ARTICLE pubs.acs.org/IECR
Design and Control of Distillation System for Methylal/Methanol Separation. Part 1: Extractive Distillation Using DMF as an Entrainer Qiaoyi Wang, Baoru Yu, and Chunjian Xu* State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, China ABSTRACT: A method for methylal/methanol separation using extractive distillation with N,N-dimethylformamide as an entrainer is presented. Rigorous steady state and dynamic simulations for this process are implemented on commercial simulators (Aspen Plus and Aspen Dynamics). On the basis of global economic optimization, a design with optimized operation conditions for this process is developed. For dynamic simulations, feed flow rate and feed composition disturbances are used to evaluate the dynamic performance of several control structures. The dynamic simulation results reveal that the control structure with fixed reflux ratio can handle all disturbances well, except one kind of composition disturbance. An improved control structure with R/F (the reflux flow rate/the feed flow rate) ratio scheme is used to maintain the two products' purity requirements. Dynamic simulation results reveal that this control structure can handle feed flow rate and composition disturbances quite effective.
1. INTRODUCTION Methylal, also called dimethoxymethane, is an important intermediate that is widely used in many fields for its exceptional dissolving ability, extremely low viscosity, amphiphilic characteristics, low surface tension, and particularly high evaporation rate, etc. It can be used in the production or synthesis of aerosols, perfumes, paints and varnishes, cleaning and degreasing solvents, pharmaceuticals, polymers, adhesives, and insecticides and as a plasticizer of resins, etc. It is also a cleaning diesel additive and substitution for freon.13 Traditionally, methylal is synthesized by reaction of methanol with formaldehyde or paraformaldehyde in the presence of catalyst.2 Because the yield of methylal is restricted by chemical equilibrium, many scientists have been working on catalytic distillation, which combines reaction and separation in a single vessel to achieve a high yield of methylal. Several catalytic distillation processes for the synthesis of methylal have been reported by Lawrence, Xuemei, Seinosuke.36 Masamoto and Matsuzaki7 have developed the world’s first commercial technique for production of methylal by reactive distillation. A high purity of methylal is needed when methylal is used for making perfume or pharmaceuticals or as a molecular weight modifier for polyacetal resin.1,8 Unfortunately, methylal and methanol forms a minimum-boiling azeotrope at atmosphere pressure with 94.06 wt % methylal. Thus a methylalmethanol mixture cannot be separated completely through a simple distillation process. Many techniques have been proposed to separate azeotropes, including azeotropic distillation, extractive distillation, reactive distillation, liquidliquid extraction, adsorption, salt addition distillation, pressure-swing distillation, and membrane pervaporation. Carretier1 has studied the purification of methylal by a membrane process; several pervaporation membranes with different operating conditions and for various methylal grades were investigated. However, due to degradations and weak permeating flows, it is hard to consider an industrial development by using organic membranes. Still others put forward a method for methylal purification by extractive distillation.9 r 2011 American Chemical Society
Extractive distillation is widely used for separation of azeotropes and other mixtures with a relative volatility of the key components below 1.1.10 Because the entrainer has different affinities to the key components, addition of the entrainer causes an increase in the relative volatility of the light and heavy key components. A fairly pure light key component can be obtained at the top of the extractive distillation column, and the heavy key component, attained at the top of the entrainer recovery column, with entrainer being recovered at the bottom then recycling to the extractive distillation column. Extractive distillation makes azeotrope separation feasible and economical, but it is inevitable that entrainer is introduced into products more or less. Figure 1 gives a conventional flowsheet for extractive distillation. The mixture AB and the entrainer S are fed into the extractive distillation column. The presence of entrainer alters the relative volatility between A and B, causing A to move toward the top and B, toward the bottom of the column. The extractive distillation column bottom's stream is fed into the entrainer recovery column to produce almost pure B at the top and almost pure S at the column bottom. Heavy entrainer S will be recycled to the extractive distillation column. To balance tiny entrainer losses in both distillates, a small makeup stream of entrainer should be added. Because entrainer is a dominant factor in the feasibility and economy of extractive distillation, several rules for entrainer selection have been proposed.11,12 More recently, computeraided molecular design of entrainer for extractive distillation based on a genetic algorithm has been presented.13 It facilitates the choice of extractive agent through a computer program. For the separation of a methylal/methanol mixture by using extractive distillation, various entrainers have been recommended, such as water, aqueous alkaline solutions, ethylene glycol(EG), paraformaldehyde, and dimethylformamide (DMF).9 Received: August 29, 2011 Accepted: December 20, 2011 Revised: November 20, 2011 Published: December 20, 2011 1281
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Table 1. Results of the Entrainer Selection for Extractive Distillation System methylal (1)/methanol (2) using the NRTL model solvent
Tb /K
k∞ 1,s
k∞ 2,s
αs1,2
EG
470.23
33.506
31.206
1.074
DMSO
463.89
30.434
17.371
1.752
DMF
424.93
15.925
8.682
1.833
developed on the basis of global optimization. Finally, two different control structures are introduced, and their dynamic performances are evaluated through dynamic simulations.
