- Email: [email protected]
Regulatory Control for the Operation of a Simultaneous Saccharification and Co-Fermentation Reactor for Bioethanol Production.
Andrzej Kraslawski and Ilkka Turunen (Editors) Proceedings of the 23 rd European Symposium on Computer Aided Process Engineering – ESCAPE 23, June 9-12, 2013, Lappeenranta, Finland 43 © 2013 Elsevier B.V. All rights reserved.
Regulatory Control for the Operation of a Simultaneous Saccharification and CoFermentation Reactor for Bioethanol Production. Hernandez-Escoto, H.,a Rodriguez-Gomez, D.,b Morales-Rodriguez, R.c* a
Departamento de Ingeniería Química, Universidad de Guanajuato, Noria Alta s/n, 36050, Guanajuato, Gto., México. b Departamento de Biotecnología, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, C.P. 09340, México, D.F., México. c Departamento de Ingeniería de Procesos e Hidráulica, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, C.P. 09340, México, D.F., México. *e-mail: [email protected]
Abstract This study presents the development and implementation of regulatory control structures in the operation of a Simultaneous Saccharification and Co-Fermentation (SSCF) using Proportional-Integral (PI) controllers. To this end, two different case studies were analyzed: 1) assuming lower cellulose and xylan fractions in the feedstock used to feed the SSCF reactor, and 2) lower efficiency of the microorganism for glucose to ethanol conversion, lower cellulose and xylan fraction content in the feedstock and slightly higher activity of class 1 and 2 enzyme. The implementation of the regulatory control structures showed that it is feasible to regulate the process with a simple control system by managing those likely uncertainties during the operation of the SSCF unit. Keywords: Simultaneous saccharification and co-fermentation, regulatory control, bioethanol production.
1. Introduction. Human dependence on fossil fuels and the reduction of the petroleum resources have led to environmental and political problems. Therefore, the search of improving the energy production from alternative renewable sources and exhaustive research on bioethanol production based on biomass wastes have increased recently. Nevertheless, there are still challenges to solve in the list of priorities of the research and development areas. The conventional bioethanol production process involves 4 main sections: pretreatment, enzymatic hydrolysis, fermentation, and downstream processes for separation of anhydrous ethanol and recovery of additives and reactants (MoralesRodriguez et al., 2011b). Recently, some studies have focused on the development of new intensified process configurations, aiming to find and improve the conversion of the employed biomass to ethanol; for instance, by the combination of the processes of enzymatic hydrolysis and (co-) fermentation in one single unit, that is known as simultaneous saccharification and (co-)fermentation (SSF and SSCF, respectively). SSF is present when one single sugar (glucose or xylose) is converted into ethanol, and SSCF when two sugars (e.g., glucose and xylose) are metabolized into ethanol. Jin et al. (2012), Kang et al. (2012) and other authors have worked in the development of SSCF processes using an experimentalbased approach, while some other works have proposed mathematical models for this
44
H. Hernandez-Escoto et al.
type of reactor (Philippidis et al., 1993). Moreover, Morales-Rodriguez et al. (2011a) proposed and validated a newer mathematical model for SSCF by combining existing mathematical models for enzymatic hydrolysis (Kadam et al., 2009) and cofermentation (Krishnan et al., 1999), showing good predictions compared with the experimental data employed for the model validation. This model offers an alternative of exploring the SSCF process under several matters of design, optimization and operation, which have not been explored and analyzed yet; including the design and control of a SSCF process carried out in a stirred tank reactor in continuous operation (CSTR). Therefore, in order to develop a feasible-in-practice SSCF process, and obtain a good picture about the probable ethanol production rate that can be achieved, and the operational problems that could be exhibited, this work is focused on analysing the dynamics and control of a class of CSTRs where a SSCF process is carried out, on the basis of the validated model above mentioned. In order to establish a reference for further studies on this class of reactors, it is aimed to maintain the highest productivity of the reacting system and the lowest variation of the concentration of produced ethanol with the simplest control system: one based on a well-tuned conventional PI controller.
