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IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com

IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com


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  • 1. Mazidah Tajjudin, Mohd Hezri Fazalul Rahiman, Norlela Ishak, Hashimah Ismail, NorhashimMohd Arshad, Ramli Adnan / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-august 2012, pp.2091-2096 Modified Relay Tuning PI with Error Switching: A Case Study on Steam Temperature Regulation Mazidah Tajjudin*, Mohd Hezri Fazalul Rahiman*, Norlela Ishak*, Hashimah Ismail#, Norhashim Mohd Arshad*, Ramli Adnan*. *Faculty of Electrical Engineering University teknologi MARA, Shah Alam, Malaysia. # Faculty of Engineering, University Selangor, Malaysia.Abstract This study evaluates the performance The closed-loop system becomes critically stablebetween reaction curve Ziegler-Nichols tuning whereby the ultimate period and gain are acquired.rules applied to FOPDT model and the open-loop This method is not easy to apply on a working plantrelay feedback tuning method based on ultimate because the system might be brought to unstable state.gain and period. Error and control signal Anyway, these methods were very limited that led toswitching were equipped to enhance the output an endless discussions and studies being conducted inresponse over a limited steam temperature order to improve it.response. The control performance was evaluated Alternatively, Astrom and Hugglandexperimentally on a steam distillation process proposed a more practical solution using a relayregulation. The proposed technique employing feedback test [9] where the automatic tuning wasrelay tuning together with error switching had performed online by switching from automatic modeproduced an astounding closed-loop response even to tuning mode. Details on the implementation can befor a non-linear steam temperature control. referred in [9–12].Keywords – steam temperature; PI control; This paper shared the idea of relayZiegler-Nichols tuning; relay feedback. perturbation on the process in order to get the process gain and frequency. But, instead of implementing a1. INTRODUCTION closed-loop auto-tuned, this method gathered the PID controller has been successfully applied information from the open-loop response. From thesein industries. However, optimal performance of the two parameters, the PID was then tuned using thecontroller is really depends on the tuning method. classical Ziegler-Nichols rules. This simple approachSome prominent tuning methods are the Ziegler- unbelievably produced better results than a model-Nichols, pole-placement and frequency domain based tuning approach as discussed in [13] for metalspecifications. The main issue in PID tuning is that, chamber temperature control. However, in thisthere was no general tuning method applicable for all application, the temperature response was quite lineartypes of applications. and the dynamic response was fast. Regulating a Aidan[1], [2] had summarized more than steam temperature was more challenging.twenty methods of PID tuning with their suitable Steam temperature control had beenapplications and process types. Cominos and Munro performed using various control techniques. Most[3] discussed on the most favorable PID tuning literatures focused on the superheated steamtechniques with their suitable application. More temperature control but some had done a study onstudies being done to improvised or synthesized a new saturated steam temperature [12]. Nevertheless,rule [4] to a specific process type such as an regardless of its temperature range, the studies hadintegrating process [5], integral plus time-delay pointed out the same non-trivial issues in steamprocess [6], and first-order plus dead-time [7]. temperature regulation which is normally comes fromMoreover, the fitness of Ziegler-Nichols rules are also the nonlinearity, slow varying process dynamics, fastdepends on the relative time-delay of the process, see disturbance and unmodeled dynamics [14–17]. These[8]. characteristics made model-based tuning ineffective Generally, Ziegler-Nichols tuning method for steam temperature regulation.was synthesized based on the step response and This study supported the statement byfrequency response methods. The first approach comparing the output gathered from the proposedrequires some information from the open-loop step tuning method and compared it against the model-response which is the gain, time lag and dead-time. based Ziegler-Nichols PID. Experimental results willThe latter is based on the closed-loop response under demonstrate this issue evidently.proportional control. Here, the gain is increased until 2. PROCESS DESCRIPTION AND MODELING Steam distillation is among the most popular method for essential oil extraction process. The proportion of different essential oils extracted by 2091 | P a g e
