Design, Optimization and Control of Extractive Distillation for the

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Design, Optimization and Control of Extractive Distillation for the Separation of Trimethyl Borate-Methanol Zhenyu Bao, Weijiang Zhang, Xianbao Cui, and Jiao Xu Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie502022m • Publication Date (Web): 28 Aug 2014 Downloaded from http://pubs.acs.org on September 9, 2014

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Industrial & Engineering Chemistry Research

Design, Optimization and Control of Extractive Distillation for the Separation of Trimethyl Borate-Methanol Zhenyu Bao, Weijiang Zhang, Xianbao Cui, Jiao Xu* Department of Chemical Engineering, Tianjin University, Tianjin 300072, China *Corresponding author. Tel.:+86 022 27402028 E-mail address: [email protected]

Abstract Separation of trimethyl borate and methanol by extractive distillation was investigated firstly using dimethyl sulfoxide as the solvent. Performance of six commonly used strong polar solvents were theoretically calculated and experimentally determined. Dimethyl sulfoxide was selected due to its outstanding separation efficiency. A flowsheet was proposed with optimized facility sizings and operation conditions. Stability and controllability of the process were evaluated by imposing disturbances. It was found difficult to cope with feed variations using pure temperature control structures due to a large solvent/feed ratio. So an improved reflux to feed ratios (improved R/F) scheme with a composition controller was put forward to deal with large fluctuations. Supervision of products’ purity confirmed that this control structure worked effectively. Keywords：：Extractive distillation; Solvent selection; Optimization; Trimethyl borate; Dynamic performance

Nomenclature β

solvent selectivity

SP

solvent power

1

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PI

performance index

γ

activity coefficient

MW molecular weight T

temperature (K)

Cp

specific heat capacity at constant pressure (J·mol-1·K-1)

α

relative volatility

1

extractive distillation column

2

solvent recovery column

D

overhead distillate

B

bottom product

C

Column

X, x

liquid mass concentration

a

trimethyl borate

b

methanol

c

dimethyl sulfoxide

P

pressure (kPa)

S

solvent flow rate (kg/h)

N

number of stages

TC

temperature controller

SFC

solvent/feed ratio controller

PC

pressure controller

LC

level controller

2


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FC

feed flow rate controller

V

valve

M

solvent makeup flow (kg/h)

SF

solvent/feed ratio

KC

proportional gain

DT

dead time

Superscripts ∞

infinite dilution conditions

Subscripts i, j

ith or jth component

S

solvent

b

boiling point

1

trimethyl borate

2

methanol

T

total

F

feed

C

condenser

R

reboiler

1. Introduction Trimethyl borate (B(OCH3)3) is an essential reagent for the production of organic boride, sodium borohydride, high-purity boron, anti-friction additive of lubricants, stabilizer and plasticizer of

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polymers, etc. Due to its particular physical and chemical properties, trimethyl borate is also used as catalyst, dehydrant, auxiliary solvent in brass-welding, and high-energy fuel in aircrafts. Natural abundance boron contains 19.8% B-10 and 80.2% B-11,1 which are the only two stable isotopes. Natural abundance trimethyl borate is normally synthesized by esterification of boric acid and methanol. It can also be synthesized by the reaction of borax, sulfuric acid and methanol. The reaction mixtures of the two synthesis methods contain trimethyl borate and methanol, which should be separated for further utilization. As B-10 has excellent neutron absorption performance, it is widely used in nuclear power plant, military equipment and medical treatment. High abundance boron used in pressurized water reactors, often exists in the form of boric acid.2 Production of high B-10 abundance boric acid is reported by Han et al.3, which inevitably forms a mixture of trimethyl borate and methanol, and the yield of target product is strongly affected by the separation of the mixture. At 101.3 kPa, trimethyl borate and methanol form a minimum-boiling azeotrope, the azeotropic point is 328.37 K and its azeotropic mixture contains 77~78% (wt.%) trimethyl borate.4 The azeotrope can not be separated by ordinary distillation, so special distillation such as extractive distillation and other separation methods are introduced to separate the azeotropic mixture. Sulfuric acid dealcoholization is one of the industrially used methods. The product is an intermediate containing 92% (wt.%) trimethyl borate. Then, a salting-out process is employed to get higher purity trimethyl borate (i.e. 98% (wt.%)). Usually, lithium chloride is applied.5 The separation process consumes a lot of sulfuric acid which is corrosive and the salting-out process is inconvenient to carry out. Extractive distillation is an important technique to separate close-boiling point mixture and

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azeotropes. It is based on the preferential affinity of solvent (entrainer) for one or more of the components over the others, thus alters the relative volatilities of the feed components. Khoury6 has given detailed descriptions on such a process. A lot of azeotropes have been separated via extractive distillation, however, up to now, the separation of the azeotrope of trimethyl borate-methanol by extractive distillation has not been reported in open literature. Solvent selection is a major task in extractive distillation. In recent years, many methods have been developed to select proper solvents, such as qualitative judgment, quantitative estimation, and experimental determination. Pretel et al.7 and Papadopoulos and Linke8 have applied computer-aided molecular design (CAMD) in single solvent design. Moreover, Karunanithi and Achenie9 have studied solvent mixtures design. Dyk and Nieuwoudt10,11 have extended genetic algorithm based CAMD model to design solvents mainly used for distillation process. Conceptual design and optimization of distillation process have been investigated by Douglas,12 Doherty and Malone13 and Rodríguez-Donis et al.14 Their researches have shown how to select solvents and optimize column sequences. Since dynamic performance proves to be a necessary way to assess the effectiveness and controllability of steady state design, Sakizlis15 has presented reviews of integrated design and control methodologies, Shirsat et al.16 and A. Ghaee et al.17 have given detailed cases concerning extractive distillation. Effective plantwide process control system includes several goals such as safe and smooth process operation, tight control of product quality in front of disturbances. Luyben et al.18-22 have presented essential tips and theories on dynamic control using Aspen Plus Dynamics and HYSYS. In their works, development of rigorous simulation of single distillation columns and sequences of columns are concluded; the interaction between steady state design and control is explained; plantwide control with emphasis on selection

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of control structures for an entire multi-unit process is introduced; feedforward, feedback and protective controls are applied to achieve automatic startup, shutdown and smooth, noninteracting control of column product composition. To fill in the gap of research on trimethyl borate - methanol separation technique, in this study, a proper solvent was selected with combination of software calculation and experiment determination, the solvent was comprehensively compared with other potential solvents. Then, the process was optimized through iteration algorithm. Optimized parameters were further testified using the single factor method. At last, control structures were proposed to improve the dynamic stability of the system.

