Research Article  Open Access
Optimal Control of a Nonlinear TimeDelay System in Batch Fermentation Process
Abstract
The main control goal in batch process is to get a high yield of products. In this paper, to maximize the yield of 1,3propanediol (1,3PD) in bioconversion of glycerol to 1,3PD, we consider an optimal control problem involving a nonlinear timedelay system. The control variables in this problem include the initial concentrations of biomass and glycerol and the terminal time of the batch process. By a timescaling transformation, we transcribe the optimal control problem into a new one with fixed terminal time, which yields a new nonlinear system with variable timedelay. The gradients of the cost and constraint functionals with respect to the control variables are derived using the costate method. Then, a gradientbased optimization method is developed to solve the optimal control problem. Numerical results show that the yield of 1,3PD at the terminal time is increased considerably compared with the experimental data.
1. Introduction
Batch processing represents the natural way to scaleup processes from the laboratory to the production environment [1]. During the batch operation, no substrate is added to the initial charge and no product is removed until the end of the process [2]. Optimal control of batch processes has received attention recently because it is a choice for reducing production costs, improving product quality, and meeting safety requirements [3–5].
1,3Propanediol (1,3PD) is one of the important products used in the chemical industry. Using glycerol for producing 1,3PD is effective from both the economical and ecological point of view. The operations of glycerol bioconversion to 1,3PD consist of batch, continuous, and fedbatch cultures. Compared with continuous and fedbatch cultures, glycerol fermentation in batch process can obtain the highest production concentration and molar yield 1,3PD to glycerol [6]. For this process, many studies have been carried out including the quantitative description of the cell growth kinetics [7, 8], enzymecatalytic kinetics [9], the parameter identification problem [10–13], and the pathway optimization problem [14]. In particular, considering the existence of timedelays in the fermentation process [15, 16], a nonlinear timedelay system was recently proposed in [13]. Numerical simulations showed that the nonlinear timedelay system can describe the batch process better than previous mathematical models. However, in batch process of glycerol bioconversion to 1,3PD, the aim is to obtain as much 1,3PD as possible at the terminal time. Mathematically, this is an optimal control problem. Although the achieved results are interesting, such optimal control problem is ignored in the above researches.
In this paper, taking the initial concentrations of biomass and glycerol and the terminal time of the fermentation process as control variables and the yield of 1,3PD as the performance index, we propose an optimal control model involving nonlinear timedelay system in [13] and subject to continuous state constraints. In fact, this problem is a free time delayed optimal control problem. By the way, there has been a mounting interest in optimal control of timedelay systems. A maximum principle for optimal control problem with a constant delay was provided in [17]. It should, however, be noted that many timedelay systems including the nonlinear timedelay system in this paper are highly nonlinear. Therefore, it is often impossible to obtain analytical solutions of the delayed optimal control problem and one has to resort to numerical solution methods. As a result, some successful algorithms, such as iterative dynamic programming method [18], control parameterization method [19], and measure theoretical approach [20], have been developed. However, terminal time in above optimal control problems is fixed. The presence of free terminal time makes the delayed optimal control problem much more complicated. Thus, to solve the optimal control problem in this work, these existing computational methods cannot be used directly and new computational techniques should be explored.
In this paper, by a timescaling transformation, we equivalently transcribe the free time delayed optimal control problem into one with fixed terminal time. It is significant to mention that this transformation yields a more complex dynamic system in which the timedelays are variable. By the costate method, we derived the gradients of the cost functional and constraints with respect to the control variables. Then, a gradientbased optimization technique is developed. Finally, numerical results show that the yield of 1,3PD at the terminal time is increased considerably compared with the experiment data.
2. Nonlinear TimeDelay Systems
In batch process, a proper quantity of biomass and glycerol is added to the reactor only once and stirred uniformly under given conditions. Then, 1,3PD is removed at the end of the process. Based on the work [13], the following nonlinear timedelay system can be used to describe the batch process: where is the state vector whose components are, respectively, the concentrations of biomass, glycerol, 1,3PD, acetate, and ethanol in the reactor at time ; is a delay argument; is the terminal time; is the initial state; is a given initial function; and the dynamics of the batch process is given by In (2), the specific growth rate of cells , the specific consumption rate of substrate , and the specific formation rates of products , , can be expressed as where , , , and are kinetic parameters and and are the critical concentrations for cells growth.