2. STEADY STATE DESIGN Figure 1. Sketch of conventional extractive distillation process.
Chemical process design is one of the most creative and challenging activities that confront many chemical engineers. Seider14 introduced several steps involved in developing a process design, including problem assessment, process creation, optimization, and plantwide controllability assessment. Process simulators, such as Aspen Plus, HYSYS, and PRO/II are frequently used to aid process creation. Throughout the process design, estimates of the cost of the equipments and other costs related to the capital investment play a crucial role in selecting the design alternatives. Douglas15 has given a summary of equipment cost correlations and utilities costs and proposed using total annual costs (TAC) as an objective function to screen optimal design from many alternatives. TAC has been widely used as the objective function to evaluate different designs.1619 The selection of an appropriate control structure is the most important decision when designing distillation control systems. Skogestad20 has reviewed distillation columns dynamics and control in detail, which covers advances in distillation process control in the past few decades. The selection of controlled variables and manipulated variables is of vital importance in designing distillation control systems. Luyben21,22 has studied the relative gain array (RGA) for controlled variables and manipulated variables pairing. On the basis of a RGA analysis, Skogestad23 proposed four different control configurations for distillation columns by using conventional PI controllers. It is found that the (L/D) (V/B) control structure is the best choice for a two-point composition control, and the L/V configuration is preferable for one-point control. Advanced control strategies have also been introduced to distillation control, such as model predictive control, dynamic matrix control, neural network control, etc.2426 Dynmic simulation, which is extensively studied in the open literature, can be used as a tool to understand the distillation process dynamics and design control systems. Luyben27 gives a detailed introduction of dynamic simulation using Aspen Dynamics and HYSYS. In this paper, an extractive distillation and a fully heat integrated pressure-swing distillation process for the separation of methylal/methanol are demonstrated. The economy and dynamic controllability of the two processes is compared. In the first part of this paper, only the extractive distillation is presented. It is organized as follow: first, a suitable entrainer is selected for methylal/methanol separation by extractive distillation. Second, an optimized design using extractive distillation is
2.1. Entrainer Selection. Since the entrainer is a key factor in extractive distillation, more attention should be paid to its selection. A criterion for entrainer selection is through the comparison of relative volatilities in the presence of different entrainers.13 The higher the relative volatility value, the easier the separation. The relative volatility is defined as
αsi, j ¼
k∞ i, s k∞ j, s
ð1Þ
where ksi,j is the relative volatility of component i and component j in the presence of entrainer, k∞ j,s is the infinite dilution K value for a trace of species i in the entrainer, and k∞ j,s is the infinite dilution K value for a trace of species j in the entrainer. Of all possible entrainers that can be used for the separation of methylal and methanol azeotrope mixture, the following three entrainers are studied in this paper: ethylene glycol (EG), dimethyl sulfoxide (DMSO), and N,N-dimethylformamide (DMF). Their K values at infinite dilution and the relative volatility are listed in Table 1. From Table 1, because DMF introduces no further azeotrope in the system and shows a higher relative volatility (1.833), it is chosen as the most suitable entrainer. This large relative volatility allows an economical separation sequence. 2.2. Residue Curve Map (RCM) for the System. Doherty2830 studied the design and synthesis of an azeotropic distillation process with RCM. A mixture with an initial composition x1(0) and x2(0) was placed in a container at a fixed pressure. A vapor stream was continuously distilled, and the composition of the remaining liquid in the vessel was plotted on the ternary diagram.31 For a distillation column under total reflux conditions,the residue curve approximates the column composition profile. A residue curve map can be used as a simple method for designing and distinguishing between feasible and infeasible sequences for a given system. The RCM of the methylalmethanolDMF ternary system mapped by Aspen Plus using NRTL the model is shown in Figure 2. It can be seen that the methylalmethanol azeotrope is the unstable node, DMF is the stable node, and both methylal and methanol are the saddles. The resulting residue curves (red lines) with arrows point to pure DMF. It can be seen that no distillation boundary exists. This is an ideal situation for selection of an extractive distillation process. The blue line stands for the material balance line. It is noticed that F1 can be separated into D1 and B1, and B1 can be separated into D2 and B2. This means that feed can be separated into relatively pure products with the aid of 1282