2. Systematic Methodology for Regulatory Control of Simultaneous Saccharification and Co-Fermentation reactor for Biothanol Production. A systemic methodology involving 5 steps was followed to perform the dynamics analysis and the regulatory control of the SSCF reactor: 1) Model development/selection: a mathematical model for a SSCF unit operation was previously developed by Morales-Rodriguez et al. (2011a) and validated with some available experimental data (Kang et al., 2012), which were employed in this study; 2) Determination of the nominal (set point) bioethanol production: this was performed with the aim of finding the maximum productivity for the reacting system, and maintaining it even in presence of certain disturbances during the operation of the reactor; 3) Disturbances case studies: various possible scenarios were analyzed in this step, in order to propose the most feasible control system/structure to maintain the desired bioethanol productivity; 4) Control system/structure selection and tuning: A selection of the type of control that would be employed in the SSCF was performed in this step, where a conventional Proportional-Integral (PI) control system to regulate the process was selected. The controller was tuned systematically by following a poleassignment approach (Zavala-Guzmán et al., 2012); 5) Control scenarios implementation: the performance of the control system was evaluated under different scenarios of disturbance rejection defined in step 3; thus, the final target was to maintain ethanol concentration in a certain value. The solution of the mathematical model and the implementation of the control structure were carried out in MatLab (The MathWorks, Inc. 2008).
3. The Process of Simultaneous Saccharification and Fermentation. The study employed a CSTR for SSCF (see Figure 1), where processes of enzymatic hydrolysis of cellulose and fermentation of reducing sugars are simultaneously carried out at certain temperature (T). The stream entering to the reactor, at certain flow rate Qin, contains cellulose and hemicellulose, enzymatic complex composed by enzyme class 1 (cellobiohydrolases (CBH) and 1,4-β-glucanase (EG)), enzyme class 2 (βglucosidase (BGD)), and yeast at watered concentrations (Se, Ee1, Ee2, and Ce,
Regulatory control for the operation of a simultaneous saccharification and cofermentation reactor for bioethanol production
45
respectively). The output stream (Qout) contains ethanol (product of interest) in addition with other by-products and non converted reactants in minor amounts, which correspond to the concentrations in the reactor of ethanol (P), glucose (G), xylose (X), cellulose-xilose (S), enzymatic complex (E1 and E2) and yeast (Ce). For the operation of this type of processes, it is assumed that its monitoring relies on the measurement of the concentrations of ethanol (yp), and it is also considered that the process can be regulated by manipulating the amount of cellulose and hemicellulose mass in the input stream (ISin), or the input flowrate (Qin).
Enzymes (class 1 and 2) Water Cell biomass Solid fraction: Cellulose Feed stream Xylan Qin Lignin Ash Other compounds
SSCF Reactor T, V Enzymes (class 1 and 2) Water Cell biomass Solid fraction: Cellulose Out stream Xylose Lignin Qout Ash Other compounds Ethanol Glucose Cellobiose Xylose
Figure 1. SSCF Continuous stirred tank reactor for bioethanol production.
The reactor is described by a set of ordinary differential equations ODEs (see details in Morales-Rodriguez et al., 2011a), yP = P. (1a) ݔሶ ൌ ݂ሺݔǡ ݑǡ ݀ǡ ሻ, x(0) = x0; u = IS or Qin (1b) x = [P, G, X, S, E1, E2, C]’, (1c) d = [Se, Ee1, Ee2, Ce, (Qin or IS)]’, p = set of 50 parameters. Although the number of states evidently is greater than one, and it can be perturbed by several input variables (d) or parameters (p), the reactor can be considered as a singleinput-single-output (SISO) system.
