Adaptive Control of Continuous Fermentation with Immobilized Yeasts Saccharomyces Cerevisiae BO 213

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1 Adaptive Control of Continuous Fermentation with Immobilized Yeasts Saccharomyces Cerevisiae BO 13 VLISLAVA LYUBNOVA 1, GORGI KOSTOV, ROSITSA DNKOVA, DSISLAVA PIRCHVA, MAYA IGNATOVA 1, MIHAIL ANGLOV 1 Department Bioengineering Institute of Systems ngineering and Robotics, Bulgarian Academy of Sciences Acad. G. Bonchev Str. Bl., Sofia 1113, BULGARIA v_lyubenova@iser.bas.bg; m_ignatova@iser.bas.bg Department Wine and Brewing Technology University of Food Technologies Maritza Boulevard 6, Plovdiv 4000 BULGARIA george_kostov@abv.bg; Abstract: In this paper, a new approach for adaptive linearizing control of continuous fermentations with mobilized yeasts Saccharomyces cerevisiae BO 13 was proposed. Three fermentations with three different procedures of cells mobilization were investigated. The switching from batch to continuous mode of cultivation was realized automatically in te when the ethanol production rate reaches its maxal value. This information was received by software sensor of ethanol production rate. The proposed control algorithm stabilizes the concentration of ethanol in the culture medium at value corresponding to maxal ethanol production rate. The sulation results are promising and could be verified in laboratory conditions. Key-Words: adaptive control, monitoring, alcohol fermentation, continuous process, mobilized yeast 1 Introduction The ethanol production is a complex technology, which outputs also a number of secondary products used in various industries. Different research concerning process efficiency increasing by technological refinement of fermentation systems were carried out. Systems with mobilized bio-catalysers with different character and supports nature were investigated and were compared with traditional fermentation processes with free cells. The comparison is done on the basis of microbial growth kinetics and the metabolite products accumulation in cultural medium. The systems with mobilized cells showed а number of advantages: high productivity, high operating stability, decreasing of final product purification costs etc. Kinetic models for continuous alcoholic fermentation with mobilized cells were developed. Another contemporary direction for biotechnological processes efficiency increasing is introduction of on-line control of the main biological variables. Such control schemes can be applied only for continuous or fed-batch cultivation reges [1]. During the last years, some results related to monitoring and control of a process for ethanol production from recombinant strains S.cerevisiae were published [3]. The proposed new control strategies and its investigations showed increasing of process production and decreasing of production costs. In this paper, a new control strategy for adaptive stabilization of ethanol concentration in the culture is proposed. The novelty consists of on-line monitoring of ethanol production rate, recognizing its maxal value and maintenance this physiological state as long as possible. This strategy is investigated for continuous fermentations with mobilized yeasts Saccharomyces cerevisiae BO 13. Materials and Methods.1 Materials, media and microorganism For the mobilized processes, the yeast Saccharomyces cerevisiae BO 13 was used. The ISBN:

2 culture medium has the following composition (g/dm 3 ): glucose ; (NH 4 ) SO 4 ; KH PO g/l, MgSO 4 7Н О - 0.5; yeast extract - 1. All the culture media were sterilized in autoclave at 11 С for 0 min. The last part of the beads is suspended in 0. % solution of Na-alginates for 60 minutes for additional ionic coating. (experent 3). The detailed information about the mobilization procedure could be found in [9]. xperental variants and yeast mobilization Thirteen different batch fermentations were made for the a of investigations - one with mobilized cells entrapped into a porous matrix (Ca-alginate) and 1 fermentation using microencapsulated yeast cells. Microcapsules with solid core were obtained by variation of the te for chitosan coating first 4 fermentations. very of these variants stayed in sodium citrate solution for constructing microcapsules with liquid core - other 4 fermentations. Microcapsules with liquid core were stabilized by another ionic polymer coating the last 4 fermentations. The fermentation activity of all mobilized cells was compared to the fermentation activity of free cells [5]. As a result the three variants with the highest fermentation activity and mechanical stability were selected and discussed in this work. A detailed description of the mobilization procedure and the fermentation condition is done below. For the mobilization, 5 g of ALGOGL 601 (Degussa) were