Control comparison of extractive distillation with two different solvents for separating acetone and tetrahydrofuran

Control comparison of extractive distillation with two different solvents for separating acetone and tetrahydrofuran

Process Safety and Environmental Protection 125 (2019) 16–30 Contents lists available at ScienceDirect Process Safety and Environmental Protection j...

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Process Safety and Environmental Protection 125 (2019) 16–30

Contents lists available at ScienceDirect

Process Safety and Environmental Protection journal homepage: www.elsevier.com/locate/psep

Control comparison of extractive distillation with two different solvents for separating acetone and tetrahydrofuran Zhaoyou Zhu a , Xueli Geng a , Guoxuan Li a , Xiaopeng Yu a , Yinglong Wang a,∗ , Peizhe Cui a , Guowu Tang b , Jun Gao c a

College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China College of Chemistry, Chemical Engineering and Materials Science, Shandong Normal University, Jinan, 250014, China c College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao, 266590, China b

a r t i c l e

i n f o

Article history: Received 6 January 2019 Received in revised form 9 February 2019 Accepted 7 March 2019 Available online 9 March 2019 Keywords: Process control Extractive distillation Quantitative structure property relationship Modelling and simulation studies

a b s t r a c t Extractive distillation is a common method to separate the mixture which has the low relative volatility, such as acetone and tetrahydrofuran mixture. Because the number of candidate solvents is high and the experiment is time-consuming, the selection method of solvent is important. In our previous work, quantitative structure property relationship was introduced to model and evaluate the relative volatility of acetone and tetrahydrofuran mixture. The flowsheet of extractive distillation was set up and the total annual cost was calculated with n-Octane and butyl ether as new and former solvent, respectively. Except the mathematical verification, we think that the dynamic control of the configuration of extractive distillation can also be a method to evaluate the feasibility of the built model. In this paper, we studied the dynamic control for extractive distillation with n-Octane and butyl ether as solvent and the integral of squared error was calculated for purity of two products. The results show that some indexes for n-Octane as solvent are better than that for butyl ether as solvent. The dynamic control performance is consistent with the results of economical and can be a way to judge the reliability of quantitative structure property relationship. © 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Nomenclature CAMD QSPR GFA ACT THF BTE OTE CS1 CS2 ISE

Computer-Aided-Molecular-Design Quantitative structure property relationship Genetic Function Approximation Acetone Tetrahydrofuran Butyl ether n-Octane Improved control structure 1 Improved control structure 2 Integral of squared error

1. Introduction Separation process is a very important part in chemical industry. In the chemical production, ordinary distillation cannot separate

∗ Corresponding author. E-mail address: [email protected] (Y. Wang).

binary azeotropes because the formation of azeotropes. In order to solve this problem, a variety of special distillation methods have been developed, such as azeotropic distillation (Chen et al., 2018; Franke, 2016; Le et al., 2015; Li et al., 2015), extractive distillation (Hsu et al., 2016; Ma et al., 2018; Wu, 2015; Zhang et al., 2018), and pressure swing distillation (Gao et al., 2017; Wang et al., 2016). Extractive distillation was proposed for the first time in 1930s (Randall and Webb, 1939; Wang et al., 2015a) and is common method to separate the mixtures which form azeotrope. The solvent was added as the third component to change the relative volatility of the mixture. The effect of solvent on azeotropic mixtures has been studied from theoretical and experimental studies (Matsuda et al., 2011; Prausnitz and Anderson, 1961; Sazonova et al., 2014). The selected solvent must have some qualities such as selectivity, stable, efficiency, noncorrosive and inexpensive, etc, and these qualities have been discussed carefully in the literature (Gil et al., 2009). It is very troublesome to choose suitable solvent from a large number of organic solvents. The first thing to consider is the main solvent properties of the separation mixture such as selectivities, distribution coefficients, solvent losses and so on. Many scholars have studied the selection of solvents and the widely used is Computer-Aided-Molecular-Design (CAMD) (Lek-utaiwan et al., 2011), it has been gradually extended to extractive distilla-

https://doi.org/10.1016/j.psep.2019.03.009 0957-5820/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