4. Process Behaviors and Operational Problems By following a bifurcation analysis and a sensitivity analysis of the process with respect to the inputs (u, d), this process only exhibits uniqueness of steady state for every value of inputs (for the sake of space, a corresponding one-line bifurcation figure is not shown). Moreover, every steady state is stable, since the corresponding eigenvalues, provided by the coefficient matrix of the linearized model, are all of negative real part. Thus, this is an auto-regulated process where the challenge in its operation will rely on a disturbance rejection problem.
5. Control System One of the main characteristics of the SSCF process is its level of sensitivity to some operating factors, and also its nonlinear feature, which can generate a complex behaviour in the system. For instance, a diminishing of the cellulose and/or xylan composition (as a result of the environmental conditions) can result in a lower ethanol
46
H. Hernandez-Escoto et al.
concentration in the output stream of the unit, which can directly affect the operation in the purification section of the process; in this case, it is advantageous to control the concentration in the output stream of the SSCF unit rather than control the operation in the distillation column, because control efforts and costs. Another example could be the variation of the enzyme quality that is directly related with its activity, whether a decrease in the enzyme activity can be found as a consequence of the microorganisms stress, resulting in a lower cellulose conversion to glucose, influencing the ethanol production of the SSCF unit. The list of challenges for solving potential troubles during the operation of the SSCF reactor can be even longer. Therefore, in order to guarantee a production rate provided by nominal conditions, it is necessary to introduce a control system in the SSCF reactor. Several approaches can be applied since conventional up to nonlinear-advanced approaches, provided the nonlinear feature of the model, but a very important question can be asked, why does not it begin with a conventional PI controller? This is the most economic among any controller, and it can be used as a reference for the further studies implementing advanced (modelbased) techniques. In addressing the control structure determination, a first key point to highlight is that in this class of processes, not all the variables can be directly measured on-line; however, the concentration of ethanol can be easily quantified. For instance, the carbon dioxide released in the process can be continuously measured, and directly correlated with ethanol production (Haloui et al., 1998). Furthermore, latest chromatographic systems allow a frequent analysis of reacting samples (Liu et al., 2001). On the other hand, with respect to control inputs, typical ones are the feed flowrate of the total mixture, or feed flowrate of individual components, among them, the most used is the substrate feeding. In terms of instrumentation, any of them only requires one valve or pump to be manipulated. Then, for constructing the control system, two options on control configurations are considered: (i) (Qin, P), or (ii) (IS, P). Then, the conventional PI controller is implemented:
Kc P P dW 0 W I
u u Kc P P ³
t
u = IS or Qin
(2)
Once the control configuration and controller are established, the remaining problem is to tune the controller gains (Kc, WI). Tuning relationships are recalled from ZavalaGuzmán et al. (2012), and applied in a straightforward manner: W p f a 2 2n 1 2n 1 Kc (3a, b) W , , i Kp n2 These relationships result from the assignment of stable poles to a second order dynamics that approximately describes the convergence of control system. The convergence dynamics is constructed with the coupling of the control law (2) with a first order model that describes the response of control output with respect to control input, in terms of a static gain (KP) and a time constant (WP). These parameters are identified on the basis of the response of the control output with respect to a step change in the control input (IS or Qin). The stable poles correspond to those of a second order dynamics of well-known behaviour through parameters as the damping factor (fa) and the time constant (WR), which is made n times “faster” than WP (WR = WP/n). Then, the tuning parameters are n and fa; the last intends to determine the type of response of the control system (i.e., underdamped if fa < 1).