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Industrial & Engineering Chemistry Research the entrainer. To balance the tiny loss of entrainer in both D1 and D2 streams, a small makeup stream of DMF should be added. 2.3. Process Design and Economic Analysis. 2.3.1. Process Design. In this paper, the raw material used is from a local plant and mainly composed of methylal and methanol with little methane chloride and water. After pretreatment, methane chloride was removed from the raw material completely. The composition was analyzed by gas chromatography. The extractive distillation process was simulated with the following data: the feed was a mixture made up of 85.8 wt % of methylal, 13.9 wt % of methanol, and 0.3 wt % of water, with a flow rate of 24 000 tons/ annum or 3000 kg/h with 8000 working hours annually. The steady state and dynamic simulations are implemented by commercial software (Aspen Plus and Aspen Dynamics). The NRTL activity model is chosen as the property package in the simulation, using the built-in binary interaction parameters in the simulator. Useful skills on how to use these software programs are covered in detail in Lyuben’s book.31 The two product specifications are set to be as follows: the methylal impurity in the methanol product is not more than 0.2 wt %, and the methylal product has a purity of 99.9 wt %. Here, we use Aspen notation of numbering stages from the top, with stage 1 being the reflux drum and the last stage being the rebolier. For an extractive distillation column (denoted as C1), there are three design degrees of freedom once the total stages,
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operating pressure and feed location are fixed: reflux ratio (RR1), entrainer flow rate (S), and reboiler heat duty (QR1). Several case studies are made to investigate the influence of the flow rate of entrainer and reflux ratio on the product composition. In each case, we fix the number of theoretical plates (NT1) with a different S and RR1, and the optimal feeding location of fresh feed (NFF) and entrainer (NFE) are gained by minimizing the C1 reboiler heat duty. Figure 3 shows the influence of S and RR1 on the compositions of the distillate stream from the extractive column C1 with NT1 = 52. Since almost all the methylal that is present in the C1 bottom product will go to the top of the entrainer recovery column (denoted as C2), to achieve the desired methanol product, the mass flow rate of methylal in the bottom product of the C1 is held at 0.85 kg/h (calculated from the material balance) by manipulating the reboiler heat duty QR1 for all the cases shown in Figure 3. Figure 3A shows that for a given entrainer flow rate, S, there is an optimum reflux ratio, RR1, which gives the maximum methylal purity. The higher the entrainer flow rate, the higher the methylal purity that can be obtained. To achieve the desired 99.9 wt % methylal purity, the entrainer flow rate must be above 2500 kg/h at a reflux ratio of about 2. Figure 3B shows how the impurity of methanol in D1 is affected by entrainer flow rate and reflux ratio. 2.3.2. Economical Optimization. From the results of section 3.1, it can be found that there is a minimal entrainer flow rate for a given theoretical plate, NT1, when the product purity specifications are fixed. It is known that for the extractive distillation process, an increase in the entrainer flow rate will reduce the heat duty of the extractive distillation column, but the heat duty of the entrainer recovery column will increase, and a larger column diameter is expected. Thus, a trade-off between the extractive distillation column costs (include both the fixed capital costs and operating costs) and entrainer recovery column costs needs to be made. So there exists an optimal entrainer flow rate that minimizes TAC for fixed theoretical plates. When the entrainer flow rate is larger than the optimal value, the costs of the entrainer recovery column become more dominant, and total annual costs increase. It is a convention to use the total annual cost as the objective function to screen process candidates. Using the following objective function, a global economic optimization is carried out on the basis of the TAC.32
Figure 2. Residue curve map for the methylalmethanolDMF system at 101.3 kPa.
TACð103 $=yearÞ ¼ Cv þ Cf þ ðir þ im Þ 3 FCI
ð1Þ
Figure 3. Effect of RR1 and entrainer flow rate S in extractive column (NT1 = 52) on (A) methylal purity and (B) impurity of methanol. 1283
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Table 2. Utility Prices utility
price ($/GJ)
medium pressure steam (10 barg)
14.19
cooling water
0.354
electricity
16.8
where the Cv is the process variable costs, mostly utilities consumption (steam, cooling water, and electricity); Cf is the annual fixed costs, such as maintenance expense and wages; FCI is the fixed capital investment; ir is the fixed capital recovery rate applied to FCI; and im is the minimum acceptable rate of return on FCI. Cf was assumed to be 10% of FCI, and ir + im was assumed to be 20% of the FCI in this optimization; thus, the objective function can be rewritten as32 TACð103 $=yearÞ ¼ Cv þ 0:3 3 FCI
ð2Þ
