In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional-integral-derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.

Automatic control of bioprocesses.

https://backend.orbit.dtu.dk/ws/files/5034408/Ravendra%20Singh_PEC09-47.pdf

Model-based computer-aided framework for design of process monitoring and analysis systems

In the manufacturing industry, for example, the pharmaceutical industry, a thorough understanding of the process is necessary in addition to a properly designed monitoring and analysis system (PAT system) to consistently obtain the desired end-product properties. A model-based computer-aided framework including the methods and tools through which the design of monitoring and analysis systems for product quality control can be generated, analyzed and/or validated, has been developed. Two important supporting tools developed as part of the framework are a knowledge base and a model library. The knowledge base provides the necessary information/data during the design of the PAT system while the model library generates additional or missing data needed for design and analysis. Optimization of the PAT system design is achieved in terms of product data analysis time and/or cost of monitoring equipment subject to the maintenance constraints of the desired product quality. The application of the model-based framework is highlighted through a case study involving the operation of a fermentation process.

Model-based Computer Aided Framework for Design of Process Monitoring and Analysis Systems

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

/pdf/model-based-methods-in-the-biopharmaceutical-process-11khoogenk.pdf

Model-Based Methods in the Biopharmaceutical Process Lifecycle.

This contribution addresses the application and comparison of model based estimation, optimization, and control methods for fed-batch bioprocesses. For the application of model based control, appropriate knowledge of the system’s state is required. The estimation quality of two constrained optimization based state estimation algorithms, namely the Bayesian maximum a posteriori based Constrained Extended Kalman-Filter (CEKF) and the Moving-Horizon-State-Estimation (MHE) is compared to the classical unconstrained Extended Kalman-Filter (EKF). The comparison is based on Monte Carlo simulations of a small mechanical and a high order grey-box model of a biological system. Moreover, EKF, CEKF, and MHE usually introduced separately, are described in a coherent setting starting from the same maximum a posteriori estimation problem. Finally the well-known nonlinear model predictive control (NMPC) is compared with a control strategy that uses online optimization of the complete future trajectory. Extensive Monte Carlo simulation studies and a real world application are considered.

Model based estimation and optimal control of fed-batch fermentation processes for the production of antibiotics

http://www.nt.ntnu.no/users/skoge/prost/proceedings/dycops-2010/Papers_CAB2010_common/WeMT1-02.pdf

Quality-by-Design Using a Gaussian Mixture Density Approximation of Biological Uncertainties

Mathematical models have been proven to be a key factor in optimizing production processes in recent years. However, in the case of biochemical processes the design is usually done using heuristics, since these systems show complex internal regulation mechanisms and strongly nonlinear behavior. This makes it difficult to find an appropriate model. In those cases, where a structured biochemical model has been successfully identified, the yield of the process can be increased significantly. Obtaining a suitable production model is usually a difficult and time consuming task, especially for biochemical systems. In this contribution the concept for an automation tool is presented which starts with the few noisy measurements of initial experiments to perform a model evolution from run to run. Thereby, the first unstructured model candidates are used for an optimal production-orientated process design whose realization will provide additional information about the dynamic behavior within the production area, thus, leading to new and improved model candidates. Due to the difficult measurement situation in biochemical processes many different model candidates may show a similar fit to the data why it is unwise to focus on one model candidate for process design, only. Furthermore, the use of more than one model candidate for the design procedure represents a kind of robustness for the planning. This cyclic procedure enables an optimal production design corresponding to the available measurement information at any time.

/pdf/rapopt-an-automation-tool-for-production-orientated-run-to-5ddd0seox5.pdf

RapOpt – An Automation Tool for Production-orientated Run-to-run Model Evaluation

Parameterschätzung für nichtlineare dynamische Modelle auf Basis stark verrauschter, statistisch unvollständig bestimmter Messdaten

An Automated Approach to Build Process Models by Detecting Biological Phenomena in (fed-)batch Experiments

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