Real Time PID Control for Hydro-diffusion Steam Distillation Essential Oil Extraction System Using

Gradient Descent Tuning Method Zakiah Mohd Yusoff#1, Zuraida Muhammad#2, Mohd Noor Nasriq Nordin#3, Mohd Hezri Fazalul Rahiman#4,

Mohd Nasir Taib#5

#Faculty of Electrical Engineering, UiTM Shah Alam,Selangor, Malaysia 1zakiah_yusoff@yahoo.com

2muhammmad_zuraida@yahoo.com 3nasriq85@yahoo.com

4hezrif@ieee.org 5dr.nasir@ieee.org

Abstract—This paper present the implementation of real time PID controller for hydro-diffusion steam distillation essential oil extraction system based on comparison of Gradient Descent (GD) and Ziegler Nichols (ZN) tuning methods. The first order of Auto Regressive Exogenous (ARX) model was used to describe the behavior of the temperature system and will be use in the controller design. A PID controller is expected to execute a robust response towards parameters changes and better control of steam temperature during distillation process. The system had been evaluated based on rise time, % overshoot, settling time and root mean square error (RMSE). The temperature control was achieved by controlling the voltage fed to the heater ranging from 0V to 5V via digital-to-analogue converter (DAC). Robustness test of the PID controller are based on: i) introduce disturbance and ii) set-point tracking. From the result, the performance of PID controller using GD tuning method reveals that this controller can be adapt for the system because being sensitive to parameters changes and robust as the response can compensate the load disturbance and set point change. Keywords— Gradient descent, Ziegler Nichols, ARX model, hydro-diffusion, distillation process, set-point tracking.

I. INTRODUCTION

Essential oils (EO) can be defined as the volatile aromatic compound by extraction process which can be found mostly from plant’s flowers, bark, wood, leaves, root, seed or resin [1, 2] and it stored in the pockets of the botanical material which can be extracted by either breaking, heating or stirring the pockets in boiling water[3]. The usage of essential oil covers in aromatherapy, food and beverages, perfume, antibacterial activity, and spa practices [2, 4, 5]. Generally, an analytical procedure for essentials oils can be categories into two steps: extraction process (such as steam distillation, hydro-distillation, simultaneous distillation-extraction and solvent extraction), and analysis process gas chromatography (GC), gas

chromatography coupled to mass spectrometry (GC-MS) [1, 4-6].

Steam distillation is the earliest and popular extraction technique for most botanical materials[7] compared to others techniques such as solvent extraction, expression and critical fluid extraction[1, 6]. The proportion of essential oils extracted by steam distillation is 93% compared to the remaining only 7% of essential oil extracted[1]. Steam distillation usually applied on the fresh and dried material[8]. Based on the review above, the author already developed new system that uses steam which is hydro-diffusion steam distillation system.

There are several factors affect the extraction yield such as temperature, pressure, distillation time, chemical composition and particle size [9]. This paper concentrating on steam temperature as a controlled variable since it is one of the significant factors in the extraction process. Controlling and maintaining the temperature at the desired is a bit challenging tasks due to various factors such as slow dynamic response and process have lag or time delay[10, 11]. In order to make sure the quality of essential oil, a good temperature controller is needed. The PID controller have been well developed for industrial and process control today [11, 12, 13] due to simplicity, ease of design, low cost and effectiveness for most linear systems [12]. The application of PID controller in hydro-diffusion steam distillation essential oil extraction system is expected to execute a robust response towards parameters changes and better control of steam temperature during distillation process.

In later part of the paper is organized as follows: In section 2, hardware set up and system configuration is presented. Section 3 describes the plant’s modeling and followed by PID controller design in section 4. The results of experimental evaluation of the proposed technique on the hydro-diffusion steam distillation are presented in Section 5, which is followed by the concluding remarks.

2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012)2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012)

978-1-4673-2036-8/12/$31.00 ©2012 IEEE 288

II. METHODOLOGY i. Hydro-diffusion steam distillation essential oil

extraction system Hydro-diffusion steam distillation essential oil extraction

system is visualized as in Figure 1 and installed at Distributed Control System Laboratory (DCS) in University Teknologi Mara, Malaysia. The distillation column with 10 litre water is heated by immersion heater to produce steam. Pressurized steam is supply from the top of the plant and passed through the botanical materials. Pressurized steam is used to vaporize the volatile oils in the plant material. The steam and oil vapor mixture is then going down through the small tube to the condenser. The implementation of heating process in pilot plant of essential oil extraction system is described in Figure 1.