Major pieces of equipment for this process are the two distillation column vessels (including column internals), reboilers, and condensers. Small items such as reflux drums, pumps, valves, and pipes are usually not considered because their costs are much lower compared with the costs of the column vessels and heat exchangers. The “tray sizing” function in Aspen Plus is employed to size the column vessels and a sieve plate is selected. Since the two columns have small diameters, we specify the tray spacing is 0.4 m instead of the default value (0.61 m) in the simulator. The heat transfer area for the condensers and reboilers is determined using the overall heat transfer coefficient and a differential temperature driving force. Here, the overall heat transfer coefficients are assumed to be 0.852 kW/(K 3 m2) and 0.568 kW/(K 3 m2) for the condenser and reboiler, respectively, which are taken from Luyben’s book.31 All the major equipment costs are estimated using a cost estimation program CAPCOST of Turton.33 The utilities consumption costs can be calculated from the heat duties of the reboilers and condensers as well as the power of all the pumps. Medium pressure steam with a pressure of 10 bar (184 °C) and cooling water with a temperature of 30 °C are supposed to be available in the plant. The utility prices taken from CAPCOST are listed in Table 2.33 The “Design Spec/Vary” feature in Aspen Plus is used to achieve the desired product quality requirements. For the extractive distillation column (C1), one design specification is set up to maintain the methylal purity of D1 at 99.9 wt % by manipulating RR1, and the other design specification is to maintain the mass flow rate of methylal in B1 at 0.85 kg/h by varying QR1. For the entrainer recovery column (C2), a “Design Spec/Vary” is used to maintain the DMF impurity in D1 at 10 ppm by varying RR2, and the other “Design Spec/Vary” is used to maintain the impurity in B2 at 10 ppm by manipulating the mass flow rate of D2. The entrainer feed temperature is also a process design variable; thus, a cooler is required. Knight and Doherty34 have suggested operating the entrainer feed temperature 515 °C below the top temperature of the extractive distillation column. In view of the cooling medium that is available, subcooling the entrainer feed temperature to 40 °C is considered in the following simulations. The design variables to be optimized in the flowsheet include the entrainer flow rate (S), total stages of the extractive distillation column (NT1), entrainer and fresh stream feeding locations (NFE and NFF), total stages of the entrainer recovery column
(NT2), and feeding location of the entrainer recovery column (NF2). Because so many design variables need to be optimized, a calculation sequence is carefully established to facilitate the optimization. For the extractive distillation column, a sequential iterative optimization search is used to find the optimal design, with NT1 as the outer iterative loop, the entrainer flow rate as middle loop, and NFE and NFF as the inner iterative loop. The optimization procedure to minimize the TAC is summarized below. 1 Optimization for extractive distillation column (C1): (1) Fix two columns top pressure (P1, P2) at 101.3 kPa. (2) Guess the total stages of extrainer recovery column (NT2). (3) Guess the total stages of extractive column (NT1). (4) Calculate the minimal entrainer flow rate (Smin) using the method as mentioned in section 2.3.1. (5) Guess the entrainer flow rate (S g Smin). (6) Guess the entrainer feeding location (NFE), the fresh feeding location (NFF), and the feeding location of C2 (NF2). (7) Change the reboiler duty (QR1) and the reflux ratio (RR1) until the two design specifications can be met. (8) Go back to step 6 and change NFE, NFF, and NF2 until QR1 + QR2 is minimized. (9) Go back to step 5 and change S until the TAC is minimized. (10) Go back to step 3 and change NT1 until TAC is minimized. 2 Optimization for extrainer recovery column (C2): (1) Fix total stages of extractive column (NT1) that has been optimized. (2) Guess the total stages of extractive column (NT2). (3) Guess the feeding location of C2 (NF2). (4) Vary the reflux ratio (RR2) and distillate rate (D2) until the two design specifications can be met. (5) Go back to step 3 and change NF2 until QR1 + QR2 is minimized. (6) Go back to step 2 and change NT2 until the TAC is minimized. Such a sequential iterative optimization procedure is clearly demonstrated in Figure 4. To determine the optimal value for the total stages of the extractive column, NT1, several cases are studied with fixed NT2. In each case, the above-mentioned optimization procedure is used to determine the optimal feeding locations and entrainer flow rate, S. Table 3 gives the detailed information for each case, and the TACs estimated from CAPCOST are also listed. As can been seen from Table 3, if there is an increase in the total stages of C1, the diameter of C1 turns out to be smaller, and the reboiler heat input decreases; this reduces heat exchanger and energy costs. However, the cost of the extractive distillation column vessel increases because the vessel is longer. The case with the minimum TAC corresponds to the optimal design when NT2 is fixed: the optimal NT1 is 52, the optimal NFE and NFF are 4 and 42, respectively, and the corresponding optimal extrainer flow rate is 2900 kg/h. Table 4 shows the detailed information for when NT1 is specified at its optimal value (NT1 = 52), with different total stages of the extrainer recovery column NT2. The optimal feed location of C2 is determined by minimizing the total heat duty, QR1 + QR2. Increasing the total stages of the extrainer recovery column NT2 would bring a decrease in the reboiler heat duty and 1284
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Figure 4. Sequential iterative optimization procedure for this system.
