Control of an Optimal Extractive Distillation Process with Mixed

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Process Systems Engineering

Control of an Optimal Extractive Distillation Process with Mixed-Solvents as Separating Agent Jesús Alberto Jaime Fonseca, Gerardo Rodríguez, and Iván Dario Gil Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b01706 • Publication Date (Web): 03 Jul 2018 Downloaded from http://pubs.acs.org on July 4, 2018

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Industrial & Engineering Chemistry Research

Control of an Optimal Extractive Distillation Process with Mixed-Solvents as Separating Agent Jesús A. Jaime, Gerardo Rodríguez and Iván D. Gil* Grupo de Procesos Químicos y Bioquímicos, Department of Chemical and Environmental Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia *[email protected]

Abstract In this work, process control is studied in an optimal Extractive Distillation (ED) system to dehydrate ethanol using a glycerol-ethylene glycol mixture as separating agent. ED system simulation and optimization with respect to the total annual cost (TAC) was made using the software platform Aspen Plus®. The control structure of the optimized system was evaluated in Aspen Dynamics. Two control strategies were studied: a conventional control and a modified control scheme. The conventional control structure uses the distillate and bottoms flow rates to control the top and bottom levels , respectively. The second strategy uses the entrainer makeup flow rate to control the sump level in the recovery column and the recovery column bottoms flow rate to control the entrainer feed flow rate to the extractive column. Dynamic simulations show the control strategy that allows obtaining a soft-regulating control while at the same time ethanol purity is above 0.995 mole fraction. In addition, the use of solvents mixture as entrainer reduces importantly the energy consumption and favors the process stability. Key words: Optimization, process control, ethanol dehydration, mixed solvents 1. Introduction. Anhydrous ethanol is widely used in gasoline mixtures as internal combustion engine fuel to increase the octane index and reduce environmental contamination associated with green-house gases. Furthermore, in chemical industry ethanol takes part in the production

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of solvents for cosmetics, paints, medicine, food and chemical synthesis of esters and ethers. The most common ethanol dehydration technologies are Extractive Distillation (ED) and adsorption with molecular sieves. Extractive distillation is an azeotropic mixtures separation technique that uses a separating agent, which modifies the relative volatilities among the mixture components. The separating agent must have specific properties to improve the operating conditions and get high purity products. On the other side, adsorption with molecular sieves uses the difference in the molecular size of the mixture components. Some studies compared both alternatives with respect to the energy consumption. As a result, the extractive distillation using ethylene glycol as separating agent and the molecular sieves had similar values, but ED has less fixed capital costs1. Ethanol dehydration by means of extractive distillation has been tested using some glycols or derivatives as entrainers. In particular, ethylene glycol and glycerol have demonstrated to be the most competitive in terms of separation, accessibility and energy consumption. However, when glycerol is used, hydraulic problems are detected in the extractive column due to its high viscosity. Nevertheless, the energy consumption in this case is lower than in ED using ethylene glycol as entrainer. Additionally, ethylene glycol is more expensive, and the solvent to feed ratio and the energy consumption are higher than in glycerol case. It is expected that a mixture of ethylene glycol and glycerol will decrease the viscosity of the separating agent and, at the same time, the global energy consumption of the process. Additionally, some advantages regarding to the entrainer cost, the separation factor, among others, can be obtained. 1,2 An important step in the process design is to choose the best value of the design variables of the system. The process optimization in extractive distillation has been deeply studied during the last years by means of deterministic and stochastic optimization approaches. The practical application of the optimization takes into account different objective functions that depend on the particular interest. These objectives can include operational costs, capital fixed costs taking into account the payment period, product recovery, energy consumption, efficiency, process safety, total annual cost (TAC), among others3. Some examples of this approach with both methodologies of optimization are in the work of Herreros et al.4, where


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the process profit of an extractive distillation system of ethanol was maximized using the simulated annealing algorithm (stochastic algorithm). This work shows the dependence between the initial value of the optimization variables and its optimum value, because the algorithm finds optimums values in the search region. In the work of Luo & Liang 5, the total annual cost for extractive and pressure swing distillation process of isopropyl alcoholdi-isopropyl ether mixture is minimized, applying a sequential iterative algorithm. An example of Global Optimization was reported by Lladosa et al.6. The optimization was carried out for the separation of the azeotropic mixture of n-propyl-ether and n-propylalcohol, comparing the pressure swing distillation and the extractive distillation. The work of Feitosa et al.7 proposes a systematic procedure using process simulators, where the discrete variables are maintained fixed and the continue variables are optimized. Finally, two works focused in the optimization with genetic algorithm as optimization methodology were proposed by You8,9. In the first, a genetic algorithm programed in Excel with visual basic for application was used to minimized the total process energy consumption and TAC for an extractive distillation system to separate the azeotropic mixture acetone-methanol with water as separating agent. in the second work, the total process energy consumption was minimized for the separation of di-isopropyl ether and isopropyl-alcohol using a genetic algorithm. For this, they coupled the process simulator with Matlab®. Extractive and azeotropic distillations are non-linear multivariable systems and provide a permanent challenge to the control system designers10. For this reason, the correct selection of control scheme, operational conditions and design parameters (number of stages and feed stages) has an important influence in the stability of the system. A good selection of control loops (best pairing between manipulated and controlled variables) allows guaranteeing the product specification in the time, the composition in the column and better safety conditions11. Different control structures have been reported by some authors. Luyben12 proposed a control scheme for the separation of CO2 and ethane. Gani et al.13 studied the dynamic control of an extractive distillation process and a partially heat-integrated pressure swing process for the separation of ethanol-tetrahydrofuran (THF) mixture. Wang et al.14 evaluated control structures for extractive and azeotropic distillation processes, using isopropanol-water mixture as study case. Grassi II15 studied control