2. Process Design and Optimization

2.1 Solvent Selection Solvent selection is the key problem for extractive distillation. The performance of solvent can be indicated by several indicators based on the activity coefficient at infinite dilution γ∞. The commonly used indicators are solvent selectivity, solvent power, relative volatility at infinite dilution in the solvent and performance index. Their definitions are as follows:9,7

γ 1∞,S solvent selectivity β = ∞ γ 2 ,S solvent power SP =

relative volatility

1

γ

α1, 2

∞ 2 ,S

⋅

(eq. 1)

MW2 MWS

(eq. 2)

γ 1∞, S P1S = ∞ S γ 2, S P2

performance index PI =

(eq. 3)

α1, 2

(eq. 4)

MWS xms 6


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where xms is the minimum solvent molar fraction to break the azeotrope. For estimation of γ∞, an activity-coefficient estimation model called Conductor-Like Screening Model for Segment Activity Coefficient (COSMO-SAC) is used, which is based on the interactions between surface charge distributions of molecules in solution.23 Molecules are first transferred from vacuum phase to an ideal conductor, where surface charge is ideally shielded, then transferred to real solvent using polarity factors, rather than directly into condensed phase. COSMO-SAC obtains molecular segments activity coefficient using solvation free energy, which avoids the violation of thermodynamic consistency principle in several boundary conditions.24-26 Most approaches in selecting solvents mainly based on the same concept of molecular generation, testing and matching the molecules with specified target molecular properties.27-30 Considering the distinct polarity difference between trimethyl borate and methanol, combined with price, toxicity and stability consideration in industrial application, several frequently used strong polar solvents such as dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF), N,N-dimethylacetamide (DMAC), ethylene glycol (EG), glycerin and N-methyl-2-pyrrolidinone (NMP) were investigated. For each solvent, ten vapor-liquid equilibrium (VLE) data points were experimental determined using the same method and facilities with Tian et al.32 Regression and correlation were made using ChemCAD. The minimum solvent molar fraction (xms) to break the azeotrope was then obtained, and the result was shown in Table 1. Meanwhile, vapor pressures of trimethyl borate and methanol were calculated using the Antoine equation with coefficients obtained from literatures.33,34

Table 1. Results of Solvent Selection

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As can be seen from Table 1, DMSO has comparatively higher β•SP and PI values than other solvents, which indicate a favorable effect on separation of the azeotrope. Moreover, it has a relatively low heat capacity, so it is more energy saving than other solvents. There is an interesting phenomenon that xms value is lower for NMP than DMSO. But with increasing solvent molar fraction, the solvent effect of DMSO increases sharply, which displays a rather high relative volatility when solvent molar fraction is 0.4 (see Figure 1). This obvious tendency was testified by further increase of DMSO molar fraction. Figure 1 shows the XY phase diagram for trimethyl borate-methanol with different solvents (solvent molar fraction is 0.4) at 101.3 kPa. Values and curves of EG and glycerin are not shown in Table 1 and Figure 1 because their miscibility and flowability are bad, moreover, the problem can be aggravated when mixing with trimethyl borate and methanol, as the temperature of the mixtures are reduced.

Figure 1. Isobaric VLE Diagram for the System of Trimethyl Borate (1) + Methanol (2) + Solvent (3) (x3=0.4) at 101.3 kPa

In Figure 1, x1’ represents the mole fraction of trimethyl borate in the liquid phase excluding the solvent, y1 is mole fraction of trimethyl borate in the vapor phase. It can be seen that DMSO can greatly enhance the relative volatility of trimethyl borate and methanol when the molar fraction is 0.4. NMP is another potential solvent with strong solvent effect, especially when separating mixtures with high trimethyl borate concentrations. Whether the mixture of DMSO and NMP can produce better performance needs further investigation. In contrast, DMAC and DMF seem to be

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not as good as the former two solvents. The overall order of the solvent performance may be resulted from group interaction difference between the mixture and the N-containing/ S-containing compounds.

2.2 Feasibility Study The feasibility study was carried out by Aspen Plus V7.2. The UNIQUAC activity coefficient model was used to calculate the vapor-liquid equilibrium, however, only the binary-interaction parameters of methanol-DMSO were found in the database. The binary-interaction parameters of trimethyl borate-methanol were obtained from Gmehling’s handbook,35 and the binary-interaction parameters of trimethyl borate-DMSO were correlated from the vapor-liquid equilibrium data calculated by COSMO-SAC. Results are shown in Table 2, and the parameters of UNIQUAC model are 0 except bij and bji.

Table 2. UNIQUAC Model Parameters of the Ternary System

Residue curve map of the ternary system was obtained through Aspen Plus and shown in Figure 2. As there exists no boundary line in the map, the azeotrope and DMSO is the unstable and stable node, respectively, so the extractive distillation process is feasible. In this figure, black bold lines are the material balance line, F stands for the composition of the material to be separated (fresh feed). F1 is the mixture of fresh feed and solvent, which can be separated into D1 (overhead of extractive distillation column) and B1 (bottom product of extractive distillation column), then B1 can be separated into D2 (overhead of solvent recovery column) and B2 (bottom product of

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solvent recovery column). D1 and D2 are trimethyl borate and methanol products, respectively, so it is feasible to separate the azeotrope into pure products with the aid of DMSO.