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Fig. 1. Optimal flow sheet with (a) BTE as solvent and (b) OTE as solvent.

tion, liquid-liquid extraction, gas absorption processes and so on (And and Achenie, 1996; Harper et al., 1999). CAMD is used to generate the molecular with certain rules and the newly formed molecular is tested and match the molecular properties which is important in extractive distillation process. To improve the accuracy of CAMD method, some additional steps have been inserted (Chen et al., 2005; Lin et al., 2005). Quantitative structure property relationship (QSPR) is an method which is used to reveal the quantitative change rules between activity of compounds and their molecular structure or physical and chemical properties using mathematical statistics method. The initial scope of use is in the study on drug synthesis (Kubinyi, 1997a, b), as the development of the technology, it started to be used to study the material properties from single

component (Guendouzi and Mekelleche, 2012; Pourbasheer et al., 2015) to binary component (Ajmani et al., 2006; Katritzky et al., 2011; Solov’Ev et al., 2015). In recent years, QSPR has been used in extractive distillation to predict the relative volatility of the mixtures (Kang et al., 2016, 2014), the researchers built the quantitative structure relative volatility relationship based on the experimental data to explain relationship between relative volatility and physicochemical properties of solvents. The results indicated that the model have great stability and predictability. In our previous work (Zhu et al., 2017), we built the QSPR model of acetone (ACT) and tetrahydrofuran (THF) system with Genetic Function Approximation Algorithm (GFA) and the coefficient of determination is up to 0.9611. To assess the stability and predictability, we introduced the external validation and the cal-

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Fig. 2. Basic control structure for BTE as solven.

Fig. 3. Slope of temperature distribution profiles for C1 and C2 with BTE as solvent.

culated values were very closed to the experiment values. We used the model to predict the relative volatility and the new solvent n-Octane (OTE) was selected out. Separation flowsheets were set up with new solvents OTE and former solvent butyl ether (BTE), respectively. The sequential iterative calculation was used to optimize the flowsheets and the optimal operation parameters were obtained. The economical was calculated and the results indicated that flowsheet with OTE as solvent can reduce the total annual cost to 10.8% and shown a better economy. Dynamic control is a key factor to confirm the applicability of the technology in industry (Qin et al., 2016; Wang et al., 2019; Vasilie et al., 2018; Illingworth et al., 2019; Luyben and Chien, 2011; Osuolale and Zhang, 2015). In chemical engineering, the flowrate

and composition of the feed always meet the disturbance (Yu et al., 2015; You et al., 2017; Zhang et al., 2016). A great technology should have the ability to deal with the disturbance in some degree, thus many researchers do a deep research about dynamic control of extractive distillation (Wang et al., 2016, 2015c; Wang et al., 2015d; Zhu et al., 2016). Zhang (2006) presented an offset-free inferential feedback control strategy by using principal component regression and partial least squares models. An example of methanol-water separation shows that the strategy is effective. Wang et al. (2015b) studied the extractive distillation focused on the tradeoff between dynamic control and steady state, the results shown that increasing the flowrate of solvent in steady state can make an improvement on the control performance in dynamic control. Ramos et al. (2016)

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Fig. 4. Dynamic performances of the basic control structure with BTE as solvent: (a) ±10% feed flow rate disturbances; (b) ±10% feed composition disturbances.

