Process analytical technology in food biotechnology

Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.Die Biotechnologie ist ein Wissenschaftsbereich, in dem hohe Genauigkeit und Wiederholbarkeit eine wichtige Rolle spielen. Dies ist der Tatsache geschuldet, dass die hergestellten Produkte sehr oft den Bereichen Nahrungsmitteln, Pharmazeutika oder Kosmetik angehöhren und daher besonders den Menschen beeinflussen. Um den menschlichen Fehler bei der Produktion zu vermeiden, die Qualität eines Produktes zu sichern und die optimale Verwertung der Rohmaterialen zu gewährleisten, wird ein besonders hohes Maß an Automation angestrebt. Die Werkzeuge, die in der Nahrungsmittel- und chemischen Industrie hierfür zum Einsatz kommen, werden in der Process Analytical Technology (PAT) Initiative zusammengefasst. Ziel der PAT ist die Entwicklung zuverlässiger neuer Methoden, um Prozesse zu beschreiben und eine automatische Regelungsstrategie zu realisieren. Biotechnologische Prozesse gehören hierbei zu den aufwändigsten Regelungsaufgaben, da in den meisten Fällen eine biologische Komponente der entscheidende Faktor ist. Entscheidend für eine erfolgreiche Regelungsstrategie ist ein hohes Maß an Prozessverständnis. Dieses kann entweder durch eine direkte Messung der entscheidenden physikalischen, chemischen oder biologischen Größen gewonnen werden oder durch einen SoftSensor. Zusammengefasst zeigt sich, dass das finale Ziel der PAT Initiative auch nach einigen Jahren des Propagierens weder komplett in der Industrie noch bei vielen Produktionsprozessen angekommen ist. Auf der einen Seite liegt dies mit Sicherheit an der Tatsache, dass noch viel Arbeit in die Generalisierung von Algorithmen gesteckt werden muss. Diese müsse einfacher zu implementieren und vor allem noch zuverlässiger in der Funktionsweise sein. Auf der anderen Seite wurden jedoch auch Algorithmen, Regelungsstrategien und eigne Ansätze für einen neuartigen Sensor sowie einen Soft-Sensors vorgestellt, die großes Potential zeigen. Nicht zuletzt müssen die möglichen Anwender neue Strategien einsetzen und Vorbehalte gegenüber unbekannten Technologien ablegen

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system

In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves fnding feed rate profles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefned concentration profles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defned. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.Fil: Fernández, Maria Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Ortiz, Oscar Alberto. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System

In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. However, in order to maintain process safety and product quality it is necessary to specify the controller performance and to tune the controller parameters. In this work, an approach is presented to establish an intelligent control system for oxidoreductive yeast propagation as a representative process biased by the aforementioned uncertainties. The presented approach is based on statistical process control and fuzzy logic feedback control. As the cognitive uncertainty among different experts about the limits that define the control performance as still acceptable may differ a lot, a data-driven design method is performed. Based upon a historic data pool statistical process corridors are derived for the controller inputs control error and change in control error. This approach follows the hypothesis that if the control performance criteria stay within predefined statistical boundaries, the final process state meets the required quality definition. In order to keep the process on its optimal growth trajectory (model based reference trajectory) a fuzzy logic controller is used that alternates the process temperature. Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. The presented experimental results show that the genetic tuned fuzzy controller is able to keep the process within its allowed limits. The average absolute error to the reference growth trajectory is 5.2 × 106 cells/mL. The controller proves its robustness to keep the process on the desired growth profile

Probing Control of a Fedbatch Fermentation. Simulation and Implementation in ABB Industrial IT

