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A Laboratory Experiment for Teaching Bioprocess Control Part 1: Hardware Setting
Proceedings of the 9th IFAC Symposium Advances in Control Education The International Federation of Automatic Control Nizhny Novgorod, Russia, June 19-21, 2012
A Laboratory Experiment for Teaching Bioprocess Control Part 1: Hardware Setting Martin J. Wolf*, Vasil Ninov**, Heiko Babel***, Kimmo Hütter***, Ralph Staudt**, Winfried Storhas***, Essameddin Badreddin**
* University of Mannheim, Mannheim, Germany (e-mail: [email protected]) ** Heidelberg University, ZITI, Automation Lab, Heidelberg, Germany (e-mail: [email protected], [email protected], [email protected]) *** Mannheim University of Applied Sciences, Institute of Technical Microbiology, Mannheim, Germany (e-mail: [email protected], [email protected], [email protected]) Abstract: In this paper, the development of a laboratory experiment for teaching bioprocess control and automation is presented. In bioprocesses, chemical reactions are performed by living microorganisms, which do not only show increased metabolic needs and certain environmental susceptibilities, but also offer huge advantages like highly sophisticated capabilities for synthesizing complex protein products in a reliable, fast, cheap, and safe manner. Therefore also the plants to be used for bioreactions exhibit, compared to conventional process technology, additional demands e.g. with respect to sterility or aeration. The corresponding lecture intentionally focuses on modern geometric [and] model-based control. As the dynamics of a bioprocess depend on the characteristics of the microorganisms’ metabolism as well as on some mechanical properties of the bioreactor used, a bioprocess model constitutes of two parts: a kinetic model of the microorganisms, and a reactor model. This paper, as a first in a series of three, focuses on the latter, including the experimental procedures for identification of the four most important characteristics of a bioreactor, which are power input, homogenization, and gas and heat transfer. Considerations on scale-up of the reactor system increase comprehension of the model. A short description of the control system used, a portable low-cost host-target real-time computer system completes this experiments’ hardware documentation. One distinctive feature of the control system is its newly developed graphical user interface. This interface was programmed in Labview and relieves operation of the plant as it does not fall short of commercial ones in any way thus well-prepares students for a latter job. Concomitant papers will in detail describe the kinetic model and the didactic design of the instructions for the experiment. Keywords: Process Control, Biotechnology, Mathematical Model, Bioreactor / Fermenter, User Interface.
In order to enhance students’ learning processes, it is meaningful to take the different learning styles into account by addressing individual preferences for visual, auditory or tactile learning, facilitating various accesses to the subject content. Therefore, a teaching experiment for the PRAE lecture was developed and is still refined based on this approach, aiming in all students to be able to reconstruct the main course topics also in the experimental part. Accordingly, the selection and alignment of content focused the foundation of knowledge construction as well as fostering deep level understanding of the relevant material. The selfimposed practical relevance of the experiment has a preliminary function, framing the goal to develop a complete fermentation running on industrial devices, and not to just heating up some water as an abstraction of a real process.
INTRODUCTION With the 1998 Sorbonne Declaration the so-called Bologna Process was initialized, a reform with a tremendous impact on higher education. Aiming for the creation of a unified European higher education area, it has resulted in numerous reforms in e.g. the German higher education system: The transition from the old German “Diplom” [master-level] degree to the B.A./M.A. degree system, accompanied by addressing and fostering relevant skills and competences, were the basis to modernize curricula. For instance, at the Mannheim University of Applied Sciences the focus of the biotechnology master curriculum was slightly moved towards the “technology” aspect, resulting in the introduction of a new mandatory 4-hours/week class on process automation (PRAE; with E denoting English as the teaching language). In this context, the first author was given the opportunity to create, develop and teach 2 hours per week of this PRAE lecture, starting with the beginning of this master’s programme in summer term 2010, and being offered every semester since. 978-3-902823-01-4/12/$20.00 © 2012 IFAC
The first author’s part of the PRAE lecture follows a dual focus: First, to teach the students how control engineers “think” by learning central facts, principles, and concepts and by solving typical control problems (with, of course, reduced mathematical complexity): Mutual understanding improves 402
10.3182/20120619-3-RU-2024.00033
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
communication between different disciplines. Second, to equip the students with a broad overview over the whole field of control (profound factual knowledge), including classical PID control as well as modern model-based control, mathematical systems analysis, controller synthesis, and implementation using Simulink as graphical programming language.
a 19” rack, connected via a backplane. All signals on the backplane are 0-10V voltage signals and are well documented, such that continuing using the measurement amplifiers while replacing the controllers with a freely programmable real-time computer, see section 1.3, is easily possible. The complete fermenter unit’s dimensions are 35 x 47 x 150 cm; the weight is (approx.) 35 kg. The complete fermenter system is portable; its glass vessel is sterilized in the autoclave. The fermenter unit requires only four external supplies: Electrical power (230V), “water in”, “water out”, and “compressed air in”.
For teaching model-based control, one needs – of course – a model of the respective process. For this, two independent parts are equally important, as both influence the overall dynamics of the bioprocess: The characteristic metabolic properties of the microorganisms grown (the “kinetic model”), and the physical and mechanical characteristics of the fermenter vessel in which the reaction takes place (the “reactor model”). Both model components interact, as e.g. the transport of heat, produced by the microorganisms, out of the reactor and the material transport (e.g. oxygen) into and its dispersion inside the reactor both depend on reactor properties, and influence biological growth. This paper, the first in a series of three, therefore completely describes the hardware used for the teaching experiment, so to say: the reactor model – starting with mechanical setup and mathematical models of this hardware (and, as a surplus, calculations on scale-up of the whole process based on dimensions analysis), over the complete control system including electrical wiring up to the development of the graphical operator’s interface. Aligned with this, accompanying and upcoming papers will contain detailed descriptions of the bioprocess (a Vibrio natriegens fermentation), of the kinetic model, of the complete simulation environment, of the teaching concept of the entire experiment, and of the experiments’ instructions. 1. BIOREACTOR SYSTEM The core component of every bioprocess (“microbial reaction”) is the bioreactor, also called fermenter. It provides well-defined bounds for the reaction, reproducibility through controlled internal conditions (e.g. homogenization) and controlled interactions with the environment (energy and material transport); it also protects the reaction against critical external distortions like biological contamination.
Fig. 1. INFORS ISF-100 Fermenter.
