Study on the Soft Sensor and Control Scheme for an Industrial Azeotropic Distillation Column1

Copyright © IFAC Advanced Control of Chemical Processes Hong Kong, China, 2003

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STUDY ON THE SOFT SENSOR AND CONTROL SCHEME FOR AN INDUSTRIAL AZEOTROPIC DISTILLATION COLUMN 1

Shi Zhang, Cuimei 80, Changyin Sun, Yanru Wang

College ofAutomation, Nanjing University of Technology, Nanjing 210009, China E-mail: [email protected]

Abstract: In this paper, the control problem for an industry azeotropic distillation column is presented. In view of the existing problem of the control system, the author first uses soft-sensing technique to construct a soft sensor of water content of the distillation column bottom, in which the soft sensor model is built based on the field data collected by DCS. For the convenience of project application, the comparatively simple regression model is chose to form the soft sensor. After that, a scheme of inferential control is design and applied to application, and then is improved according to the actual situation after a trial period. At present, the inferential control system based on soft sensor has been running smoothly over a long period of time in worksites. Copyright © 2003 IFAC Keywords: Industrial distillation column, azeotropic distillation, regression analysis, soft sensor, inferential control

I. INTRODUCTION

Therefore, in practice operation, excess purification is usually adopted, ending up with the result Increasing energy consumptions as well as decreasing the product yield. The problem mentioned above exits not only in China but also western countries.

Distillation is the most important operation unit for separation processes commonly adopted In petroleum refinery and chemical industry. Because of the difficult factors, such as nonlinear, multivariables, hard to measure online for quality parameters, the automatic control problem for distillation process has always been one of the hot subjects of automation researches. Many articles about distillation column control have been issued. Although most of those were researched in theory or digital simulation, and various examples of successful application in industry often were reported (Kumar, et al., 1999; Monroy et al., 1999). However, there still exist problems for distillation column control (Ming Rao, et aI., 2002), since overly simple control systems used in industry great deal are seldom able to guarantee the products' purity (1Lung Chien, et al., 2000).

Nowadays, DCS is often used to control industrial equipment, especially in larger petrol-chemical plant. A contradict phenomenon arose in those petrolchemical enterprises: on the one hand, the functional DCS has not been fully used; on the other hand, control schemes of some intricate equipments, such as distillation column, are so simple that there exit lots of problems. The paper does the work to improve the situation mentioned above. An advanced control system based on DCS was proposed in this paper. The system mainly relies on the present functions of DCS and handles the control problem of the industry distillation column in a proper.

I The research of paper are sponsored by the corporation. contract No OlJSNJYZIOJOl5

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2. PROCESS INTRODUCTION

The Figure 2 indicates the fonner controlling scheme of Column DA106. From the figure, it can be seen that this scheme is formed by all simple loops, without any quality-controlling loop. What's more, the current producing load goes far beyond the system's designing ability. Therefore, the former controlling scheme is hardly to meet the controlling command. Though Column DA I06 is not the product column, for the reasons mentioned above, the water content in the product is mainly controlled by the operation of this tower. In this case, the operators usually turn to the controlling method of over purifying, which means over increasing heat supply of the reboiler and therefore over decreasing the water content of the bottom. The method pledges that the water content keeps not more than 20ppm; however, it increases energy consumption uselessly, and, the water content controlling is not kept smooth.

The main subject of this article is focused on a butadiene plant, which belongs to a big petrochemical enterprise, Yangzi Branch of SINOPEC. Butadiene is the major monomer and raw material for producing styrene butadiene rubber, cis-I, 4polybutadiene mbber, nitrile rubber, ABS rosin, nylon, and so on. The distillation section of the butadiene plant includes two distillation columns, Column DAI06 and Column DAI07, whose tasks are to remove the high-boiling-point impurities, such as cis-butene-2, butadiene-I, 2, ethyl acetylene and C 5, as well as low-boiling-point impurities such as methyl acetylene and water, and to achieve the final product, butadiene-I, 3. There arc some control problems in the two distillation columns. Column DA I07 is the production column, from the top of which the important petrochemical raw material butadiene-I, 3 is distilled. On the purpose of enhancing the operating and controlling level, increasing producing load, the formerly conventional control instmment system has just been changed into DCS. On this condition, the authors cooperate with the technician of the plant and have completed a whole set of control scheme for the two towers. The research work about Column DA I07 has been reported (Shi Zhang, et al., 2002). This paper focuses on the work about the control problem of water content of bottom of Column DA 106.