Figure 1 The hydro-diffusion steam distillation essential oil extraction

system ii. System Configuration

The system is divided into 3 main parts which is process plant, data acquisition using PCI-1711 card and control unit as in Figure 2 below.

Figure 2 Block diagram of system configuration

The system consists of three phase 240 Vac immersion type heater with a power rating of 1.5kW. The sensor is a platinum sensing element, PT-100 3 wires type placed in the material tray. PT100 connected with the signal conditioning circuit varies in terms of resistance value of 100ohm at 0 oC. The temperature control was achieved by controlling the voltage fed to the heater ranging from 0V to 5V via digital-to-analogue converter (DAC). Computer was used as a control unit. PCI-1711 Advantech card was used to interface between hardware and control unit. Software for the system was developing using MATLAB programming to monitor the signal response and for real-time implementation, MATLAB Real Time Workshop (RTW) was employed.

III. PLANT’S MODELING Modeling approach basically starts with the design of

experiment to obtain input and output data, followed by model structure selection, then model estimation and lastly is model validation. The Auto Regressive Exogenous (ARX) model was used to describe the behavior of the global model based on the relationship between input and output data. The system is perturbed with Pseudo Random Binary Sequence (PRBS) as an input and temperature as an output signal. The PRBS amplitude is selected to adhere at maximum of 100% from the full scale (5V) when PRBS signal is 1 and 50% of full scale (2.5V) when the PRBS signal at 0. The output of steam temperature response and PRBS input as in Figure 3.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 500070

75

80

85

90

95

Time sample (s)

Tem

pera

ture

(oC

)

Temperature vs time sample

PB=0.4

(a)

0 500 1000 1500 2000 2500 30000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time(sec)

On/

Off

PRBS input

(b)

Figure 3 Input-output data of heating process, (a) output, (b) input.

RTD

2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012)

289

There are 5000 input-output data collected in this experiment. The PRBS signal is generated with PB=0.4 and clock period factor Nb=2.5 (Nb=1/PB). All data have been sampled at Ts=1. The initial temperature at steam tray is 70.8 oC and was gradually increased up to 94.1 oC.

i. ARX Modeling

The fundamental of ARX model structure is given by equation (1) below [14]

where the polynomials A(q) and B(q) are given by where q-1 is the delay operator and e(t) represent the white noise.

The input-output data are divided equally into 2 sets: odd number samples data (1, 3, 5...) are used for estimation, the even number samples (2, 4, 6...) are used for validation purposed by using interlacing technique. The modeling is done by using model order 1. The obtained ARX model was employed as in equation (3)

A(q) = 1 - q-1

B(q) = 0.0082 q-1

In represent the system, good model should be

representative in all condition of the system. The validation on ARX model shows the performance criteria using fitness, R2 is 98.41%.The performance criteria based on RMSE and mean square error (MSE) are 0.1037 and 0.0108 respectively. This model accepted to perform further for controller design.

IV. PID CONTROLLER DESIGN Figure 4 illustrates the block diagram of PID module for

controlling steam temperature for hydro-diffusion steam distillation plant.

Figure 4 Block diagram of PID controller

The PID controller is give in equation (4) where Kp, Ki, and Kd is the coefficients for proportional (P), integral (I) and derivative (D) respectively whereas e (t) is the error.

There are three gain parameters consists in PID controller.

In this study, GD and ZN tuning methods were applied to tune these parameters. The Ziegler-Nichols tuning method is well known and often forms the basis for tuning procedures used by control system vendors. In the ZN approach, simple formulas for PID controller settings are expressed in terms of ultimate gain Ku and ultimate period Tu of the process. The disadvantages of experimentally determining the critical parameters are that the system can be brought to a state of instability [15, 16]. To overcome these problems, gradient descent optimization is applied to tune these parameters and was done by simulation. Gradient descent is one of the optimization techniques to tune important parameter simultaneously. The method improves dynamic and steady state response and maintains the frequency at desired level [17, 18].Gradient descent tuning algorithm requires no knowledge of the plant to be controlled. This makes the algorithm robust to changes in the plant. It also makes the algorithm universally applicable to linear and nonlinear plants, with or without noise, with or without time delay. The algorithm achieves the tuning objective by minimizing an error function [19].

The PID parameters are tabulated in Table 1 for GD and Table II for ZN tuning method .The tuned parameters of PID controller that was done by simulation is tested on the real system.

