Volker C. Hass

Soft sensors in bioprocessing: A status report and recommendations

The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology - Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

Adaptive, Model-Based Control by the Open-Loop-Feedback-Optimal (OLFO) Controller for the Effective Fed-Batch Cultivation of Hybridoma Cells

Although fed‐batch suspension culture of animal cells continues to be of industrial importance for the large scale production of pharmaceutical products, existing control concepts are still insufficient. Changes in cell metabolism during cultivation and between similar cultivations, the complexity of the cell metabolism, and the lack of on‐line state variables restrict the transfer of available control strategies established in bioprocess engineering. A process control strategy designed to achieve optimized process control must account for all these difficulties and fit sophisticated requirements toward adaptability and flexibility. The combination of a fed‐batch process and an Open‐Loop‐Feedback‐Optimal (OLFO) control provides a new approach for cell culture process control that couples an efficient cultivation concept to a capable process control strategy. The application of an adaptive, model‐based OLFO controller to a hybridoma cultivation and experimental results are presented.

Operator training simulation for integrating cultivation and homogenisation in protein production

Graphical abstract

Report and recommendation of a workshop on education and training for measurement, monitoring, modelling and control (M3C) in biochemical engineering

Report and recommendation of a workshop on education and training for measurement, monitoring, modelling and control ((MC)-C-3) in biochemical engineering

Advanced Process and Control Strategies for Bioreactors

Bioreactor processes have to provide an almost optimal environment to microorganisms or cells to promote growth and product formation. The design and operation of a bioreactor as the main element of fermentation is a complex task, not only with respect to a reactors configuration and size but to the control and mode of operation. In this chapter, some fundamentals are discussed, including process and control strategies and concepts for process development. Next, examples for the use of model-based concepts for the design of experiments, feeding strategies, seed train layout, and control strategies, including simulation tools supporting biotechnological training and education, are shown.

“BioProzessTrainer” as training tool for design of experiments

Concept Design and optimization of cell culture processes requires intensive studies based on “Design of experiments”-strategies. In academia teaching of DoE-concepts is often insufficient, as in most cases only simple culture strategies (batch) can be performed, as time and money are limited. More complex tasks such as feeding strategies for fed batch culture can be discussed theoretically only. To close this gap the virtual “BioProzessTrainer”, a model based simulation tool, was developed. It supports biotechnological education with respect to process strategies, bioreactor control, kinetic analysis of experimental data and modeling. Along with a set of examples for different control and process strategies (batch, fed batch, chemostat etc.) learners are prepared for real experiments [1,2]. The “BioProzessTrainer” (Figure 1) helps to improve the quality of education by using interactive learning forms and by transmitting additional knowledge and skills. Costs for practical experiments can be minimized by reducing plant operation costs. Here a concept for teaching DoE-concepts for batch(optimization of e.g. substrate concentrations and inoculation cell density) and fed-batch-processes (evaluation and optimization of feeding strategy) using the “BioProzessTrainer” is shown.

An environment for the development of operator training systems (OTS) from chemical engineering models

Abstract In chemical engineering, process models obtain increasing importance. There are process models for the optimisation of the process strategy as well as process models for the development of advanced control strategies. Another example of the use of process models is the assessment of potential operational risks, hidden in complex reaction dynamics. These examples of process models may be referred to as engineering type models. Educational type process models represent another type. Both types of models serve specific sets of requirements. However, the frequent use of process models is restricted by the high investments, necessary for their development, combined with a relatively short lifetime. Reuse of a process model in an operator training system (OTS) is an efficient measure to prolong the lifetime, especially of an engineering type process model. The reuse task will be largely simplified, by an adequate development environment. The development environment introduced here is a combination of a coding framework, designed for the rapid design of process models and a commercial process control system. The coding framework provides tools for parameter estimation and verification, while the process control system is equipped with various tools for process control and process visualization. Models, developed in the coding framework are easily incorporated into the process control system, enabling software engineers to quickly generate a most realistic look and feel of the process. In combining the coding framework and the process control software, an elegant way to prolong the lifetime of a process model is established.

Model-Based Control of Hybridoma Cell Cultures

Abstract The optimal control of hybridoma cell cultivations requires adaptive model based control strategies like the Open-Loop-Feedback-Optirnal-(OLFO) controller In this controller, the applied models must be able to predict the states of a cultivation reasonably well. Theoretical investigations show how the online-prediction performance of a model may be evaluated. Experimental results obtained by the application of a simple unstructured process model in a simplified OLFO-strategy illustrates the potential of this controlleL Experiments have been carried out, that show the value of oxygen-uptake-rate measurements for controlling the process under glutamine limiting conditions.

Model-Based Process Control for Optimised Banana Ripening

Abstract A dynamic model of the banana ripening process as performed in the food industry was developed. In the model, the degradation of starch to soluble sugars was described based on the carbon dioxide emission. The basis of the physico-chemical and the plant model parts were dynamic energy and mass balances. The model was evaluated by the means of experimental data produced in a pilot plant banana ripening box. The functionalities of the box follow industrial standards. The model describes the characteristic courses of the carbon dioxide concentration in the air of the process, the starch concentration in the banana pulp and the peel colour. Based on this information, the ripening state of the bananas could be estimated with the help of the model. Moreover, the model was used for an automated predictive and adaptive process control. For this purpose the Open-Loop-Feedback-Optimal (OLFO) controller was applied to the banana ripening process. Two characteristic model parameters, the starch-to-CO 2 reaction rate factor and the peel colour change rate factor, were fitted to changing process courses. The control function of the banana ripening process, the set-point temperature profile, was optimised to automatically control the process to the required ripening state and the optimal storage temperature at the end of the procedure.

Optimisation of Time-space-yield for Hybridoma fed-batch Cultures with an Adaptive Olfo-controller

Ralf Portner, Bjorn Frahm, Paul Lane, Axel Munack, Kathrin Kuhn, Volker C. Hass Technische Universitat Hamburg-Harburg, Bioprozessund Bioverfahrenstechnik, Denickestr. 15, 21071 Hamburg, Germany, Bayer Technology Services, Process Technology, 51368 Leverkusen, Germany, Bundesforschungsanstalt fur Landwirtschaft, Institut fur Technologie und Biosystemtechnik, Bundesallee 50, 38116 Braunschweig, Germany, Hochschule Bremen, Neustadtwall 30, 28199 Bremen, Germany