1.1 Mechanical Setup
1.2 Reactor Model
For this experiment, a 5-liter glass fermenter IFS-100 (see Fig. 1) manufactured by Infors (Bottmingen, Switzerland) approximately in 1995 is used. This fermenter is equipped with a 2-discs blade-stirrer with baffles cage and is normally operated with 2 liters reserved as head space. The stirrer shaft, incorporating an axial face seal, is agitated from below. The reactor is tempered through a double jacket; here the temperature as well as the flow of the tempering medium is controlled at an internally fixed setpoint (but can also be operated with external, adjustable devices). All plugs for sensors, filling etc. are located in the top cover. The reactor unit initially comes with three peristaltic pumps for e.g. acid, base, and feed. Electronic control units (measurement amplifiers and controllers) are realized as individual cards in
The reactor model includes power input, homogenization, gas transfer and heat transfer. In the following the experiments for the determination of the characteristics and the formulae describing them will be discussed. The empirical equations presented in this chapter are only valid for the considered fermenter, operated at stirrer speeds of 400-1000 min 1 (below: overflow of agitator, above: too high shear forces) and an aeration rate between 0.5 min 1 and 2 min 1 . For each experiment wide-spread empirical equations were tested to describe the experimental data (Babel, 2011). Additionally the data was fit with polynomials of diverse degrees. To compare the goodness of fit the residual sum of squares (RSS) was calculated. It was found that the polynomials yielded a better fit and were subsequently used for the model. 403
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
1.2.1 Power Input
rates of 0.5, 1, 1.5 and 2 min 1 . To compare different mixing characteristics the mixing time 95% is calculated, which is the time the system needs in seconds to reach a mixing quality of M ( 95% ) 95% . It was found, that the mixing time 95% depends on the stirrer speed and the aeration rate and can best be described by (5), wherein the stirrer speed n and the aeration rate q are in min 1 , with RSS = 14.47.
The power input into the medium, i.e. the dissipative energy added, is necessary for homogenization and to facilitate gas transfer, but can also be considered as an unwanted source of heat. It is composed of two major parts: First, the pneumatic power input in Watt, approximately given by (Storhas, 1994) PG VG L g H
(1)
95% (3.15 103 n3 6.87 n 2 4.34 103 n 7.52 104 )
where VG is the gas flow in m3 /s , L is the density of the 3
(4.44 106 q 3 2.41 105 q 2 3.82 105 q 3.87 105 )
2
medium in kg/m , g is the gravity of earth in m/s and H is the height of the liquid in the fermenter in meter. The other part is the dissipative energy added by the stirrer into the medium, which can be expressed by (Bates et al., 1963) PR Ne L n d 3
5 R
1.2.3 Gas transfer The most important gas transfer that takes place in aerobic fermentation processes is the transfer of oxygen from the gas phase into the medium. The oxygen is then consumed by the microorganisms – this links/relates the reactor model to the model for kinetic growth of the microorganisms. As a model for gas transfer in the fermentation process, the so called two film theory is used. This model yields the following expression for the O2 transfer rate (Schügerl, 1991)
(2)
Here Ne is the Newton number, n the stirrer speed in s -1 , and d R the diameter of the stirrer in meter. The Newton number generally relates the resistance force to the inertia force and is a characteristic of the reactor and stirrer geometry. In the case of a bioreaction the Newton number strongly depends on the stirrer speed n and to a smaller extend on the aeration rate q (both in min 1 ). The Newton number was determined by measuring the torque at varying stirrer speeds in the range of 100-1000 min 1 and aeration rates of 0-3 min 1 . The torque was measured for at least 3 minutes and the average was used for subsequent calculations. It was assumed, that the viscosity of the medium is the same as the viscosity of water at 310 K and does not change during the process. Then, with a RSS of 5.8, it holds for the Newton number Ne : Ne (108 n3 3105 n 2 2.68 103 n 12.39) (2.5 10 1 q 2 4.27 10 1 q 5.66) 0.166
(6)
Here OTR is the oxygen transfer rate [ mol/(m3 s) ], k L a the 1
volumetric oxygen transfer coefficient [ s ], cO 2 [ mol/m3 ] the concentration of oxygen in the medium, pO 2 the partial pressure of oxygen [ Pa ], and H is Henry’s coefficient [ Pa m3 /mol ]. The volumetric oxygen transfer coefficient was than determined by recording a saturation curve of the oxygen concentration in an oxygen depleted medium. One measurement lasted for about two minutes. The same range of stirrer speed and aeration rates was used as in the experiments for the determination of the mixing quality. It was found that, with a RSS of 7.83105 , the volumetric oxygen transfer rate can best be described by:
(3)
The assumption of an isotropic reaction space, yielding in the applicability of ordinary instead of partial differential equations, is only valid if the system is well mixed. This means that transport processes in the reaction space need to be faster than the biochemical reactions. To assess the validity of this assumption, and as a characteristic for homogenization, the mixing quality was measured. This is given by (Storhas, 1994) c0 c(t ) c0 c
dcO 2 p k L a O 2 cO 2 dt H
OTR
1.2.2 Homogenization
M (t )
(5)
k L a (2.31 102 n3 47.4 n 2 2.46 104 n 5.33)
(1.33109 q 3 3.94 109 q 2 1.76 109 q 1.01108 )
(7)
1.2.4 Heat transfer
The heat transfer directly influences the reaction temperature; to describe the heat transfer, the thermal energy in the reactor jacket and in the medium was described by ordinary differential equations (Demtröder, 2008). In an integral form the thermal energy Q in Joule in the reactor jacket can be described by:
(4)
wherein c0 is the initial and c is the final concentration in
m QJ Q JL dt cH 2 O m TTemp mJ cH 2 O m J TJ ,0
mol/m3 . The mixing quality was assessed by measuring the pH after addition of hydrochloric solution. This results in a drop of the pH which was recorded. A typical measurement lasted around 20 seconds. The measurement took place at stirrer speeds of 400, 600, 800 and 1000 min 1 and aeration
QJ
t
t 0
404
(8)
9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
wherein cH 2O is the heat capacity of water in J/(kg K) , m is the mass flow through the reactor jacket in kg/s , TTemp is
1.2.5 Scale-Up Considerations Improve Comprehension Nonetheless, in science interest lies not only on the characterization of a certain bioreactor-system, but also on transfering this data to other reactors with different scales, e.g. from laboratory to production scale. Different dimensiondependent behaviors of the system’s properties due to scale, like the faster increase of volumes compared to surfaces, require a systematic approach to acquire reliable data for the scale-up. A powerful tool to do so is dimensional analysis (Zlokarnik, 2005). Dimensional analysis provides valuable information about the parameters’ relevancies and their dependencies from each other. Based on this knowledge, experiments can be planned and performed more efficiently and be supplemented with a stringent evaluation of the gained results. As a consequence high accuracy parameter and performance predictions for different plant scales can be achieved. Especially the understanding of systems behavior and the capacity to reduce the number of experiments to conduct, provided by dimensional analysis, should encourage universities to lecture this broadly applicable technique in theory and practice; hence it is included in this teaching experiment.
