Maria M. Papathanasiou

A control strategy for periodic systems – application to the twin-column MCSGP

Abstract In this work we present advanced multi-parametric control strategies for the “Multicolumn Countercurrent Solvent Gradient Purification” (MCSGP) process, which is a continuous chromatographic separation process, governed by a periodic operation profile, linked to the intensification of monoclonal antibody production. We demonstrate a seamless, step-by-step procedure for the development of multi-parametric controllers as part of our recently introduced PAROC framework and software platform. The designed controller assures optimal operating conditions, avoiding pertubations, under continuous operation, while capturing the periodic nature of the process.

Intelligent, model-based control towards the intensification of downstream processes

Abstract Process Intensification (PI) has been gaining increasing interest as industrial trends urge a shift towards more eco-efficient processes of significantly decreased operation and capital costs. In this direction we focus on the development of advanced control strategies of the Multicolumn Countercurrent Solvent Gradient Purification Process (MCSGP), an industrial, semi-continuous, chromatographic process, used for the purification of several biomolecules. We present a novel control approach that manages to drive the process towards continuous, sustainable operation. The presented controllers are designed within the PARametric Optimization and Control (PAROC) framework/software platform that enables the development of intelligent, model-based controllers through a step-by-step approach. The controllers are successfully tested against various disturbance profiles and they manage to track the predefined setpoints without significant offset.

Advanced model‐based control strategies for the intensification of upstream and downstream processing in mAb production

Current industrial trends encourage the development of sustainable, environmentally friendly processes with minimal energy and material consumption. In particular, the increasing market demand in biopharmaceutical industry and the tight regulations in product quality necessitate efficient operating procedures that guarantee products of high purity. In this direction, process intensification via continuous operation paves the way for the development of novel, eco‐friendly processes, characterized by higher productivity and lower production costs. This work focuses on the development of advanced control strategies for (i) a cell culture system in a bioreactor and (ii) a semicontinuous purification process. More specifically, we consider a fed‐batch culture of GS‐NS0 cells and the semicontinuous Multicolumn Countercurrent Solvent Gradient Purification (MCSGP) for the purification process. The controllers are designed following the PAROC framework/software platform and their capabilities are assessed in silico, against the process models. It is demonstrated that the proposed controllers efficiently manage to increase the system productivity, returning strategies that can lead to continuous, stable process operation.

A Predictive Model for Energy Metabolism and ATP Balance in Mammalian Cells: Towards the Energy-Based Optimization of mAb Production

Monoclonal antibodies (mAb) are complex molecules that exhibit high specificity and affinity making them suitable for novel diagnostic and therapeutic applications. Model-based techniques could be used to develop optimization strategies to design feeding regimes that maximize mAb titer in mammalian cell cultures. Existing feeding strategies depend mainly on glucose and glutamate supply, neglecting the exhaustion of other essential amino acids and the energy requirements for the proliferation and maintenance of cells. In this work, cell composition and energy requirements have been considered in the development of a novel dynamic predictive model for GS-NS0 cells producing cB72.3 mAb. The model describes the production and consumption of ATP based on glucose and amino acids energy metabolic networks. The successful coupling of growth kinetics equations and stoichiometric balances and the in vitro/in silico approach has enabled us to develop the first dynamic model that predicts the ATP content in mammalian cell cultures.