mixed into 00 ml of water and the mixture (Alginate solution) was stirred during 4 hours (te for reaching a viscous solution). Then the solution was sterilized in 1000 W microwave for 1 minute. The alginate solution was mixed with the yeast (previously rehydrated), then this mixture was fed into a CaCl solution (5 g into 50 ml of distilled water) slowly and by using a nozzle. In this way small drops of alginate fall down into the CaCl solution and small beads retaining the yeast was formed (experent 1). During the mobilization, the agitation of the suspension was continuous, for avoiding cells sedentation. With part of the beads prepared in such way an ionic polymeric coating was done. For this purpose the beads from Ca alginates are suspended for 60 minutes in 0.% chitosan solution (prepared with 1% solution acetic acid). After that the pearls are washed up with saline solution and place in 0.5 M solution of Na-citrate 30 minutes. In such way a liquid core is received. Then a part of the beads are washed up again to be used for fermentation. (experent )..3 Batch fermentation The used bioreactor (Fig.1) is a dm 3 volume glass cylinder (1.7 dm 3 working volume), equipped with a six-blade turbine stirrer and four repulse devises. The installation includes sensors for monitoring and controlling ph, temperature and the stirrer spеed. The control and the regulation of the temperature and ph were carried out by Sartorius Biostat B plus system. The ph is adjusted at 4.5 by feeding a solution of 0% KOH through a peristaltic pump. The temperature in all the experents was kept at 8±0.1 С. The ph control is switched on after reaching the mentioned temperature. The accuracy of the ph control system is ±0.05. The bioreactor was sterilized during 4 h, using a 0.3% solution of neomycin. After that, the bioreactor was washed with sterilized water. At the beginning of the process and every three hours, samples for ethanol, glucose and biomass determination were taken. All processes with 100 g mobilized cells are carried out. In this case, biomass concentration corresponds to this one applied in silar fermentations with free cells. Fig. 1. Bioreactor with ph and temperature control for free and mobilized batch processes ISBN:

3 .4 Analysis thanol and glucose concentration were measured by Anton Paar DMA 4500, according to BC. The method includes density and refraction index measurement for determination of glucose and ethanol concentrations. The working ranges of Anton Paar DMA 4500 are: density 0-3 [g/ml]; temperature 0-90 [ C]. The system can work in on-line and off-line mode [6]. Biomass in the mobilized process was measured as follows: 1 g mobilized cells is put into 1% sodium citrate solution. 1 g pure alginate is put into 1% sodium citrate solution and it is used as control sample. After completely dissolving the catalyst, the biomass concentration is measured by spectrophotometer at 60 nm [7]. 3 Unstructured Model for Continuous Fermentation For modeling the alcohol fermentation with mobilized process detailed models have been proposed in [5, 8]. The model for the mobilized process is silar to that of free cells but considers two other state variables, the glucose and ethanol concentrations inside the gel-beads, G and, respectively. These concentrations are determined by diffusional factors. In most cases, these diffusion factors cause a decrease on the yield of the target product in the mobilized system. The model is described as follows: dg µ X q X = K LS ( G G) (1) Y X/G Y /G d q X = K LP () dx dg ( ) + Y /G = µ X (3) ( G G )- D(G G ) = K (4) in LS d = K LP ( ) D (5) µ G max (6) µ = 1 G M ks+ G + K q= k SP q max + G G G + K SS SSP 1 MP where µ max, ( h -1 ) is maxum biomass growth rate; k S and K SS, (g/dm 3 ) are Monod constants; M ( g/dm 3 ) - inhibition constants for biomass; MP, ( g/dm 3 ) - (7) inhibition constant for ethanol; q max, ( g/(g.h) - maxal ethanol specific accumulation rate; k SP and K SSP, (g/dm 3 ) - Monod constants; µ (h -1 ) and q (g/l.h) are specific growth and ethanol production rate respectively, Y X/G, is yield factor from glucose to biomass; Y /G - yield factor from glucose to ethanol. The diffusion mechanisms in the mobilized cells is taken into account by coefficients K LS (h -1 ) and K LP (h -1 ). They are the global mass transfer coefficients for glucose and ethanol from the mobilized cells to the media and from the media to the mobilized cells, respectively. The sub index denotes concentration on the mobilized beads. 