Table 3. Optimization Results of the Extractive Distillation Column parameters
case 1
case 2
case 3
case 4
case 5
Table 4. Optimization Results of the Entrainer Recovery Column parameters
case 6
case 7
case 8
case 9
NT1
40
48
50
52
54
NT1
52
52
52
52
NT2 S (optimum, kg/h)
20 3400
20 3000
20 3000
20 2900
20 2900
NT2 S (optimum, kg/h)
16 2900
20 2900
22 2900
24 2900
NFE (optimum)
4
4
4
4
4
NF2 (optimum)
7
9
9
9
NFF (optimum)
30
38
40
42
43
ID2 (m)
0.52
0.48
0.47
0.47
ID1 (m)
0.83
0.82
0.82
0.82
0.81
QC2 (KW)
260.38
211.21
204.52
201.97
QC1 (kW)
626.25
622.66
616.65
620.90
616.46
QR2 (KW)
307.96
260.78
255.07
253.49 413.39
QR1 (kW)
813.69
788.84
783.75
782.57
779.00
Cv ($1000s/annum)
436.33
416.47
414.04
Cv ($1000s/annum)
438.51
421.12
418.89
416.47
414.85
FCI ($1000s/annum)
664.43
666.42
671.16
676.56
FCI ($1000s/annum) TAC ($1000s/annum)
636.55 629.48
654.76 617.55
659.39 616.71
666.42 616.39
672.37 616.56
TAC ($1000s/annum)
635.66
616.39
615.39
616.35
an increase in the fixed capital investment. A trade-off should be made between the fixed capital investments and the operating costs. On the basis of this optimization procedure, the economic optimal process flowsheet is obtained. Note that case 8 in Table 4 corresponds to the optimal design, since it has the lowest TAC ($615 390/annum) of all cases. Figure 5 gives the final optimal flowsheet for this system, with detailed steam information, heat duties, equipment sizes, and
operating conditions at the steady-state design conditions. Figure 6 shows the temperature profiles of the two columns for the flowsheet in Figure 5. As you can see from Figure 6A, there is a rapid rise in the temperature for stage 4 and a rapid fall for stage 42. This is because the entrainer and fresh feed are fed at stages 4 and 42, respectively. It is obvious that stage 47 displays a fairly steep slope for the extractive distillation column, and stage 6, for the extrainer recovery column. These indicate the proper temperature control point for the two columns. Figure 7 gives the extraction distillation column and the entrainer recovery column 1285
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Figure 5. Optimal process flowsheet for extractive distillation.
Figure 6. (A) Extractive distillation column temperature profile. (B) Entrainer recovery column temperature profile.
liquid composition profiles. Notice that for the extraction distillation column, the methylal purity rises rapidly to 99.9 wt % and the DMF slip down rapidly to zero, despite the fact that only four stages in the rectifying section serve the separation. For the stripping section, the variation tendency for all compositions turn out to be complex.
3. CONTROL SYSTEM DESIGN 3.1. Basic Control Structure for Extractive Distillation System with Fixed Reflux Ratio. For its economic advantage,
the steady-state design of case 8 is considered only for control system design. Before starting the dynamic simulation, the plumbling system and major equipment sizes must be specified. The commonly used heuristic for reflux drums and column bases sizing is to provide ∼5 min of liquid holdup when half full. All the control valves' pressure drops are ∼3 bar with the valve half open at the design flow rate. Then the steady state flowsheet is
pressure-checked, and the Aspen Plus file is exported to Aspen Dynamics. The following control structure is proposed for the extractive distillation control system. Figure 8 gives the basic control structure for extractive distillation system. (1) Feed is flow-controlled (reverse acting). (2) Reflux drum levels in both columns are held by manipulating the flow of distillates (direct acting). (3) Base level in extractive distillation column is held by manipulating the flow of the bottoms (direct acting). (4) Base level in entrainer recovery distillation column is held by manipulating the makeup DMF flow rate (reverse acting). (5) The total entrainer flow is in proportion to the feed flow. (6) The pressure in the two columns is controlled by manipulating the heat removal rate in the condenser of the two columns (reverse acting). (7) The reflux ratio in both columns is fixed. 1286
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Figure 7. (A) Extractive distillation column liquid composition profile. (B) Entrainer recovery column liquid composition profile.
Figure 8. Basic control structure with fixed reflux ratio.
(8) Entrainer feed temperature is held by manipulating the cooler HX heat duty (reverse acting). (9) The temperature for stage 47 in the extractive distillation column is controlled by manipulating the reboiler heat input into the extractive distillation column (reverse acting). (10) The temperature for stage 6 in the entrainer recovery column is controlled by manipulating the reboiler heat input into the entrainer recovery column (direct acting). A noteworthy feature reveals here is that the base level in the entrainer recovery distillation column is held by manipulating the
makeup DMF flow rate. As you can see from Figure 5, the makeup stream is much smaller than the total entrainer flow rate; however, the 5 min holdup time in the base of the entrainer recovery column is able to handle the dynamic changes in the entrainer circulation rate. When the feed flow rate increases, there is an immediate increase in the total entrainer flow rate under the ratio control scheme. The level in the base of the entrainer recovery column begins to drop immediately. However, as more feed is introduced to the extractive column, it eventually produces an increase in the feed to the entrainer 1287
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Figure 9. Relayfeedback test results for the basic control structure shown in Figure 8.