Figure 2. Residue Curve Map for Trimethyl Borate-Methanol-DMSO (101.3 kPa)

The dashed line across the map is the isovolatility curve.36 When DMSO is added, the isovolatility line moves towards the hypotenuse (where the concentration of methanol is zero). Meanwhile, since DMSO lies in the part below the isovolatility curve, where the relative volatility α C3 H 9 BO3 >1, causing trimethyl borate to go up the column. So trimethyl borate is the main CH 4O

product of D1.

2.3 Partial Optimization The feed to be separated contains 77% (wt.%) trimethyl borate and 23% (wt.%) methanol, and the flow rate is 3000 kg/h. The product specifications are: - D1: 99.5% (wt.%) trimethyl borate; - D2: 99.5% (wt.%) methanol. The non-optimized flowsheet is shown in Figure 3.

Figure 3. Non-Optimized Flowsheet for the Process

In Figure 3, the variables with interrogation mark behind should be optimized, which will be discussed in the following part. Traditional separation sequence is adopted. As the solvent is

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recycled for the utmost utilization, a solvent makeup flow is necessarily added to balance the tiny solvent loss. 2.3.1 Design of Extractive Column (C1) Since the price of trimethyl borate is much higher than methanol, trimethyl borate should be recovered as much as possible. Specification for the bottom product is: trimethyl borate flow rate should be no more than 0.5 kg/h (recovery rate: 99.98%). There are five variables in C1 that need to be optimized: - total stages (NT1); - fresh feed stage (NF1); - solvent feed stage (NFS); - solvent flow rate (S); - reflux ratio (RR1). At first, we set the total stages and the feed stage of solvent recovery column at 16 and 6, respectively. NT1 is given as 62, after trial and error, the minimum energy consumption value (QR1+QR2) is obtained when NF1 is set at 48 and NFS at 4. Meanwhile, NF1 and NFS other than those always result in disqualification of products. Similarly, to reach the specifications, NT1 should be no less than 62. That preliminarily confirms the minimum NT1 and the proper NF1. Then, concentrations of components in the overhead flow of the extractive column at different S are studied by changing RR1. The results have been plotted in Figure 4, in which the results are obtained when bottom specification is satisfied.

(a) (b) (c)

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Figure 4. Concentration of Components in D1 at Different RR1s and Ss

From Figure 4, we can see that trimethyl borate concentration reaches a maximum value when RR1=0.4, at the same time, the concentration of impurities are almost the least. Meanwhile, increase of S results in a higher product purity. However, more solvent also leads to a larger QR1+QR2. The green lines in Figure 4 are the purity requirements. As we have defined that product purity should be 99.5% (wt.%), S=24000 kg/h is adopted. If we change the purity requirement to 99% (wt.%), S can be lowered to 18000 kg/h, which can save a lot of energy. Adjusting the operation condition to meet the corresponding purity requirement is suggested when implementing the project. The concentration of DMSO is constant when changing S, because the specification is used, but it can be seen that solvent loss reaches the minimum when RR1=0.4. 2.3.2 Design of Solvent Recovery Column (C2) Separation of methanol and DMSO takes place in C2. It is relatively easy due to a large relative volatility (76.07, calculated using Aspen Plus built-in data). Methanol is the second target product, 99.5% (wt.%) of D2 is methanol. Most of DMSO enters B2 with trace impurity inside (1ppm). There are three variables that need to be optimized: - total stages (NT2); - feed stage (NF2); - reflux ratio (RR2). By fixing the obtained values for C1, we set NT2 at 16, then NF2 is tested to minimize the energy consumption. Stage 6 is selected for NF2 and the initial RR2 is estimated by using the same method in C1 design. NT2 other than 16 and NF2 other than 6 are discussed in the next part. As RR2 is 12


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manipulated by Aspen Plus to attain the product specification, RR2=2.0 is used as an initial assignment. 2.4 Global Economic Optimization The trade-off between economic benefits, product quality, and controllability has been studied by Brengel and Seider,37 Luyben and Floudas,38 and Palazoglu and Aarkun.39 It is a complex job to take every factor into account, so in this study, only utility consumption is considered when calculating operating cost. Total annual cost (TAC) consists of fixed capital investment (FCI) and cost of utilities (CUT). We will adopt the equation mentioned in Muñoz’s article:40 TAC (103 $/a)=CUT + 0.3FCI

(eq. 5)

By observing the proper RRs, we can see that when total stages reduces, FCI drops obviously, however, RRs need to increase to meet the specifications, which in turn rise CUT. TAC is used here to search for the optimum condition. Global optimization is undertaken in an iterative manner below: (1) Fix the pressures at the top of the two columns at 101.3 kPa. (2) Give initial estimates for NT2 and NF2. (3) Give values for NT1, NF1 and NFS. (4) Set S at the minimum value obtained in the partial optimization part. (5) Change the values of S, D1 and RR1 to achieve the specifications of C1. (6) Go back to (4) until QR1+QR2 is minimized with NT2, NF2, NT1, NF1 and NFS fixed. (7) Go back to (3) until TAC is minimized with NT2 and NF2 fixed. (8) Give values for NT2 and NF2. (9) Change the values of D2 and RR2 to achieve the specifications of C2.

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(10) Go back to (9) until QR1+QR2 is minimized with NT1, NF1, NFS, NT2 and NF2 fixed. (11) Go back to (4) until TAC is minimized with NT1, NF1 and NFS fixed. (12) Go back to (2) until TAC is totally minimized with only pressures fixed. The optimization procedure is briefly shown in Figure 5. The results have been listed in Table 3.