3.1 Models identification Parameter identification of models (1-7) was realized using experental data of each batch fermentation. An optization procedure based on evolutionary algorithm is applied. The optization criterion was chosen to obtain the minal mean square error between experental data of concrete fermentation and corresponding sulated outputs of the free mobilized cells (3-7) systems respectively. Parameter identification was done using Matlab 7.0 Math works, USA. The optal values of models parameters for the three chosen mobilized cells fermentations are summarized in Table 1. Table 1 Models parameters Parameter xper.1 xper. xper. 3 µ max q max K S K SP K SS K SSP Y X/G Y /G M MP K LS K LP Problem Solution Looking at the model (1-7), it can be remark that the input-output equation for ethanol production is the equation (5). For derivation of control algorithm the value of ethanol production rate, R, have to be estated on-line. So, as first task a software sensor of this parameter have to be designed. ISBN:

4 4.1 Software Sensor of thanol Kinetics The software sensor presented in this paper is based on measurements of ethanol. An observer based estator for the ethanol production rate, R, considered as unknown te-varying parameter, can be written as [3]: dê = Rˆ D+ C1 ( Ê) (8a) drˆ = C ( ) (8b) Ê The first equation of the estator presents a copy of balance equation for the ethanol concentration (5), and a correction term, which is a product of the estation error and a tuning parameter C 1. The second equation is an update equation for the ethanol production rate including the estation error multiplied by a tuning parameter C. For the estator (8), the dynamics of the observation error ( e = Ê ) and tracking error ( ρ = R Rˆ ) is presented by the following system: where λ is control tuning parameter; Rˆ, is estation values of ethanol production rate received by estator (8) and is the measured value of ethanol concentration in the culture medium. 5 Results and Discussion All sulation investigations are realized using programs in MATLAB environments. In Figs. and 3, the results from investigations of batch phase of fermentation for the three experents are shown. In Fig. a, b and Fig. 3 b, a comparison between model values of biomass, substrate and ethanol in culture medium (solid lines) and their experental data (points) is given. As can be seen, the model fits well with experental data. In Figs. c and 3c, values of substrate and ethanol in the beads (solid lines) are plotted. d e C = ρ C 1 1 e 0 + dr 0 ρ The tuning parameters C 1 and C are calculated as function of the eigenvalues, p o, of this error system. To be stable estator (8), the eigenvalues must lie in the left half plane. The following equations present relationships between tuning parameters and two eigenvalues selected equal to each other p o : C1 = p o po C = (9) where p o, C 1 and C are constants. The advantages of the selection of double eigenvalues are well known and described in []. 4. Adaptive Control Design As the measurements of ethanol are available the control algorithm is designed silar to linearizing adaptive control algorithms proposed in [3, 10]. * ( - ) + Rˆ D= λ, (10) Fig. Batch fermentation model (solid lines); exp. 1 (*), exp. ( ), exp. 3(+) In Fig. 3 a, a comparison between the model (solid lines) and estates values (dotted lines) of ethanol production rates is presented. The tuning parameters C 1 and C are calculated using the relationships (9) with p o =-. This value is chosen as compromise between the rate of convergence and disturbance sensibility of estation algorithm (8). The results show that estates and model curves almost coincide. For sulation investigations of the adaptive control (8), (10), the control scheme in Fig. 4 is used. The value of control tuning parameter λ is set 0.1as a compromise between the rate of convergence and disturbance sensibility of control algorithm. ISBN:

5 Fig. 3 Batch fermentation model (solid lines); exp. 1 (*), exp. ( ), exp. 3(+); ethanol production rates estates ( ) * + - Adaptive Control Control Аlgorithm Fig.4 Control Scheme Rˆ D Process Мodel Software Sensor The control sulation results for the three experents are compared in Fig In Fig. 5 model values of biomasss (subfigure a), substrate (subfigure b) and ethanol (subfigure c) in culture medium (solid lines) are presented. The substrate concentration in the feed is 40 g/dm 3. The duration of continuous fermentation is 300 h. Fig. 5. Continuous fermentation. Concentrations in the culture: model (solid lines); exp. 1 (*), exp. ( ), exp. 3(+) Fig.6. Continuous fermentation: concentrations in the beads The batch experental points are shown to be compared with continuous fermentations. The values of substrate and ethanol in beads are given in Fig. 6. In Fig. 7, the ethanol production rate and control input - dilution rate are plotted. As can be seen in Fig.5c, ethanol concentration in the culture reaches set-point and keeps it till the end of fermentation. Analyzing data in Tabl. 1 and qs. (1), (4), it may be remarked that the specific rates