recovery column, then the base level is brought back. This inventory control was suggested by Grassi35 and Luyben.36 Figure 8 demonstrates this control structure for this sytem. Conventional PI controllers are used for all controllers except the four liquid level controllers. Proportional controllers are used for all liquid levels with Kc = 2. The proportional and integral (PI) settings of the top pressure control loops for both columns are set at Kc = 20 and τI = 12 min. Three deadtime elements are inserted into the corresponding temperature control loops with a deadtime of 1 min. Relay-feedback tests are run on the temperature controllers to determine ultimate gains and periods, and TyreusLuyben tuning is used. Figure 9 shows the relay-feedback test results for the basic control structure using the tuning method mentioned above. Table 5 gives the tuning parameters for the three temperature controllers. Now the dynamic performance of the basic control structure is evaluated by feed flow rate and composition disturbances. Figure 10 shows the dynamic responses of this control structure to positive and negative 20% step changes in feed flow rate at t = 0.2 h. As you can see from Figure 10, product purities are held fairly close to their desired values at the new steady state. It is found that the two controlled tray temperatures were brought back to their set points in 3 h. Figure 11 shows the dynamic responses for feed composition disturbances at t = 0.2 h. In the first (solid lines), the feed composition is changed from 85.8/13.9/0.3 wt % to 80.8/18.9/ 0.3 wt % methylal/methanol/water. In the second (dashed lines), the feed composition is changed to 85.8/12.2/2 wt % methylal/methanol/water. As shown in Figure 11, when the methylal purity in the feed is decreased from 85.5 wt % to 80.5 wt % (with a corresponding change in methanol), product purities are held fairly close to their desired values. However, for the
Table 5. Temperature Controllers Tuning Parameters for the Basic Control Structure with Fixed Reflux Ratio parameters
TC1
TC2
TCHX
controlled variable manipulated variable
T1,47 QR1
T2,6 QR2
Trecycle QHX
transmitter range (K)
300400
310410
280360
controller output range (GJ/h)
05.364
01.837
01.495
ultimate gain
2.7237
4.3565
0.4905
ultimate period (min)
5.4
8.4
2.1
gain, Kc
0.8185
1.3614
0.1278
integral time, τI (min)
11.88
18.48
4.62
change in the water concentration in the feed from 0.3 to 2 wt % (with the corresponding changes in methanol), it produces a relatively large deviation in the purity of the distillate from the extractive distillation column; however, if the feed composition is changed from 85.8/13.9/0.3 wt % to 90.8/8.9/0.3 wt % methylal/methanol/water at t = 0.2 h, the Aspen Dynamics integrator will fail to work, and then the simulation will terminate. The reason for the strange behavior as mentioned is the high interaction of the two-column system. With the feed composition changes from 85.8/13.9/0.3 wt % to 90.8/8.9/ 0.3 wt % methylal/methanol/water, the mass fraction of DMF in the bottom of C1 increases, which leads to a higher temperature of the trays in C2. Then the heat input into the C2 reboiler will stop completely. This abrupt increase in the temperature of the trays of C2 makes the Aspen Dynamics integrator fail to work. Further study is made of the dynamic performance of the basic control structure. It is found that if a small step change of the feed composition is imposed on the system, such as changing the feed composition to 86.8/12.9/0.3 wt % methylal/methanol/water at 1288
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Figure 10. Dynamic responses for control structure with reflux ratio fixed: 20% feed flow rate disturbances.
Figure 11. Dynamic responses for the control structure with reflux ratio fixed: feed composition disturbances.
t = 0.2 h, this control structure can handle the disturbance effectively. A large step change in the feed composition (a mass fraction of methylal increase with a corresponding decrease in methanol) will bring about catastrophic breakdown. A strategy that avoids this catastrophic breakdown is to mix the feed stream with some methanol in the upstream slow. 3.2. An Improved Control Structure for Extractive Distillation System. A modified overall control strategy is to hold both reflux flow rates at constant values and change only with the fresh feed flow rate, which was recommended by Grassi.35 This can be termed an improved control structure with the reflux flow rate/
feed flow rate (R/F) ratio. Figure 12 gives the details of this improved control structure. Figure 13 gives the dynamic responses for the improved control structure to 20% feed flow rate disturbances. It is noticed that the two product compositions are maintained very close to their desired value at the new steady state. It is also found that the two tray temperature control loops perform well in bringing the temperatures back to their set points. Figure 14 gives the dynamic responses for the improved control structure to feed composition disturbances. As can be seen from Figure 14, for 90.8 wt % methylal feed disturbance, it 1289
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Figure 12. Improved control structure with R/F ratio.