strategies for extractive distillation with different azeotropic mixtures and proposed some control configurations. For example, in the conventional scheme, the reflux drum level, sump column level, column pressure and solvent flow rate are controlled with the distillate flow rate, bottoms flow rate, condenser heat duty, and solvent makeup flow rate, but with the variation, the sump level column and mixed solvent flow rate are controlled using the separating agent makeup and bottoms flow rate, respectively. These two strategies, the conventional and the variation, are studied in this work. The aim of this work is to optimize and control an extractive distillation system to produce anhydrous ethanol using a mixture glycerol-ethylene glycol as separating agent. This new alternative was proposed in our previous work2 and attempts to take advantage of the two solvents in different aspects. Initially, this operating scheme takes into account that ethylene glycol is the most commonly solvent used in the industry to dehydrate ethanol, and glycerol is a low-cost solvent available as a by-product of biodiesel production. The validation of the thermodynamic model used in the steady state simulation is performed to guarantee the correct modeling of the separation and transport phenomena. In addition, the minimization of the TAC of the extractive distillation system is carried out in order to find the best value of the design and operating variables in the proposed scheme. Finally, two control strategies are evaluated considering the effect of disturbances in the feed azeotropic flow rate and its composition to demonstrate the robustness in each case. 2. Thermodynamic model. Ethanol-water mixture has a minimum boiling-point azeotrope at 78.1 °C of mole composition 89% ethanol at normal pressure. Accordingly, Non-Random Two-Liquids (NRTL) activity coefficient model was used to predict the non-ideality of the liquid phase, while vapor phase was assumed to be ideal.


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(a)

375

T-x Ethyleneglycol-water Est. NRTL T-y Ethyleneglycol-water Est. NRTL T-x Ethyleneglycol-water Exp. T-y Ethyleneglycol-water Exp.

490

Temperature (K)

Temperature (K)

(b) Ethanol-water T-x Exp Ethanol-water T-y Exp Ethanol-water T-x Est. NRTL Ethanol-water T-y Est. NRTL

370 365 360

470 450 430 410

355 390

350

370

0

0.2

0.4 0.6 x-y Ethanol

0.8

1

0

0.6

0.8

1

(d) T-x Ethyleneglycol-Ethanol Est. NRTL T-y Ethyleneglycol-Ethanol Est. NRTL T-x Ethyleneglycol-Ethanol Exp. T-x Ethyleneglycol-Ethanol Exp

470

Temperature (K)

570

0.4

490

T-x Glycerol-water Est. NRTL T-y Glycerol-water Est. NRTL T-x Glycerol-water Exp. T-x Glycerol-water Exp.

620

0.2

x -y Glycerol

(c)

Temperature (K)

520 470

450 430 410 390

420 370

370 0

0.2

0.4

0.6

0.8

350

1

0

0.2

0.4

x-y Glycerol

0.6

0.8

1

x -y Ethanol

(e) 630

T-x Glycerol-Ethanol Est. NRTL T-y Glycerol-Ethanol Est. NRTL T-x Glycerol-Ethanol Exp. T-x Glycerol-Ethanol Exp.

580

Temperature (K)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


530 480 430 380 330 0

0.2

0.4

0.6

0.8

1

x -y Glycerol

Figure 1: T-xy experimental and estimated diagrams at 1 atm for a) ethanol-water, b) water-ethylene glycol, c) water glycerol, d) ethanol-ethylene glycol and e) ethanol-glycerol binary systems. 16- 18 The thermodynamic model was validated using experimental data for binary mixtures as water-ethanol16, water-ethylene glycol16, water-glycerol18, ethanol-ethylene glycol16 and ethanol-glycerol17. An area test was used to evaluate the thermodynamics consistency of the experimental binary data19, obtaining values between 2 and 8 percent for the five binary



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mixtures. The binary interaction parameters of the five components pairs were regressed in Aspen Properties. The T-xy plot for the different binaries is shown in Figure 1. In general, it can be noted that the experimental data fit correctly to the model in almost all the cases or at least for the more representative pairs. Because, exist a strong difference between the volatilities of the component of the mixture. Table 1 shows the binary parameters used for the process simulation. Table 1: Binary parameters of NRTL model for the vapor-liquid binary systems Component i

