2 citations · 10 across the 20 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
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…
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…