TABLE 1

PID PARAMETERS USING GD

Parameter Kp 17.22 Ki 0.0000288 Kd 0.0001583

TABLE II

PID PARAMETERS USING ZN

Parameter Kp 26.8 Ki 0.0002057 Kd 16.575

V. RESULT AND DISCUSSION i. Real Time Implementation In this paper, PID controller is designed to control the steam

temperature at the desired temperature.

(1)

(2)

(3)

(4)

2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012)

290

0 200 400 600 800 1000 1200 1400 1600 1800 200070

75

80

85

90

95

Time sample, s

Ste

am t

empe

ratu

re,

degC

Step response performance

GD

Setpoint

ZN

Figure 5 PID controllers step response

TABLE III

STEP RESPONSE PERFORMANCE

PID

CONTROLLER

Rise time (s)

%OS Settling time (s)

RMSE

GD 345 0.6700 1993 0.4239 ZN 658 0.9800 2667 0.6707

Figure 5 and Table III shows the step response

performance for real time PID controller using GD and ZN tuning method. In term of the rise time, GD gave faster response with 345 s compared with the ZN with 658 s. There is different approximately 0.31% deviation for overshoot between GD=0.67% and ZN= 0.98%. For a settling time criteria, GD reach the faster settling time with 1993 s compared with ZN= 2667 s. Meanwhile, the roots mean squared error (RMSE) showed that PID controller using GD tuning method outperformed PID controller using ZN with 0.4239 and 0.6707 respectively.

Robustness test of the PID controller are based on: i) introduce disturbance and ii) set-point tracking. The objective of the introduce disturbance is to test ability of the PID controller in comprising an abrupt disturbance during running process. The disturbance is cut off the power supply from 1.5kW to 0W for 30 seconds and start at 1000 samples.

0 500 1000 1500 2000 2500

76

78

80

82

84

86

88

90

92

Time sample,s

Ste

am t

empe

ratu

re,d

egC

Disturbance test

Figure 6 PID controllers response with disturbance

TABLE IV

DISTUREBANCE TEST PERFORMANCE

PID CONTROLLER GD ZN Tmin 87.62 oC 87.42 oC

Time taken to go back to the setpoint, (s)

151 386

From the result in Figure 6, after introduce the disturbance at sample 1000s, steam temperature decrease with Tmin= 87.62oC and Tmin= 87.42oC for GD and ZN controller respectively. However, time taken to go back to the set point after introduce disturbance for GD controller is two times faster which is only 151s compared with ZN controller = 386s. Significant improvement can be spotted to GD controller because provide fast and robust response in comprising an abrupt disturbance during running process.

The PID controller also assessed based on the set point tracking where the actual response is expected to follow the reference signal, r(k):

80 oC; for 0 <k< 700 85 oC; for 701 <k< 1200 90 oC; for 1201 <k< 1700

Without disturbance

With disturbance

r(k) =

2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012)

291

200 400 600 800 1000 1200 1400 1600

76

78

80

82

84

86

88

90

92

Time sample,s

Ste

am t

empe

ratu

re,

degC

Setpoint change performance

ZNSetpointGD

Figure 7 PID controllers with set-point tracking

Figure 7 illustrates the comparison between GD and ZN performance in controlling the system using step change input. As can be seen, at higher temperature level, both controllers performed well in tracking the stipulated set point. Regardless of lower temperature, GD still showed a consistent and better control performance compared to ZN.

VI. CONCLUSION This work demonstrates the implementation of real time

PID controller using GD and ZN tuning method for hydro-diffusion steam distillation essential oil extraction system. The performance of PID controller using GD tuning method reveals that this controller can be adapt for the system because being sensitive to parameters changes and robust as the response can compensate the load disturbance and set point change .

ACKNOWLEDGEMENT

This work was conducted on the data gathered at the Faculty of Electrical Engineering, UiTM Shah Alam.The authors would like to thank all staff involved and RMI UiTM and JPbSM UiTM.

REFERENCES

[1] H. Ebrahimzadeh, Y. Yamini, F. Sefidkon, M.

Chaloosi, and S. M. Pourmortazavi, "Chemical composition of the essential oil and supercritical CO2 extracts of Zataria multiflora Boiss," Food Chemistry, vol. 83, pp. 357–361, 2003.

[2] J. M. Roldan-Gutierrez, J. Ruiz-Jimenez, and M. D. L. d. Castro, "Ultrasound-assisted dynamic extraction of valuable compounds from aromatic plants and flowers as compared with steam distillation and superheated liquid extraction," Talanta, vol. 75, pp. 1369–1375, 2008.