During the production of pharmaceutical products, a central stage is so called fermentation process i.e. cultivation of bacteria in a biological reactor. In the scope of this Master Thesis the product is recombinant protein. The goal for the fermentation process is to keep a high cell growth and a high production of recombinant proteins. For these purposes, an automatic control algorithm has been presented in (Åkesson, 1999), with some modifications described well in (Velut et al, 2002). The algorithm is designed to avoid acetate accumulation while maintaining a high glucose feed by monitoring the dissolved oxygen tension in the biological reactor. The acetate is an undesired byproduct that inhibits both cell replication and production of recombinant proteins. The automatic control algorithm and a process model has first been implemented in Matlab and later in Control Builder,(a developing environment for IEC 61131 Control Languages. It is part of the Industrial IT 800xA System developed by ABB and is a comprehensive process automation system. The Control Builder is mainly used to implement the controller programs while the Industrial IT 800xA System has been used to control/monitor/log different variables during simulation and later in the field test. After verifying the implementation in Control Builder by comparing the simulation in Soft controller (a simulation tool for real time simulation) with simulation in Matlab, the controller programs are ready for the final test. The final test was performed in a physical biological reactor located at division of Biotechnology at the Chemical Center of Lund Institute of Technology where the controller programs were downloaded into an ABB AC800M controller. The final test of the controller programs in Lund showed to be very successful at the first attempt. It managed to keep a high cell growth and at the same time it has prevented production of acetate

Complex Dynamics in Fed-Batch Systems: Modeling, Analysis and Control of Alcoholic Fermentations

Modeling and control of fed-batch fermentation processes has been a subject of great interest to realize high productivity and yields from the fermentation technique. The goal of this dissertation was to gain insights into how the complex dynamic behaviors exhibited in fed-batch fermentation systems affect the stability of standard single-loop as well as non-standard feedback control structures. Novel PID stability theorems were established to help construct the controller stabilizing regions

Probing Control : Analysis and Design with Application to Fed-Batch Bioreactors

In most control problems the objective is to control the output at a desired value in spite of disturbances. In some cases, the best setpoint is not known a priori and it should be found online to optimize the process performance. This thesis examines a probing strategy that can be applied for this class of problems. The focus is on the application of the technique to the control of feed supply in fed-batch fermentations of the bacterium Escherichia coli. The thesis is divided into three parts. In the first part, the convergence properties of the probing algorithm are examined. The analysis is limited to processes modeled by a linear time-invariant dynamic in series with a static nonlinearity. Stability and performance analysis taking into account the process dynamic is performed. Tuning guidelines that help the user for the design are also derived. The second part presents a novel cultivation technique based on the probing approach. The fermentation technique combines the advantages of probing control and temperature-limited fed-batch technique. The feeding strategy is well adapted for prolonged operation at the maximum oxygen transfer capacity of the reactor. The efficiency of the method is demonstrated by simulations and experimental results. The strategy leads to a high biomass and it limits the degradation of the recombinant protein activity in the late production phase. In the third part, the probing feeding strategy is evaluated in industrial-scale bioreactors. Based on experimental results the influence of scale and complex medium is discussed. It is shown that the flexibility and robustness of the technique makes it a useful tool for process development

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

On-Line Estimation of Oxygen Transfer Rate with Oxygen Enriched Air using Off-Gas Sensor for Escherichia coli

An online estimator for the Oxygen Transfer Rate OTR for Escherichia coli cultured in bioreactors was developed, which allowed for improved culture outcomes. E. coli are used to manufacture recombinant proteins used as therapeutics, such as insulin and human growth hormone. E. coli cultures require high levels of oxygen in order to produce the therapeutics efficiently. Previous methods to estimate OTR used values for the volumetric mass transfer coefficient, kLa, which had been determined from separate experiments and thus set to a constant value or by stopping oxygen flow periodically to the bioreactor to update the kLa value. In this work, the kLa value was estimated in real-time and continuously from the on-line dissolved oxygen concentration and off-gas measurements. The gas phase mixing in the head space and time response of the off-gas sensor were accounted for in the model. The improved estimates of OTR were incorporated into a model of E. coli metabolism to better predict the metabolic state of E. coli, such that glucose could be fed to E. coli at near optimal rates. Additionally, the effects of enriching the air with pure oxygen were accounted for in the estimator model, as this enrichment is necessary to reach final cell densities representative of the industrial process