In view of this case, the authors adopt the inferential control based on soft sensor instead of the former fixed value control loop of the reboiler heat supply, in order to realize the direct quality control of water content. The key problem of the work is to build the soft sensor of water content. 3. THE SOFT SENSOR OF WATER CONTENT In recent years, it is widely reported that the soft sensing technique has been introduced into the component estimation (Jingshou Vu, Ailun Liu, Kejin Zhang, 2000). Lots soft sensors in the form of commercial software have been released and utilised in practice production. Nevertheless, this kind of software is so expensive that they were rarely introduced by the domestic enterprises.

The basic process flow of the distillation section of the butadiene plant is shown in the Fig.l. Though most of the impurity has been dropped out from the raw material C 4 in the previous extraction distillation process, some impurities whose relative volatilities are close to I with butadiene-I, 3 are still exist. These impurities are to be dropped out in the columns ofDAl06 and DAI07. Column DA I06 is an azeotropic distillation column with the main task of dropping out methyl acetylene, azeotropic water and other lighter components. Methyl acetylene and other lighter components rise into the top of Column DA I06 because of their low boiling point. Then they will be cut out with part of butadiene-l, 3, as the tail-gas through a torch. The saturated water in feeding, as the azeotropic matter of butadiene-l, 3, is also taken to the top of the column. It is del aminated with butadiene in the reflux dmm after condensation, and then drain out from the bottom of the drum.

3. I SecondGlJ! variahles' selecting and field data's processing In this work, the possible process variables related with water content which can be obtained are as follows: two temperatures (the reboiler heating temperature T500-25 and the bottom temperature T500-26), three flows (the reboiler heating flow F133, the feeding F122, and the reflux F134), one pressure (the bottom pressure P 116) and one liquid level (the bottom liquid level L I 19). As for the seven variables shown above, we choose T500-25, T500-26, F122, F134, PI16 as process secondary variables. With the action of fixed value controlling loop, F 133 and L119 are kept stable, and have little to do with the change of the column's bottom water content, so they are omitted. According to the result of the regressing analysis method, the five variables above-mentioned are obviously connected with the water content at bottom, while F 133 and L 119 give no effect on it.

Water content in feeding is about 500 ppm, and according to products' purity criterion, the product water content should not be over 20 ppm. The task of Column DA I07 is to take off high-boiling mixture, namely, all the water at the bottom of Column DA 106 will be carried into products, besides, through the experience of operators, if the water content is well controlled, and the methyl acetylene consistency content is able to be guaranteed. As a result, the water content of Column DA I06's bottom is a vital controlling target.

Since lots of high frequency noises existing in measured value of the flows, which may influence the modelling work, the measured value of FI22 and FI34 must be filtered at first, and then processed with regression analysis.

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After defining the secondary variables, we should go on analyzing the real measure values of the water content at the bottom of Column DA I06. When building the soft sensor model with industry field data, off-line sampling analysis data are commonly as the real measure value. But a little difference appears in this work. Although there is a sampling point at the bottom of Column DA I06, it usually do not be used, only the water content in the product drum (that is connected with the exit of Column DA I 07 ) is sampled and analyzed every 8 hours. In order to get real value of water content at the bottom of Column DA I06, many methods have been tried, but all the results are not ideal. Firstly, since the saturation water is determined under a certain pressure and temperature, the water content is tried to be calculated, however, the error of the results is unacceptable comparing with the ppm level's unit, even through the data (pressure, temperature, etc) are practically measured. Then, the method to calculate water content through a mechanism model of Column DA 106 is tried. It is a pity that, to an azeotropic distillation column like Column DA I06, the correlative data about vapor-Iiquid equilibrium cannot be acquired from literature or databases. The idea of getting the relation of vapor-Iiquid equilibrium through experiment also had to be given up due to the expensive experimental cost caused by the high work pressure (OA-0.5MPa) . Considering the water content will not change a lot from the bottom of Column DA I 06 to the product drum, it can be supposed reasonably that water content in the product drum is only the express of water content in Column DA I 06 bottom after a period of delaying-time ( T). Therefore the problem above-mentioned is to be simplified greatly. Supporting our supposition, the operators give us an approximate value of the delaying-time, and then by means of some additive experiment, the value of T is confirmed about I hour.