Figure 5. Optimization Procedure Sequence

Table 3. Global Optimization Result

In Table 3, D1/2 stands for the diameter of the columns. CUT includes the cost of cooling water (0.354 $/GJ), steam (14.19 $/GJ) and electricity (16.8 $/GJ), which are taken from Turton’s book.41 Pumps are considered as electricity-consuming devices. Cost of facilities such as columns, condensers and reboilers are included in FCI, because these large equipments need annual maintenance. In section 2.3 (partial optimization), NF1 and NFS have been optimized with the minimum energy consumption when NT1=62. Since 62 is verified to be the optimum value for NT1, then NF1=48 and NFS=4 are fixed in each case except case 1 (NF1=50 is optimal). For now, TAC values are calculated to compare the best configuration of the other variables. Case 1 and case 2 show how NT1 affects the TAC. Herein, NT2 and NF2 are fixed with the optimum value. As can be seen, TAC is lower when NT1=62, which is also the minimum NT1 that can achieve the specifications we set. Likewise, case 3, case 4 and case 5 show how NT2 affects TAC. In these cases, middle part of the columns are selected as NF2. It is clear that NT2=16 is superior to NT2=18 and 14. Case 4, case 2, 14


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and case 6 demonstrate the best NF2 should be 6. RR1 is not the best value we estimate due to surplus purification than we defined. But it does not matter that we choose RR1=0.4 as operation condition, purer products can be obtained in that case. For smaller reflux ratios such as 0.33, they can lead to a larger output rate, which is beneficial for a plant. 2.5 Analysis of Optimization Result Single factor method is used to testify the validity of the optimization procedure. NT1, NF1, NFS, NT2 and NF2 are studied besides the above stated RRs and Ss. Results are shown in Figure 6.

(a) (b) (c) (d) (e) Figure 6. Correlations between Product Purity and NT1 (a), NF1 (b), NFS (c), NT2 (d), NF2 (e)

In Figure 6 (a), NT1 is found to be exactly suitable in achieving the product specification at 62, and the purity of trimethyl borate increases with the increase of NT1. Figure 6 (b) and (c) show that the optimal NF1 and NFS values are achieved at 48 and 4, respectively, which are found to be the only values satisfying the given specifications. These locations are influencing because they are either near the bottom or near the top of the column. Increasing NF1 or decreasing NFS results in sharp decrease of product purity. In comparison, a more gradual increase of product purity is observed in C2 when NT2 is larger than 16, as shown in Figure 6 (d). Moreover, product purity stops increase when NF2 is larger than 6, which is demonstrated by Figure 6 (e). The different tendencies between the two columns occur due to the difference in separation difficulties. The more difficult to separate a mixture, the greater impact variables cause on product purity. In conclusion, purity of products can reach the specifications we expected with the optimized values.

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Diameters of the two columns denote the internal diameter which is calculated by Aspen inner algorithm and rectified by eq. 6:42

F = Vmax ρ V = 1

(eq. 6)

, where Vmax is the maximum vapor velocity (ft/s) and ρV is the corresponding vapor density (lb/ft3). The maximum vapor volumetric flow rate and ρV are inquired in hydraulic parameters, then Vmax is calculated by eq. 6. Cross-sectional area and internal diameter of the column are thus obtained. By comparing with the column diameter in tray sizing tab, tray spacing is adjusted to make them equal. The tray spacing is 0.24 m for C1 and 0.253 m for C2. This procedure can ensure the column height correct, which is essential for economic evaluation. At last, the total solvent loss is calculated by material balance equation, thus makeup flow rate is 4.077 kg/h. Global flowsheet is presented in Figure 7 with detailed stream and facility operation information in it.

Figure 7. Optimal Design Flowsheet

The optimal values of parameters shown in Figure 7 are consistent with the case 2 in Table 3. It can be seen that solvent usage is economized, as the S/F ratio is less than 0.14% (wt.%) when operating. Even so, the amount of recycled solvent is large, further utilization of the tremendous energy that the recycled flow possessed is beneficial. Temperature distribution along the columns and concentration of the three components in liquid phase are displayed in Figure 8.

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(a) (b) Figure 8. Temperature and Liquid Concentration Profiles

Figure 8 gives a description on how separation occurs on each stage. For C1, the temperature rises rapidly in the above 4 stages and turns to be flat between stage 4 and 47, then becomes steep again, that is because cold solvent and fresh material are fed on stage 4 and 48, respectively. Three sections are divided apparently with each one has a special function. We may as well label these three sections as s1, s2 and s3 from column top to bottom. As can be seen from colored lines in Figure 8 (a), DMSO concentration drops sharply from stage 4 to stage 1 in s1, as high purity trimethyl borate is required in D1. S2 serves to minimize methanol concentration in the overhead, but methanol concentration drops slowly from stage 48 to stage 4, which is due to the difficulty in azeotrope separation. Trimethyl borate is stripped off in s3, while methanol and DMSO enter C2. Similarly, but seems simpler in C2, only tiny amount of trimethyl borate appears between stage 2 and 6, the primary task for this column is to split methanol and DMSO. 3. Control System Design To achieve the control purpose, many control strategies with different combination of manipulated variables configurations have been proposed by Skogestad.43 He has elaborated these strategies from theoretical points. More recently, Luyben42 has discussed a variety of control structures with the aid of computer softwares, one category of them is called “Single end” control structure where the temperature on one tray is controlled. Moreover, base on Luyben’s ‘slope criterion’42 in the determination of control point, temperature measurement is easy, fast, accurate, and composition is closely related with temperature, the stage that have rapid temperature change (large slope in

