Adaptive Steam Temperature Regulation for Essential Oil Extraction Process

Essential oil is volat ile and sensitive to excessive heat. Many studies had shown that temperature during extraction process had a great impact on the oil quality. Despite of that, until now there are very few research had been published on the control development of essential oil extraction system. Hence, this study was commenced part icularly on the development of a regulated essential oil extract ion system using self-tuning control. A self-tuning control was applied using pole-assignment method to regulate the steam temperature throughout the extraction process. Combination of controller poles in real and imaginary axis may influence the closed-loop response so that the steam can reach the set point faster but yet with min imal overshoot. Extensive analysis was done by simulation in order to understand the effect of the poles location and also the selection of sampling time over the closed-loop response. Outcome from the simulation was applied on the real process where the controller produced satisfactory result as expected. The controller was able to regulate the steam temperature at a desired level and maintained within ±2% output boundary.


Introduction
Steam distillation is among the most popular method for essential oil ext raction process. The proportion of different essential oils extracted by steam distillation is 93% and the remain ing 7% is extracted by other methods such as hydro distillat ion [1]. Th is method applies hot steam to extract the essential oil fro m the raw materials. The mixture of oil and steam will be condensed and separated at their liquid form. In the majority of cases the oil is less dense than water and so forms the top layer of the distillate and can be separated easily using proper method and instruments.
Continual exposure to excessive heat may degrade the quality of the essential oil as had been studied and reported in [2] fo r ginger ext raction. The study proposed that steam temperature needs to be regulated below saturated temperature throughout the extraction process. This finding had been supported by lots of other published works [3][4][5][6][7]. Nevertheless, there were very few literature on the control development for ext raction system was found.
This study in it iates the develop ment of an auto mated essential o il ext ract ion system us ing steam d ist illat ion technique. The system was able to perform steam d istillation at a regulated temperature up to 100℃.
Steam temperature control had been studied and applied using various methods. Most of the publications focused on the superheated steam temperature control. There is very litt le publication so far that discussed on the steam temperature regulat ion below 100 ℃ . Nonetheless, other studies pointed out the same non triv ial issues in steam temperature regulation wh ich is normally co mes fro m the nonlinearity, slow varying process dynamics, fast disturbance and unmodeled dynamics [8][9][10][11]. Dynamics of the process may be changed due to load variations and sometimes fro m unpredictable causes. These attributes made fixed parameter controller such as PID is inefficient to perform satisfactory control for steam temperature.
It is not doubtful that PID controllers are still widely used in industries nowadays despite of the advancement in process control technology. It has been reported that more than 97% of the controllers in process control industries are of PID type [12]. The ability of PID control mode to compensate most practical industrial processes has led to their wide acceptance such as in pulp and papers industries. In addition, it simple structure and easy to understand has made tuning procedures more easy to comprehend and can be accomplished by trial and error by technical personnel. Furthermore, the presence of integral term will ensure zero steady-state error for a step change in input signal. Nevertheless, PID controller will only performed well within limited operating range where tuning was performed unless the process is linear. Unfortunately, even though the control structure seems simple, there were no generic tuning procedures that can sustain satisfactory performance over variation of process types. This drawback has led to continual research in PID control leading to different kind of tuning approaches. Some of the renowned methods applying phase margin and gain marg in, the internal mode control (IM C) design method, direct synthesis method, graphical technique, optimization technique, and frequency response analysis [13]. An extensive summary of some well-known PID tuning techniques can be found in [14]and [15]. An adaptive PID or the self-tuning PID on the other hand will automatically adjust the controller parameters based on the control laws. Th is control scheme is attractive especially if there is only little informat ion about the process to be controlled is known. In fact, there are lot of on-going research in refining the algorith ms to tailor the needs of specific applicat ions [16].
This paper focused on applying the self-tuning PID to regulate steam temperature in steam distillation process for essential oil extract ion. Steam d istillation process possessed a slow varying dynamics with varying time-delay and process gain over its full-range. Specific operating range need to be identified where the control can be acco mplished successfully. The process was described by autoregressive with exogeneous input (ARX). The unknown parameters were updated using Recursive Least Square (RLS) method while controller design was based on pole placement.
An overview about steam distillation for essential oil extraction process was discussed in Section 2. Section 3 explained on the self-tuning control design and algorithm using pole-placement method. Some simu lation results to show the effect of poles selection will be discussed in section 4. The simulat ion will then be verified with experimental evaluation in section 5.