Ethanol

Ethanol

Ethanol

Water

Component j

water

Glycerol Ethyleneglycol Glycerol

Water

Glicerol

Ethyleneglycol Ethyleneglycol

Binary Parameters NRTL AIJ

-0,8009

0

14,639

-7,449

0,347

0

AJI

3,4578

0

-0,111

6,5862

-0,056

0

BIJ

246,18

442,713

-4664,405

2809,74

34,823

-347,582

BJI

-586,08

36,139

283,649

-425,41

-147,137

298,143

CIJ

0,3

0,3

0,638

0,293

0,3

0,3

3. Steady state analysis 3.1.

Process simulation

The conventional production process of anhydrous ethanol comprises three main steps: (1) Fermentation, (2) Distillation and (3) Dehydration. The fermentation product is an aqueous solution of ethanol of approximately 10-mol%. This solution is rectified until it reaches an ethanol-water azeotropic mixture. Finally, the azeotropic mixture is dehydrated until the 99.5 mol% of ethanol. The steady state simulation studied here only takes into account the dehydration step of the ethanol. For this reason, the simulation was performed for an ethanol feed composition (raw material) close to the azeotrope (88-mol% ethanol). Anhydrous ethanol production was fixed in 300 cubic-meters per day considering the typical capacity of the bioethanol plants in Colombia. Ethanol purity and ethanol, water and ethylene glycol recoveries were used as design specifications in the simulation. The extractive distillation system evaluated in this work has two columns (extractive and recovery columns) as shown in Figure 2. The top pressures of the extractive and recovery


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columns are set at 1 atm and 0.2 atm, respectively. Likewise, a packing pressure drop of 0.20 atm was assumed for the extractive column and of 0.05 atm for the recovery column. The recovery column is operated at low pressure according to the scheme proposed by Meirelles et al.17. In addition, low operating pressures avoid the thermal decomposition of glycols mixture18, which is not normally considered in process design simulations. In the scheme used in this work, the azeotropic ethanol and mixed solvents are fed to the extractive column. The top product is anhydrous ethanol and the bottom product is a water, ethylene glycol and glycerol mixture. The mixed solvents are recovered in a second column and mixed with a stream of fresh solvents to maintain constant the solvent to feed ratio and the ethylene glycol concentration in the entrainer stream. The steady state design was simulated in the Aspen Plus® 8.6 software platform using the RadFrac module in rate-based model. The simulation was accomplished using as base the design parameters such as reflux ratio, distillate to feed ratio, number of stages, feed stages and solvent to feed ratio and the results obtained in our previous work2. However, the reflux ratios and distillate to feed ratios of both columns were adjusted using the tool “Design spec”, with the top product purity and recovery as process variable. Because in our previous work, the columns were simulated using an equilibrium model. The columns used Nutter-Ring random packing to estimate the column diameter and the stage efficiencies. Table 2: Extractive distillation simulation parameters using rate based model. Parameter

Extractive column

Recovery Column

Reflux ratio

0.484

0.272

Distillate to feed molar ratio

0.485

0.136

Number of stages

32

16

Entrainer feed stage

23

--

Azeotropic feed stage

4

7

Glycerol flow rate (kmol/h)

78.2

--

Ethylene Glycol flow rate(kmol/h)

117.3

--

Reboiler duty (kJ/s)

4281

993



Table 2 shows the results obtained in the steady state simulation, and the reboiler duty of both columns. The solvent to feed ratio has a high impact in the energy consumption and this justifies the need of an optimization of the system considering the main design variables. 3.2.

Process optimization

Design and operating variables were optimized by minimizing the ratio between Total Annual Cost (TAC) and annual mass ethanol production ( ). The objective function was defined as: 1 + $ #$% # & ' = ℎ and subjected to the following constraints: % $()* ≥ 0.995

% ()* ≥ 0.995

% $ (0 * ≥ 0.995

% $ (ℎ$ 1$ * ≥ 0.995 % (1$ * ≤ 0.45

is the operating cost of the process (steam, cooling water, solvent makeup) and ' is the

fixed cost of the process (equipment). The methodology of Ulrich & Vasudevan22 was used to estimate the steam and cooling water cost, while the equipment cost estimation (distillation columns, heat exchangers, pumps) was accomplished using the equations proposed by Seider et al.23. Other equipment as valves, reflux pumps and instrumentation was not considered, because the cost is lower than in the heat exchanger and distillation columns (with internals).


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101.3 kPa 78,3 °C 4118.14 kW

Extractive Column

21.3 kPa 57.6 °C 616.8 kW

Cooling Water

Recovery Column

2 4

Cooling Water

2

Ethanol Azeotropic feed 213.16 kmol/h 0.88 ethanol 0.12 water

22

214.21 kmol/h 0.996 ethanol 0.003 water 0,0007 MEG 0.0003 glycerol

RR=1.759 ID=1.71 m

205.8 kmol/h 0.004 Ethanol 0.138 Water 0.531 MEG 0.327 glycerol

31

Makeup solvent

Water 29.43 kmol/h 0.029 ethanol 0.96 water 0.011 MEG 0.0001 glycerol

7 RR=0.71 ID=1.21 m

13

0.486 kmol/h 0.999 MEG 0.001 glycerol

Steam 194,9 °C 4908.5 kW

Steam

174.8 °C 1188.5 kW

176.38 kmol/h 0.001 water 0.617 MEG 0.382 glycerol

Figure 2: Optimal Flowsheet Design for Anhydrous Ethanol Process, MEG=ethyleneglycol.