the temperature of the water flowing into the jacket in K , TJ ,0 is the initial temperature in the jacket in K , mJ is the mass of the water in the jacket in kg and Q JL is the flow of thermal energy across the reactor wall in Watt. Similarly, it holds for the thermal energy in the medium QL : QL
t
t 0
P
R
PG Q JL dt cH 2O mL TL ,0
(9)
wherein mL is the mass of the medium, and TL ,0 is the initial temperature of the medium. In this integral the heat production of the microorganisms is not considered, but if known it could be easily added and would represent another link to the kinetic model. From the heat energy the respective temperatures can be calculated by (Demtröder, 2008): T
Q cm
(10)
herein T denotes the temperature in K, Q denotes the thermal energy in J, c is the specific heat capacity of the object in J/(kg K) and m is its mass in kg. After calculating the temperature in the reactor and in the jacket with (10), the heat transfer across the reactor wall can be calculated by (Demtröder, 2008): Q JL k A (TJ - TL )
The essence of dimensional analysis is to arrange the parameters of a considered problem in such proportions, that they result as dimensionless numbers. As long as these so called pi-quantities are constant, the system behaves the same in any scale. Otherwise, functional dependencies of piquantities can be investigated by varying a single pi-quantity, e.g. the relationship between the Reynolds and the Newton number of stirrers is a popular example. The basic steps are as follows:
(11)
here k denotes the heat transfer coefficient in W/(m 2 K) , A is the interphase area of heat transfer in m 2 , TJ and TL are the temperature in K in the jacket and the medium respectively. With (11), the temperature of the medium can finally be calculated.
Step 1
List all relevant substance- and process-parameters as well as natural constants
Step 2
Perform a matrix transformation to obtain the piquantities
The heat transfer coefficient k was determined by heating up the medium with an external thermostat at a constant temperature and flow rate. The measurement was performed until the temperature inside the reactor reached the vicinity of the thermostat’s temperature, which lasted up to 1 hour. The heat transfer coefficient was determined at jacket flow rates of 10 and 24 dm3 /min , the stirrer speed and aeration rate were varied in the same range of values as in the previous experiments. The above equations from the literature yielded a poor fit. It was, however, empirically found that k depends on the stirrer speed n , the aeration rate q , and the flow rate of water in the jacket V in the following way:
Step 3
Interpret the pi-quantities
Step 4
Perform the consequent experiments
In the context of this teaching experiment theoretical scale-up investigation using dimensional analysis were fulfilled, including volumetric oxygen transfer coefficient kLa, heat-up time, and mixing time (Hütter, 2012). In the following example only the kLa analysis will be discussed due to comprehensive reasons. Step 1: For the kLa-value in a geometrically similar continuously stirred reactor, i.e. having a uniform shape at different scales, the following parameters are of relevance: volumetric power input P/V, superficial gas velocity ug, acceleration due to gravity g, dynamic viscosity η, liquid density ρ and the diffusion coefficient D. Since the different coalescence phenomena are not completely understood at this point, they are, as a didactic simplification, disregarded.
J
k 1/ (1.14 102 0.865 / (68.1 0.377 VJ ) 0.975 / ((1.67 103 0.265 n) 2
(12)
2
(9.4310 1.2110 q)))
wherein n and q are in the unit min 1 , VJ is in dm3 /min ,
Step 2: A dimensional matrix is created subsequently, which contains the parameters as columns and their dimensions’ potencies in the respective rows (see Table 1). The core
3
and the calculated residual sum of squares is 3.8 10 .
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9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
Thus, according to equation (16) and (17) it appears that the influence on n by µ is directly (~µ-2/3), and additionally indirectly by having an impact on the Newton number (~(NeL/NeP )1/3). Numerical comparison between the effects of the direct and the indirect influences on the stirrer speed proves the indirect influence to be insignificant (data not shown). Thus, to adjust the stirrer speed according to scale, equation (16) can finally be reduced to:
matrix, which preferably consists of invariable parameters, is then transformed into an identity matrix. The resulting residue matrix yields the pi-quantities. To extract the piquantities, each column-parameter of the residue matrix has to be divided by the corresponding core-matrix-parameters, having the indicated exponents, e.g. to obtain Π1 the kLa must be divided by ρ with the exponent 1/3, by η with the exponent -1/3, and by g with the exponent 2/3. All extracted pi-quantities are (kinematic viscosity ν = η /ρ): 1/3
1/3
Π1 ≡ kLa · η / (ρ
2/3
nP = nE µ-2/3
2 1/3
· g ) = kLa · (ν / g )
Π2 ≡ P/V / (ρ2/3 · η1/3 · g4/3) = P/V / (ρ · (ν · g4)1/3) Π3 ≡ ug · ρ1/3 / (η1/3 · g1/3) = ug / (ν · g)1/3
Table 1. Dimensional matrix transformation. Subdivided in core matrix (top-left), residual matrix (top-right), and residual matrix after transformation (bottom-right with corresponding core-parameters being reindicated on the right).
(13)
Π4 ≡ D · ρ / η = D / ν Step 3: Assuming the diffusion coefficient D to be constant, Π2, Π3, and Π4 can be kept constant by controlling P/V and ug on a non-varying level, since they are the only scale dependent parameters. As a consequence Π1 including the kLa will be unchanged as well. In conclusion, independently from scale the kLa stays idem if both, the volumetric power input P/V and the superficial gas velocity ug remain constant. This finding is consistent with the broadly used empirical equation to characterize the kLa-value kLa = K · (P/V)A · ugB
(18)
ρ
η
g
kLa
P/V
ug
D
M
1
1
0
0
1
0
0
L
-3
-1
1
0
-1
1
2
T
0
-1
-2
1
-3
-1
-1
M+T+2Z
1
0
0
1/3
2/3
-1/3
-1
ρ
(3M+L–Z)/2
0
1
0
-1/3
1/3
1/3
1
Η
(3M+L+2T)/3 = Z
0
0
1
2/3
4/3
1/3
0
g
(14) Step 4: Measurements of the kLa-value in geometrically similar reactors of different scales could provide validation of the theoretically obtained up-scaling rules for stirring and the aeration (not performed).