The terminal time in system (1) is a control variable. Define where and are the lower and upper bounds for the terminal time, respectively. Any is called an admissible terminal time. Moreover, since no product comes into being at the initial point, . Hence, let and define where and are real numbers such that . Any is called an admissible initial vector of biomass and glycerol.
For the nonlinear timedelay system (1), there exists a unique continuous solution corresponding to each on [21]. Furthermore, there exist critical concentrations of biomass, glycerol, and products, outside which cells cease to grow. Hence, it is biologically meaningful to restrict the concentrations of biomass, glycerol, and products within a set defined as
3. Optimal Control Problems
In the batch process, it is desired that the yield of 1,3PD should be maximized at the end of the process. This is achieved by manipulating some control variables and . Thus, the optimal control problem in batch process can be formulated as
Note that (OCP) is of nonstandard type because the terminal time and the initial values of biomass and glycerol are variables to be determined. Thus, the (OCP) is actually a free time optimal control problem. It is difficult to solve the (OCP) using existing numerical techniques [18–20]. The main difficulty is the implicit dependence of the system state on the terminal time. We now employ a timescaling transformation from to as follows: where is a new time variable. Clearly, corresponds to , and corresponds to . Let . Then The initial conditions become Thus, system (1) is transformed into the following form: Let denote the solution of system (11) corresponding to each on . Then, (OCP) can be transcribed into the following equivalently optimal control problem with fixed terminal time:
Note that the timedelay in system (11) depends on the control variable . Thus, when applied to timedelay systems, the timescaling transformation (8) yields a more complex dynamic system in which the timedelay is variable.
4. Computational Approaches
(EOCP) is in essence an optimization problem with continuous state inequality constraint (13), which has an infinite number of constraints and can be viewed as a semiinfinite programming problem. An efficient algorithm transforming this type of problems to standard mathematical programming problems was discussed in [22]. We will now briefly discuss the application of this algorithm to (EOCP).
Let The continuous state constraint (13) becomes where However, the equality constraint (15) is nondifferentiable at the points when . We replace (15) with the following inequality constraint: where , , and Thus, (EOCP) is approximated by a sequence of standard mathematical programming problems {()} defined by replacing constraint (15) with (16). Moreover, each of {()} can be solved by a gradientbased optimization method (e.g., sequential quadratic programming (SQP) [23]). However, this optimization method requires the gradients of the cost functional and constraints. Now, we will derive these required gradients using the socalled costate method, which is a commonly used technique in the optimal control domain [22, 24, 25].
Define The gradients of the cost functional defined in (12) with respect to the control variables are given in the following theorem.
Theorem 1. Let . Then where is the solution of the following costate system:with the initial conditions
Proof. The derivations of the gradients of the cost functional with respect to and are similar. Thus, only the derivation of the gradient of the cost functional with respect to is given below.
Let be an arbitrary function that is continuous and differentiable almost everywhere. Then, we may express the cost functional as follows:
Applying integration by parts to the last integral term gives
Differentiating (24) with respect to yieldsPerforming a change of variable in the last term on the righthand side of (25) gives
Substituting (26) into (25) yields
Choosing and substituting (21)(22) into the above equation, we obtain the conclusion (19). The gradient formula (20) can be derived similarly. The proof is complete.
The gradients of the constraint defined in (16) with respect to the control variables are given in the next theorem.
Theorem 2. Let . Then where is the solution of the following costate system: with the initial condition
Proof. The derivations of the gradients of the constraint with respect to and are similar. Thus, only the derivation of the gradient of the constraint with respect to is given below.
Let be an arbitrary function that is continuous and differentiable almost everywhere. Then, we may express the constraint as follows:
Applying integration by parts to the last integral term gives
Differentiating (33) with respect to yieldsPerforming a change of variable in the last term on the righthand side of (34) gives
Substituting (35) into (34) yields
Choosing and substituting (30) and (31) into the above equation, we obtain the conclusion (28). The gradient formula (29) can be derived similarly. Thus, the proof is complete.