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Fig. 5. CS1 for BTE as solvent.

invested the influence of solvent on the controllability of an extractive distillation system with heat integration. De Figueiredo et al. (2014) studied the effect of solvent on the dynamic and found that the different solvent have different influence on the control performance. Based on the above research, in this paper, we think the dynamic control performance of extractive distillation can also be a criterion to judge which solvent is better for the technology and can indirect evaluate the reliability of QSPR in our previous work, so we do the dynamic control for extractive distillation with OTE and BTE as solvent. 2. Dynamic control of extractive distillation The separated mixture is set at 1000 kg/h with 62 wt % ACT and 38 wt % THF, the specify purity for two product were set no less than 99.9 wt %. The new and former solvents were OTE and BTE, respectively. The steady state and optimal operating parameters with OTE and BTE as solvent were obtained in our previous work and the corresponding parameters were displayed in Fig. 1. Before beginning the study about dynamic control, the reflux drum size and the column sump size were confirmed. A commonly used heuristic is to set a 10 min holdup if full (Luyben and Chien, 2011). The proper pressures were set in pumps and valves to ensure the dynamic operation. 2.1. Dynamic control of the configuration with BTE as solvent 2.1.1. Basic control structure The basic control structure of extractive distillation with BTE as solvent was shown in Fig. 2. The profiles of the temperature slope for C1 and C2 were shown in Fig. 3. The stages 7, 42 and 49 have obvious temperature fluctuations in C1 column and the stages 5, 8 and 11 have obvious fluctuations in C2 column. For C1 column, the flash feed location is stage 42 and the solvent feed location is stage 7. For C2 column, the feed location is stage 8. The temperature fluctuation of feed stage will affect the stability of the control process, so, these feed locations are not selected as the temperature control

points. Stage 49 in C1 column and stage 11 in C2 column were first selected as temperature sensitive stages (Grossel, 2006; Li et al., 2013; Yu et al., 2012). The details of basic control structure are as follows: 1 The column pressures were controlled by manipulating condensers heat duty of the two columns. 2 The solvent flowrate was in proportion to the feed flow rate. 3 The reflux drum levels were controlled by manipulating the distillate rate (direct acting). 4 The sump level of the C1 column was controlled by manipulating the bottoms rate (direct acting). 5 The sump level of the C2 column was controlled by manipulating the flow rate of the makeup stream (reverse acting). 6 The temperature of stage 49 in C1 column and the temperature of stage 11 in C2 column were controlled by manipulating the reboiler heat duty. (reverse acting) 7 The temperature of the solvent feed was controlled by manipulating the cooler HEDC heat duty (reverse acting). 8 The reflux ratio was fixed in two columns. 9 Fresh feed flowrate was controlled by feed valve. (reverse acting) Temperature and composition measurements are not instantaneous. For temperature and composition measurements, the time lags of 0.5–1 min and 3–30 min are typically experienced, respectively. To get fairly conservative controller tuning, three dead-time blocks with 1 min dead time were inserted into the temperature controlle (Luyben and Chien, 2011). Relay-feedback tests of temperature controllers were carried out to confirm the ultimate gains and periods and the parameters using Tyreus − Luyben tuning (Luyben, 1996) are shown in Table 1. The integral times (␶I) and gains (Kc) of the level controllers were set 9999 min and 2, respectively. The flow rate controllers had Kc = 0.5 and ␶I = 0.3 min. ±10% feed flow rate and composition disturbances were introduced to the basic control structure system after 0.5 h. The corresponding dynamic responses are shown in Fig. 4. The results shown that it cost a little time for the purities of ACT and THF to

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Table 1 Transmitter Ranges, Controller Output Ranges, and Tuning Parameters of Three Temperature Controllers for basic control structure with BTE as solvent. Variable

TC1

TC2

HE

controlled variable manipulated variable transmitter range (K) controller output range gain Kc integral time  I (min)

T1,49 QR 273.15–438.25 0–2.5 GJ/h 2.55 11.88

T1,11 QR 273.15–533.42 0–0.89 GJ/h 0.98 13.2

T recycle QHK 273.15–362.49 −1.22–0 GJ/h 0.16 3.96

Fig. 6. Dynamic performances of CS1 with BTE as solvent: (a) ±10% feed flow rate disturbances; (b) ±10% feed composition disturbances.