Figure 13. Dynamic responses for improved control structure with R/F ratio: 20% feed flow rate disturbances. 1290
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Figure 14. Dynamic responses for the improved control structure with R/F ratio: feed composition disturbances.
Figure 15. The influence of water feed mass faction on the control structure with reflux flow fixed.
can be handled well under the R/F ratio scheme without the problem exhibited as mentioned under the fixed reflux ratio control scheme. Compared with the control structure with RR fixed, the reflux flow rate of C2 under the R/F ratio scheme is bigger, since the mass fraction of methanol decreases from 13.9 to 8.9 wt %. This prevents the temperature of the C2 from rising too high to make the Aspen Dynamics integrator fail to work. For the R/F ratio scheme, there is a large deviation when the water concentration in the feed is increased to 2 wt % (with corresponding decrease in methanol concentration), and it takes quite a long time to reach a new steady state. To reveal the influence of the water on the overall control structure, a step change in the water concentration in the feed to 1 wt % (with a corresponding decrease in the methanol concentration) is imposed on the system; the dynamic response is shown in Figure 15. Although only a small amount of water is present in the feed, a great impact on the overall dynamic performance is observed. To get a high-purity methylal product, it is recommended that a pretreatment of the feed be done, such as using a water-absorbing agent to remove some of the water until the mass fraction of water in the column feed decreases to below 1 wt %.
It is found that the improved control structure performed nicely in maintaining the high purity of the two products, despite a very wide feed flow rate and composition disturbance variations.
4. CONCLUSIONS Design and control of an extractive distillation process for separation of methylal/methanol are investigated in this part of the paper. Using a criterion for entrainer selection, DMF is chosen as a suitable entrainer. Using the total annual cost as the objective function, the optimal design of the extractive distillation process is presented. According to the simulation results, it is found that the optimal mass flow rate of the entrainer (S) is 2900 kg/h, and the optimal total number of stages is 52 for the extractive distillation column and 22 for the entrainer recovery distillation column, respectively. Two control structures are used for this extractive distillation process. Feed flow rate and composition disturbances are made to verify their effectiveness. It is found that the basic control structure with a fixed reflux ratio does not work for a 90.8 wt % 1291
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’ AUTHOR INFORMATION Corresponding Author
*Tel: +86 022-27404440. Fax: +86 0222-27404440. E-mail:
[email protected].
’ ACKNOWLEDGMENT The authors are thankful for support by the Programme of Introducing Talents of Discipline to Universities (No. B06006) and assistance from the staffs in the State Key Laboratories of Chemical Engineering (Tianjin University). ’ NOMENCLATURE αi,j = separation factor of components i and j C1 = extractive distillation column C2 = entrainer recovery column IDn = internal diameter for column n (m) Ki = K factor of component i NFE = feeding location for the entrainer NFF = feeding location for the fresh feed NF2 = feeding location for the feed to column C2 NTn = number of theoretical plates for column n QCn = condenser heat removal for column n (KW) QHX = heat duty of the heat exchanger HX QRn = reboiler heat input for column n (KW) RRn = reflux ratio for column n ($1000s/annum) T = absolute temperature (K) TAC = total annual costs Tn,m = temperature for tray m in column n (K) ’ REFERENCES (1) Carretier, E.; Moulin, Ph.; Beaujean, M.; Charbit, F. Purification and Dehydration of Methylal by Pervaporation. J. Membr. Sci. 2003, 217, 159–171. (2) Li, S. Study on the Preparation of Methylal by Slurry Catalytic Distillation Process. Ph.D. thesis, Tianjin University, Tianjin, China, 2005. (3) Satoh, S.; Yukio T. Process for Producing Methylal. U.S. Patent 6,379,507, April 30, 2002. (4) Lawrence, S. A., Jr. Process for Making Acetals. U.S. Patent 6,015,875, January 18, 2000. (5) Zhang, X.; Zhang, S.; Jian, C. Synthesis of Methylal by Catalytic Distillation. Chem. Eng. Res. Des. 2011, 89, 573–580. (6) Hagen, G. P.; Spangler, M. J. Preparation of Polyoxymethylene Dialkane Ethers, By Catalytic Conversion of Formaldehyde Formed by Dehydrogenation of Methanol or Dimethyl Ether. U.S. Patent 6,350,919, February 26, 2002. (7) Masamoto, J.; Matsuzaki, K. Development of Methylal Synthesis by Reactive Distillation. J. Chem. Eng. Jpn. 1994, 27, 1–5. (8) Tanaka, Y.; Yamamoto, S. Process for Purification of Methylal. U.S. Patent 6,160,185, December 12, 2000. (9) Muller, W. H. E.; Kaufhold, M. Process for the Recovery of Pure Methylal from Methanolmethylal Mixtures. U.S.Patent 4,385,965, May 31, 1983.
