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

Biotechnological processes are used to produce many products in society, e.g., bioplastics, beer, vaccines, enzymes, and biopharmaceuticals. A promising biopharmaceutical is monoclonal antibodies (mAbs), which have been applied for treatment of various diseases including cancer, autoimmune disorders, and infectious diseases such as coronavirus disease 2019 (COVID-19). In 2017, the 6 top-selling biopharmaceuticals were mAbs. In 2022 the mAb market size was valued at 210.06 billion USD and the expected market size in 2030 is 494.52 billion USD. The subject of this thesis is economic nonlinear model predictive control (ENMPC) for biotechnological processes and uncertainty quantification (UQ) of closed-loop systems. We develop an ENMPC algorithm for profit maximization of an mAb fermentation process and a high-performance Monte Carlo simulation toolbox for UQ of closed-loop systems. The purpose of this thesis is to demonstrate, by simulation, that ENMPC can increase the profit of the mAb fermentation process and that Monte Carlo simulation can quantify uncertainties and closed-loop performance for the process. In this work, we develop 1) a high-performance Monte Carlo simulation toolbox for UQ of closed-loop systems, 2) a modeling methodology for processes consisting of reactive systems conducted in reactors, and 3) an ENMPC algorithm for profit maximization. In addition, we develop a target-tracking nonlinear model predictive control (NMPC) algorithm for testing purposes. This thesis introduces the Monte Carlo simulation toolbox, the modeling methodology, the NMPC algorithms, and a set of main numerical results. The Monte Carlo simulation toolbox applies Open Multi-Processing (OpenMP) for parallelization on shared memory architectures and shows almost linear parallel scaling for both proportional-integral-derivative (PID) controllers and the developed NMPC algorithms. The Monte Carlo simulation approach is applicable for quantification of uncertainties and closed-loop performance with respect to any selected key performance indicators (KPIs), which makes the approach versatile. The modeling methodology separates modeling of the reactor and the reactive system. In this way, the reactor equations are not required to be updated to change the considered process. We apply the modeling methodology to introduce three models in this thesis. The ENMPC algorithm consists of a continuous-discrete extended Kalman filter (CD-EKF) for state estimation and an economic regulator for profit maximization. We apply the ENMPC algorithm for profit maximization of an mAb fermentation process, where the economic regulator depends on mAb and glucose prices. The numerical results show that availability of Monte Carlo simulations enables closed-loop performance quantification with respect to selected KPIs and simplifies comparison between controllers. Closed-loop simulations with the ENMPC algorithm indicate that ENMPC can increase the profit of the mAb fermentation process. In addition, a set of operational insights summarizes the ENMPC operational strategy, which we apply to design a simple controller. Monte Carlo simulation quantifies the simple controller performance and show practically identical performance as the ENMPC algorithm. These results demonstrate the “from-simple-viacomplex-to-lucid” approach, where the ENMPC technology enables design of a simple controller with practically identical performance. This thesis consists of a summary report and a collection of seven research papers and a technical report. Six papers are published or accepted for publication in conference proceedings and one paper is submitted to Journal of Process Control. The technical report is not peer-reviewed