Figure 3: Block diagram of connection between Aspen Plus® and Matlab®

The optimization was carried out using the genetic algorithm incorporated in Matlab® with a maximum generation number of 300, population of 50 and fitness function and constraint tolerance of 1 45 . The optimization variables were reflux ratio, distillate to feed ratio,

number of stages, feed stage in each column, and glycerol and ethylene glycol mole flow rate in the entrainer stream. At each step, the algorithm randomly generates individuals



using the optimization variables as a genetic code, which are exported to Aspen plus Software platform. In Aspen Plus® the optimization variables are evaluated and the ethanol, water and ethylene glycol recoveries, ethanol purity and objective function values are exported to Matlab® again. The best individuals from the current population are used as parents to generate the children for the next generation. The communication between programs is shown in Figure 4, the optimal values of the variables are shown in Table 3, and Figure 2 shows the optimal flowsheet for the system. Table 3: Extractive Distillation Simulation Parameters for Optimum Case Using Rate Based Model. Parameter

Extractive Column

Recovery column

Reflux Ratio

0.759

0.376

Molar Distillate to Feed ratio

0,5103

0.143

Number of Stages

32

14

Solvent Feed Stage

22

--

Feed Stage

4

6

Glycerol Flow (kmol/h)

67.3

--

Ethylene Glycol Flow(kmol/h)

109.3

--

4. Control strategy design The controllability analysis needs to convert the distillation columns from the rate-based model to the equilibrium model.

The simulation was exported to a pressure-driven

simulation in Aspen Dynamics. Before this, it is necessary sizing the reflux drums and column sumps using a diameter to height ratio of 0.5 and a residence time of 10 minutes, with a holdup of 50% liquid level. Pumps and valves are sized using the characteristics curves of Aspen and providing adequate pressure drops. The control variables were chose to guarantee the control of inventory, energy and quality in the system. The selection of control variables follows the rules proposed by Luyben11.

4.1.

Control strategy 1


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The characteristics of the control loops of this strategy were determined by the following configuration, as shown in the figure 5. -

The flow of the azeotropic feed is controlled by manipulating its own valve.

-

The temperatures in the most sensible stage of the columns are controlled using the steam flow rate to the reboiler.

-

The reflux drum levels in each column are controlled by manipulating the valves in the distillate stream.

-

The reflux flow is controlled by manipulating the reflux valve in the reflux stream.

-

The reflux ratios are held constant using a ratio control between the distillate and the reflux flow rate.

-

The top column pressure is controlled by manipulating the water flow rate to condenser.

-

The solvent flow is controlled by a ratio control with the azeotropic feed flow rate. 95

Solvent feed temperature (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


90 85 80 75 70 65

-20% azeotropic feed +20% azeotropic feed

60 0

2

4

6

8

10

Time (h) Figure 4: solvent feed temperature The temperature of the azeotropic ethanol feed was kept constant; in consequence, the solvent feed temperature is in open loop and changes when there is a disturbance in the feed flow rate of azeotropic ethanol. In Figure 4 the behavior is shown. The temperature change more or less 14°C, but the column dynamic is not disturbed markedly. This phenomenon



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occurs due to the thermal integration between the azeotropic feed and the solvent recycle streams, then there is no a degree of freedom available to be used as manipulated variable. PC

PC

Cooling Water

Cooling Water

2 LC

4

2

Ethanol FC

FC

22

Azeotropic feed

RC

7 RC

RC

FC

LC

LC

31 TC

LC

Water

Extractive Column

FC

Recovery Column

13 TC

Makeup solvent Steam

Steam

Figure 5: Scheme of Control Strategy 1 The column temperature profile was controlled using only one column temperature. The temperature control location was selected in the stage with the highest slope in the temperature profile. The temperature profiles of both columns are shown in Figure 6. The recovery column had two stages with high slopes, the 5th and the 9th stage. The 9th stage was used as control temperature, because the control temperature in the 4th stage has higher dead and response times. In addition, Figure 6(d) shows that this point has a major change in the temperature when the reboiler heat duty is disturbed. The 31st stage in the extractive column was chosen, since it has the major slope and is the closer stage to the reboiler. In the strategy 2, the dynamic behavior of the temperature of both columns is similar to the strategy 1, where the temperature control loop allows the system to be invariable to the disturbances.


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170

(a)

165

(b)

150

145

Temperature (°C)

Temperature (°C)

155

135 125 115 105 95

130 110 90 70

85 75

50 0

4

8

12

16

20

24

28

32

1

3

5

Stage 1

7

9

11

13

Stage 15

(c) Temperature Difference (°C)

Temperature Difference (°C)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0,5 0

-0,5 -1 -5 % heat duty

(d)

10 5 0 -5

+5 % heat duty

-5 % heat duty

+5 % heat duty

-10

-1,5 1

4

7

10

13

16

19

22

25

28

31

1

3

Stage

5

7

9

11

13

Stage

Figure 6: Temperature Profiles of (a) Extractive and (b) Entrainer Recovery Columns. Temperature difference when exist a disturbance of +/- 5% in (c) extractive and (d) recovery reboiler duty 4.2.