Regulatory control for the operation of a simultaneous saccharification and cofermentation reactor for bioethanol production
47
6. Performance of the Control System
15.20 15.18 15.16 15.14 15.12 15.10 15.08 15.06 15.04 15.02 15.00
a)
Set Point No Control Control
0
200
400
600
800
1000
1200
3.4x10
-3
3.3x10
-3
3.2x10
-3
3.1x10
-3
3.0x10
-3
2.9x10
-3
2.8x10
-3
2.7x10
-3
2.6x10
-3
2.5x10
-3
Q (L/hr)
Ethanol concentration (g/L)
To evaluate the performance of both control systems (controlling P by manipulating Qin, or controlling P by manipulating IS), it was considered the following perturbed scenario, that could be likely found in the bioethanol production process: a lower cellulose and xylan concentration in the raw material is fed to the SSCF reactor, a lower enzyme activity is considered to be fed into the reactor due to the quality decrease in the production process of the enzymes and a yeast known to be a lower producer of ethanol. The scenario was constructed by making changes in corresponding model parameters. This scenario resulted in a lower ethanol concentration in the output stream. The performances of both control systems are illustrated in Figure 2. By controlling P with Qin (Figure 2a, b), the ethanol concentration is maintained by allowing a longer residence time of the reacting mixture inside the reactor; indeed, the total input flowrate is significantly diminished (15%) compared to the nominal one. The settling in the SSCF reactor was obtained approximately 210 h, faster than the time of the natural response of the process. By manipulating IS (Figure 2c, d), the ethanol concentration is maintained as well; and it works by inferring the amount of available insoluble solids in the reactor that would allow more production of glucose and xylose, and subsequently ethanol. The regulation of the ethanol concentration in the output stream was obtained just about 140 hrs faster than in the case of Qin. Moreover, it is worthwhile to notice that this would be a minor effect on the performance of the distillation column during the purification of ethanol for both control systems.
t (hr)
c)
14.5 Set Point No Control Control
13.0
0
200
400
600
t (hr)
800
1000 1200
Insoluble solids concentration (g/L)
Ethanol concentration (g/L)
15.0
13.5
Nominal Control 0
200
400
600
800
1000 1200
t (hr)
15.5
14.0
b)
100 90
d)
80 70 60
Nominal Control
50 40 30
0
200
400
600
800
1000 1200
t (hr)
Figure 2. Control system performance: (a, b): Qin-P control pair; (c, d): IS-P control pair
7. Conclusion It was shown the operating scheme of a Simultaneous Saccharification and CoFermentation process carried out in a continuous stirred tank reactor. The study also
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proposed a systematic methodology where one of the key steps (number 3) was the appropriate identification of possible critical scenarios during the operation of the SSCF reactor. Although this class of processes is complex due to the nature of its components and its kinetics, it exhibits uniqueness of steady state for its respective inputs, and it exhibits stability, as well. Moreover, a conventional PI conventional controller was capable of rejecting disturbances and maintaining the production rates of ethanol in a smoother and faster way than the natural response of the process. It is also important to highlight the relevance of this type of analysis for the bioethanol production process. Besides, it is still necessary to look for new alternatives to overcome some issues regarding to uncertainties during the operation of the process, such as, lower enzyme activity, variation on biomass composition, toxic compounds generated in previous sections of the process that can affect the microorganisms efficiency for ethanol production, etc. This work intends to be considered as a reference for further studies.
References A. Berlin, M. Balakshin, N. Gilkes, J. Kadla, V. Maximenko, S. Kubo, J.N. Saddler, 2006, Inhibition of cellulase, xylanase and b-glucosidase activities by softwood lignin preparations. Journal of Biotechnology, 125, 198–209. R.P. Chandra, R. Bura, W.E. Mabee, A. Berlin, X. Pan, J.N. Saddler, 2007, Substrate pretreatment: the key to effective enzymatic hydrolysis of lignocellulosics? Advances in Biochemical Enginerring / Biotechnology, 108, 67–93. N.E. Haloui, D. Picque, G. Corrieu, 1988, Alcoholic Fermentation in Winemaking: On-Line Measurement of Density and Carbon Dioxide Evolution. Journal of Food Engineering, 8, 1730. M. Jin, C. Gunawan, V. Balan, M.W. Lau, B.E. Dale, 2012, Simultaneous saccharification and co-fermentation (SSCF) of AFEXTM pretreated corn stover for ethanol production using commercial enzymes and Saccharomyces cerevisiae 424A(LNH-ST). Bioresource Technology, 110, 587–594. K.L. Kadam, E.C. Rydholm, J.D. McMillan, 2004, Development and validation of a kinetic model for enzymatic saccharification of lignocellulosic biomass, Biotechnology Progress, 20, 698-705. L. Kang, W. Wang, Y.Y. Lee, 2010, Bioconversion of kraft paper mill sludges to ethanol by SSF and SSCF. Applied Biochemistry and Biotechnology, 161, 53-66. M.S. Krishnan, N.W.Y. Ho, G.T. Tsao, 1999, Fermentation kinetics of ethanol production from glucose and xylose by recombinant Saccharomyces1400(pLNH33). Applied Biochemistry and Biotechnology, 78, 373-388. Y.