On the basis of Theorems 1 and 2, we can develop the following algorithm to solve the (OCP).
Algorithm 1.
Step 1. Choose initial values of , , , and .
Step 2. Solve () using SQP [23] to give ().
Step 3. Check feasibility of , for .
Step 4. If is feasible, then go to Step 5. Otherwise, set , where is a given positive constant. If , where is a prespecified positive constant, then go to Step 6. Otherwise, go to Step 2.
Step 5. Set , where is a given positive constant. If , then go to Step 2. Otherwise, go to Step 6.
Step 6. Output and stop.
At the conclusion of Steps 1–6, is an approximate optimal solution of the (OCP).
5. Numerical Results
Algorithm 1 was applied to seek the optimal control variables in (OCP) and all computations were implemented in Fortran 6.5. We obtained that and h. Here, nonlinear timedelay system (1) was solved using the sixorder RungeKutta method with Lagrange interpolation [26]. The timedelay h, the initial state , the initial function , the terminal time h, and the kinetic parameters and critical concentrations are listed in Table 1. In the optimization process, we assume that , , and . In addition, the smoothing and feasible parameters were initially selected as and . The parameters and were chosen as 0.1 and 0.01 until the solution obtained was feasible for the (OCP). The process terminated when and .

Under the obtained optimal control strategy, we obtained the optimal yield of 1,3PD at the optimal terminal time is mmol h^{−1}, which is increased by 90.04% compared with experiment data [13]. Furthermore, we plotted the optimal yield change of 1,3PD with respect to the fermentation time in Figure 1. From Figure 1, we can see that the yield of 1,3PD at the terminal time is actually increased.
6. Conclusion
In this paper, the optimal control problem in batch process was investigated. We presented the optimal control problem involving a nonlinear timedelay system and with free terminal time. We then transcribe the free time optimal control problem into a new one with fixed terminal time and variable timedelay. We developed a computational method based on the gradients of the cost and constraint functionals with respect to the control variables. Numerical results showed the effectiveness of the developed computational method.
Conflict of Interests
The author declares that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
The supports of the Natural Science Foundation for the Youth of China (no. 11201267) and the Shandong Province Natural Science Foundation of China (no. ZR2011AL003) are gratefully acknowledged.
References
 D. Bonvin, “Optimal operation of batch reactors—a personal view,” Journal of Process Control, vol. 8, no. 56, pp. 355–368, 1998. View at: Publisher Site  Google Scholar
 B. McNeil and L. M. Harvey, Practical Fermentation Technology, John Wiley & Sons, Chichester, UK, 2008.
 B. Srinivasan, S. Palanki, and D. Bonvin, “Dynamic optimization of batch processes I. Characterization of the nominal solution,” Computers and Chemical Engineering, vol. 27, no. 1, pp. 1–26, 2003. View at: Publisher Site  Google Scholar
 Z. K. Nagy and R. D. Braatz, “Openloop and closedloop robust optimal control of batch processes using distributional and worstcase analysis,” Journal of Process Control, vol. 14, no. 4, pp. 411–422, 2004. View at: Publisher Site  Google Scholar
 L. Wang, Z. Xiu, Y. Zhang, and E. Feng, “Optimal control for multistage nonlinear dynamic system of microbial bioconversion in batch culture,” Journal of Applied Mathematics, vol. 2011, Article ID 624516, 11 pages, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 B. Gtinzel, Mikrobielle herstellung von 1, 3propandiol durch Clostridium butyricum und adsorptive abtremutng von diolen, [Ph.D. thesis], TU Braunschweig, Braunschweig, Germany, 1991.
