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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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. 7

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

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Fig. 8

IAE and ISE variations of different controllers for set point tracking

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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. 10

Comparison of the designed controllers for noise suppression

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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. 12

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



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