  • 2. Mazidah Tajjudin, Mohd Hezri Fazalul Rahiman, Norlela Ishak, Hashimah Ismail, NorhashimMohd Arshad, Ramli Adnan / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-august 2012, pp.2091-2096steam distillation is 93% and the remaining 7% isextracted by other methods such as hydro distillation[18]. This method applies hot steam to extract theessential oil from the raw materials. The mixture ofoil and steam will be condensed and separated at theirliquid form. In the majority of cases the oil is lessdense than the water and so forms the top layer of thedistillate and can be separated easily using propermethod and instruments. A pilot-scale steam distillation plantdeveloped for this study consists of a stainless steelcolumn of 26 cm inner diameter and a verticallymounted steel condenser to convert the steam intoliquid form. Figure 1 shows the simplified schematicdiagram of the plant. Two RTDs were installed; Fig.1 Schematic of a steam distillation processRTD1 was immersed in the water to monitor water A)temperature while RTD2 was installed 40cm from B) FOPDT from open-loop responseRTD1 to monitor the steam temperature inside the In process industry, first-order plus dead-column. The distance of the sensor from its heat time (FOPDT) is the most common model structuresource will caused transport delay in steam that was used to represent a process [7], [25], [26]temperature measurement. because the parameters are easier to identify and During operation, steam will be generated by understandable. Most PID control tuning techniquesboiling the water inside the distillation column. In were derived based on FOPDT model acquired fromnormal operation, the water volume is 10 liters. The the open-loop step test. From the response in figure 2,water was heated up by a 1.5kW coil-type heater. It it can be observed that the steam temperature is not atook about 3500s to boil the water. The open-loop linear variable over its full-range and obviouslyresponse under normal operating condition is shown cannot be represented by the FOPDT modelin Figure 2. Column temperature that represents the accurately. However, the response are linear withinsteam temperature started to rise gradually after 1500s specific range i.e. between [30 50]oC, [50 70]oC andwhen water temperature is around 70oC. The steam [70 100]oC.temperature increased exponentially until 80oC where This study was concerned to regulate thethe steam rate hiked to 100oC and saturates. steam temperature at 85oC where the essential oil During closed-loop operation, the steam extraction took place. So, the operating range wastemperature will be measured by RTD2. RTD2 was limited to 80oC to 100oC only.installed over the raw material to monitor thetemperature of steam that passed through the raw Open-loop testmaterial instead of measuring the steam temperature 100that will enter the raw material bed. Continualexposure to excessive heat may degrade the quality of 90 Steam Waterthe essential oil as had been studied and reported in 80 Temperature,degC[19] for ginger extraction. This finding was supported 70by lots of literatures [20–24] from the area of 60botanical and chemical studies. Signal conversion was necessary to convert 50the output from RTD (resistance) to voltage signal 40that was compatible with the acquisition card PCI 301711. The signal converter converts 0oC to 120oC to 0 500 1000 1500 2000 2500 3000 3500 4000 4500 50001V to 5V. 1 volt offset was intentionally put to avoid Time,smisleading during measurement error. Control signal Fig.2 Open-loop response of steam and waterfrom the controller manipulated the heater power by temperatureproviding a dc voltage from 0V to 5V to a continuous General form of the FOPDT model is as follows:power controller. Y(s) K p e −θs = (1) X(s) τs + 1 where X(s) is the input Y(s) is the output of the process Kp is the process gain τ is the time constant 2092 | P a g e