[3] F. Chemat, M. E. Lucchesi, J. Smadja, L. Favretto, G. Colnaghi, and F. Visinoni, "Microwave accelerated steam distillation of essential oil from lavender: A rapid, clean and environmentally friendly approach," Analytica Chimica Acta, vol. 555, pp. 157–160, 2006.

[4] E. Cassel, R. M. F. Vargas, N. Martinez, D. Lorenzo, and E. Dellacassa, "Steam distillation modeling for essential oil extraction process," Industrial Crops and Products, vol. 29, pp. 171–176, 2009.

[5] N. Kasuan, M. Yunus, M. H. F. Rahiman, S. R. S.Aris, and M. N. Taib, "Essential Oil Composition of Kaffir Lime: Comparative analysis between Controlled Steam Distillation and Hydrodistillation Extraction Process," Proceedings of 2009 Student Conference on Research and Development (SCOReD 2009), 2009.

[6] F. Peng, L. Sheng, B. Liu, H. Tong, and S. Liu, "Comparison of different extraction methods: steam distillation, simultaneous distillation and extraction and headspace co-distillation, used for the analysis of the volatile components in aged flue-cured tobacco leaves," Journal of Chromatography A, vol. 1040, pp. 1–17, 2004.

[7] P. Masango, "Cleaner production of essential oils by steam distillation," Journal of Cleaner Production, vol. 13, pp. 833-839, 2005.

[8] M. Rouatbi, A. Duquenoy, and P. Giampaoli, "Extraction of the essential oil of thyme and black pepper by superheated steam," Journal of Food Engineering, vol. 78, pp. 708–714, 2007.

[9] N. Kasuan, Z. Yusuf, M. N. Taib, M. H. F. Rahiman, N. Tajuddin, and M. A. A. Aziz, "Robust Steam Temperature Regulation for Distillation of Essential Oil Extraction Process using Hybrid Fuzzy-PD plus PID Controller," World Academy of Science, Engineering and Technology vol. 71, pp. 932-937, 2010.

[10] Z. Yusuf, Z. Janin, and M. N. Taib, "Application of Fuzzy Logic Controller for Glycerin Bleaching Process," Proceedings of 2009 IEEE Student Conference on Research and Development (SCOReD 2009), pp. 438-441, 2009.

[11] N. Tajjudin, Z. Yusuf, M. H. F. Rahiman, and M. N. Taib, "Self tuning PID Controller for Steam Distillation Essential Oil Extraction System," 6th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 259-262, 2010.

[12] K. S. Tang, K. F. Man, G. Chen, and S. Kwang, "An Optimal Fuzzy PID Controller," IEEE Transactions On Industrial Electronics, vol. 48, pp. 757-765, 2001.

[13] Z. Yusuf, Z. Janin, and M. N. Taib, "Self Tuning Fuzzy Logic Controller with Smith Structure for Controlling Glycerin Bleaching Temperature," 2010 IEEE Control and System Graduate Research Colloquium, pp. 39-43, 2010.

[14] N. Ismail, N. Tajjudin, M. H. F. Rahiman, and M. N. Taib, "Modeling of Dynamic Response of

2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012)

292

Essential Oil Extraction Process," 2009 5th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 298-301, 2009.

[15] M. Tajjudin, N. Ishak, H. Ismail, M. H. F. Rahiman, and R. Adnan, "Optimized PID Control using Nelder-Mead Method for Electro-hydraulic Actuator Systems," 2011 IEEE Control and System Graduate Research Colloquium, pp. 90-93, 2011.

[16] R. Adnan, M. Tajjudin, N. Ishak, H. Ismail, and M. H. F. Rahiman, "Self-tuning Fuzzy PID Controller for Electro-Hydraulic Cylinder," IEEE 7th International Colloquium on Signal Processing and its Applications, pp. 395-398, 2011.

[17] X. Dong and D.-X. Zhou, "Learning gradients by a gradient descent algorithm," Journal of Mathematical Analysis and Applications, vol. 341, pp. 1018-1027, 2008.

[18] G. Mallesham, S. Mishra, and A. N. Jha, "Optimization of Control Parameters in AGC of Microgrid using Gradient Descent Method " 16th National Power Systems Conference, pp. 37-42, 2010.

[19] F. Lin, R. D.Brandt, and G. Saikalis, "Self-Tuning of PID controller by adapitve interaction," Proceedings of America Control Conference, pp. 3676-3681, 2010.

.

2012 IEEE Control and System Graduate Research Colloquium (ICSGRC 2012)

293