Computational tools in the assistance of personalized healthcare

Abstract Process Systems Engineering has been many years in the forefront, advancing the standards in healthcare and beyond. Gradually, integrated methods that utilize both experimental and/or clinical data, as well as in silico tools are becoming popular among the medical community. In silico tools have already demonstrated their great potential in various sectors, assisting the industry to produce experiments of significantly reduced cost that allow thorough investigation of the system at hand. Similarly, in biomedical systems, the advancement of the current state of the art through the development of intelligent computational tools can lead to personalized healthcare protocols. The first part of this chapter serves as a brief review of the computational tools commonly used in healthcare, such as big data analytics and dynamic mathematical models. The challenges characterizing biomedical systems, such as data availability and patient variability, are also discussed here. We present the advantages and limitations of the various methods and we suggest a generic framework for the design and testing of advanced in silico platforms. The PARametric Optimization and Control (PAROC) framework presented here is based on the design of high-fidelity, dynamic, mathematical models that are then validated using experimental and/or clinical data. Such models provide the basis for the execution of optimization and control studies for the design of patient-specific treatment protocols. The final part of the chapter is dedicated to the application of PAROC to three different biomedical examples, namely: (i) acute myeloid leukemia, (ii) the anesthesia process, and (iii) diabetes mellitus. The challenges of each case are discussed and the application of the relevant PAROC steps is demonstrated.

Advanced control strategies for a periodic, two-column chromatographic process

In this work we investigate the design and application of advanced control strategies to a chromatographic process. For the Multicolumn Countercurrent Solvent Gradient Purification process (MCSGP), a nonlinear process, described by a periodic profile, we develop a step-by-step procedure that enables the seamless development of approximate explicit advanced MPC controllers for such systems. The designed controllers assure optimal operating conditions, periodic input profiles and continuous process monitoring.

Explicit MPC in real-world applications: The PAROC framework

PAROC is an integrated framework and software platform for the development of explicit MPC controllers for real-world applications. Starting from a validated mathematical representation of the system under consideration, suitable model reduction and system identification techniques provide an approximate model which is used in combination with state-of-the-art explicit MPC algorithms to solve the underlying optimal control problem offline. The resulting solution is then seamlessly incorporated into the high fidelity model based closed-loop system, where it is validated without the need to solve an optimization problem online. The capabilities and applicability of this framework are highlighted via two case studies: the control of a combined heat and power (CHP) cogeneration system for residential use, as well as the optimal control of intravenous anaesthesia. Challenges such as hybrid systems and nonlinearity are discussed, along with new directions in the development of powerful novel multi-parametric programming algorithms and tools for a wide range of optimization and control problems, thereby paving the way for the wider applicability of the proposed PAROC framework to real-world applications.

Development of advanced control strategies for periodic systems: An application to chromatographic separation processes

In this work we investigate the design and application of advanced control strategies to a periodic chromatographic process. For the Multicolumn Countercurrent Solvent Gradient Purification process (MCSGP), a nonlinear process described by a periodic profile, we present a step-by-step procedure that enables the seamless development of explicit advanced MPC controllers for such systems. The designed controllers assure optimal operating conditions, periodic input profiles and continuous process monitoring.

Computational tools for the advanced control of periodic processes - Application to a chromatographic separation

Abstract In this work we focus on the development of advanced, model-based controllers for periodic processes and we demonstrate their operation capabilities using the example of a chromatographic separation process. We demonstrate the design of a multi-parametric controller for the “Multicolumn Countercurrent Solvent Gradient Purification” (MCSGP) process, a semi-continuous, two-column, periodic chromatographic separation process, governed by a periodic operation profile, linked to production of monoclonal antibodies. For the controller development we use PAROC framework and software platform. The designed controllers are tested in-silico, against the process model and demonstrate stable operation, characterized by cyclic input profiles.

Development of advanced computational tools for the intensification of monoclonal antibody production

Abstract In this work we demonstrate an advanced, model-based control strategy for the maximization of the productivity of a mammalian cell culture system, using GS-NS0 cells. We consider an unstructured, differential and algebraic equation model that describes the events taking place in the bioreactor, based on glucose and four key amino acids. In particular, we monitor and maximize the accumulation of the monoclonal antibody (mAb) in the cell culture system using the feed flow rate as the control input. For the development of the controller we are following the PAROC framework and software platform, based on multi-parametric policies. The performance of the controller is also assessed in-silico against the process model. The designed optimal control policy significantly the antibody concentration within the culture and indicates a biologically significant input profile.