The examining set data are used to examine the model's generalization ability. The result is showed in Fig.3. In the Fig.3, the abscissa values show sampling numbers. It is obvious that the precision of the proposed model is very high, and the generalization ability can also satisfy the control request. Moreover, the model is simple in form and easy to realize in the project. 3.3 On-line correction oflhe soft sensor model

Because of the influences from time varying, non linear and non-integrality of modeling factor established in product process, the soft sensor model has to be corrected on-line (Jingshou Yu, 2002). The correction work may classify into two types. One is long-time correction, that is, after the distillation process running for a period of time, a new set of data should be recollected, and a new model will be reconstmcted and substituted the fonner model; the other is short-time correction, which is carried out on-line. The main idea is to correct the model with the error between the off-line lab analysis data set every 8 hours and the calculating result of soft sensor model. What requires our attention is that the time when the analysis data is sent to the control-room is a little bit later than the sampling time, so we should keep the calculating results at the moment of sampling, in order to compare the two groups of data at the same time. Correction formula is as follows:

M'13Q't) = a x (Sa13Q't - 5) - S13Q't - 5» Where: !'. SI30(l): The corrected value of the water content at t; a: The corrected coefficient, here is 0.5; ,): The analysis delay-time; Sa 130 (t- [, ): The lab analysis value of the water content at t- Ii S 130 (t- Ii ): The model estimated value of the water content at t- [, .

3.2 Building and examining the soft sensor model

A group of data in situ (350 points) is divides into two sets, with one set (200 points) is used to build the soft sensor model, and another (150 points) to examine the model. After the regressing analysis with the data of the modeling set (200 points), the regression model can be obtained as follows:

The later type of correction has been studied by simulation. As the time that the analysis data is sent to the control-room (the correcting-time) is not always fixed, while the sampling time is fixed, therefore, the delay-time between sampling time and correcting time is not always same. In the simulation study, the delay-time is presumed 8 hours (That is much greater than it in normal case.). The simulation result is showed in FigA. In practical operation, the delay-time usually is about I hour, so if the correction is running practically, the correct effects should be much better.

5

S130(t) = La;

x

x(t -r)

;=0

Where: Sl30 (I): the estimated value of the column bottom water content at time t; T: delay-time; x; (t - T) ( i = 1,2, ·.. ,5): 5 secondary variables; a, U = 0,1, 2, ''', 5) : regression coefficients. The values are' -84.677507 ao 2.841364 al -0.655388 a2 -1.731556 aJ 1.582286 a4 231.365314 as

From the FigA, on the condition that the generalization ability of the soft sensor model is not very good (see the Fig.3), as long as through on-line correcting, the accuracy of the soft sensor model is also able to satisfactory

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4. THE CONTROL SCHEME AND PRACTICAL APPLICATION

(,,,,,,(.): the maximal heating corresponding to the maximal reflux flow; IminO: the minimal heating corresponding to the minimal reflux flow. Here {n",,(.) and {minO are decided by following process: (I) Through analyzing the process, the technique parameters are decided which are all related with heating of reboiler, including the feeding F122, the F134, the temperature of heater's input and output TI33 and T500-25, the column bottom temperature T500-26 and pressure P 116; (2) The regress relation, {(FI22, F134, T133, T500-25, T500-26, PI16), was obtained by regression analysis with the field data; (3) Supposing the reflux flow is set 1.7 or 1.3 times separately, then the maximal heating and the minimal heating functions fC) are listed as follow: (,,,,,,(.)=fCFI22. I.7x F134, T133. T500-25, T500-26, P116) fmi"c)=fCFI22, 1.3x F134. Tl33, T500-25, T500-26. P116)

As shown above, the prime problem about control loop of the bottom column is that there are not direct or indirect quality control loops. So basing on sampling analysis data every 8 hours, only can the operators adjust the set point value in control loop of reboiler heating flow by hand to control water content of the column bottom. In this case, the inferential control scheme based on soft sensor is the best choice.