Control strategy 2

Figure 7 shows the control strategy two. The temperature, pressure and level control loops are similar to those of control strategy 1. In this case, the solvent flow rate is controlled by manipulating the flow rate of the recovery column bottoms and the recovery column sump level is controlled using the make-up flow rate. This control strategy has one main problem: the response time is higher due to the interaction between the manipulated and the control variable. The level, flow and pressure controllers were tuned in using the values proposed by Luyben11. The temperature controllers were tuned varying the Proportional-IntegralDerivative (PID) parameters until the best controller response is achieved. The flow rates and pressures were controlled using a Proportional-Integral (PI) controller, the level used a proportional controller and the temperature controllers were PID. The flow controls had



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67 = 0.5 and 89 = 0.3 and the pressure controller used 67 = 20 and 89 = 12. The level

controller in the reflux drum had 67 = 2 and the column sump level was 67 = 20, because

this sub-system needs a faster dynamics due to the internal flows24. PC

PC

Cooling Water

Cooling Water

2 LC

4

2

Ethanol FC

FC

22

Azeotropic feed

7 RC

RC

LC

FC

TC

RC

LC

31

Makeup solvent

LC

Water

Extractive Column

FC

Recovery Column

13 TC

Steam

Steam

Figure 7: Scheme of control strategy 2

The dynamic behavior was analyzed using two study cases: (1) changes are more or less than 20% in the mole flow rate of azeotropic ethanol feed and (2) azeotropic feed composition changes to 80 mol% and 84 mol% of ethanol. The first case was accomplished taking into account that the feed flow rate is not constant in the industries, because it depends on the previous process and its disturbances. The second case is based on the rectifying column, where aqueous ethanol is concentrated to azeotropic composition. Some disturbances in this process stage can decrease the composition of the feed to the extractive distillation system. The dynamic responses of the most representative variables are shown in Figures 8 and 9 for the control strategy 1 and in Figures 10 and 11 for the control strategy 2.


130.8 130.6 130.4 130.2 130.0 129.8 129.6

0.998

4 6 Time (h)

8

0.997

0.996

0.995 8

(e)

250 230 210 190 170 0

2

0.44

4 6 Time (h)

8

0.42 0.40 0.38 0.36 0.34 0.32 2

4 6 Time (h)

8

140.92 140.88 140.84 140.80

10

2

0.972

4 6 Time (h)

8

10

8

10

8

10

8

10

(d)

0.968 0.964 0.960 0.956 0.952 0.948 0

2

38

4 6 Time (h) (f)

36 34 32 30 28 26 24 22

10

(g)

0

140.96

10

Distillate flow rate (kmol/h) - RC

270

4 6 Time (h)

(b)

141.00

0 Water mole fraction - Distillate

(c)

2

141.04

10

0 Mixed solvents flow rate (kmol/h)

Ethanol mole fraction -Distillate

2

0

Ethanol flow rate (kmol/h) -EC

Temperature stage 7th (°C) -RC

(a)

0

Glycerol mole fraction-mixed solvents

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Temperature stage 31th (°C)-EC

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2

4 6 Time (h) (h)

210 190 170 150 130 0

2

4 6 Time (h)

Figure 8: Feed flow dynamic responses for the control strategy 1. (─) +20% azeotropic feed and (- - -) -20% azeotropic feed


130,36


130,32 130,30 130,28 130,26 130,24 130,22 130,20 130,18 2

4

6

8

10

140,92 140,90 140,88 140,86 140,84 140,82 140,80 0

Water mole fraction - Distillate


0,996

0,995 2

4

6

8


205 200 195 190 2

4 6 Time (h)

0,386

8

(g)

0,385 0,384 0,383 0,382 0,381 0

2

4

6

8

10

10

6

8

10

8

10

8

10

0,970 0,965 0,960 0,955 2

4

Time (h)

44

(f)

42 40 38 36 34 32 30 28 0

10

8

(d)

0

2

4

6

Time (h) Mixed solvents flow rate (kmol/h)

Distillate flow rate (kmol/h) - EC

210

6

Time (h)

0,975

10

(e)

0

4

0,980

Time (h) 215

2

0,985

(c)

0

(b)

140,94

Time (h) 0,997

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140,96

(a)

130,34

0


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



174,9

(h)

174,6 174,3 174,0 173,7 173,4 0

Time (h)

2

4

6 Time (h)

Figure 9: Dynamic responses for the control strategy 1 with disturbance in the feed composition. (─) 80 mol% ethanol and (- - -) 84 mol% ethanol


130.8

141.15

130.6 130.4 130.2 130.0 129.8 129.6 2

4

6

Time (h)

0.997

8

141.00 140.95 140.90 140.85 140.80 0

2

4

0.995 4

6

8

210 190 170 6

8

2

4

6

38

8

10

(f)

