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Research Papers

Two Degrees-of-Freedom Fractional-Order Proportional–Integral–Derivative-Based Temperature Control of Fermentation Process

[+] Author and Article Information
Nikhil Pachauri

Department of Electrical and
Electronics Engineering,
Delhi Technical Campus (DTC),
28/1, Knowledge Park-III,
Greater Noida 201306, Uttar Pradesh, India
e-mail: nikhilpchr@gmail.com

Vijander Singh

Instrumentation and Control
Engineering Division,
Azad Hind Fauz Marg, NSIT,
Dwarka Sec-3,
New Delhi 110078, India
e-mail: vijaydee@gmail.com

Asha Rani

Instrumentation and Control
Engineering Division,
Azad Hind Fauz Marg, NSIT,
Dwarka Sec-3,
New Delhi 110078, India
e-mail: ashansit@gmail.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 19, 2016; final manuscript received November 29, 2017; published online January 19, 2018. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 140(7), 071006 (Jan 19, 2018) (10 pages) Paper No: DS-16-1505; doi: 10.1115/1.4038656 History: Received October 19, 2016; Revised November 29, 2017

Temperature is one of the essential parameter in a fermentation process, which affects the thermal movement of cells. The temperature range for such processes is very tight and must be maintained precisely for efficient operation. Therefore, in this work combination of fractional calculus and two degrees-of-freedom proportional–integral–derivative (2DOF-PID) controller is proposed for desired temperature control of bioreactor. The 2DOF-PID controller incorporates an extra control loop, whereas fractional operator offers additional tractability for alteration in system dynamics. In order to achieve efficient execution of the control strategies, design parameters are optimized with the help of nondominated sorted genetic algorithm-II (NSGA-II) and Cuckoo search algorithm (CSA). NSGA-II-tuned controllers perform better than the CSA-tuned controllers. Further, the results show that the proposed controller regulates the temperature of bioreactor in a more robust and efficient manner in comparison to other designed controllers.