Steam Distillation Essential Oil Extraction Process
A pilot-scale steam d istillation plant developed for this study consists of a stainless steel colu mn o f 26 cm inner diameter and a vertically mounted steel condenser to convert the steam into liquid form. Figure 1 shows the simplified schematic diagram of the plant. Two RTDs were installed; RTD1 was immersed in the water to monitor water temperature wh ile RTD2 was installed 40cm fro m RTD1 to monitor the steam temperature inside the column. The distance of the sensor fro m its heat source will caused transport delay in steam temperature measurement.
During operation, steam will be generated by boiling the water inside the distillation colu mn. In normal operation, the water volu me is 6 litres. The water was heated up by a 1.5kW coil-type heater. It took about 3500s to boil the water. The open-loop response under normal operating condition is shown in Figure 2. Colu mn temperature that represents the steam temperature started to rise gradually after 1500s when water temperature is around 70℃. The steam temperature increased exponentially until 80℃ where the steam rate hiked to 100 o C and saturates. During this state, temperature within the colu mn is at homogeneous.
The objective of this research is to regulate the steam temperature below 100 o C as to preserve the oil fro m burning and consequently preserving its quality. During closed-loop operation, the steam temperature will be measured by RTD2. RTD2 was installed over the raw material to mon itor the temperature of steam that passed through the raw material instead of measuring the steam temperature that will enter the raw material bed.
Some signal conversion needs to be done to convert the resistance from RTD to voltage signal that was compatib le with the acquisition card PCI 1711. The signal converter converts 0℃ to 120℃ to 1V to 5V. This signal was the measured variab le of the process. Control signal fro m the controller manipulated the heater power by providing a d.c voltage fro m 0V to 5V to a continuous power controller.  System Identification Toolbo x and fine-tuned to min imize the residual from experimental data. The best fit model was given by equation (3).
The process gain for the specified range is 4.5 o C/ V. Co mparison between experimental data and the predicted model output gives RMSE of 0.042℃.

Self-tuning Pole Placement
Self-tuning control (STC) contains two algorithms; one for the online parameter estimation and the other is for control law imp lementation. This method uses the informat ion fro m model parameters that must be updated recursively in order to synthesize a new controller parameters based on specified design requirements. In so me self-tuning controller, the recursive process estimat ion was not necessary. This type of controller is referred to as implicit self-tuning controller.
Most of the explicit STCs apply certainty equivalence principle where model uncertainties during parameter estimation were not considered. It is assumed that these values correspond to their actual values. Theoretical details of the principal can be found in pro minent textbooks of adaptive control [16] [17]. Figure 3 shows a block diagram of an explicit self-tuning control structure. The control structure based on pole-assignment method is shown in Figure 4. For exp licit self-tuning control, a parametric model is more appropriate because the plant parameters need to be updated at each sampling interval. For this study, second-order ARX structure had been identified as the most suitable model structure. The process transfer function is given by where A(z -1 ) and B(z -1 ) are polyno mials in the fo rm The transfer function of a controller is Where E(z) = W(z) -Y(z) or the closed-loop system error and By substituting P(z -1 ) and Q(z -1 ) into equation (5), the controller output becomes ( ) = 0 ( ) + 1 ( − 1 ) + 2 ( − 2 ) + ( 1 − ϒ ) ( − 1 ) + ϒ ( − 2) (6) q 0 , q 1 , q 2 , and ϒ will be determined according to design specifications. The closed-loop transfer function is then becomes with characteristics polynomial of ( −1 ) ( −1 ) + ( −1 ) ( −1 ) = ( −1 ) (8) where D(z -1 ) is the desired characteristics polynomial in the form ( −1 ) = 1 + 1 −1 + 2 −2 + 3 −3 + 4 −4 (9) For easier determination of system overshoot and response speed, the follo wing characteristics polynomial is preferable [19].
The ARX regression model in recursive form can be written as where �( ) is the predicted output. Parameter vector is therefore can be determined by minimizing the loss function given in equation (15) using the recursive least square (RLS) algorith m that is widely used and can be referred in [20]and [21] for mo re detail.