36 34 32 30 28 26 24 22 0

2

4

6

Time (h) Mixed solvents flow rate (kmol/h)

(g) 0.390 0.380 0.370 0.360 0.350 0.340 4

10

0.952

10

0.400

2

8

0.956

Time (h)

0

10

0.960

0



230

4

8

0.964

Time (h)

250

2

10

0.968

10

(e)

0

8

(d)

Time (h) 270

6

0.972

0.996

2

141.05

Time (h)

(c)

0

(b)

141.10

10




(a)

0


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



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6

8

10

(h)

210 190 170 150 130 0

2

Time (h)

4

6

Time (h)

Figure 10: Feed flow dynamic responses for the control strategy 2. (─) +20% azeotropic feed and (- - -) -20% azeotropic feed


130,36


130,28 130,24 130,20 130,16 0

2

4

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140,96

(a)

130,32

6

8

(b)

140,94 140,92 140,90 140,88 140,86 140,84 140,82 140,80

10

0

2

4

Time (h) Ethanol mole fraction -Distillate

0,997


(c)

0,995 0

2

4

6

8

0,986 0,984 0,982 0,980 0,978 0,976 0,974 0,972 0,970 0,968 0,966

10

0



212 208 204 200 196 192 2

4

6

8

10

2

4

Mixed solvents flow rate (kmol/h)

(g)

0,385

0,380 0

2

4

6

8

10

42

6

8

10

8

10

8

10

(f)

40 38 36 34 32 30 28 0

Time (h)

0,390

10

Time (h)

(e)

0

8

(d)

Time (h) 216

6

Time (h)

0,996


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



2

4

6

Time (h)

175,2

(h)

175,0 174,8 174,6 174,4 174,2 174,0 173,8 0

Time (h)

2

4

6

Time (h)

Figure 11: Dynamic responses for the control strategy 2 with disturbance in the feed composition. (─) 80 mol% ethanol and (- - -) 84 mol% ethanol


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The dynamic response of the temperature control in the columns is excellent for both strategies. The control loop allows rejecting the disturbances, because it makes the temperature reach the set-point value in a short period of time. When the ethanol feed flow rate is disturbed, the maximum variation of temperature was 0.5°C and less than 0.1°C for the extractive and the recovery columns, respectively. However, when the azeotropic ethanol feed composition is changed, the temperature in the extractive column changes only 0.1°C and in the recovery column the temperature variation is less than 0.1°C. The low changes in the temperature are linked to the feed flow rate. For example, when the feed flow rate increases, the internal flows of the column augments too, then, the liquid and vapor inventory in the column is higher, decreasing the column temperatures. For this reason, the valve located in the steam fed to the reboiler jacket opens to increase the energy flow to the material inside the reboiler. Additionally, The mole fraction of the top products of extractive and recovery columns are less affected by the disturbances in the feed flow

20

12 8 4

Valve opening (%)

16

0 0

2

4

6

8

10

Mixed solvent mole flowrate (kmol/h)

Time (h) 180

1,45

55 50

1,44 45 40 1,43 35 1,42

30 0

2

4

6

8

10

Time (h) Variable Valve

(c)

175

Variable Valve

(b)

Valve opening (%)

Variable Valve

(a)

60 50

170 165

40

160 155

30

150 20

145 140

Valve opening (%)

180 175 170 165 160 155 150 145 140 135 130

Reboiler level - Recovery column (m)

rate and composition. Mixed solvents flow rate (kmol/h)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


10

135 130

0 0

2

4

6

8

10

Time (h)

Figure 12: Dynamic response of the (a) mixed solvents feed flow rate, (b) reboiler level of recovery column in the strategy 1 and (c) mixed solvent feed flow rate strategy 2, for a disturbance of -20% of the azeotropic ethanol mole flow rate.



Likewise, the anhydrous ethanol purity is not affected by the changes in the feed flow rate and feed composition in both alternatives, because this variable only decreases 0.4-mol% ethanol. As a result, the top product is always maintained above the specification. The dynamic response is good, because the solvent to feed ratio and reflux ratio in the extractive column are held in a constant value. This avoids the solvent dilution effect in the system and maintains the concentration and mass transfer inside the column unchanging. The major stabilization time of the ethanol mole fraction exists in a negative disturbance of the azeotropic ethanol mole flow rate. In this case, the solvent to feed ratio changes in a period of time, modifying distillation curves. The water mole composition in the top product of the entrainer recovery column has the same performance. It only increases 2% when the ethanol feed composition decreases until 84-mol% of ethanol, because the water flow in the system increase, but the solvent to feed ratio is constant for the ratio feed control. In this condition, the recovery column has a major proportion of light key. In addition, the difference in the boiling point and the relative volatilities allows an easier separation. The principal difference between the two strategies is the stabilization time. The alternative two has a stabilization time between one (1) and two (2) hours approximately, while the control strategy 1 has a stabilization time between three (3) and five (5) hours when a change takes place in the feed flow rate of azeotropic ethanol. In case of disturbances in the composition of azeotropic ethanol fed to extractive column, the stabilization time of both alternatives is less than 2 hours. One of the most important variables in the process is the glycerol mole fraction in the mixed solvents. The reason is that if the glycerol concentration increases in the column, the viscosity will also increase, causing hydraulic problems inside the column and into the pipelines. Therefore, the glycerol mole fraction in the mixed solvents feed stream and the columns stages has to be less than 40%. Figures 8(g) and 10(g) show the dynamic performance of the glycerol mole fraction in the mixed solvents feed stream to extractive column with disturbances in the feed flow rate for the two control strategies. In the first strategy, the system has a poor control of the solvent flow rate when the azeotropic ethanol