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temperature profile) can be chosen as an excellent control point. Because this temperature can be used as an obvious reflection of the key component composition variation. Maintaining the temperature of this tray can keep the composition profile in the column unchanged and thus prevent impurities from entering the withdraw flow. Based on that, stage 61 and stage 3 are chosen for C1 and C2, respectively. Temperature drops on the feed stages are also large, but it is not suitable for control, as the fluctuation of feed conditions can easily destroy the effectiveness of the control system. An effective and practical control structure needs precise control point and accurate equipment dimensions. Commonly used heuristic is applied here:42 the reflux drum and column base are supposed to provide 10min liquid holdup, and length to diameter ratio are both 2:1. In this case, reflux drum is 0.72 m and 0.62 m in diameter for C1 and C2, respectively. Column base is 1.55 m and 1.49 m in diameter for C1 and C2, respectively. 3.1 Control Structure with Fixed RRs As we have obtained RR1 and RR2 in the optimal operation condition, they are entered in the multipliers. Other manipulated variables and their control actions are listed as follows: (1) Feed flow rate is controlled by adjusting inlet valve (Controller: FC, reverse acting). (2) Solvent flow rate is controlled by adjusting V2B, which is 8 times feed flow rate (Controller: SFC, direct acting). (3) Temperatures of the certain stage in both columns are controlled by adjusting reboiler heat duties (Controllers: TC1 and TC2, reverse acting). (4) Pressures in both columns are controlled by adjusting the condenser heat duties (Controllers: PC1 and PC2, reverse acting).

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(5) Temperature of recycled solvent is controlled by adjusting heat removal rate of the cooler (Controller: TC3, reverse acting). (6) Reflux drum levels in both columns are controlled by adjusting withdraw flow rate of distillates (Controllers: LC1D and LC2D, direct acting). (7) Base level in extractive distillation column is controlled by adjusting withdraw flow rate of B1 (Controller: LC1B, direct acting). (8) Base level in solvent recovery column is controlled by adjusting solvent makeup flow rate (Controller: LC2B, reverse acting).

(a) (b) Figure 9. Ultimate Control Structure Scheme with Fixed RRs

In Figure 9, solvent flow rate is manipulated by V2B rather than VM because makeup flow is too small to handle fluctuates when operating. SFC is a cascade controller, it can precisely regulate V2B according to the ratio of SF output (8 times feed flow rate) and total solvent flow rate. PID controllers are used here except temperature controllers and pressure controllers. Level controllers are set at KC=2 and integral time=9999 min, since proportional-only control is used. Flow controllers use the conventional tuning: KC=0.5, integral time=0.3 min. Pressure controllers use default settings. Temperature controllers are PIDincr controllers, and they are tuned with dead time=1 min. Tyreus-Luyben tuning rule is selected to update calculated gains and integral times. Close loop tuning is used with relay amplitude of 5% output range. Detailed tuning parameters are listed in Table 4.

19



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Table 4. Tuning Parameters of Temperature Controllers

Dynamic performance is tested by feed flow rate and feed composition disturbances when time is 0.1 min. However, this control structure is not so effective to deal with several disturbances. It works well for +20% feed flow rate (3000 kg/h→3600 kg/h), but it needs 20 h to keep the system steady if the feed flow rate decreases 1% (3000 kg/h→2970 kg/h). For composition disturbances, integrator fails to work when trimethyl borate concentration in the feed changes 1% (both positive and negative). The results for feed flow rate disturbances are shown in Figure 10.

(a) (b) (c) (d) Figure 10. Dynamic Responses of Feed Flow Rate Disturbances for Fixed RRs Scheme

It can be seen that variables are brought to new steady state in 3 h. New temperatures for both columns are consistent with original ones, but purity of products are changed. More pronounced is methanol concentration changes in D2 when +20% feed flow rate is implemented. As more fresh feed is introduced into the system, solvent flow increase dramatically due to SFC control. The increment of flow rate results in higher liquid and vapor rates in the column which give a higher final pressure on stage 3 (102.7 kPa→103.3 kPa), constant temperature can not keep the compositions unchanged. For now, another control structure is investigated to prevent integrator from failing to work. 3.2 Control Structure with Fixed R/F Ratios

20


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Fixed R/F ratios control takes more account of feed fluctuations, so it is supposed to be more stable when dealing with disturbances. The control structure scheme is shown in Figure 11.

Figure 11. Ultimate Control Structure Scheme with Fixed R/F Ratios

Temperature controllers are tuned again, then disturbances are exerted on the system. This time, ±20% feed flow rates and ±2% composition changes can be handled by the structure. The results are shown in Figure 12 and Figure 13.

(a) (b) (c) (d) Figure 12. Dynamic Responses of Feed Flow Rate Disturbances for Fixed R/F Ratios Scheme

(a) (b) (c) (d) Figure 13. Dynamic Responses of Composition Disturbances for Fixed R/F Ratios Scheme

As can be seen, all variables are brought to steady after 12 h despite large deviations. Vibration is observed for temperature of stage 61 in C1, but the new temperature is exactly equal with the original one. Strict control on feed flow rate and composition is needed, as fluctuations lead to the low purity of products. This control structure is not recommended for application, because the purity of methanol is strongly affected by the +20% feed disturbance and the +2% composition change. After all, this structure can prevent the strong interaction between two columns from breaking down, which is an improvement compared with the fixed RRs structure.

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3.3 Control Structure with Fixed QR/F Ratios QR/F structure improves the dynamic response to temperature changes when disturbances are imposed. The control structure scheme is shown in Figure 14.

Figure 14. Control Structure Scheme with Fixed QR/F Ratios

Tunings are made on temperature controllers to get gains and integral times, then ±20% feed flow rates and ±2% composition changes are imposed on the structure. The results are shown in Figure 15 and Figure 16.

(a) (b) (c) (d) Figure 15. Dynamic Responses of Feed Flow Rate Disturbances for Fixed QR/F Ratios Scheme

(a) (b) (c) (d) Figure 16. Dynamic Responses of Composition Disturbances for Fixed QR/F Ratios Scheme

It is clear that methanol product purity can be controlled well when feed flow fluctuates, which is better than fixed R/F ratios structure. Other performances are the same with the fixed R/F ratios structure. This structure is also not recommended because methanol product purity is still seriously affected by +2% composition change. Since the above three pure temperature control structures are unable to provide satisfactory control effect we desired, an improvement of the structure is to be made.