wherein K, A, and B represent constants (Storhas, 1994). To achieve a constant ug the aeration rate q [vvm] has to be adjusted according to scale. By considering ug to be the volumetric gas flow of the reactor divided by its cross sectional area AR following relationship can be obtained: 3
1.3 Control System
2
ug = q · V / AR ~ q · L / L = q · L → qP = qE · LE / LP = qE / µ
As programmable real-time control-system a host-target computer (Wolf, 2009) is used, running the real-time operating system QNX as target- and Windows as host operating system. Programming is done using Simulink together with Mathworks’ the Real-Time Workshop (now called “Simulink Coder”). The graphical user interface for controlling the fermentation is realized by Labview. The Labview Add-on “Simulation Interface Toolkit” (SIT) allows controlling a Simulink model via the Labview graphical interface. Labview, Simulink and the SIT server run on the host computer without real-time requirements. Reference values are entered from the user at the Labview GUI and are sent from Labview to the SIT server via TCP/IP. The SIT server communicates these values to Simulink; for this, only a block enabling Simulink to read from the SIT server has to be inserted into the Simulink model, nothing else has to be changed. For data acquisition two S-626 PCI DAQ boards from Sensoray, Inc. are used. Connections are made directly to the backplane of the reactor electronics unit. For automation of the reactor, using the following analog signals is sufficient: Aeration (“flow”), temperature, stirrer speed, pO2, and pH. If the number of analog-I/O provided by these cards is insufficient, the controller software can either be distributed among multiple target computers using Opal-RT’s RT-Lab Software (as the number of S-626 boards per
(15)
with V being the volume of the reactor, L a characteristic length and µ = LP / LE the scale-up factor. The indices E and P denote the experimental and the production scale. However, according to Π2 the attainment of a constant kLa requires another question to be answered: How has the major power input, i.e. the stirrer speed, to be adjusted in order to achieve a constant volumetric power input? Since a constant ug leads, independently from scale, to a constant pneumatic power input per volume, only the power input by stirring has to be considered. According to equation (2) the following relationship can be obtained for the stirrer speed n: P/V ~ NeE · nE3 · LE2 = NeP · nP3 · LP2 → nP = nE · µ-2/3 · (NeE / NeP )1/3
(16)
Thereby, the Newton number itself is with aeration a function of scale and can, for either scale, be calculated according to the empirical equation (Neo = non-aerated Ne; dR = reactor diameter): Ne = Neo · (1 + 490 · ug · (g · dR)-1/2 )-1/3
(17)
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9th IFAC Symposium Advances in Control Education Nizhny Novgorod, Russia, June 19-21, 2012
A history-function stores selected values in an “.lvm-file”. All values are stored both, periodically after an adjustable time interval, and whenever the value changes. This function is realized using the Labview function “write to measurement file”, and local variables.
computer is limited to two), and/or additional DAQ-boards from National Instruments, e.g. PCI-6229 or PCIe-6323 cards, could be used within the one target computer. In the latter case the data handling for those cards is done by Labview (an additional Labview „Real-Time Module“ is needed), and this data can also be provided to Simulink via Labview’s SIT server.
A “predefined values” functionality allows the user to start and/or operate the application using reasonable (initial) values. For the values initialization, a sequence diagram is used. The realization uses the predefined Labview functions “read from measurement file”, “index array”, “constant”, and local variables. The selection of these "predefined values" modes is made using radio button. Some modes such as “sterilization” and “fermentation” are already defined and implemented as single buttons. In this way, even the appearance of the GUI can be adjusted accordingly (e.g. displayed temperature ranges etc.). Some other properties such as "parameter hiding" can protect parameters from being accidentally set by the user in certain modes of operation; this is for example used for controlling the pump (to prevent immediate direction switches etc.).
1.4 Operator Interface The user interface for controlling the fermentation, see Fig. 2, is implemented using Labview (Ninov, 2011). The following parameter- or state variables’ values can be displayed by or set (reference values) via the user interface: Temperature, stirrer speed, pH, oxygen partial pressure pO2, pressure in the reactor, valves positions, concentrations of biomass, substrate, and synthesized product, pumps directions and velocities, and filling level. Additionally, the user interface shows a pictorial display of the most important values: At a glance the operator can see if the stirrer is in motion (animated picture), if the reactor is aerated (bubbles), and in which temperature range the reactor contents is in (dark blue, light blue, light red, dark red).
ACKNOWLEDGMENTS The authors would like to thank Silke Weiß, HDZ, Freiburg University, for annotations improving this manuscript as well as Dipl.-Ing. (FH) Michael Reuter, Mannheim University of Applied Sciences, and Dipl.-Ing. Frank Stolzenberger, Heidelberg University, for professional technical support. REFERENCES Babel, H. (2011). Modeling the dynamics of a stirred-tank bioreactor for a Vibrio natriegens fermentation. Lab Project, Mannheim University of Applied Sciences. Bates, R., Fondy, P. & Corpstein, R. (1963). Examination of Some Geometric Parameters of Impeller Power. Industrial & Engineering Chemistry Process Design and Development, 2, 310-314. Demtröder, W. (2008). Experimentalphysik 1: Mechanik und Wärme, Springer, Berlin/Heidelberg/New York. Hütter, K. (2012). Scale-Up: Calculations and Simulinkbased Modelling of Thermal Sterilization. Lab Project, Mannheim University of Applied Sciences. Ninov, V. (2011). Design und Implementierung einer Bedienerschnittstelle in Labview für die Simulinkbasierte Echtzeit-Regelung eines Bioreaktors. Bachelor’s Thesis, Heidelberg University. Schügerl, K. (1991). Bioreaktoren und ihre Charakterisierung, Salle, Frankfurt am Main. Storhas, W. (1994). Bioreaktoren und periphere Einrichtungen, Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden. Wolf, M., Staudt, R., Gambier, A., Storhas, W., Badreddin, E. (2009). On setting-up a mobile low-cost real-time control system for research and teaching with application to bioprocess pH control. IEEE Conference on Control Applications (CCA), 1631-1636, St. Petersburg, Russia. Zlokarnik, M. (2005) Scale-Up – Modellübertragung in der Verfahrenstechnik. 2. Auflage. 2005. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Fig. 2. Labview Operator Interface.
The user interface contains an overview tab where the most important values are displayed numerically and can coarsely and quickly be set. All functions on the overview tab are also implemented on separate single tabs providing additional (e.g. for threshold values) and more precise elements for setting these values. Single functions are defined as subroutines. A subroutine in Labview is called SubIV. The SubIV is used as an object in the overview window. The link between the overview window parameters and the single tabs parameters is realized using local variables. The user interface is multilingual. The respective implementation is realized using the predefined Labview functions “radio buttons”, “text-ring”, “index array”, “property node”, “constant”, and local variables. Each label of a functional element of the user interface is a text-ring where the respective translations and the associated indexes are entered. Multi-language functionality of the tabs’ headings, though, can not be implemented using text rings; they are addressed using index array and property node of text-rings instead.
