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MODEL BASED ANALYSIS OF TEMPERATURE PROCESS UNDER VARIOUS CONTROL STRATEGIES | J4RV3I12010

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This paper analyze the temperature process in an empirical model. From the empirical model the system behavior is determined by transfer function and the basic controller strategies Ziegler-Nichols & Cohen-Coon method are implemented in it. With these tuning methods the best control strategies are obtained at the final stage by interfacing the system with NI-myRIO kit.

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Journal for Research | Volume 03| Issue 12 | February 2018 ISSN: 2395-7549 All rights reserved by www.journal4research.org 39 Model based Analysis of Temperature Process under Various Control Strategies Christy Arockia Rani A. Karthik selvan A. Assistant Professor U.G. Student Department of Instrumentation and Control Engineering Department of Instrumentation and Control Engineering Saranathan College of Engineering, Tiruchirappalli-12, India Saranathan College of Engineering, Tiruchirappalli-12, India Lohachandar M. Ramkumar K. U.G. Student U.G. Student Department of Instrumentation and Control Engineering Department of Instrumentation and Control Engineering Saranathan College of Engineering, Tiruchirappalli-12, India Saranathan College of Engineering, Tiruchirappalli-12, India Sakthivel S. U.G. Student Department of Mechanical Engineering Saranathan College of Engineering, Tiruchirappalli-12, India Abstract This paper analyze the temperature process in an empirical model. From the empirical model the system behavior is determined by transfer function and the basic controller strategies Ziegler-Nichols & Cohen-Coon method are implemented in it. With these tuning methods the best control strategies are obtained at the final stage by interfacing the system with NI-myRIO kit. Keywords: PID - ZN II, CC, System Identifications _______________________________________________________________________________________________________ I. INTRODUCTION Nowadays, Proportional-Integral-Derivative (PID) control is the most common control algorithm used in industry and it has been universally accepted in industrial control purposes. The PID controllers plays a vital role in industrial applications for its robust performance in a wide range of operating conditions and for its functional simplicity, which allows the engineer to operate them in a simpler and easier way. As the name suggests, PID algorithm comprises three basic coefficients: proportional, integral and derivative, which are varied accordingly to get optimal response. Closed loop systems, the theory of classical PID and the effects of tuning a closed loop control system are discussed in this paper. To make the control loops work accurately, the PID loop must be tuned. The entire idea of this algorithm lies in manipulating the error which is the difference that occurs between the Process Variable and Set point [1]. The Ziegler - Nichols tuning method is a heuristic method of tuning a PID controller. It was developed by John G. Ziegler and Nathaniel B. Nichols. The Ziegler-Nichols tuning creates a "1/4 wave decay" which is an acceptable result for certain conditions, but not optimal for all the applications. This results in a loop that overshoots its set point after the disturbance or a set point change in it. The response is a semi oscillatory function, with a marginal loop and it can withstand for small changes in process controller [2]. The Cohen-Coon tuning rules are next to the Ziegler-Nichols tuning rules. Cohen and Coon published the tuning method in 1953, which is eleven years later than the Ziegler-Nichols tuning rule. The Cohen-Coon tuning rule is widely applied, where the dead time is less than twice that of the time constant. The Cohen-Coon rule is used to obtain a quarter - amplitude damping response. Even though quarter - amplitude damping type tuning provides very fast disturbance rejection, it tends to be very oscillatory and frequently interacts with the other tuned loops present in the system. Quarter - amplitude damping type tuning also makes the loop to be unstable if the process gain or dead time increases twice of its value. However, this can be minimized by reducing the controller gain into half of its value obtained [3]. Skogestad’s method principle is similar to the dead-time compensation and approximation cannot be achieved in time delay. With dead-time compensation, Skogestad’s method gives good set point tracking and also formula for the integral time Ti can be obtained, through which slow disturbance compensation can be reduced. Other controller design methods which are based on pole-zero cancellations have a chance for the occurrence of slow disturbance compensation if the cancelled pole is close to zero (corresponding to cancellation of a large process time constant using a large Ti)[4].
Model based Analysis of Temperature Process under Various Control Strategies (J4R/ Volume 03 / Issue 12 / 008) All rights reserved by www.journal4research.org 40 In this paper, the important and proposed work is to interface the temperature trainer kit with a virtual instrumentation workbench LabVIEW, via myRIO kit. The process variable is obtained from the temperature sensor and sent to the software via NI-myRIO. The obtained variable is processed with the three basic control strategies namely ZN, C-C, SKOGESTAD. Even these had been implemented earlier by many researchers, our proposed work is to calculate its efficiency in virtual software or via virtual instrumentation workbench. II. TEMPERATURE PROCESS Temperature control is important in heating processes as it can make the materials to react accordingly to its physical properties. Proportional -Integral - Derivative (PID) controllers are the workhorses of many industrial controllers, the frequently used method is Ziegler Nichols, also called as ZN. The need for improved performance of the process has led to the development of robust and optimal controllers. The objective of the work is to maintain the temperature of water in the liquid tank in a desired value. System identification of this temperature process is done by empirical method, which is a nonlinear response and it approximated to be a (First Order plus Dead Time) FOPDT model. In this paper, the major process is to maintain the temperature in the desired value with a help of basic controller strategies and a solid state relay. Fig. 1.1: Block Diagram of Temperature Process The real time temperature values are obtained through RTD sensor and fed to NI-myRIO were the signals are manipulated in the virtual instrument and the control output is fed to the solid state relay to turn on the heater placed in the temperature trainer kit. In the process of controlling, the water is heated by a heater which is controlled by the controller. Fig. 1.2: Experimental Setup for Temperature Process III. SYSTEM IDENTIFICATION Empirical modeling is a useful approach for the analysis of different problems across numerous areas/fields. This type of modeling is particularly used when parametric models cannot be constructed due to some physical constraints. Based on different methodologies and approaches, empirical modeling allows the analyst to obtain an initial understanding of the relationships that exist among the different variables that belong to a particular system or process. In some cases, the results from empirical models can be used in order to make decisions about control variables, with the intent of resolving a given problem in the real-life applications. The most commonly used model to describe the dynamics of the industrial temperature process is a general First Order plus Time Delay Process (FOPTD). And the FOPTD model structure is given in equation as, G(s) = k / (τs+1) e-t d s Where, td – Time delay, K – Process gain, τ - Time constant. IV. CONTROLLER DESIGN In the general, the controller set point (r) and process variable (y) is fed to the comparator and the variable (e) represents the tracking error. This error signal (e) is fed to the PID controller, and the controller computes both the derivative and the integral coefficient of this error signal with respect to time. The control signal (u) of the process is equal to the proportional gain (Kp) times the magnitude of the error plus the integral gain (Ki) times the integral of the error plus the derivative gain (Kd) times the derivative of the error.
Model based Analysis of Temperature Process under Various Control Strategies (J4R/ Volume 03 / Issue 12 / 008) All rights reserved by www.journal4research.org 41 This control signal (u) is fed to the plant and the controller output (y) is obtained. The controller output (y) is given as s feedback signal to compare with the reference signal and to find the error signal (e). The controller considers this error signal and computes the control input. The general relation for obtaining the proportional gain, integral gain and derivative gain is given below Where Kp = proportional gain, Ki = integral gain, and Kd = derivative gain. Ziegler-Nichols Method It is performed by setting the I (integral) and D (derivative) gain to be zero. The proportional gain Kp is then increased (from zero) until it reaches the ultimate gain Ku, at which the output of the control loop has a stable and consistent oscillations. Ku and the oscillation period Tu are used to set the P, I, and D gains depending on the type of controller used: Table - 4.1 Zn Controller Tunning Controller K Ti Td kp PID 98.1 4.686 1.1715 58.86 Cohen-Coon Method By performing a step test at initial steady state conditions, the parameters of a FOPTD (first order plus time delay) model are obtained. This process is carried out to until the process settles at a new steady state and the process parameters t1, τ, τ del, K, r are calculated by, Τ = t 3 – t 1 T = t 1 – t 0 K r Table - 3.2 Cohen-Coon Tunning Controller K Ti Td Kp PID 98.1 7.418 1.10 66.20 V. PERFORMANCE INDEX The objective function considered is based on the error performance criterion. The performance of a controller is best evaluated in terms of error criterion. Such criterions are available in the proposed work and the controller performance is evaluated in terms of Integral of Absolute Errors (IAE) criterion, given by The IAE weighs the error with time and hence emphasizes the error values over the range of 0 to T where T is the expected settling time. VI. RESULTS & COMPARISON The tuned values through the traditional as well as the proposed techniques are analyzed for their responses to a unit step input, with the help of simulation and the real time application for the liquid heating tank is made. A tabulation of the time domain specifications comparison and the performance index comparison for the obtained models with the designed controllers is represented. Fig. 6.1: MATLAB Simulated Output
Model based Analysis of Temperature Process under Various Control Strategies (J4R/ Volume 03 / Issue 12 / 008) All rights reserved by www.journal4research.org 42 It is clear from the responses that the C-C based controller has the advantage of a better closed loop time constant, which enables the controller to act faster with a minimum overshoot and settling time. The response of Skogestad’s controller is unstable than the CC based controller. The time domain specification comparison is done for the C-C and Skogestad’s based controllers for the responses obtained and it is tabulated below. Table - 6.1 Time Domain Spefication Comparison Pid ZN-II Skogestad CC-Method Rise time (seconds) 3.45 30 6.285 Peak time (seconds) 7.8 - 6.8 Overshoot 0.6 - 0.4 Settling time (seconds) 48.5 1009 2.7 VII.CONCLUSION The developed controller tuning for various set points can be suitably tracked by providing a program which can allow the system to choose that value based on the set point selected. The various results presented prove that C-C tuned PID settings are better than the Skogestad’s tuned values. The simulation responses for the models validated reflect the effectiveness of the C- C based controller in terms of time domain specifications. The performance index under the various error criterions for the proposed controller is always less than the tuned controller. In addition to it, the real time responses confirm the validity of the proposed C-C based tuning for the liquid heating tank system. C - C presents multiple advantages to a designer by operating with a reduced number of design methods and parameters to establish the type of the controllers, giving a possibilities of configuring the dynamic performance of the control system with ease and starting the design with a reduced amount of information about the controller, also it focuses on the performance of the control system. These features are illustrated in this work by considering the problem of designing a control system for a plant of first-order system with time delay and deriving the possible results. The future scope of the work is aimed at providing an on-line self-tuning PID controller with the proposed algorithm so as to solve complex issues in real time problems. REFERENCES [1] B.Wayne Bequette “Process Control Modeling, Design, and Simulation” by Prentice-Hall, Inc. 2003. [2] E.Zafiriou, and Morari, M., Robust “A complete IMC design procedure Process Control, Prentice-Hall, Upper Saddle River, NJ. [3] R. Oonsivilai and A. Oonsivilai„PSOs Turbine Optimal PID Tuning by Genetic Algorithm using MSE‟ World Academy of Science, Engineering and Technology. [4] K.S. Tang, K.F. Man, S. Kwongand Q.HE “Genetic Algorithm and their Apllications” Pintu Chandra Shill, Md. Faijul Amin, M. A. H. Akhand, and Kazuyuki Murase Optimization of Interval Type-2 Fuzzy Logic Controller Using Quantum Genetic Algorithms WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012. [5] Sameh F. Desouky Howard M. Schwartz A Novel Technique to Design a Fuzzy Logic Controller Using Q learning and Genetic Algorithms in The PursuitEvasion game IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 [6] S. Nithya, Abhay Singh Gour, N. Sivakumaran, T. K. Radhakrishnanand N. Anantharaman, Model Based Controller Design for Shell and Tube Heat Exchanger, Sensors and Transducers Journal, Vol. 84, Issue 10, pp. 1677-1686, October 2007. [7] D. Rathikarani, D. Sivakumar and S. Anita Janet Mary, Genetic algorithm based optimization of controller tuning for processes with dead time, Proceedings of the International conference on Advances in control and optimization of dynamical systems (ACDOS), 2007. [8] Psong-Wook Shin, Young-Joo Song, Tae-Bong Lee and Hong-KyooChoi, Genetic algorithm for identification of time delay systems from step responses, International Journal of Control, Automation, and Systems, Vol. 5, No. 1, Feb 2007, pp. 79-85. [9] M. V. Sadasivarao and M. Chidambaram, PID controller tuning of cascade control systems using genetic algorithm, Journal of Indian Institute of Science, 86, July-Aug 2006, pp. 343-354. [10] N.Nithyarani, S.M.Girirajkumar and Dr.N.Anatharaman, Modeling and Control of Temperature Process using Genetic Algorithm, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. Vol.2, Issue 11, November 2013. [11] N.Nithyarani and S.Renganathan, Advances in Control Techniques and Process Analysis with LabVIEW and DCS, International Journal of Electronics, Communication & Instrumentation Engineering Research and Development. Vol.3, Issue 2, Jun 2013. [12] N.Nithyarani. Advanced process Analysis on LabVIEW, International Journal of Advanced Research in Electrical and Electronics Engineering. Vol.1, No.1 Nov 2013. [13] Brian D.O., Anderson, (1992), “Controller Design: Moving from Theory to Practice.”IEEE Control systems, pp 16-25. [14] Goldberg D., (1989), “Genetic Algorithms in Search, Optimization and Machine Learning.” Addison Wesley, ISBN: 0201157675. [15] I.J.Nagrath and M.Gopal, “Control System Engineering.” New Age international, ISBN: 8122411924.

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MODEL BASED ANALYSIS OF TEMPERATURE PROCESS UNDER VARIOUS CONTROL STRATEGIES | J4RV3I12010

  • 1. Journal for Research | Volume 03| Issue 12 | February 2018 ISSN: 2395-7549 All rights reserved by www.journal4research.org 39 Model based Analysis of Temperature Process under Various Control Strategies Christy Arockia Rani A. Karthik selvan A. Assistant Professor U.G. Student Department of Instrumentation and Control Engineering Department of Instrumentation and Control Engineering Saranathan College of Engineering, Tiruchirappalli-12, India Saranathan College of Engineering, Tiruchirappalli-12, India Lohachandar M. Ramkumar K. U.G. Student U.G. Student Department of Instrumentation and Control Engineering Department of Instrumentation and Control Engineering Saranathan College of Engineering, Tiruchirappalli-12, India Saranathan College of Engineering, Tiruchirappalli-12, India Sakthivel S. U.G. Student Department of Mechanical Engineering Saranathan College of Engineering, Tiruchirappalli-12, India Abstract This paper analyze the temperature process in an empirical model. From the empirical model the system behavior is determined by transfer function and the basic controller strategies Ziegler-Nichols & Cohen-Coon method are implemented in it. With these tuning methods the best control strategies are obtained at the final stage by interfacing the system with NI-myRIO kit. Keywords: PID - ZN II, CC, System Identifications _______________________________________________________________________________________________________ I. INTRODUCTION Nowadays, Proportional-Integral-Derivative (PID) control is the most common control algorithm used in industry and it has been universally accepted in industrial control purposes. The PID controllers plays a vital role in industrial applications for its robust performance in a wide range of operating conditions and for its functional simplicity, which allows the engineer to operate them in a simpler and easier way. As the name suggests, PID algorithm comprises three basic coefficients: proportional, integral and derivative, which are varied accordingly to get optimal response. Closed loop systems, the theory of classical PID and the effects of tuning a closed loop control system are discussed in this paper. To make the control loops work accurately, the PID loop must be tuned. The entire idea of this algorithm lies in manipulating the error which is the difference that occurs between the Process Variable and Set point [1]. The Ziegler - Nichols tuning method is a heuristic method of tuning a PID controller. It was developed by John G. Ziegler and Nathaniel B. Nichols. The Ziegler-Nichols tuning creates a "1/4 wave decay" which is an acceptable result for certain conditions, but not optimal for all the applications. This results in a loop that overshoots its set point after the disturbance or a set point change in it. The response is a semi oscillatory function, with a marginal loop and it can withstand for small changes in process controller [2]. The Cohen-Coon tuning rules are next to the Ziegler-Nichols tuning rules. Cohen and Coon published the tuning method in 1953, which is eleven years later than the Ziegler-Nichols tuning rule. The Cohen-Coon tuning rule is widely applied, where the dead time is less than twice that of the time constant. The Cohen-Coon rule is used to obtain a quarter - amplitude damping response. Even though quarter - amplitude damping type tuning provides very fast disturbance rejection, it tends to be very oscillatory and frequently interacts with the other tuned loops present in the system. Quarter - amplitude damping type tuning also makes the loop to be unstable if the process gain or dead time increases twice of its value. However, this can be minimized by reducing the controller gain into half of its value obtained [3]. Skogestad’s method principle is similar to the dead-time compensation and approximation cannot be achieved in time delay. With dead-time compensation, Skogestad’s method gives good set point tracking and also formula for the integral time Ti can be obtained, through which slow disturbance compensation can be reduced. Other controller design methods which are based on pole-zero cancellations have a chance for the occurrence of slow disturbance compensation if the cancelled pole is close to zero (corresponding to cancellation of a large process time constant using a large Ti)[4].
  • 2. Model based Analysis of Temperature Process under Various Control Strategies (J4R/ Volume 03 / Issue 12 / 008) All rights reserved by www.journal4research.org 40 In this paper, the important and proposed work is to interface the temperature trainer kit with a virtual instrumentation workbench LabVIEW, via myRIO kit. The process variable is obtained from the temperature sensor and sent to the software via NI-myRIO. The obtained variable is processed with the three basic control strategies namely ZN, C-C, SKOGESTAD. Even these had been implemented earlier by many researchers, our proposed work is to calculate its efficiency in virtual software or via virtual instrumentation workbench. II. TEMPERATURE PROCESS Temperature control is important in heating processes as it can make the materials to react accordingly to its physical properties. Proportional -Integral - Derivative (PID) controllers are the workhorses of many industrial controllers, the frequently used method is Ziegler Nichols, also called as ZN. The need for improved performance of the process has led to the development of robust and optimal controllers. The objective of the work is to maintain the temperature of water in the liquid tank in a desired value. System identification of this temperature process is done by empirical method, which is a nonlinear response and it approximated to be a (First Order plus Dead Time) FOPDT model. In this paper, the major process is to maintain the temperature in the desired value with a help of basic controller strategies and a solid state relay. Fig. 1.1: Block Diagram of Temperature Process The real time temperature values are obtained through RTD sensor and fed to NI-myRIO were the signals are manipulated in the virtual instrument and the control output is fed to the solid state relay to turn on the heater placed in the temperature trainer kit. In the process of controlling, the water is heated by a heater which is controlled by the controller. Fig. 1.2: Experimental Setup for Temperature Process III. SYSTEM IDENTIFICATION Empirical modeling is a useful approach for the analysis of different problems across numerous areas/fields. This type of modeling is particularly used when parametric models cannot be constructed due to some physical constraints. Based on different methodologies and approaches, empirical modeling allows the analyst to obtain an initial understanding of the relationships that exist among the different variables that belong to a particular system or process. In some cases, the results from empirical models can be used in order to make decisions about control variables, with the intent of resolving a given problem in the real-life applications. The most commonly used model to describe the dynamics of the industrial temperature process is a general First Order plus Time Delay Process (FOPTD). And the FOPTD model structure is given in equation as, G(s) = k / (τs+1) e-t d s Where, td – Time delay, K – Process gain, τ - Time constant. IV. CONTROLLER DESIGN In the general, the controller set point (r) and process variable (y) is fed to the comparator and the variable (e) represents the tracking error. This error signal (e) is fed to the PID controller, and the controller computes both the derivative and the integral coefficient of this error signal with respect to time. The control signal (u) of the process is equal to the proportional gain (Kp) times the magnitude of the error plus the integral gain (Ki) times the integral of the error plus the derivative gain (Kd) times the derivative of the error.
  • 3. Model based Analysis of Temperature Process under Various Control Strategies (J4R/ Volume 03 / Issue 12 / 008) All rights reserved by www.journal4research.org 41 This control signal (u) is fed to the plant and the controller output (y) is obtained. The controller output (y) is given as s feedback signal to compare with the reference signal and to find the error signal (e). The controller considers this error signal and computes the control input. The general relation for obtaining the proportional gain, integral gain and derivative gain is given below Where Kp = proportional gain, Ki = integral gain, and Kd = derivative gain. Ziegler-Nichols Method It is performed by setting the I (integral) and D (derivative) gain to be zero. The proportional gain Kp is then increased (from zero) until it reaches the ultimate gain Ku, at which the output of the control loop has a stable and consistent oscillations. Ku and the oscillation period Tu are used to set the P, I, and D gains depending on the type of controller used: Table - 4.1 Zn Controller Tunning Controller K Ti Td kp PID 98.1 4.686 1.1715 58.86 Cohen-Coon Method By performing a step test at initial steady state conditions, the parameters of a FOPTD (first order plus time delay) model are obtained. This process is carried out to until the process settles at a new steady state and the process parameters t1, τ, τ del, K, r are calculated by, Τ = t 3 – t 1 T = t 1 – t 0 K r Table - 3.2 Cohen-Coon Tunning Controller K Ti Td Kp PID 98.1 7.418 1.10 66.20 V. PERFORMANCE INDEX The objective function considered is based on the error performance criterion. The performance of a controller is best evaluated in terms of error criterion. Such criterions are available in the proposed work and the controller performance is evaluated in terms of Integral of Absolute Errors (IAE) criterion, given by The IAE weighs the error with time and hence emphasizes the error values over the range of 0 to T where T is the expected settling time. VI. RESULTS & COMPARISON The tuned values through the traditional as well as the proposed techniques are analyzed for their responses to a unit step input, with the help of simulation and the real time application for the liquid heating tank is made. A tabulation of the time domain specifications comparison and the performance index comparison for the obtained models with the designed controllers is represented. Fig. 6.1: MATLAB Simulated Output
  • 4. Model based Analysis of Temperature Process under Various Control Strategies (J4R/ Volume 03 / Issue 12 / 008) All rights reserved by www.journal4research.org 42 It is clear from the responses that the C-C based controller has the advantage of a better closed loop time constant, which enables the controller to act faster with a minimum overshoot and settling time. The response of Skogestad’s controller is unstable than the CC based controller. The time domain specification comparison is done for the C-C and Skogestad’s based controllers for the responses obtained and it is tabulated below. Table - 6.1 Time Domain Spefication Comparison Pid ZN-II Skogestad CC-Method Rise time (seconds) 3.45 30 6.285 Peak time (seconds) 7.8 - 6.8 Overshoot 0.6 - 0.4 Settling time (seconds) 48.5 1009 2.7 VII.CONCLUSION The developed controller tuning for various set points can be suitably tracked by providing a program which can allow the system to choose that value based on the set point selected. The various results presented prove that C-C tuned PID settings are better than the Skogestad’s tuned values. The simulation responses for the models validated reflect the effectiveness of the C- C based controller in terms of time domain specifications. The performance index under the various error criterions for the proposed controller is always less than the tuned controller. In addition to it, the real time responses confirm the validity of the proposed C-C based tuning for the liquid heating tank system. C - C presents multiple advantages to a designer by operating with a reduced number of design methods and parameters to establish the type of the controllers, giving a possibilities of configuring the dynamic performance of the control system with ease and starting the design with a reduced amount of information about the controller, also it focuses on the performance of the control system. These features are illustrated in this work by considering the problem of designing a control system for a plant of first-order system with time delay and deriving the possible results. The future scope of the work is aimed at providing an on-line self-tuning PID controller with the proposed algorithm so as to solve complex issues in real time problems. REFERENCES [1] B.Wayne Bequette “Process Control Modeling, Design, and Simulation” by Prentice-Hall, Inc. 2003. [2] E.Zafiriou, and Morari, M., Robust “A complete IMC design procedure Process Control, Prentice-Hall, Upper Saddle River, NJ. [3] R. Oonsivilai and A. Oonsivilai„PSOs Turbine Optimal PID Tuning by Genetic Algorithm using MSE‟ World Academy of Science, Engineering and Technology. [4] K.S. Tang, K.F. Man, S. Kwongand Q.HE “Genetic Algorithm and their Apllications” Pintu Chandra Shill, Md. Faijul Amin, M. A. H. Akhand, and Kazuyuki Murase Optimization of Interval Type-2 Fuzzy Logic Controller Using Quantum Genetic Algorithms WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012. [5] Sameh F. Desouky Howard M. Schwartz A Novel Technique to Design a Fuzzy Logic Controller Using Q learning and Genetic Algorithms in The PursuitEvasion game IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 [6] S. Nithya, Abhay Singh Gour, N. Sivakumaran, T. K. Radhakrishnanand N. Anantharaman, Model Based Controller Design for Shell and Tube Heat Exchanger, Sensors and Transducers Journal, Vol. 84, Issue 10, pp. 1677-1686, October 2007. [7] D. Rathikarani, D. Sivakumar and S. Anita Janet Mary, Genetic algorithm based optimization of controller tuning for processes with dead time, Proceedings of the International conference on Advances in control and optimization of dynamical systems (ACDOS), 2007. [8] Psong-Wook Shin, Young-Joo Song, Tae-Bong Lee and Hong-KyooChoi, Genetic algorithm for identification of time delay systems from step responses, International Journal of Control, Automation, and Systems, Vol. 5, No. 1, Feb 2007, pp. 79-85. [9] M. V. Sadasivarao and M. Chidambaram, PID controller tuning of cascade control systems using genetic algorithm, Journal of Indian Institute of Science, 86, July-Aug 2006, pp. 343-354. [10] N.Nithyarani, S.M.Girirajkumar and Dr.N.Anatharaman, Modeling and Control of Temperature Process using Genetic Algorithm, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. Vol.2, Issue 11, November 2013. [11] N.Nithyarani and S.Renganathan, Advances in Control Techniques and Process Analysis with LabVIEW and DCS, International Journal of Electronics, Communication & Instrumentation Engineering Research and Development. Vol.3, Issue 2, Jun 2013. [12] N.Nithyarani. Advanced process Analysis on LabVIEW, International Journal of Advanced Research in Electrical and Electronics Engineering. Vol.1, No.1 Nov 2013. [13] Brian D.O., Anderson, (1992), “Controller Design: Moving from Theory to Practice.”IEEE Control systems, pp 16-25. [14] Goldberg D., (1989), “Genetic Algorithms in Search, Optimization and Machine Learning.” Addison Wesley, ISBN: 0201157675. [15] I.J.Nagrath and M.Gopal, “Control System Engineering.” New Age international, ISBN: 8122411924.