22


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3.4 Control Structure with Improved R/F Ratios Composition control is more directly and has sound effects on maintaining products purity, but composition measurements typically have larger dead times and lags than temperature does. Here, we insert a 3 min dead time element to the control structure with fixed R/F ratios. The ultimate structure scheme and control panel are shown in Figure 17.

(a) (b) Figure 17. Control Structure Scheme and Control Panel with Improved R/F Ratios

TC1 is shifted to cascade control as shown in Figure 17 (b), because its input signal is provided by cc1 output. Temperature controllers and composition controllers are tuned and updated with the calculated gain and integral time. Tuning results are listed in Table 5.

Table 5. Tuning Parameters of the Temperature Controllers and the Composition Controller

Disturbances of ±20% feed flow rate and ±2% composition are imposed on the system. Figure 18 and Figure 19 indicate that concentration of each component is handled well by this control structure. Temperature of stage 61 in C1 reaches another steady state because the increment of flow rate changes the pressure on the stage. However, vibration is observed and it costs more time to attain a steady state. This due to the strong interaction between the two columns, and the dead time element of composition control leads to a delay of response. Even so, vigorous control is exerted and the purity of products are fairly close to specifications in front of large fluctuations.

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(a) (b) (c) (d) Figure 18. Dynamic Responses of Feed Flow Rate Disturbances for Improved R/F Ratios Scheme

(a) (b) (c) (d) Figure 19. Dynamic Responses of Composition Disturbances for Improved R/F Ratios Scheme

Comparing the four structures, the main concerns of choice have been listed in Table 6.

Table 6. Comparison of Control Structures

It can be seen from Table 6 that the new steady state for the purity of trimethyl borate under +20% feed flow rate disturbance is 98.8%, which is the most affected one among the purities investigated. This is resulted from the rigorous design of the columns. Therefore, margins for total number of stages should be given in practical applications. Meanwhile, the maximum deviation of purity is observed for methanol under -20% feed flow rate disturbance. This 3.1% deviation occurred from the sharp decrease of feed flow rate causes the rapid increase of temperature in C2, which further leads to the escape of DMSO from D2 due to the excessive heating provided by the reboiler. The deviation lasts for about 0.5 h and finally brings to normal by TC2. Nevertheless, the improved R/F structure performs comparatively better in both purity and deviation control, which confirms the effectiveness of this structure.

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4. Conclusion With the concern of industrial practice in the separation of trimethyl borate and methanol, a non-corrosive and industrial-oriented extractive distillation process was designed and evaluated. DMSO was selected as the solvent due to a higher separation efficiency compared with other solvents. This conclusion was drawn after theoretical calculation and experimental verification. The total number of stages for the extractive distillation column and the solvent recovery column should be 62 and 16, respectively. The solvent/feed ratio was 8 and the solvent was recovered with a purity of 99.9999% (wt.%). Global control strategies were proposed to test the stability of the system. Given disturbances could be handled well by the improved R/F ratios control structure, and the new steady state of the products’ purity were quite close to their original levels. Therefore, this work confirms the feasibility of the separation of trimethyl borate and methanol by simulation method, and provides a useful guide for the application of extractive distillation process in chemical industry.

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Acknowledgments The first author would like to extend sincere thanks to Professor Xianbao Cui, Professor Jiao Xu, Wuke Lang, Zheng Tian, Bo Li for their instructions on optimization part and language usage. Ming Xia and Baoru Yu are also appreciated for their assistance on controller tuning.

References [1] Han, L. G.; Yu, J. Y.; Zhang, W. J. The enrichment technology for the separation and production of boron isotopes. Journal of Isotopes 2006, 19 (1), 48-52. [2] Xu, J.; Zhang, W. J. The application of enriched 10B boric acid in pressurization water reactor. Nuclear Science and Engineering 2012, 32 (3), 238-243. [3] Han, M.; Zhao, B.; Zhang, W. J. Synthesis of boric-10 acid. Chemical Engineering (China) 2007, 35 (8), 70-73. [4] Gmehling, J.; Onken, U. Vapor-liquid equilibrium data collection. DECHEMA: Frankfurt, 1982, 1 (2a). [5] Li, X. B.; Yang, X.; Ma, Y. J. Measurement of liquid-liquid equilibrium of trimethyl borate-methanol-lithium chloride. Guangdong Chemical Industry 2009, 36 (12), 154-155. [6] Khoury, F. M. Multistage separation process. CRC:New York, Chapter 10, 2005. [7] Pretel, E. J.; Lopez, P. A.; Bottini, S. B. Computer-aided molecular design of solvents for separation processes. AIChE J. 1994, 40 (8), 1349-1359. [8] Papadopoulos, A. I.; Linke. P. A unified framework for integrated process and molecular design. Institution and Chemical Engineers 2005, 83 (6), 674-678.