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A Laboratory Experiment for Teaching Bioprocess Control Part 1: Hardware Setting Martin J. Wolf*, Vasil Ninov**, Heiko Babel***, Kimmo Hütter***, Ralph Staudt**, Winfried Storhas***, Essameddin Badreddin**
* University of Mannheim, Mannheim, Germany (e-mail: [email protected]) ** Heidelberg University, ZITI, Automation Lab, Heidelberg, Germany (e-mail: [email protected], [email protected], [email protected]) *** Mannheim University of Applied Sciences, Institute of Technical Microbiology, Mannheim, Germany (e-mail: [email protected], [email protected], [email protected]) Abstract: In this paper, the development of a laboratory experiment for teaching bioprocess control and automation is presented. In bioprocesses, chemical reactions are performed by living microorganisms, which do not only show increased metabolic needs and certain environmental susceptibilities, but also offer huge advantages like highly sophisticated capabilities for synthesizing complex protein products in a reliable, fast, cheap, and safe manner. Therefore also the plants to be used for bioreactions exhibit, compared to conventional process technology, additional demands e.g. with respect to sterility or aeration. The corresponding lecture intentionally focuses on modern geometric [and] model-based control. As the dynamics of a bioprocess depend on the characteristics of the microorganisms’ metabolism as well as on some mechanical properties of the bioreactor used, a bioprocess model constitutes of two parts: a kinetic model of the microorganisms, and a reactor model. This paper, as a first in a series of three, focuses on the latter, including the experimental procedures for identification of the four most important characteristics of a bioreactor, which are power input, homogenization, and gas and heat transfer. Considerations on scale-up of the reactor system increase comprehension of the model. A short description of the control system used, a portable low-cost host-target real-time computer system completes this experiments’ hardware documentation. One distinctive feature of the control system is its newly developed graphical user interface. This interface was programmed in Labview and relieves operation of the plant as it does not fall short of commercial ones in any way thus well-prepares students for a latter job. Concomitant papers will in detail describe the kinetic model and the didactic design of the instructions for the experiment. Keywords: Process Control, Biotechnology, Mathematical Model, Bioreactor / Fermenter, User Interface.
In order to enhance students’ learning processes, it is meaningful to take the different learning styles into account by addressing individual preferences for visual, auditory or tactile learning, facilitating various accesses to the subject content. Therefore, a teaching experiment for the PRAE lecture was developed and is still refined based on this approach, aiming in all students to be able to reconstruct the main course topics also in the experimental part. Accordingly, the selection and alignment of content focused the foundation of knowledge construction as well as fostering deep level understanding of the relevant material. The selfimposed practical relevance of the experiment has a preliminary function, framing the goal to develop a complete fermentation running on industrial devices, and not to just heating up some water as an abstraction of a real process.
INTRODUCTION With the 1998 Sorbonne Declaration the so-called Bologna Process was initialized, a reform with a tremendous impact on higher education. Aiming for the creation of a unified European higher education area, it has resulted in numerous reforms in e.g. the German higher education system: The transition from the old German “Diplom” [master-level] degree to the B.A./M.A. degree system, accompanied by addressing and fostering relevant skills and competences, were the basis to modernize curricula. For instance, at the Mannheim University of Applied Sciences the focus of the biotechnology master curriculum was slightly moved towards the “technology” aspect, resulting in the introduction of a new mandatory 4-hours/week class on process automation (PRAE; with E denoting English as the teaching language). In this context, the first author was given the opportunity to create, develop and teach 2 hours per week of this PRAE lecture, starting with the beginning of this master’s programme in summer term 2010, and being offered every semester since. 978-3-902823-01-4/12/$20.00 © 2012 IFAC
The first author’s part of the PRAE lecture follows a dual focus: First, to teach the students how control engineers “think” by learning central facts, principles, and concepts and by solving typical control problems (with, of course, reduced mathematical complexity): Mutual understanding improves 402
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communication between different disciplines. Second, to equip the students with a broad overview over the whole field of control (profound factual knowledge), including classical PID control as well as modern model-based control, mathematical systems analysis, controller synthesis, and implementation using Simulink as graphical programming language.
a 19” rack, connected via a backplane. All signals on the backplane are 0-10V voltage signals and are well documented, such that continuing using the measurement amplifiers while replacing the controllers with a freely programmable real-time computer, see section 1.3, is easily possible. The complete fermenter unit’s dimensions are 35 x 47 x 150 cm; the weight is (approx.) 35 kg. The complete fermenter system is portable; its glass vessel is sterilized in the autoclave. The fermenter unit requires only four external supplies: Electrical power (230V), “water in”, “water out”, and “compressed air in”.
For teaching model-based control, one needs – of course – a model of the respective process. For this, two independent parts are equally important, as both influence the overall dynamics of the bioprocess: The characteristic metabolic properties of the microorganisms grown (the “kinetic model”), and the physical and mechanical characteristics of the fermenter vessel in which the reaction takes place (the “reactor model”). Both model components interact, as e.g. the transport of heat, produced by the microorganisms, out of the reactor and the material transport (e.g. oxygen) into and its dispersion inside the reactor both depend on reactor properties, and influence biological growth. This paper, the first in a series of three, therefore completely describes the hardware used for the teaching experiment, so to say: the reactor model – starting with mechanical setup and mathematical models of this hardware (and, as a surplus, calculations on scale-up of the whole process based on dimensions analysis), over the complete control system including electrical wiring up to the development of the graphical operator’s interface. Aligned with this, accompanying and upcoming papers will contain detailed descriptions of the bioprocess (a Vibrio natriegens fermentation), of the kinetic model, of the complete simulation environment, of the teaching concept of the entire experiment, and of the experiments’ instructions. 1. BIOREACTOR SYSTEM The core component of every bioprocess (“microbial reaction”) is the bioreactor, also called fermenter. It provides well-defined bounds for the reaction, reproducibility through controlled internal conditions (e.g. homogenization) and controlled interactions with the environment (energy and material transport); it also protects the reaction against critical external distortions like biological contamination.
Fig. 1. INFORS ISF-100 Fermenter.