-C. Liu, F.-S. Wang, W.-C. Lee, 2001, On-line monitoring and controlling systems for fermentation processes. Biochemical Engineering Journal, 7, 17-25. R. Morales-Rodriguez, K.V. Gernaey, A.S. Meyer, G. Sin, 2011a, A mathematical model for simultaneous saccharification and co-fermentation of C6 and C5 sugars. Chinese Journal of Chemical Engineering, 19, 185–191. R. Morales-Rodriguez, A.S. Meyer, K.V. Gernaey, G. Sin, 2011b, Dynamic Model-based Evaluation of Process Configurations for Integrated Operation of Hydrolysis and CoFermentation for Bioethanol Production from Lignicellulose. Bioresource Technology, 102, 1174-1184. G.P. Philippidis, T.K. Smith, C.E. Wyman, 1993, Study of the enzymatic hydrolysis of cellulose for production of fuel ethanol by the simultaneous saccharification and fermentation process, Biotechnology and Bioengineering, 41, 846-853. A.M. Zavala-Guzmán, H. Hernández-escoto, S. Hernández, J.G. Segovia-Hernández, 2012, Conventional proportional-integral (PI) control of dividing wall distillation columns: systematic tuning. Industrial & Engineering Chemistry Research, 51, 10869-10880.
Regulatory Control for the Operation of a Simultaneous Saccharification and CoFermentation Reactor for Bioethanol Production. Hernandez-Escoto, H.,a Rodriguez-Gomez, D.,b Morales-Rodriguez, R.c* a
Departamento de Ingeniería Química, Universidad de Guanajuato, Noria Alta s/n, 36050, Guanajuato, Gto., México. b Departamento de Biotecnología, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, C.P. 09340, México, D.F., México. c Departamento de Ingeniería de Procesos e Hidráulica, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, C.P. 09340, México, D.F., México. *e-mail: [email protected]
Abstract This study presents the development and implementation of regulatory control structures in the operation of a Simultaneous Saccharification and Co-Fermentation (SSCF) using Proportional-Integral (PI) controllers. To this end, two different case studies were analyzed: 1) assuming lower cellulose and xylan fractions in the feedstock used to feed the SSCF reactor, and 2) lower efficiency of the microorganism for glucose to ethanol conversion, lower cellulose and xylan fraction content in the feedstock and slightly higher activity of class 1 and 2 enzyme. The implementation of the regulatory control structures showed that it is feasible to regulate the process with a simple control system by managing those likely uncertainties during the operation of the SSCF unit. Keywords: Simultaneous saccharification and co-fermentation, regulatory control, bioethanol production.
1. Introduction. Human dependence on fossil fuels and the reduction of the petroleum resources have led to environmental and political problems. Therefore, the search of improving the energy production from alternative renewable sources and exhaustive research on bioethanol production based on biomass wastes have increased recently. Nevertheless, there are still challenges to solve in the list of priorities of the research and development areas. The conventional bioethanol production process involves 4 main sections: pretreatment, enzymatic hydrolysis, fermentation, and downstream processes for separation of anhydrous ethanol and recovery of additives and reactants (MoralesRodriguez et al., 2011b). Recently, some studies have focused on the development of new intensified process configurations, aiming to find and improve the conversion of the employed biomass to ethanol; for instance, by the combination of the processes of enzymatic hydrolysis and (co-) fermentation in one single unit, that is known as simultaneous saccharification and (co-)fermentation (SSF and SSCF, respectively). SSF is present when one single sugar (glucose or xylose) is converted into ethanol, and SSCF when two sugars (e.g., glucose and xylose) are metabolized into ethanol. Jin et al. (2012), Kang et al. (2012) and other authors have worked in the development of SSCF processes using an experimentalbased approach, while some other works have proposed mathematical models for this
