Economic Nonlinear Model Predictive Control for Integrated and Optimized Non-Stationary Operation of Biotechnological Processes

Morten Ryberg Wahlgreen: Optimal operation of biotechnological processes for improved peptide and vaccine production

In society there is a growing needed for production of peptides (e.g. insulin) and vaccines. Insulin production is a key element in fighting diabetes, while the need of vaccine production is more needed than ever in light of the 2020 COVID-19 pandemic.

Biotechnological processes are part of peptide and vaccine production. Among these are chromatographic processes, which are modelled as convection-diffusion-reaction partial differential equation systems with steep fonts, making them mathematically hard to simulate. Biotechnological processes are continuously operated at single steady-state in industrial practice. While this is a simple way to operate the processes, there is evidence in the literature that this is non-optimal.

Today, the manufacturing of peptides and vaccines are conducted without the use of feedback control and optimization. Without this it is nearly impossible (if not impossible) to have non-stationary or periodic operation of the processes. Consequently, the operation of such biotechnological processes offers significant improvement potential. We will achieve such improvements by developing economic nonlinear model predictive control (NMPC) technology. The optimal control provided by this technology will enable operation of biotechnological processes in an integrated dynamic manner, which may not be at steady-state, but can also be non-stationary and periodic. The developed technology is expected to 1) enable optimal operation of biotechnological processes resulting in much higher economic yields and 2) disrupt the current operation practice for biotechnological processes.

To achieve such advanced mathematical technology, we will develop scientific computing software including software for solution of ordinary differential equations (ODE’s), partial differential equations (PDE’s) based on spectral methods, and stochastic differential equations (SDE’s), together with software for state estimation, parameter estimation and optimization. The software will be developed in a high-performance programming language.

PhD project

By: Morten Ryberg Wahlgreen

Section: Scientific Computing

Principal supervisor: John Bagterp Jørgensen

Co-supervisors: Dimitri Boiroux, Allan Peter Engsig Karup

Project title: Economic Nonlinear Model Predictive Control for Integrated and Optimized Non-Stationary Operation of Biotechnological Processes

Term: 01/08/2020 → 31/07/2023


Morten Ryberg Wahlgreen
PhD student
DTU Compute


John Bagterp Jørgensen
DTU Compute
+45 45 25 30 88


Allan Peter Engsig-Karup
Associate professor
DTU Compute
+45 45 25 30 73