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[9] Karunanithi, A. T.; Achenie, L. E. K. A new decomposition-based computer-aided molecular/mixture design methodology for the design of optimal solvents and solvent mixtures. Ind. Eng. Chem. Res. 2005, 44 (13), 4785-4797. [10] Dyk, B. V.; Nieuwoudt, I. Design of solvents for extractive distillation. Ind. Eng. Chem. Res. 2000, 39 (5), 1423-1429. [11] Dyk, B. V.; Nieuwoudt, I. Computer aided molecular design of solvents for distillation process. International Scientific Committee: Stellenbosch, 2001. [12] Douglas, J. M. Conceptual design of chemical processes. McGraw-Hill: New York, 1988. [13] Doherty, M. F.; Malone, M. F. Conceptual design of distillation systems. McGraw-Hill: New York, 1988. [14] Rodríguez-Donis, I.; Gerbaud V.; Joulia X. Entrainer selection rules for the separation of azeotropic and close-boiling-temperature mixtures by homogeneous batch distillation process. Ind. Eng. Chem. Res. 2001, 40 (12), 2729-2741 [15] Sakizlis, V.; Perkins, J. D.; Pistikopoulos, E. N. Recent advances in optimization-based simultaneous process and control design. Comput. Chem. Eng. 2004, 28 (10), 2069-2086. [16] Shirsat, S. P.; Dawande, S. D.; Kakade, S. S. Simulation and optimization of extractive distillation sequence with pre-separator for the ethanol dehydration using n-butyl propionate. Korean J. Chem. Eng. 2013, 30 (12), 2163-2169. [17] Ghaee, A.; Sotudeh-Gharebagh, R.; Mostoufi, N. Dynamic optimization of the benzene extractive distillation unit. Brazilian Journal of Chemical Engineering 2008, 25 (4), 765-776. [18] Luyben, W. L. Practical distillation control. Van Nostrand reinhold: New York, 1992.



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[19] Luyben, W. L. Plantwide dynamic simulators in chemical processing and control. Marcel Dekker: New York, 2002. [20] Luyben, M. L.; Luyben, W. L. Essentials of process control. McGraw-Hill: New York, 1997. [21] Luyben, W. L.; Tyreus, B. D.; Luyben, M. L. Plantwide process control. McGraw-Hill: New York, 1998. [22] Buckley, P. S.; Luyben, W. L.; Shunta, J. P. Design of distillation column control system. Edward Amold: North Carolina, 1985. [23] Albright, L. F. Albright’s chemical engineering handbook; Taylor & Francis Group: New York, 2009. [24] Wang, S.; Sandler, S. I.; Chen, C. C. Refinement of COSMO-SAC and the applications. Ind. Eng. Chem. Res. 2007, 46 (22), 7275-7288. [25] Hsieh, C. M.; Sandler, S. I.; Lin, S. T. Improvements of COSMO-SAC for vapor-liquid and liquid-liquid equilibrium predictions. Fluid Phase Equilib. 2010, 297 (1), 90-97. [26] Wang, S. Thermodynamic properties predictions using the COSMO-SAC solvation method. University of Delaware: Newark, 2007. [27] Bieker, T.; Simmrock, K. H. Knowledge integrating system for the selection of solvents for extractive and azeotropic distillation. Comput. Chem. Eng. 1994, 18 (Suppl. 1), S25-29. [28] Lin, B.; Chavalib, S.; Camardab, K.; Miller, D. C. Computer-aided molecular design using Tabu search. Comput. Chem. Eng. 2005, 29 (2), 337-347. [29] Papadopoulos, A. I.; Linke, P. A decision support grid for integrated molecular solvent design and chemical process selection. Comput. Chem. Eng. 2009, 33 (1), 72-87.


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[30] Weis, D. C.; Visco, D. P. Computer-aided molecular design using the signature molecular descriptor: Application to solvent selection. Comput. Chem. Eng. 2010, 34 (7), 1018-1029. [31] NIST Chemistry WebBook. http://webbook.nist.gov/chemistry, (Last used: 2014.5.14). [32] Tian, Z.; Cui, X. B.; Cai, J. L.; Zhang, Y.; Feng, T. Y.; Peng, Y. M.; Xue, L. X. Isobaric VLE data for the system of butan-1-ol + butyl ethanoate + 1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide. Fluid Phase Equilib., 2013, 352, 75-79. [33] Plank, C. A. and Christopher, P. M. Vapor-liquid equilibriums of methyl borate-carbon tetrachloride and methyl borate-benzene systems. J. Chem. Eng. Data, 1976, 21(2), 211-212. [34] Yaws, C. L. and Yang, H. C. To estimate vapor pressure easily, Hydrocarbon Processing, 1989, 68 (10), 65. [35] Gmehling, J.; Onken, U. Vapor-liquid equilibrium data collection. DECHEMA: Frankfurt, 1982, 1 (5). [36] Luyben, W. L. Design and Control of Distillation Systems for Separating Azeotropes. John Wiley &Sons, Inc: New York, 2010. [37] Brengel, D. D.; Seider, W. D. Coordinated design and control optimization of nonlinear processes. Comput. Chem. Eng. 1992, 16 (9), 861-886. [38] Luyben, M. L.; Floudas, C. A. Analyzing the interaction of design and control: I. A multiobjective framework and application to binary distillation synthesis. Comput. Chem. Eng. 1994, 18 (10), 933-969. [39] Palazoglu, A.; Aarkun, Y. Multiobjective approach to design chemical plants with robust dynamic operability characteristics. Comput. Chem. Eng. 1986, 10 (6), 567-570.



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[40] Muñoz, R.; Montón, 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 (2), 175-183. [41] Turton, R.; Bailie, R. C.; Whiting, W. B.; Shaeiwitz, J. A. Analysis, synthesis, and design of chemical processes. Prentice Hall PTR, N. J.: Upper Saddle River, 1998. [42] Luyben, W. L. Distillation design and control using AspenTM simulation. John Wiley &Sons, Inc: New York, 2006. [43] Skogestad, S. Control structure design for complete chemical plants. Comput. Chem. Eng. 2004, 28 (1-2), 219-234.