44
H. Hernandez-Escoto et al.
type of reactor (Philippidis et al., 1993). Moreover, Morales-Rodriguez et al. (2011a) proposed and validated a newer mathematical model for SSCF by combining existing mathematical models for enzymatic hydrolysis (Kadam et al., 2009) and cofermentation (Krishnan et al., 1999), showing good predictions compared with the experimental data employed for the model validation. This model offers an alternative of exploring the SSCF process under several matters of design, optimization and operation, which have not been explored and analyzed yet; including the design and control of a SSCF process carried out in a stirred tank reactor in continuous operation (CSTR). Therefore, in order to develop a feasible-in-practice SSCF process, and obtain a good picture about the probable ethanol production rate that can be achieved, and the operational problems that could be exhibited, this work is focused on analysing the dynamics and control of a class of CSTRs where a SSCF process is carried out, on the basis of the validated model above mentioned. In order to establish a reference for further studies on this class of reactors, it is aimed to maintain the highest productivity of the reacting system and the lowest variation of the concentration of produced ethanol with the simplest control system: one based on a well-tuned conventional PI controller.
2. Systematic Methodology for Regulatory Control of Simultaneous Saccharification and Co-Fermentation reactor for Biothanol Production. A systemic methodology involving 5 steps was followed to perform the dynamics analysis and the regulatory control of the SSCF reactor: 1) Model development/selection: a mathematical model for a SSCF unit operation was previously developed by Morales-Rodriguez et al. (2011a) and validated with some available experimental data (Kang et al., 2012), which were employed in this study; 2) Determination of the nominal (set point) bioethanol production: this was performed with the aim of finding the maximum productivity for the reacting system, and maintaining it even in presence of certain disturbances during the operation of the reactor; 3) Disturbances case studies: various possible scenarios were analyzed in this step, in order to propose the most feasible control system/structure to maintain the desired bioethanol productivity; 4) Control system/structure selection and tuning: A selection of the type of control that would be employed in the SSCF was performed in this step, where a conventional Proportional-Integral (PI) control system to regulate the process was selected. The controller was tuned systematically by following a poleassignment approach (Zavala-Guzmán et al., 2012); 5) Control scenarios implementation: the performance of the control system was evaluated under different scenarios of disturbance rejection defined in step 3; thus, the final target was to maintain ethanol concentration in a certain value. The solution of the mathematical model and the implementation of the control structure were carried out in MatLab (The MathWorks, Inc. 2008).
3. The Process of Simultaneous Saccharification and Fermentation. The study employed a CSTR for SSCF (see Figure 1), where processes of enzymatic hydrolysis of cellulose and fermentation of reducing sugars are simultaneously carried out at certain temperature (T). The stream entering to the reactor, at certain flow rate Qin, contains cellulose and hemicellulose, enzymatic complex composed by enzyme class 1 (cellobiohydrolases (CBH) and 1,4-β-glucanase (EG)), enzyme class 2 (βglucosidase (BGD)), and yeast at watered concentrations (Se, Ee1, Ee2, and Ce,
Regulatory control for the operation of a simultaneous saccharification and cofermentation reactor for bioethanol production
45
respectively). The output stream (Qout) contains ethanol (product of interest) in addition with other by-products and non converted reactants in minor amounts, which correspond to the concentrations in the reactor of ethanol (P), glucose (G), xylose (X), cellulose-xilose (S), enzymatic complex (E1 and E2) and yeast (Ce). For the operation of this type of processes, it is assumed that its monitoring relies on the measurement of the concentrations of ethanol (yp), and it is also considered that the process can be regulated by manipulating the amount of cellulose and hemicellulose mass in the input stream (ISin), or the input flowrate (Qin).