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(10) Seader, J. D.; Henley, E. J. Separation Process Principles. John Wiley & Sons, Inc: New York, 1998; p 604. (11) Gani, R.; Brignole, E. A. Molecular Design of Solvents for Liquid Extraction Based on UNIFAC. Fluid Phase Equilib. 1983, 13, 331–340. (12) Brignole, E. A.; Bottini, S.; Gani, R. A Strategy for the Design and Selection of Solvents for Separation Processes. Fluid Phase Equilib. 1986, 29, 125. (13) Dyk, B. V.; Nieuwoudt, I. Design of Solvents for Extractive Distillation. Ind. Eng. Chem. Res. 2000, 39, 1423–1429. (14) Seider, W. D.; Seader, J. D.; Lewin, D. R. Product and processes design principles. John Wiley & Sons, Inc: New York, 2004; pp 613. (15) Douglas, J. M. Conceptual Design of Chemical Processes. McGraw Hill: New York, 1998; pp 568577. (16) Mane, A.; Jana, A. K. A New Intensified Heat Integration in Distillation Column. Ind. Eng. Chem. Res. 2010, 49, 9534–9541. (17) Arifin, S.; Chien, I. Design and Control of an Isopropyl Alcohol Dehydration Process via Extractive Distillation Using Dimethyl Sulfoxide as an Entrainer. Ind. Eng. Chem. Res. 2008, 47, 790–803. (18) Luyben, W. L. Comparison of Extractive Distillation and Pressure-Swing Distillation for Acetone Methanol Separation. Ind. Eng. Chem. Res. 2008, 47, 2696–2707. (19) Luyben, W. L. Heuristic Design of Reaction/Separation Processes. Ind. Eng. Chem. Res. 2010, 49, 11564–11571. (20) Skogestad, S. Dynamic and Control of Distillation Columns: A Critical Survey. Model. Ident. Control 1997, 18, 177–217. (21) Luyben, W. L. Sensitivity of Distillation Relative Gain Arrays to Steady-State Gains. Ind. Eng. Chem. Res. 1987, 26, 2076–2078. (22) Luyben, W. L. Practical Distillation Control. Van Norstrand Reinhold: NewYork, 1992. (23) Skogestad, S. Selecting the Best Distillation Control Configuration. AIChE J. 1990, 36, 753–764. (24) Richalet, J. Industrial Applications of Model Based Predictive Control. Automatica 1993, 25, 1251–1274. (25) McDonald, K. A.; McAvoy, T. J. Application of Dynamic Matrix Control to Moderate- And High-Purity Distillation Towers. Ind. Eng. Chem. Res. 1987, 26, 1011–1018. (26) Dutta, P.; Rhinehart, R. R. Application of Neural Network Control to Distillation and an Experimental Comparison with Other Advanced Controllers. ISA Trans. 1999, 38, 251–278. (27) Luyben, W. L. Plantwide Dynamic Simulators in Chemical Processing and Control. Marcel Dekker, Inc: New York. 2002. (28) Phama, H. N.; Doherty, M. F. Design and Synthesis of Heterogeneous Azeotropic Distillations—II. Residue Curve Maps. Chem. Eng. Sci. 1990, 45, 1837–1843. (29) Dongen, D. V.; Doherty, M. F. Design and Synthesis of Homogeneous Azeotropic Distillations. 1. Problem Formulation for a Single Column. Ind. Eng. Chem. Fundam. 1985, 24, 454–463. (30) Doherty, M. F; Caldarola, G. A. Design and Synthesis of Homogeneous Azeotropic Distillations. 3.The Sequencing of Columns for Azeotropic and Extractive Distillations. Ind. Eng. Chem. Fundam. 1985, 24, 474–485. (31) Luyben, W. L. Distillation Design and Control Using Aspen Simulation. John Wiley &Sons, Inc: New York, 2006. (32) Mu noz, R.; Monton, J. B.; Burguet, M. C.; Torre, J. Separation of Isobutyl Alcohol and Isobutyl Acetate by Extractive Distillation and Pressure-Swing Distillation: Simulation and Optimization. Sep. Purif. Technol. 2006, 50, 175–183. (33) Turton, R.; Bailie, R. C.; Whiting, W. B.; Shaeiwitz, J. A. Analysis, Synthesis and Design of Chemical Processes. Prentice Hall: New York, 2009. (34) Knight, J. R.; Doherty, M. F. Optimal Design and Synthesis of Homogeneous Azeotropic Distillation Sequences. Ind. Eng. Chem. Res. 1989, 28, 564–572. (35) Grassi, V. G. Practical Distillation Control. Van Nostand Reinhold Press: New York, 1992. (36) Luyben, W. L. Plantwide Control of an Isopropyl Alcohol Dehydration Process. AIChE J. 2006, 52, 2290–2296. 1292
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