1.1 Mechanical Setup
1.2 Reactor Model
For this experiment, a 5-liter glass fermenter IFS-100 (see Fig. 1) manufactured by Infors (Bottmingen, Switzerland) approximately in 1995 is used. This fermenter is equipped with a 2-discs blade-stirrer with baffles cage and is normally operated with 2 liters reserved as head space. The stirrer shaft, incorporating an axial face seal, is agitated from below. The reactor is tempered through a double jacket; here the temperature as well as the flow of the tempering medium is controlled at an internally fixed setpoint (but can also be operated with external, adjustable devices). All plugs for sensors, filling etc. are located in the top cover. The reactor unit initially comes with three peristaltic pumps for e.g. acid, base, and feed. Electronic control units (measurement amplifiers and controllers) are realized as individual cards in
The reactor model includes power input, homogenization, gas transfer and heat transfer. In the following the experiments for the determination of the characteristics and the formulae describing them will be discussed. The empirical equations presented in this chapter are only valid for the considered fermenter, operated at stirrer speeds of 400-1000 min 1 (below: overflow of agitator, above: too high shear forces) and an aeration rate between 0.5 min 1 and 2 min 1 . For each experiment wide-spread empirical equations were tested to describe the experimental data (Babel, 2011). Additionally the data was fit with polynomials of diverse degrees. To compare the goodness of fit the residual sum of squares (RSS) was calculated. It was found that the polynomials yielded a better fit and were subsequently used for the model. 403
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1.2.1 Power Input
rates of 0.5, 1, 1.5 and 2 min 1 . To compare different mixing characteristics the mixing time 95% is calculated, which is the time the system needs in seconds to reach a mixing quality of M ( 95% ) 95% . It was found, that the mixing time 95% depends on the stirrer speed and the aeration rate and can best be described by (5), wherein the stirrer speed n and the aeration rate q are in min 1 , with RSS = 14.47.
The power input into the medium, i.e. the dissipative energy added, is necessary for homogenization and to facilitate gas transfer, but can also be considered as an unwanted source of heat. It is composed of two major parts: First, the pneumatic power input in Watt, approximately given by (Storhas, 1994) PG VG L g H
(1)
95% (3.15 103 n3 6.87 n 2 4.34 103 n 7.52 104 )
where VG is the gas flow in m3 /s , L is the density of the 3
(4.44 106 q 3 2.41 105 q 2 3.82 105 q 3.87 105 )
2
medium in kg/m , g is the gravity of earth in m/s and H is the height of the liquid in the fermenter in meter. The other part is the dissipative energy added by the stirrer into the medium, which can be expressed by (Bates et al., 1963) PR Ne L n d 3
5 R
1.2.3 Gas transfer The most important gas transfer that takes place in aerobic fermentation processes is the transfer of oxygen from the gas phase into the medium. The oxygen is then consumed by the microorganisms – this links/relates the reactor model to the model for kinetic growth of the microorganisms. As a model for gas transfer in the fermentation process, the so called two film theory is used. This model yields the following expression for the O2 transfer rate (Schügerl, 1991)
(2)
Here Ne is the Newton number, n the stirrer speed in s -1 , and d R the diameter of the stirrer in meter. The Newton number generally relates the resistance force to the inertia force and is a characteristic of the reactor and stirrer geometry. In the case of a bioreaction the Newton number strongly depends on the stirrer speed n and to a smaller extend on the aeration rate q (both in min 1 ). The Newton number was determined by measuring the torque at varying stirrer speeds in the range of 100-1000 min 1 and aeration rates of 0-3 min 1 . The torque was measured for at least 3 minutes and the average was used for subsequent calculations. It was assumed, that the viscosity of the medium is the same as the viscosity of water at 310 K and does not change during the process. Then, with a RSS of 5.8, it holds for the Newton number Ne : Ne (108 n3 3105 n 2 2.68 103 n 12.39) (2.5 10 1 q 2 4.27 10 1 q 5.66) 0.166
(6)
Here OTR is the oxygen transfer rate [ mol/(m3 s) ], k L a the 1
volumetric oxygen transfer coefficient [ s ], cO 2 [ mol/m3 ] the concentration of oxygen in the medium, pO 2 the partial pressure of oxygen [ Pa ], and H is Henry’s coefficient [ Pa m3 /mol ]. The volumetric oxygen transfer coefficient was than determined by recording a saturation curve of the oxygen concentration in an oxygen depleted medium. One measurement lasted for about two minutes. The same range of stirrer speed and aeration rates was used as in the experiments for the determination of the mixing quality. It was found that, with a RSS of 7.83105 , the volumetric oxygen transfer rate can best be described by:
(3)
The assumption of an isotropic reaction space, yielding in the applicability of ordinary instead of partial differential equations, is only valid if the system is well mixed. This means that transport processes in the reaction space need to be faster than the biochemical reactions. To assess the validity of this assumption, and as a characteristic for homogenization, the mixing quality was measured. This is given by (Storhas, 1994) c0 c(t ) c0 c
dcO 2 p k L a O 2 cO 2 dt H
OTR
1.2.2 Homogenization
M (t )
(5)
k L a (2.31 102 n3 47.4 n 2 2.46 104 n 5.33)
(1.33109 q 3 3.94 109 q 2 1.76 109 q 1.01108 )
(7)
1.2.4 Heat transfer
The heat transfer directly influences the reaction temperature; to describe the heat transfer, the thermal energy in the reactor jacket and in the medium was described by ordinary differential equations (Demtröder, 2008). In an integral form the thermal energy Q in Joule in the reactor jacket can be described by:
(4)
wherein c0 is the initial and c is the final concentration in
m QJ Q JL dt cH 2 O m TTemp mJ cH 2 O m J TJ ,0
mol/m3 . The mixing quality was assessed by measuring the pH after addition of hydrochloric solution. This results in a drop of the pH which was recorded. A typical measurement lasted around 20 seconds. The measurement took place at stirrer speeds of 400, 600, 800 and 1000 min 1 and aeration
QJ
t
t 0
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(8)
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wherein cH 2O is the heat capacity of water in J/(kg K) , m is the mass flow through the reactor jacket in kg/s , TTemp is
1.2.5 Scale-Up Considerations Improve Comprehension Nonetheless, in science interest lies not only on the characterization of a certain bioreactor-system, but also on transfering this data to other reactors with different scales, e.g. from laboratory to production scale. Different dimensiondependent behaviors of the system’s properties due to scale, like the faster increase of volumes compared to surfaces, require a systematic approach to acquire reliable data for the scale-up. A powerful tool to do so is dimensional analysis (Zlokarnik, 2005). Dimensional analysis provides valuable information about the parameters’ relevancies and their dependencies from each other. Based on this knowledge, experiments can be planned and performed more efficiently and be supplemented with a stringent evaluation of the gained results. As a consequence high accuracy parameter and performance predictions for different plant scales can be achieved. Especially the understanding of systems behavior and the capacity to reduce the number of experiments to conduct, provided by dimensional analysis, should encourage universities to lecture this broadly applicable technique in theory and practice; hence it is included in this teaching experiment.