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References

Shuller, M. L. , and Kragi, F. , 2002, Bioprocess Engineering: Basic Concepts, 2nd ed., Prentice hall, Upper Saddle River, NJ.
Ławryńczuk, M. , 2008, “ Modelling and Nonlinear Predictive Control of a Yeast Fermentation Biochemical Reactor Using Neural Network,” Chem. Eng. J., 145(2), pp. 290–307. [CrossRef]
Schaum, J. , Moreno, A. , Salgado, J. D. , and Alvarez, J. , 2008, “ Dissipativity-Based Observer and Feedback Control Design for a Class of Chemical Reactor,” J. Process Control, 18(9), pp. 896–905. [CrossRef]
Yin, Y. , Liu, F. , and Shi, P. , 2011, “ Finite-Time Gain-Scheduled Control on Stochastic Bioreactor Systems With Partially Known Transition Jump Rates,” Circuits, Syst., Signal Process., 30(3), pp. 609–627. [CrossRef]
Imtiaz, U. , Assadzadeh, A. , Jamuar, S. S. , and Sahu, J. N. , 2013, “ Bioreactor Temperature Profile Controller Using Inverse Neural Network (INN) for Production of Ethanol,” J. Process Control, 23(5), pp. 731–742. [CrossRef]
Imtiaz, U. , Jamuar, S. S. , Sahu, J. N. , and Ganesan, P. B. , 2014, “ Bioreactor Profile Control by a Nonlinear Auto Regressive Moving Average Neuro and Two Degree of Freedom PID Controllers,” J. Process Control, 24(11), pp. 1761–1777. [CrossRef]
Pachauri, N. , Rani, A. , and Singh, V. , 2017, “ GA-Tuned 2DOFPID-Based Biomass Concentration Control of Bioreactor,” International Conference on Intelligent Communication, Control and Devices (ICICCD), Dehradun, India, Apr. 2–3, pp. 879–885.
Harja, G. , Nascu, I. , Muresan, C. , and Nascu, I. , 2016, “ Improvements in Dissolved Oxygen Control of an Activated Sludge Wastewater Treatment Process,” Circ., Syst., Signal Process., 35(6), pp. 2259–2281. [CrossRef]
Chan, L. L. T. , Chen, T. , and Chen, J. , 2016, “ PID Based Nonlinear Process Control Model Uncertainty by Using Gaussian Process Model,” J. Process Control, 42, pp. 77–89. [CrossRef]
Sondhi, S. , and Hote, Y. V. , 2014, “ Fractional Order PID Controller for Load Frequency Control,” Energy Convers. Manage., 85, pp. 343–353. [CrossRef]
Debbarma, S. , Saikia, L. C. , and Sinha, N. , 2014, “ Automatic Generation Control Using Two Degree of Freedom Fractional Order PID Controller,” Electr. Power Energy Syst., 58, pp. 120–129. [CrossRef]
Yi, S. , Chen, F. J. , and Lin , 2013, “ Decentralized PID Neural Network Control for Five Degree-of-Freedom Active Magnetic Bearing,” Eng. Appl. Artif. Intell., 26(3), pp. 962–973. [CrossRef]
Rajasekhar, A. , Jatoth, R. K. , and Abraham, A. , 2014, “ Design of Intelligent PID/PIλDμ Speed Cotroller for Chopper Fed DC Motor Drive Using Opposition Based Artificial Bee Colony Algorithm,” Eng. Appl. Artif. Intell., 29, pp. 13–32. [CrossRef]
Araki, M. , and Taguchi, H. , 2003, “ Two-Degree of Freedom PID Controllers,” Int. J Control Autom. Syst., 1(4), pp. 401–411. http://ijcas.com/admin/paper/files/401-411.pdf
Alfaro, V. M. , and Vilanova, R. , 2012, “ Model-Reference Robust Tuning of 2DoF PI Controllers for First- and Second-Order Plus Dead-Time Controlled Processes,” J. Process Control, 22(2), pp. 359–374. [CrossRef]
Ghosh, A. , Krishan, T. R. , Tejaswy, P. , Mandal, A. , Pradhan, J. K. , and Ranasingh, S. , 2014, “ Design and Implementation of a 2-DOF PID Compensation for Magnetic Levitation Systems,” ISA Trans., 53(4), pp. 1216–1222. [CrossRef] [PubMed]
Sharma, R. , Gaur, P. , and Mittal, A. P. , 2015, “ Performance Analysis of Two-Degree of Freedom Fractional Order PID Controllers for Robotic Manipulator With Payload,” ISA Trans., 58(3), pp. 279–291. [CrossRef] [PubMed]
Pachauri, N. , Singh, V. , and Rani, A. , 2017, “ Two Degree of Freedom PID Based Inferential Control of Continuous Bioreactor for Ethanol Production,” ISA Trans., 68, pp. 235–250. [CrossRef] [PubMed]
Tiwari, D. , Pachauri, N. , Rani, A. , and Singh, V. , 2016, “ Fractional Order PID (FOPID) Controller Based Temperature Control of Bioreactor,” IEEE International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, Mar. 3–5, pp. 2968–2973.
Pachauri, N. , Rani, A. , and Singh, V. , 2017, “ Bioreactor Temperature Control Using Modified Fractional Order IMC-PID for Ethanol Production,” Chem. Eng. Res. Des., 122, pp. 97–112. [CrossRef]
Aiba, S. , and Shoda, M. N. , 1968, “ Kinetics of Product Inhibition in Alcoholic Fermentation,” Biotechnol. Bioeng., 10(6), pp. 845–864. [CrossRef]
Oustaloup, A. , Levron, F. , Mathieu, B. , and Nanot, F. M. , 2000, “ Frequency-Band Complex Noninteger Differentiator: Characterization and Synthesis,” IEEE Trans Circ. Syst. I: Fundam. Theory Appl., 47(1), pp. 25–39. [CrossRef]
Tepljakov, A. , Petlenkov, E. , and Belikov, J. , 2011, “ FOMCON: A MATLAB Toolbox for Fractional-Order System Identification and Control,” Int. J. Microelectron. Comput. Sci., 2, pp. 51–62. https://www.researchgate.net/profile/Juri_Belikov/publication/259741855_FOMCON_a_MATLAB_toolbox_for_fractional-order_system_identification_and_control/links/541c91380cf2218008cb44fc/FOMCON-a-MATLAB-toolbox-for-fractional-order-system-identification-and-control.pdf
Srinivas, N. , and Deb, K. , 1994, “ Multiobjective Function Optimization Using Non-Dominated Sorting Genetic Algorithms,” Evol. Comput., 2(3), pp. 221–248. [CrossRef]
Deb, K. , Pratap, A. , Agarwal, S. , and Meyarivan, T. , 2002, “ A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II,” IEEE Trans. Evol. Comput., 6(2), pp. 182–197. [CrossRef]
Yadav, J. , Rani, A. , and Singh, V. , 2016, “ Performance Analysis of Fuzzy-PID Controller for Blood Glucose Regulation in Type-1 Diabetic Patients,” J. Med. Syst., 40, p. 254. [CrossRef] [PubMed]

Figures

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Fig. 1

Schematic diagram of continuous bioreactor: CS, glucose concentration; CP, ethanol concentration; CX, cell concentration; Tr, reactor temperature; Tag, jacket temperature; Fag, flow of cooling agent; CO2, dissolved oxygen concentration

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Fig. 2

Basic internal structure of 2DOF-FOPID control scheme for bioreactor

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Fig. 3

(a) Flowchart for NSGA-II and (b) flowchart for CSA

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Fig. 4

Pareto-optimal sets of optimization problem for: (a) PID, (b) 2DOF-PID, and (c) 2DOF-FOPID using NSGA-II

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Fig. 5

Convergence curve for (a) PID, (b) 2DOF-PID, and (c) 2DOF-FOPID using CSA

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Fig. 6

Comparison of CSA and NSGA-II tuned controllers: (a) reactor temperature using PID controller, (b) reactor temperature using 2DOF-PID controller, and (c) reactor temperature using 2DOF-FOPID controller

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Fig. 12

Comparison of NSGA-II tuned controllers for ±5% uncertainty

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Fig. 11

Comparison of designed controllers for added disturbance 0.55 sin 20t in controller output: (a) reactor temperature and (b) jacket temperature

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Fig. 10

Comparison of the designed controllers for noise suppression

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Fig. 9

Noise suppression by PID, 2DOF-PID, and 2DOF-FOPID controllers: (a) reactor temperature and (b) controller output

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Fig. 8

IAE and ISE variations of different controllers for set point tracking

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Fig. 7

Set point tracking performance of the designed controllers: (a) reactor temperature and (b) manipulating variable (Fag)

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