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feed flow rate decreases. This occurs because the makeup stream is not able to decrease the solvent flow rate, even though the valve is completely closed and the recovery column level is maintained constant. Both dynamic responses are shown in Figures 11(a) and 11(b). Consequently, the inventory solvent reduction is associated to the loss of both solvents in the top products. In addition, the ethylene glycol has a higher relative volatility than glycerol, and then the losses of glycerol are less than those of ethylene glycol. The glycerol composition in the solvent feed stream increases until the solvent mole flow rate stabilizes, as shown Figure 8(h). In case of positive disturbances, when the flow of the solvent makeup augments, the flow of solvent increases quickly, while the valve is opening. In the control strategy 2, this problem does not exist because the manipulated variable in the control loop of the entrainer flow is linked to the recovery column bottoms flow rate. For this reason, the reboiler level increase or decrease for a period of time, depending on the disturbance. Figure 11(c) shows the dynamic behavior of the entrainer flow with a negative disturbance for the control strategy 2. The top products flow rate or the inventory control is an important factor to avoid the accumulation of material inside the columns. In this case, the reflux drum level control loops that use the distillate mole flow rate allow a rapid stabilization of the level. Approximately, the system comes to a new steady state in less of 1 hour, when the disturbance is in the feed flow rate. In contrast, when the disturbance is in the feed composition the steady state is reached in 1 hour. Additionally, both control strategies have a good control of the changes of the composition in the ethanol fed to the extractive column, maintaining the purity of the top and bottoms products. The dynamic results obtained in this work are similar to those of our previous one, which studied the control of an extractive distillation system with glycerol as separating agent21. The main differences between both works are: (1) our previous work uses an equilibrium model in the distillation columns in the initial simulation, (2) the distillation configuration was obtained by sensitivity analysis of energy consumption, (3) for disturbances of the temperature of both columns, the maximum changes in the temperature are 7°C and 2°C and the system with glycerol as entrainer has a lower stabilization time than this work. This phenomenon can be associated to the differences in the volatilities of the separating agents,



the sensitivity of the control temperature stage and the slopes of temperature profiles. Finally, in both studies exist a high response time of the anhydrous ethanol fraction for the conventional control structure, because the control of mole flow rate of separating agent has a poor dynamic for negative disturbances of mole flow rate of azeotropic ethanol as it was explained previously.

5. Conclusions This paper developed the simulation, optimization and control of an extractive distillation system for the production of anhydrous ethanol using a glycerol-ethylene glycol mixture as separating agent. The process was simulated using NRTL as property package, which describes correctly the experimental data for all the binary components pairs. The optimization allows obtaining an optimal configuration with low energy consumption and a good total annual cost. This is an attractive alternative to be applied at industrial level. The main result after the optimization was the decrease of the solvent to feed ratio of the system without affecting the ethanol purity. The glycerol and ethylene glycol mixture is a good alternative as separating agent in extractive distillation, because it allows using a solvent to feed ratio similar to the process with only glycerol. In addition, the proposed process takes advantage of the glycerol low cost and the good hydraulics of the ethylene glycol. The principal disadvantage is the high boiling point of the separating agent due to the increase in the temperature of the columns bottoms, needing high-pressure steam as utility. The two control strategies evaluated in this work have a good performance maintaining the process stability. The strategy 2 has a better response compared with the strategy 1 to maintain the glycerol composition close to the initial value when the system has disturbances in the feed flow rate and the feed composition. Furthermore, both control strategies allow a good control of the ethanol purity. Finally, control strategy 2 is appropriate for an extractive distillation process of ethanol using mixed solvents as entrainers.


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The use of a ratio control between the mixed solvents and the azeotropic ethanol mole flow rate allows maintaining the ethanol purity within the specification, because this avoid the dilution and concentration effect of the solvent inside the extractive column.

Acknowledgments The authors thank the “División de Investigación – Sede Bogotá”, for the financial support of this work through the Project Code No. 35726.