the temperature of the water flowing into the jacket in K , TJ ,0 is the initial temperature in the jacket in K , mJ is the mass of the water in the jacket in kg and Q JL is the flow of thermal energy across the reactor wall in Watt. Similarly, it holds for the thermal energy in the medium QL : QL
t
t 0
P
R
PG Q JL dt cH 2O mL TL ,0
(9)
wherein mL is the mass of the medium, and TL ,0 is the initial temperature of the medium. In this integral the heat production of the microorganisms is not considered, but if known it could be easily added and would represent another link to the kinetic model. From the heat energy the respective temperatures can be calculated by (Demtröder, 2008): T
Q cm
(10)
herein T denotes the temperature in K, Q denotes the thermal energy in J, c is the specific heat capacity of the object in J/(kg K) and m is its mass in kg. After calculating the temperature in the reactor and in the jacket with (10), the heat transfer across the reactor wall can be calculated by (Demtröder, 2008): Q JL k A (TJ - TL )
The essence of dimensional analysis is to arrange the parameters of a considered problem in such proportions, that they result as dimensionless numbers. As long as these so called pi-quantities are constant, the system behaves the same in any scale. Otherwise, functional dependencies of piquantities can be investigated by varying a single pi-quantity, e.g. the relationship between the Reynolds and the Newton number of stirrers is a popular example. The basic steps are as follows:
(11)
here k denotes the heat transfer coefficient in W/(m 2 K) , A is the interphase area of heat transfer in m 2 , TJ and TL are the temperature in K in the jacket and the medium respectively. With (11), the temperature of the medium can finally be calculated.
Step 1
List all relevant substance- and process-parameters as well as natural constants
Step 2
Perform a matrix transformation to obtain the piquantities
The heat transfer coefficient k was determined by heating up the medium with an external thermostat at a constant temperature and flow rate. The measurement was performed until the temperature inside the reactor reached the vicinity of the thermostat’s temperature, which lasted up to 1 hour. The heat transfer coefficient was determined at jacket flow rates of 10 and 24 dm3 /min , the stirrer speed and aeration rate were varied in the same range of values as in the previous experiments. The above equations from the literature yielded a poor fit. It was, however, empirically found that k depends on the stirrer speed n , the aeration rate q , and the flow rate of water in the jacket V in the following way:
Step 3
Interpret the pi-quantities
Step 4
Perform the consequent experiments
In the context of this teaching experiment theoretical scale-up investigation using dimensional analysis were fulfilled, including volumetric oxygen transfer coefficient kLa, heat-up time, and mixing time (Hütter, 2012). In the following example only the kLa analysis will be discussed due to comprehensive reasons. Step 1: For the kLa-value in a geometrically similar continuously stirred reactor, i.e. having a uniform shape at different scales, the following parameters are of relevance: volumetric power input P/V, superficial gas velocity ug, acceleration due to gravity g, dynamic viscosity η, liquid density ρ and the diffusion coefficient D. Since the different coalescence phenomena are not completely understood at this point, they are, as a didactic simplification, disregarded.
J
k 1/ (1.14 102 0.865 / (68.1 0.377 VJ ) 0.975 / ((1.67 103 0.265 n) 2
(12)
2
(9.4310 1.2110 q)))
wherein n and q are in the unit min 1 , VJ is in dm3 /min ,
Step 2: A dimensional matrix is created subsequently, which contains the parameters as columns and their dimensions’ potencies in the respective rows (see Table 1). The core
3
and the calculated residual sum of squares is 3.8 10 .
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Thus, according to equation (16) and (17) it appears that the influence on n by µ is directly (~µ-2/3), and additionally indirectly by having an impact on the Newton number (~(NeL/NeP )1/3). Numerical comparison between the effects of the direct and the indirect influences on the stirrer speed proves the indirect influence to be insignificant (data not shown). Thus, to adjust the stirrer speed according to scale, equation (16) can finally be reduced to:
matrix, which preferably consists of invariable parameters, is then transformed into an identity matrix. The resulting residue matrix yields the pi-quantities. To extract the piquantities, each column-parameter of the residue matrix has to be divided by the corresponding core-matrix-parameters, having the indicated exponents, e.g. to obtain Π1 the kLa must be divided by ρ with the exponent 1/3, by η with the exponent -1/3, and by g with the exponent 2/3. All extracted pi-quantities are (kinematic viscosity ν = η /ρ): 1/3
1/3
Π1 ≡ kLa · η / (ρ
2/3
nP = nE µ-2/3
2 1/3
· g ) = kLa · (ν / g )
Π2 ≡ P/V / (ρ2/3 · η1/3 · g4/3) = P/V / (ρ · (ν · g4)1/3) Π3 ≡ ug · ρ1/3 / (η1/3 · g1/3) = ug / (ν · g)1/3
Table 1. Dimensional matrix transformation. Subdivided in core matrix (top-left), residual matrix (top-right), and residual matrix after transformation (bottom-right with corresponding core-parameters being reindicated on the right).
(13)
Π4 ≡ D · ρ / η = D / ν Step 3: Assuming the diffusion coefficient D to be constant, Π2, Π3, and Π4 can be kept constant by controlling P/V and ug on a non-varying level, since they are the only scale dependent parameters. As a consequence Π1 including the kLa will be unchanged as well. In conclusion, independently from scale the kLa stays idem if both, the volumetric power input P/V and the superficial gas velocity ug remain constant. This finding is consistent with the broadly used empirical equation to characterize the kLa-value kLa = K · (P/V)A · ugB
(18)
ρ
η
g
kLa
P/V
ug
D
M
1
1
0
0
1
0
0
L
-3
-1
1
0
-1
1
2
T
0
-1
-2
1
-3
-1
-1
M+T+2Z
1
0
0
1/3
2/3
-1/3
-1
ρ
(3M+L–Z)/2
0
1
0
-1/3
1/3
1/3
1
Η
(3M+L+2T)/3 = Z
0
0
1
2/3
4/3
1/3
0
g
(14) Step 4: Measurements of the kLa-value in geometrically similar reactors of different scales could provide validation of the theoretically obtained up-scaling rules for stirring and the aeration (not performed).
