Volume 4 Issue 07 July-2016 Pages-5537-5550 ISSN(e):2321-7545 Website: http://ijsae.in DOI: http://dx.doi.org/10.18535/ijsre/v4i07.12 Simulation of Intelligent Controller for Temperature of Heat Exchanger System using MATLAB Authors Md. Aftab Alam 1, Dr. Ramjee Parsad Gupta 2 Electrical engineering department, BIT Sindri, Dhanbad, Jharkhand Email address: mdaftabalam1986@gmail.com; ramjee_gupta@yahoo.com ABSTRACT Heat exchanger system is widely used in chemical plants because it can sustain wide range of temperature and pressure. Actual purpose of a heat exchanger system is to transfer heat from a hot fluid to a cooler fluid, so temperature control of outlet fluid is of prime concern. In General, temperature control system has the characteristics of non-linearity, large inertia and time variability. It is difficult to overcome the effects of these factors and get the satisfactory results by using the normal PID controller. Therefore, controllers are implemented in this paper based on comparative analysis to control the output temperature of the heat exchanger system. Feed-forward controller is also employed for better disturbance rejection and more optimal control. The PID, IMC, FUZZY and ANFIS with feedback and PID, IMC, FLC and ANFIS with feedback plus Feed-forward controllers are used for the comparison and based on their overshoot and settling time the conclusions are given using simulation results. Keywords: PID Controller, Feed Forward Controller, Internal Model controller (IMC), Fuzzy Logic Controller (FLC), ANFIS, Heat exchanger 1. INTRODUCTION: Heat exchanger is extensively used in industries such as chemical processing plants as it involves production or absorption of heat. Practically all chemical process involves production or absorption of energy in the form of heat [1] [2]. Heat exchanger is commonly used in a chemical process to transfer heat from the hot fluid through a solid wall to a cooler fluid. Different types of heat exchanger are used in the industry, but most of the industry use shell and tube type heat exchanger system [3] [4] [5]. Shell and tube heat exchangers are probably the most common type of heat exchangers applicable for a wide range of operating temperatures and pressures. In shell and tube heat exchanger one fluid flows through the tubes and a second fluid flows within the space between the tubes and the shell [6]. This paper reports a work on a shell and tube heat exchanger. The outlet temperature of the shell and tube heat exchanger system has to be kept at a desired set point according to the process requirement. First of all, a Conventional PID controller is implemented in a feedback control loop so as to achieve the control objectives. PID controller exhibits undesirable high overshoots. To minimize the overshoot and reduce the settling time internal model based controller, Fuzzy logic controller and ANFIS is implemented. In this paper eight types of controllers (PID, IMC, FLC & ANFIS with feedback and PID, IMC, FLC & ANFIS with feedback and Feed-forward) are designed to achieve the control objective and comparative study between the controllers are evaluated. Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5537
2. DESCRIPTION OF HEAT EXCHANGER: Typical interacting chemical process for heating consists of a chemical reactor and a shell and tube heat exchanger system. The super-heated steam comes from the boiler and flows through the tubes. Whereas, the process fluid flows through the shells of the shell and tube heat exchanger system. The output of the chemical reactor, i.e., process fluid is stored in the storage tank. The storage tank supplies the fluid to the heat exchanger system. The heat exchanger heats up the fluid to a desired set point using super-heated steam supplied from the boiler. The storage tank supplies the process fluid to a heat exchanger system using a pump and a non returning valve. The super heated steam comes from the boiler and flows through the tubes, whereas, the process fluid flows through the shells of the shell and tube heat exchanger system. Fig. No-1(Shell and tube heat exchanger control scheme) The sensing element, thermocouple is implemented in the feedback path of the control architecture. The temperature of the outgoing fluid is measured by the thermocouple and the output of the thermocouple is sent to the transmitter unit, which eventually converts the thermocouple output to a standardized signal in the range of 4-20mA. Output of the transmitter unit is given to the controller unit. The controller implements the control algorithm, compares the output with the set point and then gives necessary command to the final control element via the actuator unit. The actuator unit is a current to pressure converter and the final control unit is an air to open valve. The actuator unit takes the controller output in the range of 4-20mA and converts it in to a standardized pressure signal in the range of 3-15 psig. The valve actuates according to the controller decisions. Fig. No-1 shows the feedback control scheme adopted in heat exchanger system. 2.1. Sources of disturbances: There are two types of disturbances in this process. The flow variation of input fluid The temperature variation of input fluid. 2.2. Experimental data used for modeling: Here the heat exchanger system, actuator, valve, sensor are mathematically modeled using the available experimental data. The experimental process data s are summarized below [16]. Exchanger response to the steam flow gain Time constants Exchanger response to variation of process fluid flow gain Exchanger response to variation of process temperature gain Control valve capacity for steam Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5538
Time constant of control valve The range of temperature sensor Time constant of temperature sensor From the experimental data, transfer functions and the gains are obtained as below. Transfer function of process =G Gain of valve Transfer function of valve Gain of current to pressure converter Transfer function of disturbance variables (i) Flow (ii) Temperature Transfer function of thermocouple (dominant) Figure 2 represents the transfer function block diagram of feedback control of shell and tube heat exchanger system. Fig.No-2(Transfer function model of heat exchanger system) 2.3. Assumptions: (a) Inflow and the outflow rate of fluid are same; therefore fluid level is maintained constant in the heat exchanger. (b) The heat storage capacity of the insulating wall is negligible. 3. DIFFERENT CONTROLLING TECHNIQUES: 3.1.Proportional-Integral-Derivative (PID) Controller: PID controllers are the most prevailing & operative controllers in industry. The integral (I) term guarantees tracing the steady state, while derivative (D) & Proportional (P) provides stability & proper transition behavior. Even complex industrial control systems may comprise a control network whose main control building block is a PID control module. In PID controller Proportional (P) control is not able to remove steady state error or offset error in step response. This offset can be eliminated by Integral (I) control action. Output of I controller at any instant is the area under actuating error signal curve up to that instant. I control removes offset, but may lead to oscillatory response of slowly decreasing amplitude or even increasing amplitude, both of which are undesirable. Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5539
Derivative (D) control action has high sensitivity. It anticipates actuating error, initiates an early correction action and tends to increase stability of system [15]. Ideal PID controller in continuous time is given as Y (t) = (e(t) + + ) (1) Laplace domain representation of ideal PID controller is G c (s) = = ( + ) (2) Fig.No-3(PID Controller) 3.1.1. Tuning of PID Controller: Ziegler and Nichols proposed rules for determining values of based on the transient response characteristics of a given plant. Closed loop oscillation based PID tuning method is a popular method of tuning PID controller. In this kind of tuning method, a critical gain is induced in the forward path of the control system. The high value of the gain takes the system to the verge of instability. It creates oscillation and from the oscillations, the value of frequency and time are calculated. Table 1 gives experimental tuning rules based on closed loop oscillation method. Table 1. Closed loop oscillation based tuning methods Type of Controller P 0 PI 0.83T 0 PID 0.5T 0.125T The characteristic equation 1 + G (S) H(S) = 0 in this case is obtained as below 900 + 420 + 43s + 0.78 + 1 = 0 (3) Applying Routh stability criterion in above equation gives Auxiliary equation is, 420 + 0.78 1 = 0 (4) Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5540
Substituting gives and For the PID controller the values of parameters obtained using Ziegler Nichols closed loop oscillation based tuning methods are Usually, initial design values of PID controller obtained by all means needs to be adjusted repeatedly through computer simulations until the closed loop system performs or compromises as desired. These adjustments are done in MATLAB simulation. 3.2. Feed-Forward Controllers: The disturbance input introduces error in the system performance. In several systems the disturbance can be predicted and its effect can be eliminated with the help of feed forward controller before it can change output of the system. The combined effect of feedback and feed forward controller reduces the overshoot value. Figure 2 shows the transfer function representation of system with feedback and feed-forward controller. =, = The transfer function of the feed-forward controller is = = Here, l is filter parameter. Its range is from 0 to1. Fig. No- 4(Feed-forward plus feedback control block diagram of heat exchanger) 3.3. Internal Model Control (IMC): IMC philosophy relies on Internal Model Principle which states that control can be achieved only if control system encapsulates some representation of process to be controlled either implicitly or explicitly. If control scheme has been developed based on exact model of process, then perfect control is theoretically possible. The main feature of internal model controller is that the process model is in parallel with the actual process. These values are adjusted in MATLAB simulation. Fig.No-5 shows the scheme of IMC. Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5541
Fig.No-5(Internal model control scheme) The process model is factored into two parts, that is invertible part and non-invertible part.the non-invertible part consists of RHP zeros and time delays. This factorization is performed so as to make the resulting internal model controller stable [1, 6, and 16]. Next we set Where is a low pass function defined as Where λ is closed loop time constant. A good rule of thumb is to choose λ to be twice fast as open loop response. Hence λ=10 (Open loop time constant ) Taking 3 rd order low pass filter i.e. n=3 we get the controller for IMC as Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5542
Table 2. Nomenclature Proportional gain Plant transfer function Integral time Model transfer function Derivative time Invertible part of K Gain of step response Non invertible part of T Time constant IMC Controller transfer function L Dead time or Delay Controller transfer function 3.4. Fuzzy logic controller (FLC): The design of fuzzy logic controller is attempted in heat exchanger. The sugeno or Takagi-Sgeno-Kang, method of fuzzy inference, introduce in 1985.It is similar to the Mamdani method in many respects. The first two parts of the fuzzy inference process, fuzzifying the inputs and applying the fuzzy operator, are the same. The main difference between Mamdani and Sugeno is that the Sugeno output membership functions are either linear or constant. The fuzzy controller are designed with single input variable and single output variable. Triangular membership functions are used for input variables. Fig. No-6 (fuzzy control system) 3.5.ANFIS Controller: An adaptive neuro- fuzzy inference system or adaptive network based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi-Sugeno fuzzy inference system. The technique was developed in the early 1990s. Since it integrates both neural network and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference systems corresponds to a set of fuzzy IF-THEN rules that have learning capability to approximate nonlinear functions. Hence ANFIS is considered to be a universal estimator.anfis is the implementation of fuzzy inference system (FIS) to adaptive networks for developing fuzzy rules with suitable membership functions to have required inputs and outputs. Generally learning type in adaptive ANFIS is hybrid learning. General structure of the ANFIS is illustrated in Figure. Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5543
Fig. No-7 (ANFIS Control System) 4. SIMULATION: The simulation for different control mechanism discussed above was carried out in Simulink in MATLAB and simulation results have been obtained. Fig 8, 9, 10 and 11 shows the simulink model of PID controller, IMC, FLC and ANFIS with feedback and Fig. 12, 13,14 and 15 shows the simulink model of PID controller, IMC and FLC with feedback and feed-forward respectively. Fig. No-8 (Simulink model of heat exchanger system with feedback PID Controller) Fig. No-9 (Simulink model of heat exchanger system with feedback IMC Controller) Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5544
Fig. No-10(Simulink model of heat exchanger system with feedback FLC Controller) Fig. No-11(Simulink model of heat exchanger system with feedback ANFIS Controller) Fig. No-12 (Simulink model of heat exchanger system with feedback plus feed-forward PID) Fig. No-13 (Simulink model of heat exchanger system with feedback plus feed-forward IMC Controller) Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5545
Fig. No-14 (Simulink model of heat exchanger system with feedback plus feed-forward FLC Controller) Fig. No-15 (Simulink model of heat exchanger system with feedback plus feed-forward ANFIS Controller) Fig 16,17, 18,19,20,21,22 and 23 shows the step response of above controllers which is obtained by simulating the model in MATLAB. Here x axis denote time in sec. and y axis denote set point value Fig. No-16(Step response of PID with feedback) Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5546
Fig. No-17 (Step response of IMC with feedback) Fig. No-18 (Step response of FLC with feedback) Fig. No-19 (Step response of ANFIS with feedback) Fig.No-20 (Step response of PID with feedback and feed-forward) Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5547
Fig. No-21 (Step response of IMC with feedback and feed-forward) Fig. No- 22(Step response of FLC with feedback and feed-forward) Fig. No- 23 (Step response of ANFIS with feedback and feed-forward) Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5548
5. SIMULATION RESULT AND DISCUSSION: To evaluate the performance of the different controller s two vital parameters of the step response of the system in this paper is considered. The first parameter is the maximum overshoot and the second parameter is the settling time. In this paper control of temperature of heat exchanger is done by 8 different controllers. A comparative study of their performance has been in the table 3 below. Table 3. Comparison of different parameters in controllers: Control system Maximum Overshoot (%) Settling Time (sec) 1.Feedback with PID 200 39 2.Feedback with IMC 140 0 3.Feedback with FLC 18 0 4.Feedback with ANFIS 115 12 5.PID with Feedback plus Feed-forward 205 49 6.IMC with Feedback plus Feed-forward 132 0 7.FLC with Feedback plus Feed-forward 12 0 8.ANFIS with Feedback plus Feed-forward 110 9 From the above observation it is clear that in conventional PID controller with feedback loop the heat exchanger produces an overshoot is 39% which is undesirable. That is why we implement IMC, ANFIS and FLC with feedback and get desired result. Overshoot is 0% in both IMC and FLC and 12% in ANFIS with feedback but settling time of IMC with feedback is (140 sec.), settling time of ANFIS with feedback(115sec) and settling time of FLC is (18sec.) with feedback. Also from the above observation conventional PID with feedback plus Feed-forward gives 49% overshoot and205 sec settling time. Now we implement IMC, ANFIS and FLC with feedback plus feed forward and get desires result. After comparing results for different controllers, we obtain that fuzzy logic controller with feedback and Feed-forward are the one which gives quick response without any oscillations. Finally a Fuzzy logic controller with feedback plus Feed-forward is developed. FLC is an intelligent controller as it resembles human decision making with an ability to generate precise solutions from certain or approximate information. The response is smooth as well as fastest as compared to previous controllers. So FLC is recommended because it is easy to implement, low cost and no need to know exact plant parameters. 6. CONCLUSION: In this paper, a comparative study of performance of conventional (PID and IMC) and intelligent (FLC & ANFIS) controllers are studied. The aim of the proposed controller is the regulation of temperature of the outgoing fluid of a shell and tube heat exchanger system to a desired temperature in the shortest possible time and minimum or no overshoot. Eight different types of controllers are used to control the outlet fluid temperature. Firstly a classical PID Controller, IMC, ANFIS and FLC with feedback are used and then classical PID Controller, IMC, ANFIS and FLC with feedback and Feed-forward are used. Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5549
After comparing results for different controllers, we obtain that fuzzy logic controller with feedback and Feed-forward are the one which gives quick response without any oscillations. REFERENCES: 1. Subhransu Padhee, Yuvraj Bhushan Khare, Yaduvir Singh Internal Model Based PID Control of Shell and Tube Heat Exchanger System, IEEE, JAN 2011. 2. Kiam Heong Ang, Gregory Chong and Yun Li, "PID Control System Analysis, Design, and Technology," IEEE Trans., Control Syst. Technol., vol. 13, no. 4, pp. 559-576, Ju12005. 3. Mridul Pandey, K. Ramkumar & V. Alagesan Design of Fuzzy Logic Controller for a Cross Flow Shell and Tube Heat-Exchanger, IEEE, Mar 2012 4. Ian G Hom et.al, "Improved Filter Design in Internal Model Control," Ind. Eng. Chem. Res., vol. 35, no.10, pp. 3437-3441, Oct 1996 5. Orlando Duran et.al, "Neural Networks for Cost Estimation of Shell and Tube Heat Exchangers, " in Proc. IntI Multi Corif- Eng. Com put. Scient., vol II, pp. 1584-1589,Mar 2008. 6. T. Liu and F. GAO, "New Insight in to Internal Model Control Filter Design for Load Disturbance Rejection, let Control the. Appl., vol. 4, issue 3, pp. 448-460, Mar 2010 7. Ernesto Araujo, Improved Takagi-Sugeno Fuzzy Approach, IEEE International Conference on Fuzzy Systems (FUZZ 2008), pp. 1154-1158, 2008. 8. Chuen Chien Lee, Fuzzy Logic in Control Systems: Fuzzy Logic controller Part 1, IEEE, 1990 9. Takagi T. and M. Sugeno, Fuzzy identification of system and its applications to modeling and control, Proc. IEEE Trans. on System Man and Cybernetics, Vol. SMC-15, No. 1, pp. 116-132, 1985. 10. Sugeno M. and G. T. Kang, Structure identification of fuzzy model, Proc. on the Fuzzy Sets and Systems, Vol. 28, pp. 15-33, 1988. 11. He SZ, Tan H, Xu FL, Wang PZ., "PID self tuning using a fuzzy adaptive mechanism," In: Proc IEEE IntConf on Fuzzy Control System, p.708-713,1993. 12. Tzafestas, NP. Papanikolopoulos, Incremental fuzzy expert PID control, IEEE Trans. Ind. Electron, vol. 37, 1990, p. 107-111 13. I. Skrjanc, D. Matko, predictive functional control base on fuzzy model for heat-exchanger pilot plant, IEEE Transactins of fuzzy systems, vol. 8, Dec. 2000 14. CC. Hnng, KJ. Astrom, Ho WK., Refinements of the Ziegler Nichols tuning formula, Proc IEE, vol. 138, p. 111-138, 1991 15. Kiam Heong Ang, Gregory Chong and Yun Li, "PID Control System Analysis, Design, and Technology," IEEE Trans., Control Syst. Technol., vol. 13, no. 4, pp. 559-576, Ju12005 16. Gang Fu et.al, "An IMC-PID Controller Tuning Strategy Based on the DE and NLJ Hybrid Algorithm," in Proc. Int. Colloq Com put, Commun, Control, Manage., pp. 307-310, Aug 2009. 17. J.-S.R. Jang, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. Systems, Man, and Cybernetics,vol. 23, no. 3, pp. 665-684, May/June 1993. 18. J.S.R. Jang and C.T. Sun, Neuro-Fuzzy Modeling and Control, Proc. IEEE, vol. 83, no. 3, pp. 378-406, Mar. 1995. 19. J. shing and R. Jang, Input Selection for ANFIS Learning, Proc. IEEE Fifth Int l Conf. Fuzzy Systems, Sept. 1996. Md. Aftab Alam, Dr. Ramjee Parsad Gupta IJSRE Volume 4 Issue 7 July 2016 Page 5550