 A. Zeng and W. Deckwer, “A kinetic model for substrate and energy consumption of microbial growth under substratesufficient conditions,” Biotechnology Progress, vol. 11, no. 1, pp. 71–79, 1995. View at: Publisher Site  Google Scholar
 Z. Xiu, A. Zeng, and L. An, “Mathematical modelling of kinetics and research on multiplicity of glycerol bioconversion to 1,3propanediol,” Journal of Dalian University of Technology, vol. 40, pp. 428–433, 2000. View at: Google Scholar
 Y. Q. Sun, W. T. Qi, H. Teng, Z. L. Xiu, and A. P. Zeng, “Mathematical modeling of glycerol fermentation by Klebsiella pneumoniae: concerning enzymecatalytic reductive pathway and transport of glycerol and 1,3propanediol across cell membrane,” Biochemical Engineering Journal, vol. 38, no. 1, pp. 22–32, 2008. View at: Publisher Site  Google Scholar
 J. Wang, J. Ye, H. Yin, E. Feng, and L. Wang, “Sensitivity analysis and identification of kinetic parameters in batch fermentation of glycerol,” Journal of Computational and Applied Mathematics, vol. 236, no. 9, pp. 2268–2276, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 L. Wang, Z. Xiu, Z. Gong, and E. Feng, “Modeling and parameter identification for multistage simulation of microbial bioconversion in batch culture,” International Journal of Biomathematics, vol. 5, no. 4, Article ID 1250034, 12 pages, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 J. Gao, L. Wang, E. Feng, and Z. Xiu, “Modeling and identification of microbial batch fermentation using fuzzy expert system,” Applied Mathematical Modelling, vol. 37, no. 1617, pp. 8079–8090, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 C. Liu, “Modelling and parameter identification for a nonlinear timedelay system in microbial batch fermentation,” Applied Mathematical Modelling, vol. 37, no. 1011, pp. 6899–6908, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 J. Yuan, X. Zhang, X. Zhu, E. Feng, and Z. Xiu, “Modelling and pathway identification involving the transport mechanism of a complex metabolic system in batch culture,” Communications in Nonlinear Science and Numerical Simulation, vol. 19, no. 6, pp. 2088–2103, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 K. Menzel, A. Zeng, H. Biebl, and W. Deckwer, “Kinetic, dynamic, and pathway studies of glycerol metabolism by Klebsiella pneumoniae in anaerobic continuous culture: I. The phenomena and characterization of oscillation and hysteresis,” Biotechnology and Bioengineering, vol. 52, no. 5, pp. 549–560, 1996. View at: Google Scholar
 Z. Xiu, B. Song, L. Sun, and A. Zeng, “Theoretical analysis of effects of metabolic overflow and time delay on the performance and dynamic behavior of a twostage fermentation process,” Biochemical Engineering Journal, vol. 11, no. 23, pp. 101–109, 2002. View at: Publisher Site  Google Scholar
 G. L. Kharatishvili, “Maximum principle in the theory of optimal timedelay processes,” Doklady Akademii Nauk USSR, vol. 136, pp. 39–42, 1961. View at: Google Scholar
 S. Dadebo and R. Luus, “Optimal control of timedelay systems by dynamic programming,” Optimal Control Applications and Methods, vol. 13, no. 1, pp. 29–41, 1992. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 K. H. Wong, L. S. Jennings, and F. Benyah, “The control parametrization enhancing transform for constrained timedelayed optimal control problems,” The ANZIAM Journal, vol. 43, pp. E154–E185, 2002. View at: Google Scholar  MathSciNet
 S. Barati, “Optimal control of constrained time delay systems,” Advanced Modeling and Optimization, vol. 14, no. 1, pp. 103–116, 2012. View at: Google Scholar  MathSciNet
 J. K. Hale and S. M. Verduyn Lunel, Introduction to FunctionalDifferential Equations, Springer, Berlin, Germany, 1993. View at: Publisher Site  MathSciNet
 K. L. Teo, G. J. Goh, and K. H. Wong, A Unified Computational Approach to Optimal Control Problems, Longman Scientific & Technical, 1991.
 J. Nocedal and S. J. Wright, Numerical Optimization, Springer, New York, NY, USA, 1999. View at: MathSciNet
 R. Loxton, Q. Lin, and K. L. Teo, “Minimizing control variation in nonlinear optimal control,” Automatica, vol. 49, no. 9, pp. 2652–2664, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 Q. Lin, R. Loxton, and K. L. Teo, “The control parameterization method for nonlinear optimal control: a survey,” Journal of Industrial and Management Optimization, vol. 10, no. 1, pp. 275–309, 2014. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 J. Stoer and R. Bulirsch, Introduction to Numerical Analysis, Springer, New York, NY, USA, 1980. View at: MathSciNet
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Copyright © 2014 Yongsheng Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.