4.1 Basic control scheme The basic control scheme is shown in the following Fig.5. In the figure, the soft sensor means the regression model mentioned above. In view of the possibility of project realization, the water content controller only adopts PID arithmetic. So, except the soft sensor model needs to program in Command Language of DCS, all the control schemes are easily carried out by configuration in DCS.

4.3 The practical application of control scheme

The control scheme in Fig. 5 has run for a period of time. It is proved that the scheme is available. Firstly, the control effects of water content is so ideal that the value can be controlled between 18-20ppm. Secondly, the heating of reboiler was decreased than before.

The control scheme has been applied in the industrial column, and received satisfactory result. Figure 7 gives the real time control effect. 5. CONCLUSIONS The former control scheme of an industrial azeotropic distillation column is improved. Firstly, a soft sensor model and an inferential control scheme based on the soft sensor model are designed and built. Then, according to the practical running, the control scheme is improved. As a result of constraint relationship added to the control arithmetic, the robustness of the control system is strengthened. So the system can adapt preferably great fluctuation of work situations. Through the several months of practical application, it proved that the control system is feasible and available to nm smoothly for long time.

4.2 Improvement ofcontrol scheme

At the same time, the authors found that this control scheme could not run steadily for a long time, because the great fluctuation of control variable occurred when the work situation changed. Through analyzing the production process, the reason caused this phenomenon was found. When control variables are changed greatly, the rising vapor flow within the column is influenced at once. It can be seen in Figure 2 that, for the reflux is the control variable of liquid level of reflux drum, the large change of the rising vapor flow quickly causes the reflux fluctuates greatly too. However, this fluctuation is not undertaken. According to the control criterion, the reflux in the column should be limited between 1.3 to 1.7 times of feeding. For these reasons, on the base of inferential control, the constraint control technique is used to solve the problem. The new improved scheme is shown in the Fig.6.

The following work is to improve the control scheme of the distillation column's top. The experience accumulated in the job of this paper may offer much help in following work. Lacking of appropriate quality control loop in the top of the distillation column, concentration of exhaust gas emission cannot be ensured, which not only influences the product quality, but also seriously depresses the product yield. The basic control idea in the top of distillation column is still the inferential control based on soft sensor. But, when the two quality control schemes are applied in both the top and the bottom of distillation column at the same time, relative effects would come into being between the two loops. On the condition of finite resource (limited by Command Language's programming ability of the DCS), how to solve the coupling problem is the key to the following work in this project.

The constraint relation between input and output is carried out in the selector:

l

/m.. (.)

out = out AC !;nin( I

Im" (.) Imin (.) ~ out ~ Imax (.) if out4C ::; Imax (.) if out AC ~

if

A(

Where: out: the output of selector; out4C: the output of water content's controller;

140

.

Exh.:mst gas emission

---+\..-11

Drainage

High boiling mixture outflow

Fig. I. The flow sheet ofbutadiene distillation workshop section

Fig. 2. The control schematic of column DA 106

25

H2 0

20 15 10

10

---actual -----rectificd after Bh

5

o

0.1..----'---.1...----1...--1----'----''----'

o

o Data set for modelling

Data set for examining

20

10

40

30

60

50

Fig. 4. Rectified result of soft-sensor model

Fig. 3. Multicomponent linear regression model

Reboiler heating

SP

Process (water content)

Water content

Secondary variables

Water content analysis value

Fig. 5. Water content inferential control system block diagram of the bottom of column DA I06

Process secondary variable

Water content soft sensor & constraint controller 30

H,O

25 20

15 10

20

10

60

80

lOO

120

140

160

IBO

Fig. 7. The practical effect of inferential control about water content

Fig. 6. Improve inferential control scheme

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reactive distillation column, Industrial &

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