6. References (1) Gil Chaves , I. D. Diseño, Montaje y Puesta en Marcha de un Sistema de Destilación Extractiva a Nivel Piloto para la Producción de Alcohol Anhidro. Universidad Nacional de Colombia, Bogotá, 2006. (2) Gil, I.; García, L; Rodríguez, G. Simulation of Ethanol Extractive Distillation with Mixed Glycols as Separating Agent. Brazilian Journal of Chemical Engineering, 2014, 31 (01), 259270. (3) Rangaiah, G. P. Multi-Objective Optimization-Techniques and Applications in Chemical Engineering, Singapore: World Scientific. (4) García-Herreros, P.; Gomez, J. M.; Gil , I. D.; Rodríguez, G. Optimization of the Design and Operation of an Extractive Distillation System for the Production of Fuel Grade Ethanol Using Glycerol as Entrainer. Industrial Engineering Chemestry Research, 2011, 3977-3985. (5) Luo, H.; Liang, C. Comparison of Pressure-Swing Distillation and Extractive Distillation Methods for Isopropyl Alcohol/Diisopropyl Ether Separation. Industrial Engineering Chemical Research, 2014, 15167-15182, (6) Lladosa, E.; Montón , J. B.; Burguet , M. Separation of Di-n-Propyl Ether and n-Propyl Alcohol by Extractive Distillation and Pressure-Swing Distillation: Computer Simulation and Economic Optimization. Chemical Engineering and Processing, 2011, 50, 1266-1274. (7) Feitosa, M. d. F.; Pontual , B.; Monteiro de Araújo, J. M.; Gonzaga Sales, L. Pereira Brito, R. Optimal Design of Extractive Distillation Columns—A Systematic Procedure Using a Process Simulator. Chemical Engineering Research and Design, 2011, 89 (3), 341-346. (8) YOU, X.; RODRIGUEZ-DONIS, I.; GERBAUD, V. Low pressure design for reducing energy cost of extractive distillation for separating Diisopropyl ether and Isopropyl alcohol. Chemical Engineering Research and Design, 2016, 109, 540-552.



(9) YOU, X.; Rodriguez Donis, I.; Gerbaud, V. Investigation of separation efficiency indicator for the optimization of the acetone-methanol extractive distillation with water. Industrial & Engineering Chemestry Research, 2015, 54 (43), 10863-10875. (10) Gani, R.; Romagnoli, J. A. ; Stephanopoulos, G. Control Studies in an Extractive Distillation Process: Simulation and Measurement Structure. Chemical Engineering Communications, 1986, 40, 281-302. (11) Luyben, W. L. Practical Distillation Control. VAN NOSTRAND REINHOLD, New York , 1992. (12) Luyben, W. L. Control of an Extractive Distillation System for the Separation of CO2 and Ethane in Enhanced Oil Recovery Processes. Industrial & Engineering Chemestry Research, 2013, 52 (31), 10780-10787. (13) Gani, R., Stephanopoulos, G. Control Studies in an Extractive Distillation Process: Simulation and Measurement Structure," Chemical Engineering Communications, 1985, 281-302. (14) Wang, H.; Li, Y.; Su, W.; Zhang, Y.; Guo, J.; Li, C. Design and Control of Extractive Distillation Based on an Effective Relative Gain Array. Chemical Engineering Technology, 2016, 2339–2347. (15) Grassi II, V. G. Process Desing and Control of Extractive Distillation-Practical Distillation Control, W. L. Luyben, Ed., New York, Van Nostrand Reinhold, 1992, 370-404. (16) Kamihama, N.; Matsuda, H.; Kurihara, K.; Oba, S. Isobaric Vapor-Liquid Equilibria for Ethanol + Water + Ethylene Glycol and Its Constituent Three Binary Systems. Journal of Chemical Engineering Data, 2012, 339-344. (17) Oliveira, M.; Teles, A. Q. A.; Coutinho, J. Phase Equilibria of Glycerol Containing Systems and Their Description with the Cubic-Plus-Association (CPA) Equation of State. Fluid Phase Equilibria, 2009, 22-29. (18) Chen, D. H.; Thompson, A. R. Isobaric Vapor-Liquid Equilibria for the Systems GlycerolWater and Glycerol-Water Saturated wtin Sodium Chloride. Journal of Chemical Engineering Data, 1970, 471-474. (19) Voutsas, E.; Louli, V.; Boukouvalas, C.; Magoulas, K.; Tassios, D. Thermodynamic Property Calculations with the Universal Mixing Rule for EoS/GE Models: Results with the Peng– Robinson EoS and a UNIFAC model. Fluid Phase Equilibria, 2006, 241, 216-228. (20) Meirelles, A.; Weiss, S.; Herfurth, H. "Ethanol Dehydration by Extractive Distillation," Journal of Chemical Technology and Biotechnology, 1992, 53, 181-188. (21) Dow 2014. Available: Chemical, Dow Answer Center, [Online]. https://dowac.custhelp.com/app/answers/detail/a_id/5280. (Accessed 25 05 2017). (22) Ulrich, G. D.; Vasudevan, P. T. How to Estimate Utility Cost," Chemical Engineering, 2006, 66-69.


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(23) Seider, W. D.; Seader, J.; Lewin, D. R. Product & Process Design Principles: Synthesis, Analysis and Evaluation, Pennsylvania: Wiley, 2003.

(24) Gil, I. D.; Gómez, J. M.; Rodríguez, G."Control of an Extractive Distillation Process to Dehydrate Ethanol using Glycerol as Entrainer," Computers and Chemical Engineering, 2012, 129-142.

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Control of an Optimal Extractive Distillation Process with Mixed

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