  • 3. Mazidah Tajjudin, Mohd Hezri Fazalul Rahiman, Norlela Ishak, Hashimah Ismail, Norhashim Mohd Arshad, Ramli Adnan / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-august 2012, pp.2091-2096 θ is the dead-time 3. PID CONTROLLER TUNING This section explained on the PID tuning There were two methods available when methods applied during this study. Table 1 estimating FOPDT model from the process reaction summarizes the Ziegler-Nichols tuning rules based on curve [27]. The first method required an inflection process reaction curve parameter that is Kp = 4.5, τ = point to be determined to acquire an accurate model 160 and θ = 350. These rules make use of the parallel whereas the second method requires data at specific structure PID where Kc is the controller gain, Ti is the points which is much easier to be acquired. Using the integral time constant, and Td is the derivative time second method of the process reaction curve will lead constant. The equation for parallel form PID is given to the following equations: in equation 5. The calculated parameter was tabulated Y θ + τ = ∆Y 1 − e−1 = 0.632 ∗ ∆Y in Table 3. τ −1 (2) Y θ+ = ∆Y 1 − e 3 = 0.283 ∗ ∆Y 3 1 𝐺𝑐 𝑠 = 𝐾𝑐 1 + + 𝑇𝑑 𝑠 (5) 𝑇𝑖 𝑠 Thus, the values of time at which the output TABLE 1 ZIEGLER-NICHOLS FROM FOPDT [27] reaches 28.3% and 63.2% of its final value are used to Kc Ti Td calculate the model parameters according to equation P 1 𝜏 - - 3. τ 𝐾𝑝 𝜃 t 28% = θ + 3 PI 0.9 𝜏 3.3𝜃 - 𝐾𝑝 𝜃 t 63% = θ + τ (3) PID 1.2 𝜏 2.0𝜃 0.5𝜃 τ = 1.5(t 63% − t 28% ) 𝐾𝑝 𝜃 θ = t 63% − τ The Ziegler-Nichols tuning PID from This study applied the second method reaction curve method was compared with an open- because it minimizes any doubt during inflection loop relay tuning. The proposed method was based on determination and hence gave more consistent result. an open-loop on-off switching around the operating The FOPDT model was given in equation 4. Model point. In this study, temperature set point was at 85oC. validation was done by comparing the output of the To minimize the effect of measurement error, model and experiment when subjected to 5 volt input hysteresis band of ±5oC was introduced. Manual on- signal. The comparison produced RMSE = 0.3838oC. off switch was equipped in the system to turn the Figure 4a shows the output from the model and controller into fully on or off when steam temperature experiment while figure 4b shows the error between approaches 80oC and 90oC. Steam temperature was both models. then oscillated between 80oC to 90oC. From the relay output, PID gain was calculated using Ziegler-Nichols 4.5 rules based on frequency response analysis of the G s = e−350 s , 80 ≤ 𝑇 ≤ 100℃ (4) 160s + 1 ultimate period and gain. The process was started without a controller FOPDT model validation and the heater was controlled by a relay switch. The 100 relay was turned ON whenever steam temperature 95 reached 80oC and turned OFF when steam Steam temperature,degC 90 temperature was at 90oC. The relay operation was 85 summarized by the following equation: 80 75 5 , 𝑇 ≤ 80℃ 𝑢 𝑡 = 0 500 1000 1500 Time,sec 0 , 𝑇 ≥ 90℃ (6) Fig. 4a Experimental output and model output This process was repeated until a sustained validation oscillation was observed as in figure 5. Figure 5a Model error shows the steam temperature response while figure 5b 2.5 2 1.5 1 shows the relay switching. The critical gain, Ku andModel error,degC 0.5 0 critical period, Pu could be found from these output -0.5 -1 and input signal. Ku was calculated using the -1.5 following equation: -2 -2.5 0 500 1000 1500 Time,sec Fig. 4b Error between experimental and model (7) output where d = magnitude of relay (input) a = magnitude of steam temperature (output) 2093 | P a g e
  • 4. Mazidah Tajjudin, Mohd Hezri Fazalul Rahiman, Norlela Ishak, Hashimah Ismail, NorhashimMohd Arshad, Ramli Adnan / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-august 2012, pp.2091-2096 information about Pu and Ku that was obtained from Relay feedback output the open-loop relay switching. The method was 96 easier to implement rather than the model-based 94 Ziegler-Nichols tuning which requires a perfect 92 model in order to perform well. This study compared 90 Steam temperature,degC the closed-loop response between these two methods. 88 The PI controller was developed using 86 MATLAB/Simulink software. Since the PI was tuned 84 between 80oC and 90oC, a control switch was set 82 prior to the controller block to enable the PI control 80 only after the steam temperature was greater than 78 80oC. Another switch was set before the controller to 76 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 reset the error value when temperature was 80oC. By Time,sec applying both switching, the controller gain will performed accordingly as it was tuned. The switchingFig. 