Enzymes (class 1 and 2) Water Cell biomass Solid fraction: Cellulose Feed stream Xylan Qin Lignin Ash Other compounds
SSCF Reactor T, V Enzymes (class 1 and 2) Water Cell biomass Solid fraction: Cellulose Out stream Xylose Lignin Qout Ash Other compounds Ethanol Glucose Cellobiose Xylose
Figure 1. SSCF Continuous stirred tank reactor for bioethanol production.
The reactor is described by a set of ordinary differential equations ODEs (see details in Morales-Rodriguez et al., 2011a), yP = P. (1a) ݔሶ ൌ ݂ሺݔǡ ݑǡ ݀ǡ ሻ, x(0) = x0; u = IS or Qin (1b) x = [P, G, X, S, E1, E2, C]’, (1c) d = [Se, Ee1, Ee2, Ce, (Qin or IS)]’, p = set of 50 parameters. Although the number of states evidently is greater than one, and it can be perturbed by several input variables (d) or parameters (p), the reactor can be considered as a singleinput-single-output (SISO) system.
4. Process Behaviors and Operational Problems By following a bifurcation analysis and a sensitivity analysis of the process with respect to the inputs (u, d), this process only exhibits uniqueness of steady state for every value of inputs (for the sake of space, a corresponding one-line bifurcation figure is not shown). Moreover, every steady state is stable, since the corresponding eigenvalues, provided by the coefficient matrix of the linearized model, are all of negative real part. Thus, this is an auto-regulated process where the challenge in its operation will rely on a disturbance rejection problem.
5. Control System One of the main characteristics of the SSCF process is its level of sensitivity to some operating factors, and also its nonlinear feature, which can generate a complex behaviour in the system. For instance, a diminishing of the cellulose and/or xylan composition (as a result of the environmental conditions) can result in a lower ethanol
46
H. Hernandez-Escoto et al.
concentration in the output stream of the unit, which can directly affect the operation in the purification section of the process; in this case, it is advantageous to control the concentration in the output stream of the SSCF unit rather than control the operation in the distillation column, because control efforts and costs. Another example could be the variation of the enzyme quality that is directly related with its activity, whether a decrease in the enzyme activity can be found as a consequence of the microorganisms stress, resulting in a lower cellulose conversion to glucose, influencing the ethanol production of the SSCF unit. The list of challenges for solving potential troubles during the operation of the SSCF reactor can be even longer. Therefore, in order to guarantee a production rate provided by nominal conditions, it is necessary to introduce a control system in the SSCF reactor. Several approaches can be applied since conventional up to nonlinear-advanced approaches, provided the nonlinear feature of the model, but a very important question can be asked, why does not it begin with a conventional PI controller? This is the most economic among any controller, and it can be used as a reference for the further studies implementing advanced (modelbased) techniques. In addressing the control structure determination, a first key point to highlight is that in this class of processes, not all the variables can be directly measured on-line; however, the concentration of ethanol can be easily quantified. For instance, the carbon dioxide released in the process can be continuously measured, and directly correlated with ethanol production (Haloui et al., 1998). Furthermore, latest chromatographic systems allow a frequent analysis of reacting samples (Liu et al., 2001). On the other hand, with respect to control inputs, typical ones are the feed flowrate of the total mixture, or feed flowrate of individual components, among them, the most used is the substrate feeding. In terms of instrumentation, any of them only requires one valve or pump to be manipulated. Then, for constructing the control system, two options on control configurations are considered: (i) (Qin, P), or (ii) (IS, P). Then, the conventional PI controller is implemented:
Kc P P dW 0 W I
u u Kc P P ³
t
u = IS or Qin
(2)
Once the control configuration and controller are established, the remaining problem is to tune the controller gains (Kc, WI). Tuning relationships are recalled from ZavalaGuzmán et al. (2012), and applied in a straightforward manner: W p f a 2 2n 1 2n 1 Kc (3a, b) W , , i Kp n2 These relationships result from the assignment of stable poles to a second order dynamics that approximately describes the convergence of control system. The convergence dynamics is constructed with the coupling of the control law (2) with a first order model that describes the response of control output with respect to control input, in terms of a static gain (KP) and a time constant (WP). These parameters are identified on the basis of the response of the control output with respect to a step change in the control input (IS or Qin). The stable poles correspond to those of a second order dynamics of well-known behaviour through parameters as the damping factor (fa) and the time constant (WR), which is made n times “faster” than WP (WR = WP/n). Then, the tuning parameters are n and fa; the last intends to determine the type of response of the control system (i.e., underdamped if fa < 1).
