited

A fast and memory-efficient spectral Galerkin scheme for distributed elliptic optimal control problems

Lasse Hjuler Christiansen, John Bagterp Jørgensen

Many scientific and engineering challenges can be formulated as optimization problems which are constrained by partial differential equations (PDEs). These include inverse problems…