5a Output from manual relay operation sequences were presented in equation 8 and 9 both for the error and control switching respectively. Relay input 0 , 𝑇 < 80℃ (8) 5 𝑒 𝑡 = 𝑒(𝑡) , 𝑇 ≥ 80℃ 4 Control output, volt 5 , 𝑇 < 80℃ 3 𝑢 𝑡 = (9) 𝑢(𝑡) , 𝑇 ≥ 80℃ 2 1 Figure 6a shows the output response with PI controller tuned using reaction curve method. The 0 resulting controller gain was smaller compared to the 0 500 1000 1500 2000 2500 Time,sec 3000 3500 4000 4500 5000 relay method and thus, producing a slower response. The ability of PI controller in regulating desired set point was evaluated with the presence of externalFig. 5b Relay switching input signal disturbance. Steam temperature was disturbed by adding 1 liter of water inside the tank when t = From the values of Pu and Ku, PID 3500s.controller can be calculated using Ziegler-Nichols Water temperature will drop whichrules in table 2. eventually reduced the steam temperature. From figure 6a, steam temperature was dropped to 78oCTABLE 2 ZIEGLER-NICHOLS FROM FREQUENCY 500 s after the water temperature. The controllerDOMAIN[27] reacts by producing a higher control signal that Kc Ti Td increased the temperature to its desired value.P 0.5𝐾 𝑢 Despite of the presence of integral form, the closed-PI 𝐾𝑢 𝑃𝑢 loop response produced some offset during steady- 2.2 1.2 state but the steam temperature might settled down atPID 𝐾𝑢 𝑃𝑢 𝑃𝑢 the desired set point beyond 7000s as can be 1.7 2 8 observed from the output. PI with reaction curve tuning This study implement a PI controller as the 100process can be estimated with a first-order system. 90The calculated PI parameters from both methods 80were tabulated in Table 3. Temperature,degC 70 60 ReferenceTABLE 3 TUNED PID PARAMETERS Steam 50 Water Tuning Kc Ti 40 method Disturbance 30 Reaction curve 0.09 1155 20 Relay feedback 0.9 900 0 1000 2000 3000 Time sec 4000 5000 6000 70004. EXPERIMENTAL RESULTS Fig. 6a Steam temperature with reaction curve This section analyzed the output response tuning PIregulated using PI controller but with different tuningmethod. The proposed method was using the 2094 | P a g e
  • 5. Mazidah Tajjudin, Mohd Hezri Fazalul Rahiman, Norlela Ishak, Hashimah Ismail, NorhashimMohd Arshad, Ramli Adnan / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-august 2012, pp.2091-2096 5 P and I action (frequency tuning) 6 4.5 P action I action 5 4 Switched Error Control signal, volt 3.5 4 3 3 2.5 2 2 1.5 1 1 0 0.5 0 -1 0 1000 2000 3000 4000 5000 6000 7000 Time,sec -2 -3Fig. 6b PI control signal with reaction curve tuning 0 1000 2000 3000 Time,sec 4000 5000 6000 7000 The same procedure was repeated for the PI Fig. 7c P and I control signal with error switchingcontroller tuned with the relay input. The output andcontrol signal was given in figure 7a and 7b 5. CONCLUSIONrespectively. Generally, the relay tuning PI produced Modified tuning based on relay input anda faster and more accurate response. The output practical implementation of PI controller with erroroscillated around the set point within ±2% marginal. switching had been proposed and evaluated experimentally on a non-linear steam temperature PI with frequency tuning regulation. The method was easy to implement but 100 the performance was outstanding when compared 90 with the classical PI with reaction curve tuning. 80 Temperature,degC 70 Reference REFERENCES 60 Steam Water [1] A. O Dwyer, ―A summary of PI and PID 50 controller tuning rules for processes with 40 time delay . Part 1 : PI controller tuning 30 Disturbance rules,‖ in IFAC Workshop on Digital 20 Control, 2000, pp. 175-180. A. O Dwyer, ―PI and PID controller tuning 0 1000 2000 3000 4000 5000 6000 7000 Time,s [2] rules for time delay processes : a summary .Fig. 7a Steam temperature with frequency tuning PI Part 2 : PID controller tuning rules,‖ in Proceedings of the Irish Signals and Systems The control signal was relatively better Conference, pp. 339-346.where it was smoother with no excessive turning on [3] P. Cominos and N. Munro, ―PIDand off compared with the reaction curve tuning. controllers:recent tuning methods and designFigure 7c shows the individual control action of P to specification,‖ in IEE Proceeding inand I together with error signal when switching was Control Theory and Applications, 2002.activated. The advantage of error switching was that [4] Q.-guo Wang, T.-heng Lee, H.-wang Fung,the error magnitude can be limited within 5oC only. Q. Bi, and Y. Zhang, ―PID tuning forThis could minimize the integral wind-up that will improved performance,‖ IEEE Transactionsincrease the control signal until the error returned to on Control Systems Technology, vol. 7, no.negative magnitude. As a result, a smoother control 4, pp. 457-465, Jul. 1999.signal was attained as shown in figure 7b. [5] B. Rice and D. Cooper, ―Design and Tuning of PID Controllers for Integrating ( Non-Self 5 Switched PI: frequency tuning Regulating ) Processes,‖ Time, 2002. 