of substrate consumption, biomass growth and ethanol accumulation are high and follow the normal behavior of silar processes described in [1, 5]. Taking into account that ethanol is the target product for this process the specific ethanol production rate is the main parameter. For the investigated processes, this parameter has a local maxum, which appears between 30 th and 36 th hour of fermentation (Fig. 3a). In framework of this period a switching from batch to continuous mode of cultivation was realized automatically in te when the ethanol production rate reaches its maxal value. Then, this parameter decreases slowly and reaches a steady state value. It varies in the range from 0.1 till 0.5 g/(g.h) for the investigated fermentations (Fig. 7). Sultaneously the dilution rates decrease from maxal h -1 till steady state value within the boundaries of 0.01 till 0.0 h -1. These values guarantee a constant productivity of the process keeping the necessary difference ( -) for permanent difusion of ethanol produced in the cell into the culture medium. From the Fig. 5c, 6b and Fig. 7b an inverse proportional relationship between ethanol concentrations (in the culture and in the beads) and dilution rate can be observed. This effect is natural one because the lower dilution rate keeps higher ethanol concentrations in the medium. ISBN:

6 laboratory are planned where the control will be realized using LABVIW system. Acknowledgment: This work was supported by Bulgarian National Fund Scientific Researches under contract No DTK 0/7/ Fig. 7. Continuous fermentation. thanol production rate and dilution rate-control input The chosen concentration of substrate in the feed, S in, leads to steady state with almost full substrate depleting after period 3/D which is optal case from expert s point of view. Obseving the te elapse of substrate concentration in the beads, it decreases slowly and becomes almost zero for all processes in steady state (Fig. 6a). This proves that all feeding substrate is transformed into ethanol. In steady state of continuous phase the process productivity is g/(dm 3.h) ethanol, with glucose transformation degree 95%. Some investigations with higher value of substrate in the feeding are investigated. The results show the same productivity of the process but the glucose assilation for ethanol production is 75-80% only which is considered as a disadvantage for such continuous system. The sulations of process model show slowly biomass growth and substrate decreasing in the medium during the steady state (Figs. 5a and 5b). These results are not as a whole in accordance with the experental data presented in [1] where the beads keep contant concentration. In spite of all the model describes sufficiently the dynamics of silar processes and could be used for prelinary investigations. 6 Conclusions The proposed control strategy stabilizes the ethanol concentration in the culture medium in such value that guarantees maxum ethanol production rate. The strategy is applied to continuous fermentation with mobilized yeasts Saccharomyces cerevisiae BO 13. The sulation results demonstrate a good performance of the proposed control algorithm. Future verifications in References: [1] Angelov M., G.Kostov, Bioengineering Aspects of Fermentation Processes with Free and Immobilized Microbial Cells, Agency 7D, Plovdiv, Bulgaria 011, 01pages (in Bukgarian). [] Claes J.., J.F. Van Impe, On-line station of Specific Growth Rate Based on Viable Biomass Measurements: xperental Validation, Bioprocess ngineering, Vol. 1, 1999, pp [3] Ignatova M., V. Lyubenova, Control of Biotechnological Processes - New Formalization of Kinetics: Theoretical Aspects and Applications. LAP LAMBRT Academic Publishing, GmbH & Co. Saarbrücken Germany, 011, 10 pages. [4] Kobayashi, F., and Y. Nakamura. Mathematical model of direct ethanol production from starch in mobilized recombinant yeast culture. Biochemical ngineering Journal 1 (004) [5] Kostov G, Denkova R, Pircheva D, Lubenova V, Nakov L, Ignatova M, Angelov M. (013). Kinetics investigation of bio-ethanol production with free and mobilized cells. In: NOLCOS, 4-6 September, Toulouse, France. [6] Lyubenova V. M.N. Ignatova, K. Salonen, K. Kiviharju,T. erikäinen,, Control of α-amylase Production by Bacillus subtilis, Bioprocess Biosyst ng, Vol. 34, No 3, 011, pp [7] Nakasaki, K. T. Murai, and T. Akiyama Dynamic Modeling of Immobilized Cell Reactor: Application to thanol Fermentation. Biotechnology and Bioengineering, Vol. 33, Pp (1989). [8] Willaert, R. G., Immobilized cell, Springer, Berlin, 001 [9] [10] Zhou, Y.,. Martins, A.Groboillot, C.Champagne, R.Neufeld, Spectrophotometric quantification of lactic bacteria in alginate and control of cell release with chitosan coating, Journal of Applied Microbiology, vol.84, 1998, p ISBN: