June  2017, 7(2): 235-257. doi: 10.3934/mcrf.2017008

Optimal control of a multi-level dynamic model for biofuel production

1. 

Institut de Mathématiques de Bourgogne, COMUE Université Bourgogne-Franche Comté, 9 Avenue Alain Savary, 21078 Dijon, France

2. 

Department of Mathematical Sciences and Center, for Computational and Integrative Biology, Rutgers University 311 N 5th St, 08102 Camden NJ, USA

* Corresponding author

Received  March 2016 Revised  October 2016 Published  April 2017

Dynamic flux balance analysis of a bioreactor is based on the coupling between a dynamic problem, which models the evolution of biomass, feeding substrates and metabolites, and a linear program, which encodes the metabolic activity inside cells. We cast the problem in the language of optimal control and propose a hybrid formulation to model the full coupling between macroscopic and microscopic level. On a given location of the hybrid system we analyze necessary conditions given by the Pontryagin Maximum Principle and discuss the presence of singular arcs. For the multi-input case, under suitable assumptions, we prove that generically with respect to initial conditions optimal controls are bang-bang. For the single-input case the result is even stronger as we show that optimal controls are bang-bang.

Citation: Roberta Ghezzi, Benedetto Piccoli. Optimal control of a multi-level dynamic model for biofuel production. Mathematical Control and Related Fields, 2017, 7 (2) : 235-257. doi: 10.3934/mcrf.2017008
References:
[1]

J. Alford, Bioprocess control: Advances and challenges, Computers & Chemical Engineering, 30 (2006), 1464-1475.  doi: 10.1016/j.compchemeng.2006.05.039.

[2]

P. T. Benavides and U. Diwekar, Optimal control of biodiesel production in a batch reactor: Part Ⅰ: Deterministic control, Fuel, 94 (2012), 211-217. 

[3]

M. S. Branicky, Introduction to hybrid systems, In Handbook of Networked and Embedded Control Systems, Control Eng. , pages 91-116. Birkhäuser Boston, Boston, MA, 2005. doi: 10.1007/0-8176-4404-0_5.

[4]

A. Bressan and B. Piccoli, Introduction to the Mathematical Theory of Control, volume 2 of AIMS Series on Applied Mathematics. American Institute of Mathematical Sciences (AIMS), Springfield, MO, 2007.

[5]

É. Busvelle and J.-P. Gauthier, On determining unknown functions in differential systems, with an application to biological reactors, ESAIM Control Optim. Calc. Var., 9 (2003), 509-551.  doi: 10.1051/cocv:2003025.

[6]

M. Caponigro, R. Ghezzi, B. Piccoli and E. Trélat, Regularization of chattering phenomena via bounded variation control, preprint, 2013, arXiv: 1303.5796.

[7]

Y. ChitourF. Jean and E. Trélat, Singular trajectories of control-affine systems, SIAM J. Control Optim., 47 (2008), 1078-1095.  doi: 10.1137/060663003.

[8]

M. W. CovertC. Schilling and B. Palsson, Regulation of gene expression in flux balance models of metabolism, J Theor Biol., 213 (2001), 73-88. 

[9]

M. W. CovertN. XiaoT. J. Chen and J. R. Karr, Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli, Bioinformatics, 24 (2008), 2044-2050. 

[10]

M. D. Di Benedetto and A. Sangiovanni-Vincentelli, Hybrid Systems: Computation and Control Lecture Notes in Comput. Sci. 2034. Springer-Verlag, Berlin, Heidelberg, 2001.

[11]

T. EeveraK. Rajendran and S. Saradha, Biodiesel production process optimization and characterization to assess the suitability of the product for varied environmental conditions, Renewable Energy, 34 (2009), 762-765.  doi: 10.1016/j.renene.2008.04.006.

[12]

A. T. Fuller, Study of an optimum non-linear control system, J. Electronics Control (1), 15 (1963), 63-71.  doi: 10.1080/00207216308937555.

[13]

M. Garavello and B. Piccoli, Hybrid necessary principle, SIAM J. Control Optim., 43 (2005), 1867-1887 (electronic).  doi: 10.1137/S0363012903416219.

[14]

J.-P. GauthierH. Hammouri and S. Othman, A simple observer for nonlinear systems applications to bioreactors, IEEE Trans. Automat. Control, 37 (1992), 875-880.  doi: 10.1109/9.256352.

[15]

J. L. Hjersted and M. A. Henson, Optimization of fed-batch Saccharomyces cerevisiae fermentation using dynamic flux balance models, Biotechnol. Prog., 22 (2006), 1239-1248. 

[16]

J. L. Hjersted and M. A. Henson, Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae, IET Systems Biology, 3 (2009), 167-179. 

[17]

J. L. HjerstedM. A. Henson and R. Mahadevan, Genome-Scale Analysis of Saccharomyces cerevisiae Metabolism and {E}thanol Production in Fed-Batch Culture, Biotechnology and Bioengineering, 97 (2007), 1190-1204. 

[18]

E. JungS. Lenhart and Z. Feng, Optimal control of treatments in a two-strain tubercolosis model, Discrete and Continuous Dynamical Systems-Series B, 2 (2002), 473-482.  doi: 10.3934/dcdsb.2002.2.473.

[19]

D. KirschnerS. Lenhart and S. Serbin, Optimal control of the chemotherapy of HIV, J. Math. Biol., 35 (1997), 775-792.  doi: 10.1007/s002850050076.

[20]

A. Kremling, K. Bettenbrock and E. Gilles, Analysis of global control of Escherichia coli carbohydrate uptake BMC Systems Biology, 1 (2007), p42. doi: 10.1186/1752-0509-1-42.

[21]

R. MahadevanJ. Edwards and F. Doyle, Dynamic flux balance analysis of diauxic growth in Escherichia coli, Biophys J., 83 (2002), 1331-1340. 

[22]

J. Moreno, Optimal time control of bioreactors for the wastewater treatment, Optimal Control Applications Methods, 20 (1999), 145-164.  doi: 10.1002/(SICI)1099-1514(199905/06)20:3<145::AID-OCA651>3.0.CO;2-J.

[23] B. O. Palsson, Systems Biology -Property of Reconstructed Networks, Cambridge University Press, 2006. 
[24]

L. S. Pontryagin, V. G. Boltyanskiǐ, R. V. Gamkrelidze and E. F. Mishchenko, The Mathematical Theory of Optimal Processes ,"Nauka", Moscow, fourth edition, 1983.

[25]

A. Rapaport and D. Dochain, Minimal time control of fed-batch processes with growth functions having several maxima, IEEE Trans. Automat. Contr., 56 (2011), 2671-2676.  doi: 10.1109/TAC.2011.2159424.

[26]

H. J. Sussmann, A nonsmooth hybrid maximum principle, In Stability and stabilization of nonlinear systems (Ghent, 1999), volume 246 of Lecture Notes in Control and Inform. Sci. , pages 325-354. Springer, London, 1999. doi: 10.1007/1-84628-577-1_17.

[27]

S. TiwariP. VermaP. Singh and R. Tuli, Plants as bioreactors for the production of vaccine antigens, Biotechnology Advances, 27 (2009), 449-467.  doi: 10.1016/j.biotechadv.2009.03.006.

[28]

K. Yamuna Rani and V. S. Ramachandra Rao, Control of fermenters -a review, Bioprocess and Biosystems Engineering, 21 (1999), 77-88.  doi: 10.1007/PL00009066.

show all references

References:
[1]

J. Alford, Bioprocess control: Advances and challenges, Computers & Chemical Engineering, 30 (2006), 1464-1475.  doi: 10.1016/j.compchemeng.2006.05.039.

[2]

P. T. Benavides and U. Diwekar, Optimal control of biodiesel production in a batch reactor: Part Ⅰ: Deterministic control, Fuel, 94 (2012), 211-217. 

[3]

M. S. Branicky, Introduction to hybrid systems, In Handbook of Networked and Embedded Control Systems, Control Eng. , pages 91-116. Birkhäuser Boston, Boston, MA, 2005. doi: 10.1007/0-8176-4404-0_5.

[4]

A. Bressan and B. Piccoli, Introduction to the Mathematical Theory of Control, volume 2 of AIMS Series on Applied Mathematics. American Institute of Mathematical Sciences (AIMS), Springfield, MO, 2007.

[5]

É. Busvelle and J.-P. Gauthier, On determining unknown functions in differential systems, with an application to biological reactors, ESAIM Control Optim. Calc. Var., 9 (2003), 509-551.  doi: 10.1051/cocv:2003025.

[6]

M. Caponigro, R. Ghezzi, B. Piccoli and E. Trélat, Regularization of chattering phenomena via bounded variation control, preprint, 2013, arXiv: 1303.5796.

[7]

Y. ChitourF. Jean and E. Trélat, Singular trajectories of control-affine systems, SIAM J. Control Optim., 47 (2008), 1078-1095.  doi: 10.1137/060663003.

[8]

M. W. CovertC. Schilling and B. Palsson, Regulation of gene expression in flux balance models of metabolism, J Theor Biol., 213 (2001), 73-88. 