4.5 [6] W. Hu, G. Xiao, and X. Li, ―An analytical 4 method for PID controller tuning with 3.5 specified gain and phase margins for integral Control signal, volt 3 2.5 plus time delay processes.,‖ ISA 2 transactions, vol. 50, no. 2, pp. 268-76, Apr. 1.5 1 2011. 0.5 [7] S. Tavakoli and T. Mahdi, ―Optimal Tuning 0 Of PID Controllers for First Order plus 0 1000 2000 3000 4000 5000 6000 7000 Time,sec Time Delay Models Using Dimensional Analysis,‖ in The Fourth InternationalFig. 7b PI control signal with frequency tuning Conference on Control and Automation (ICCA), 2003, vol. 1, no. June, pp. 942-946. 2095 | P a g e
  • 6. Mazidah Tajjudin, Mohd Hezri Fazalul Rahiman, Norlela Ishak, Hashimah Ismail, NorhashimMohd Arshad, Ramli Adnan / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-august 2012, pp.2091-2096 [8] K. Astrom and T. Hagglund, ―Revisiting the of Temperature on Extraction Rate,‖ Journal Ziegler-Nichols step response method for of American Oil Chemists Society, no. 11, PID control,‖ Journal of Process Control, pp. 1-4, 1951. vol. 14, no. 6, pp. 635-650, Sep. 2004. [22] N. C. Nikolic, S. M. Cakic, S. M. Novakovi, [9] A. Leva, ―PID autotuning algorithm based M. D. Cvetkovic, and M. Z. Stankovic, on relay feedback,‖ IEE proceedings-D, vol. ―Effect of Extraction Techniques on Yield 140, no. 5, pp. 328-338, 1993. and Composition of Soybean Oil,‖ [10] K. J. Astrom, T. H. Lee, K. K. Tan, and K. Macedonian Journal of Chemistry and H. Johansson, ―Recent Advances in Relay Chemical Engineering, vol. 28, no. 2, pp. Feedback Methods-A SUrvey,‖ 1995, pp. 173-179, 2009. 2616-2621. [23] H. Fadel, F. Marx, A. El-Sawy, and A. El- [11] C. C. Hang, K. J. Astrom, and Q. G. Wang, ghorab, ―Effect of extraction techniques on ―Relay feedback auto-tuning of process the chemical composition and antioxidant controllers — a tutorial review,‖ Journal of activity of Eucalyptus camaldulensis var . Process Control, vol. 12, pp. 143-162, 2002. brevirostris leaf oils,‖ Z Lebensm Unters [12] K. J. Astrom and T. Hagglund, ―Practical Forsch A, vol. 208, pp. 212-216, 1999. Experiences of Adaptive Techniques,‖ no. [24] Z. Burkus and F. Temelli, ―Effect of 1599, pp. 1599-1606. Extraction Conditions on Yield , [13] S.-jer Huang and Y.-ho Lo, ―Metal Chamber Composition , and Viscosity Stability of Temperature control by Using Fuzzy PID Barley β-Glucan Gum,‖ vol. 75, no. 6, pp. Gain Auto-tuning strategy,‖ WSEAS 805-809, 1998. Transactions On Systems, vol. 4, no. 1, pp. [25] B. Yang, W.-zhou Li, and F. Yang, ―A new 1-10, 2009. PSO-PID tuning method for time-delay [14] R. N. Silva, P. O. Shirley, J. M. Lemos, and processes,‖ 2008 2nd International a. C. Gonçalves, ―Adaptive regulation of Symposium on Systems and Control in super-heated steam temperature: a case Aerospace and Astronautics, pp. 1-6, Dec. study in an industrial boiler,‖ Control 2008. Engineering Practice, vol. 8, no. 12, pp. [26] J. Chen, ―System Identification and Control 1405-1415, Dec. 2000. of FOPDT or SOPDT Processes,‖ Journal of [15] H. Liu, L. Nie, and H. Huang, ―Superheated Advanced Materials Research, vol. 317– Steam Temperature Control Based on 319, pp. 2393-2397, Aug. 2011. Active Disturbance Rejection Control & [27] T. E. Marlin, Process Control:Designing Neural Network,‖ in Asia Pasific Power and Processes and Control Systems for Dynamic Energy Engineering Conference, 2010, pp. Performance. 2000, pp. 179-181. 25-28. [16] D. Zhao and P. Liang, ―Support Vector Machine Predictive Control for Superheated Steam Temperature Based on Particle Swarm Optimization,‖ in Asia Pacific Power and Energy Engineering Conference (APPEEC), 2010, no. 05006518, pp. 1-4. [17] G. L. Hou, J. H. Zhang, J. Wang, and Q. H. Wu, ―Adaptive Sliding Mode and Fuzzy Gain Scheduling Control for Steam Temperature in Power Plants,‖ in UK International Conference on Control, 2003. [18] P. Masango, ―Cleaner production of essential oils by steam distillation,‖ Journal of Cleaner Production, vol. 13, pp. 833-839, 2005. [19] N. A. Mohamed, ―Study on Important Parameters Affecting the Hydrodistillation for Ginger Oil Production,‖ 2005. [20] E. Cassel, R. M. F. Vargas, N. Martinez, D. Lorenzo, and E. Dellacassa, ―Steam distillation modeling for essential oil extraction process,‖ vol. 9, pp. 171-176, 2008. [21] M. R. Wingard, R. C. Phillips, and B.-knox Division, ―Solvent Extraction IV. The Effect 2096 | P a g e