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Fig. 7. Dynamic performances of CS1 with BTE as solvent: (a) ±20% feed flow rate disturbances; (b) ±20% feed composition disturbances.

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Fig. 8. CS2 for BTE as solvent.

reach to new stability when ±10% feed flow rate disturbance was introduced. While when the ±10% composition disturbances were introduced, the purities of ACT and THF do not reach stability after 20 h. So it was concluded that the ±10% composition disturbances can not be effective controlled using basic control structure. 2.1.2. Improved control structure 1 (CS1) Through analyzing the purity change of THF, we found that a large number of solvent was distillated up in the distillate after the disturbance beginning. The consequence is that the makeup flowrate can not guarantee the amount of solvent and then the liquid level in C2 column can not be controlled. The composition controller was inserted in C2 column. Product purity of C2 column is controlled by manipulating reflux ratio (reverse acting). The improved control structure with BTE as solvent was shown in Fig. 5. 3 min dead time was added into the composition controller, after Relay-feedback tests and Tyreus − Luyben tuning, Kc = 47.56, (␶I) = 62.04 min. ± 10% feed flow rate and composition disturbances were introduced to the control system after 0.5 h. The dynamic responses were shown in Fig. 6. The results shown that the improved control structure can handle ±10% feed flow rate and composition disturbances, the purity of ACT and THF returned to specify value after introducing ±10% feed flow rate and -10% composition disturbance while the purity of ACT have little deviation from specify value facing +10% composition disturbance but can be accepted. To deal with greater uncertainty, ±20% feed disturbances were introduced and the dynamic response were shown in Fig. 7. The results shown that the improved control structure can effective control ±20% feed disturbances, the purity of ACT and THF returned to specify value after introducing ±10% feed flow rate and -10% composition disturbance, but the purity of ACT have a large deviation from specify value facing +20% composition disturbance which can not be accepted. 2.1.3. Improved control structure 2 (CS2) To handle +20% composition disturbances, a composition controller was added in the extractive column to control the purity of

ACT. The Kc and (␶I) were 621.91 and 48.84 min, respectively. The improved control structure 2 was shown in Fig. 8. ± 10% and ±20% feed disturbances were introduced and the dynamic responses were shown in Figs. 9 and 10. The results indicated that the improved control structure 2 can handle the ±10% and ±20% feed flow rate and composition disturbances well, the purity of two products reached to specify value in short time. 2.2. Dynamic control of the configuration with OTE as solvent 2.2.1. Basic control structure The basic control structure with OTE as solvent was same with that of BTE as solvent. The profiles of temperature and the temperature slope were shown in Fig. 11. Stage 60 in C1 column and stage 12 in C2 column were selected as temperature control stages. The detailed parameters of three temperature controllers were shown in Table 2. The corresponding dynamic response was shown in Fig. 12 when introducing ±10% feed disturbances. The results shows that the basic control structure can handle ±10% feed flow rate disturbance but can not handle ±10% composition disturbance. The purity of ACT was undesirable and do not reach stable eventually. 2.2.2. Improved control structure Through observing the purity profiles of THF with time, the reason for undesirable control was same with the control with OTE as solvent in basic control structure. So a composition controller was also added in the C2 column. 3 min dead time was added into the composition controller, after Relay-feedback tests and Tyreus − Luyben tuning, Kc = 51.67, ( I ) = 64.68 min. ± 10% and ±20% feed disturbances were introduced and dynamic responses were shown in Figs. 13 and 14. Results indicated that the purity of ACT and THF were well controlled when facing +10% and +20% composition disturbances, in which the final purity had small deviation for ACT, however it was acceptable. Thus the improved control structure could handle the disturbance very well.

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Fig. 9. Dynamic performances of CS2 with BTE as solvent: (a) ±10% feed flow rate disturbances; (b) ±10% feed composition disturbances.

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Fig. 10. Dynamic performances of CS2 with BTE as solvent: (a) ±20% feed flow rate disturbances; (b) ±20% feed composition disturbances.