Simulation Results
Before the controller was imp lemented on the real process, some simu lation analysis was done to evaluate the effect of poles location and to discuss on the effect of actuator constraint towards control performance. As had been explained in Section 2, the actuator which is the power controller has limited output between 0 to 5V. This constraint will limit the effect of poles location on the controlled output. From simu lation study, the most optimal poles setting will be evaluated based on the output response.

Effect of Poles Location
When the control signal was suppressed as in experimental setup, the effect of poles location towards the output became less evident. To investigate the effect of actuator constraint, 16 controller settings was simulated. The simulation was run by varying three controller variables; sampling time, alpha, and omega. The sampling time determined a period where control signal will be updated. Sampling times were changed between 10s and 20s while alpha and omega were changed from 0.1 to 0.9 and evaluated consecutively. The results were compared in terms of the settling time, % overshoot and the error in steady-state. Significant results from the simulation were summarized in Table 2.
Theoretically, s maller value of α will produce faster response while s maller ω will produce less overshoot. There was not much different was observed in % overshoot when ω was changed from 0.0 to 0.1. The result can be seen from case A1 and A2. The unsaturated cases show 100% overshoot compared to 0.67% when the controller output was saturated.
Adversely, the overshoot may be caused by a fast settling time when α was set to 0.1 and 0.5. This observation was obtained by comparing the results between A2 and A3. The overshoot was reduced to 8% but settling time was increased to 144s. The largest overshoot was expected when we set small α and large ω as in case A8. Th is case gave overshoot of 160% but the slowest to achieve steady-state because of high oscillation. Figure 5 shows the plots of A1, A2, A3 and A8.  Fro m this study, it can be seen that there were mutual effect between α and ω. So, the step response cannot be evaluated fro m the value of α or ω alone. The best response was observed from A3 where this setting gave no overshoot, fastest settling time and no steady-state error under both conditions. The effect of actuator constraint towards controlled output can be observed for cases A2, A3, A7, A10, and A15. Figure 6 to 10 show the output response during unconstrained and constrained controller output for the respective cases.
The output responses show almost similar results when the controller output was saturated. This is shown in Figure 11. Therefore, optimal setting of poles location was made from the unconstrained condition where A3 satisfy the control requirements for this process.      in the order of tenth of seconds. Based on researcher experimental experiences, sampling time of 10 seconds would be adequate to capture the changes in process dynamics. Samp ling time faster than this will only caused chattering in control signal and jeopardized the system performance. Figure 12 shows comparison between two samp ling times; 10 seconds and 20 seconds. A2 and A10 have common poles setting but A2's sampling time was 10 seconds while A10's was 20 seconds. From the figure, A 2 was updated more frequent compared to A10 and consequently had smaller settling time. The same condition was observed from A7 and A15 as shown in figure 13. Based on these observations, the sampling time for the self-tuning controller was set to 10 seconds.

Experimental Results
Fro m simulat ion results, the optimal setting for self-tuning controller was finalized. The sampling time was set to 10 seconds, α = 0.5 and ω = 0.1. The co mputer-based control was imp lemented using MATLAB Simu lin k R2009a. Init ial condition for the parameter estimation was set to {0.01; 0.02; 0.03; 0.04} respectively. The controller was set to regulate the steam temperature at 85℃. Figure 14 shows the experimental steam temperature output that was regulated fro m roo m temperature. It took about 3000s for the steam to reach 50℃ and gain its energy. The steam temperature respond as expected where there was no overshoot and the steam temperature was maintained at 85 o C for the whole duration.
It is considered as normal in the real process to have some fluctuations in the output caused by external disturbances and uncertainties. The rule of thumb in p rocess regulation is to maintain the controlled variable within ±2% boundary. This gave tolerance of ±2℃ fro m the set point temperature. Figure 15 shows a closed-up view during the steady-state. Steam temperature fluctuates between 84℃ to 86℃ fro m the final temperature and within acceptable boundaries.

Conclusions
The self-tuning PID based on pole-placement method was adopted to regulate the steam temperature o f a steam distillat ion essential oil ext raction process. The controller cascaded four additional poles to the process plant to influence the closed-loop response. These poles tailored to the percentage overshoot and response speed requirements which is of utmost important in process control. Simu lation study helped to determine the optimal setting of the poles. Experimental imp lementation on a real process proved that the self-tuning controller can regulate the steam temperature at a desired set point without temperature overshoot when the poles were set to 0.5 and 0.1 with 10 sec. sampling time.