Regulatory control for the operation of a simultaneous saccharification and cofermentation reactor for bioethanol production
47
6. Performance of the Control System
15.20 15.18 15.16 15.14 15.12 15.10 15.08 15.06 15.04 15.02 15.00
a)
Set Point No Control Control
0
200
400
600
800
1000
1200
3.4x10
-3
3.3x10
-3
3.2x10
-3
3.1x10
-3
3.0x10
-3
2.9x10
-3
2.8x10
-3
2.7x10
-3
2.6x10
-3
2.5x10
-3
Q (L/hr)
Ethanol concentration (g/L)
To evaluate the performance of both control systems (controlling P by manipulating Qin, or controlling P by manipulating IS), it was considered the following perturbed scenario, that could be likely found in the bioethanol production process: a lower cellulose and xylan concentration in the raw material is fed to the SSCF reactor, a lower enzyme activity is considered to be fed into the reactor due to the quality decrease in the production process of the enzymes and a yeast known to be a lower producer of ethanol. The scenario was constructed by making changes in corresponding model parameters. This scenario resulted in a lower ethanol concentration in the output stream. The performances of both control systems are illustrated in Figure 2. By controlling P with Qin (Figure 2a, b), the ethanol concentration is maintained by allowing a longer residence time of the reacting mixture inside the reactor; indeed, the total input flowrate is significantly diminished (15%) compared to the nominal one. The settling in the SSCF reactor was obtained approximately 210 h, faster than the time of the natural response of the process. By manipulating IS (Figure 2c, d), the ethanol concentration is maintained as well; and it works by inferring the amount of available insoluble solids in the reactor that would allow more production of glucose and xylose, and subsequently ethanol. The regulation of the ethanol concentration in the output stream was obtained just about 140 hrs faster than in the case of Qin. Moreover, it is worthwhile to notice that this would be a minor effect on the performance of the distillation column during the purification of ethanol for both control systems.
t (hr)
c)
14.5 Set Point No Control Control
13.0
0
200
400
600
t (hr)
800
1000 1200
Insoluble solids concentration (g/L)
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Figure 2. Control system performance: (a, b): Qin-P control pair; (c, d): IS-P control pair
7. Conclusion It was shown the operating scheme of a Simultaneous Saccharification and CoFermentation process carried out in a continuous stirred tank reactor. The study also
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proposed a systematic methodology where one of the key steps (number 3) was the appropriate identification of possible critical scenarios during the operation of the SSCF reactor. Although this class of processes is complex due to the nature of its components and its kinetics, it exhibits uniqueness of steady state for its respective inputs, and it exhibits stability, as well. Moreover, a conventional PI conventional controller was capable of rejecting disturbances and maintaining the production rates of ethanol in a smoother and faster way than the natural response of the process. It is also important to highlight the relevance of this type of analysis for the bioethanol production process. Besides, it is still necessary to look for new alternatives to overcome some issues regarding to uncertainties during the operation of the process, such as, lower enzyme activity, variation on biomass composition, toxic compounds generated in previous sections of the process that can affect the microorganisms efficiency for ethanol production, etc. This work intends to be considered as a reference for further studies.
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