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Fig. 11. Slope of temperature distribution profiles for C1 and C2 with OTE as solvent.

Table 2 Transmitter Ranges, Controller Output Ranges, and Tuning Parameters of Three Temperature Controllers for basic control structure with OTE as solvent. Variable

TC1

TC2

HE

controlled variable manipulated variable transmitter range (K) controller output range gain Kc integral time  I (min)

T1,60 QR 273.15–426.97 0–2.06 GJ/h 1.8 10.56

T1,12 QR 273.15–495.26 0–0.63 GJ/h 0.60 17.16

T recycle QHK 273.15–363.15 −0.37–0 GJ/h 0.18 3.96

Table 3 Calculation of ISE of two products with OTE and BTE as solvent facing ±10%, ±20% feed flow rate and composition disturbances. 10% disturbances

ACT THF ACT THF

ISE OTE ISE BTE

20% disturbances

+10 flow rate

−10 flow rate

+10 composition

−10 composition

+20 flow rate

−20 flow rate

+20 composition

−20 composition

3.89E-09 4.24E-08 1.65E-08 1.79E-08

2.24E-09 4.52E-07 1.52E-08 1.28E-07

7.26E-07 6.58E-06 1.74E-08 9.09E-06

3.02E-07 1.33E-06 2.52E-08 1.82E-06

2.35E-08 6.45E-08 3.60E-08 9.25E-08

3.14E-08 7.27E-06 1.20E-08 5.22E-06

4.30E-06 4.27E-05 6.61E-08 6.81E-05

7.72E-07 2.55E-06 4.96E-08 3.50E-06

3. Results and discussion Control performance indexes are commonly encountered as integral functions and basically evaluate the numerical discrepancy between a measured variable and its setpoint, within a time length. In order to represent the control performance more vividly, integral of squared error (ISE) was introduced (Krohling and Rey, 2001), and the equation is defied as follow:

t ISE =



y − ysp



2

dt

t0

Where y is the purity values with time and ysp is the specify value which is 0.999. The ISE were calculated for the two final control structure with different solvents. The results was shown in Table 3. The results shown that the ISE of ±10% feed flow rate and composition disturbances for OTE as solvent is smaller than that for BTE as solvent, while the ISE for ±20% composition disturbances for OTE as solvent were larger than that for BTE as solvent. In addition that the final control structure for flowsheet with BTE as solvent is more complex than that with OTE as solvent. Connecting the work for mathematical analysis and the economical calculation in our

previous work, the total annual cost for OTE as solvent was smaller than that for BTE as solvent, we concluded that the OTE was better than BTE as solvent and the dynamic control performance can be a criterion to assess the accuracy of QSPR. 4. Conclusions The dynamic control of extractive distillation for ACT and THF with OTE and BTE as solvent was investigated in this paper. The information of feed was same with our previous work and the operation parameters after optimization was obtained from our previous work. The basic control structure of extractive distillation flowsheet with BTE as solvent can not meet the control requirement for ±10% and ±20% feed disturbances. After adding two composition controllers, all ±10% and ±20% feed disturbances were handled. For flowsheet with OTE as solvent, adding one composition controller that can achieve the control goal. The control performance indexe ISE was calculated for purity of ACT and OTE with different solvent, the results show that some indexes for OTE as solvent are better than that for BTE as solvent. Connecting the former work of economical, we think that dynamic control and economical can be the criterion to assess which solvent is best and can indirectly judge the QSPR.

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Fig. 12. Dynamic performances of basic control structure with OTE as solvent: (a) ±10% feed flow rate disturbances; (b) ±10% feed composition disturbances.

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Fig. 13. Dynamic performances of improved control structure with OTE as solvent: (a) ±10% feed flow rate disturbances; (b) ±10% feed composition disturbances.

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Fig. 14. Dynamic performances of improved control structure with OTE as solvent: (a) ±20% feed flow rate disturbances; (b) ±20% feed composition disturbances.

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