wherein K, A, and B represent constants (Storhas, 1994). To achieve a constant ug the aeration rate q [vvm] has to be adjusted according to scale. By considering ug to be the volumetric gas flow of the reactor divided by its cross sectional area AR following relationship can be obtained: 3
1.3 Control System
2
ug = q · V / AR ~ q · L / L = q · L → qP = qE · LE / LP = qE / µ
As programmable real-time control-system a host-target computer (Wolf, 2009) is used, running the real-time operating system QNX as target- and Windows as host operating system. Programming is done using Simulink together with Mathworks’ the Real-Time Workshop (now called “Simulink Coder”). The graphical user interface for controlling the fermentation is realized by Labview. The Labview Add-on “Simulation Interface Toolkit” (SIT) allows controlling a Simulink model via the Labview graphical interface. Labview, Simulink and the SIT server run on the host computer without real-time requirements. Reference values are entered from the user at the Labview GUI and are sent from Labview to the SIT server via TCP/IP. The SIT server communicates these values to Simulink; for this, only a block enabling Simulink to read from the SIT server has to be inserted into the Simulink model, nothing else has to be changed. For data acquisition two S-626 PCI DAQ boards from Sensoray, Inc. are used. Connections are made directly to the backplane of the reactor electronics unit. For automation of the reactor, using the following analog signals is sufficient: Aeration (“flow”), temperature, stirrer speed, pO2, and pH. If the number of analog-I/O provided by these cards is insufficient, the controller software can either be distributed among multiple target computers using Opal-RT’s RT-Lab Software (as the number of S-626 boards per
(15)
with V being the volume of the reactor, L a characteristic length and µ = LP / LE the scale-up factor. The indices E and P denote the experimental and the production scale. However, according to Π2 the attainment of a constant kLa requires another question to be answered: How has the major power input, i.e. the stirrer speed, to be adjusted in order to achieve a constant volumetric power input? Since a constant ug leads, independently from scale, to a constant pneumatic power input per volume, only the power input by stirring has to be considered. According to equation (2) the following relationship can be obtained for the stirrer speed n: P/V ~ NeE · nE3 · LE2 = NeP · nP3 · LP2 → nP = nE · µ-2/3 · (NeE / NeP )1/3
(16)
Thereby, the Newton number itself is with aeration a function of scale and can, for either scale, be calculated according to the empirical equation (Neo = non-aerated Ne; dR = reactor diameter): Ne = Neo · (1 + 490 · ug · (g · dR)-1/2 )-1/3
(17)
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A history-function stores selected values in an “.lvm-file”. All values are stored both, periodically after an adjustable time interval, and whenever the value changes. This function is realized using the Labview function “write to measurement file”, and local variables.
computer is limited to two), and/or additional DAQ-boards from National Instruments, e.g. PCI-6229 or PCIe-6323 cards, could be used within the one target computer. In the latter case the data handling for those cards is done by Labview (an additional Labview „Real-Time Module“ is needed), and this data can also be provided to Simulink via Labview’s SIT server.
A “predefined values” functionality allows the user to start and/or operate the application using reasonable (initial) values. For the values initialization, a sequence diagram is used. The realization uses the predefined Labview functions “read from measurement file”, “index array”, “constant”, and local variables. The selection of these "predefined values" modes is made using radio button. Some modes such as “sterilization” and “fermentation” are already defined and implemented as single buttons. In this way, even the appearance of the GUI can be adjusted accordingly (e.g. displayed temperature ranges etc.). Some other properties such as "parameter hiding" can protect parameters from being accidentally set by the user in certain modes of operation; this is for example used for controlling the pump (to prevent immediate direction switches etc.).
1.4 Operator Interface The user interface for controlling the fermentation, see Fig. 2, is implemented using Labview (Ninov, 2011). The following parameter- or state variables’ values can be displayed by or set (reference values) via the user interface: Temperature, stirrer speed, pH, oxygen partial pressure pO2, pressure in the reactor, valves positions, concentrations of biomass, substrate, and synthesized product, pumps directions and velocities, and filling level. Additionally, the user interface shows a pictorial display of the most important values: At a glance the operator can see if the stirrer is in motion (animated picture), if the reactor is aerated (bubbles), and in which temperature range the reactor contents is in (dark blue, light blue, light red, dark red).
ACKNOWLEDGMENTS The authors would like to thank Silke Weiß, HDZ, Freiburg University, for annotations improving this manuscript as well as Dipl.-Ing. (FH) Michael Reuter, Mannheim University of Applied Sciences, and Dipl.-Ing. Frank Stolzenberger, Heidelberg University, for professional technical support. REFERENCES Babel, H. (2011). Modeling the dynamics of a stirred-tank bioreactor for a Vibrio natriegens fermentation. Lab Project, Mannheim University of Applied Sciences. Bates, R., Fondy, P. & Corpstein, R. (1963). Examination of Some Geometric Parameters of Impeller Power. Industrial & Engineering Chemistry Process Design and Development, 2, 310-314. Demtröder, W. (2008). Experimentalphysik 1: Mechanik und Wärme, Springer, Berlin/Heidelberg/New York. Hütter, K. (2012). Scale-Up: Calculations and Simulinkbased Modelling of Thermal Sterilization. Lab Project, Mannheim University of Applied Sciences. Ninov, V. (2011). Design und Implementierung einer Bedienerschnittstelle in Labview für die Simulinkbasierte Echtzeit-Regelung eines Bioreaktors. Bachelor’s Thesis, Heidelberg University. Schügerl, K. (1991). Bioreaktoren und ihre Charakterisierung, Salle, Frankfurt am Main. Storhas, W. (1994). Bioreaktoren und periphere Einrichtungen, Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden. Wolf, M., Staudt, R., Gambier, A., Storhas, W., Badreddin, E. (2009). On setting-up a mobile low-cost real-time control system for research and teaching with application to bioprocess pH control. IEEE Conference on Control Applications (CCA), 1631-1636, St. Petersburg, Russia. Zlokarnik, M. (2005) Scale-Up – Modellübertragung in der Verfahrenstechnik. 2. Auflage. 2005. WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Fig. 2. Labview Operator Interface.
The user interface contains an overview tab where the most important values are displayed numerically and can coarsely and quickly be set. All functions on the overview tab are also implemented on separate single tabs providing additional (e.g. for threshold values) and more precise elements for setting these values. Single functions are defined as subroutines. A subroutine in Labview is called SubIV. The SubIV is used as an object in the overview window. The link between the overview window parameters and the single tabs parameters is realized using local variables. The user interface is multilingual. The respective implementation is realized using the predefined Labview functions “radio buttons”, “text-ring”, “index array”, “property node”, “constant”, and local variables. Each label of a functional element of the user interface is a text-ring where the respective translations and the associated indexes are entered. Multi-language functionality of the tabs’ headings, though, can not be implemented using text rings; they are addressed using index array and property node of text-rings instead.
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