[9]

M. W. CovertN. XiaoT. J. Chen and J. R. Karr, Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli, Bioinformatics, 24 (2008), 2044-2050. 

[10]

M. D. Di Benedetto and A. Sangiovanni-Vincentelli, Hybrid Systems: Computation and Control Lecture Notes in Comput. Sci. 2034. Springer-Verlag, Berlin, Heidelberg, 2001.

[11]

T. EeveraK. Rajendran and S. Saradha, Biodiesel production process optimization and characterization to assess the suitability of the product for varied environmental conditions, Renewable Energy, 34 (2009), 762-765.  doi: 10.1016/j.renene.2008.04.006.

[12]

A. T. Fuller, Study of an optimum non-linear control system, J. Electronics Control (1), 15 (1963), 63-71.  doi: 10.1080/00207216308937555.

[13]

M. Garavello and B. Piccoli, Hybrid necessary principle, SIAM J. Control Optim., 43 (2005), 1867-1887 (electronic).  doi: 10.1137/S0363012903416219.

[14]

J.-P. GauthierH. Hammouri and S. Othman, A simple observer for nonlinear systems applications to bioreactors, IEEE Trans. Automat. Control, 37 (1992), 875-880.  doi: 10.1109/9.256352.

[15]

J. L. Hjersted and M. A. Henson, Optimization of fed-batch Saccharomyces cerevisiae fermentation using dynamic flux balance models, Biotechnol. Prog., 22 (2006), 1239-1248. 

[16]

J. L. Hjersted and M. A. Henson, Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae, IET Systems Biology, 3 (2009), 167-179. 

[17]

J. L. HjerstedM. A. Henson and R. Mahadevan, Genome-Scale Analysis of Saccharomyces cerevisiae Metabolism and {E}thanol Production in Fed-Batch Culture, Biotechnology and Bioengineering, 97 (2007), 1190-1204. 

[18]

E. JungS. Lenhart and Z. Feng, Optimal control of treatments in a two-strain tubercolosis model, Discrete and Continuous Dynamical Systems-Series B, 2 (2002), 473-482.  doi: 10.3934/dcdsb.2002.2.473.

[19]

D. KirschnerS. Lenhart and S. Serbin, Optimal control of the chemotherapy of HIV, J. Math. Biol., 35 (1997), 775-792.  doi: 10.1007/s002850050076.

[20]

A. Kremling, K. Bettenbrock and E. Gilles, Analysis of global control of Escherichia coli carbohydrate uptake BMC Systems Biology, 1 (2007), p42. doi: 10.1186/1752-0509-1-42.

[21]

R. MahadevanJ. Edwards and F. Doyle, Dynamic flux balance analysis of diauxic growth in Escherichia coli, Biophys J., 83 (2002), 1331-1340. 

[22]

J. Moreno, Optimal time control of bioreactors for the wastewater treatment, Optimal Control Applications Methods, 20 (1999), 145-164.  doi: 10.1002/(SICI)1099-1514(199905/06)20:3<145::AID-OCA651>3.0.CO;2-J.

[23] B. O. Palsson, Systems Biology -Property of Reconstructed Networks, Cambridge University Press, 2006. 
[24]

L. S. Pontryagin, V. G. Boltyanskiǐ, R. V. Gamkrelidze and E. F. Mishchenko, The Mathematical Theory of Optimal Processes ,"Nauka", Moscow, fourth edition, 1983.

[25]

A. Rapaport and D. Dochain, Minimal time control of fed-batch processes with growth functions having several maxima, IEEE Trans. Automat. Contr., 56 (2011), 2671-2676.  doi: 10.1109/TAC.2011.2159424.

[26]

H. J. Sussmann, A nonsmooth hybrid maximum principle, In Stability and stabilization of nonlinear systems (Ghent, 1999), volume 246 of Lecture Notes in Control and Inform. Sci. , pages 325-354. Springer, London, 1999. doi: 10.1007/1-84628-577-1_17.

[27]

S. TiwariP. VermaP. Singh and R. Tuli, Plants as bioreactors for the production of vaccine antigens, Biotechnology Advances, 27 (2009), 449-467.  doi: 10.1016/j.biotechadv.2009.03.006.

[28]

K. Yamuna Rani and V. S. Ramachandra Rao, Control of fermenters -a review, Bioprocess and Biosystems Engineering, 21 (1999), 77-88.  doi: 10.1007/PL00009066.

Figure 1.  Bioprocess scheme exhibiting full coupling between metabolic activity and external dynamics
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