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Tables in the article Table 1. Results of Solvent Selection Solvent

Tb/K

γ 1∞, S

γ 2∞, S

β

SP

β•SP

CP/a J·mol-1·K-1

xms

α1,2

100×PI

DMSO DMF DMAC EG Glycerol NMP

462.15 426.15 438.15 470.35 563.15 476.15

1.2685 0.9874 0.9706 3.0951 2.8917 0.9181

0.3925 0.5161 0.3478 0.9719 0.9571 0.3047

3.2323 1.9133 2.7906 3.1846 3.0213 3.0129

1.0449 0.8494 1.0574 0.5311 0.3635 1.0608

3.3774 1.6252 2.9508 1.6913 1.0982 3.1961

149.39 146.05 178.2 149.8 218.9 412.4

0.33 0.44 0.36 --0.29

1.13 0.87 1.16 --0.95

4.37 2.72 3.71 --3.30

a: data from ‘NIST Chemistry WebBook’ 31

Table 2. UNIQUAC Model Parameters of the Ternary System comp,i comp,j

C3H9BO3 CH4O

C3H9BO3 DMSO

CH4O DMSO

bij bji

-629.79 76.44

-162.60 100.66

129.36 23.49

Table 3. Global Optimization Result Parameters

Case1

Case2

Case3

Case4

Case5

Case6

NT1 NT2 NF1/NFS NF2 RR1 RR2 D1(m) D2(m) QC1(kW) QR1(kW) QC2(kW) QR2(kW) CUT(103$) FCI(103$) TAC(103$)

64 16 50/4 6 0.3 1.73 1.563 1.010 -261.49 2449.81 -573.41 805.15 2606.25 747.82 2830.59

62 16 48/4 6 0.33 1.71 1.565 1.011 -266.30 2448.43 -569.78 807.58 2572.47 723.02 2789.38

62 18 48/4 9 0.33 1.92 1.565 1.035 -266.38 2448.50 -614.17 859.17 2626.77 737.11 2847.90

62 16 48/4 8 0.33 2.32 1.565 1.072 -266.30 2448.43 -697.23 935.03 2625.86 729.76 2844.78

62 14 48/4 7 0.33 2.95 1.565 1.137 -266.30 2448.43 -830.00 1060.02 2645.61 724.38 2862.93

62 16 48/4 4 0.33 1.82 1.565 1.023 -266.36 2448.48 -593.29 831.09 2582.34 724.17 2799.59

Table 4. Tuning Parameters of Temperature Controllers Parameters

TC1

TC2

TC3

Controlled variable Manipulated variable Process variable range/K Output range/kW

T1,Stage61 QR1 350~464.4 0~4896.859

T2,Stage3 QR2 350~456.3 0~1538.285

TB23 QCooler 273.15~323.15 -4746.116~0



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Ultimate gain Ultimate period/min Gain Integral time/min

4.7152 3 1.4735 6.6

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1.5396 5.4 0.4811 11.88

0.3415 0.6 0.1067 1.32

Table 5. Tuning Parameters of Temperature Controllers and Composition Controller Parameters

TC1

TC2

TC3

cc1

Controlled variable Manipulated variable Process variable range Output range Ultimate gain Ultimate period/min Gain Integral time/min Action

T1,Stage61

T2,Stage3

TB23

XC3H9BO3,Stage61

QR1

QR2

QCooler

TC1 input

0~300K

350~456.3K

273.15~323.15K

0~0.001

0~4896.44kW 14.9384

0~1538.285kW 2.2273

-4746.116kW~0 0.1617

273.15~573.15K 0.3909

3

8.4

1.8

8.4

4.6682 6.6 Reverse

0.6960 18.48 Reverse

0.0505 3.96 Reverse

0.1222 18.48 Direct

Table 6. Comparion of Control Structures Major difference

Fixed RRs

Fixed R/F

Fixed QR/F

Improved R/F

RR1=0.33 (fixed)

R1/F=0.254 (fixed)

QR1/F=0.002938 (GJ/kg) (fixed)

R1/F=0.254 (fixed)

RR2=1.71 (fixed)

R2/F=0.389 (fixed)

R2/F=0.389 (fixed) QR2/F=0.000969 (GJ/kg) (fixed)

Transient time

New product purity (a: trimethyl borate; b: methanol)

+20% feed: 3h

+20% feed: 11h

+20% feed: 8h

XC3H9BO3 on Stage61 (controlled) +20% feed: 14h

Others: Break down (except ≤ -1% feed )

-20% feed: 6h

-20% feed: 13h

-20% feed: 3h

+2% compn.: 8h

+2% compn.: 8h

+2% compn.: 7h

+20% feed: a: 99.5%; b: 91.0%

-2% compn.: 9h +20% feed: a: 98.2%; b: 93.3%

-2% compn.: 9h +20% feed: a: 96.0%; b: 99.5%

-2% compn.: 5h +20% feed: a: 98.8%; b: 99.5%

--

-20% feed: a: 96.9%; b: 99.8%

-20% feed: a: 96.9%; b: 99.7%

-20% feed: a: 99.5%; b: 99.5%

+2% compn.:

+2% compn.:

+2% compn.:


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Largest negative purity deviation (a: trimethyl borate; b: methanol)

--

a: 98.5%; b: 91.6%

a: 98.5%; b: 91.6%

a: 99.3%; b: 99.5%

-+20% feed: a:0.1%; b: 8.5%

-2% compn.: a: 96.3%; b: 99.5% +20% feed: a: 1.8%; b: 10%

-2% compn.: a: 96.3%; b: 99.5% +20% feed: a: 3.5%; b: 2.9%

-2% compn.: a: 99.5%; b: 99.5% +20% feed: a: 1.0%; b: 1.0%

--

-20% feed: a: 2.6%; b: 3.0%

-20% feed: a: 2.6%; b: 0.4%

-20% feed: a: 0.5%; b: 3.1%

--

+2% compn.: a: 1.2%; b: 9.5%

+2% compn.: a: 1.2%; b: 9.4%

+2% compn.: a: 0.3%; b: 0.2%

--

-2% compn.: a: 3.2%; b: 0%

-2% compn.: a: 3.2%; b: 0%

-2% compn.: a: 0%; b: 0.1%



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Figure 1. Isobaric VLE Diagram for the System of Trimethyl Borate (1) + Methanol (2) + Solvent (3) (x3=0.4) at 101.3 kPa 73x60mm (600 x 600 DPI)


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Figure 2. Residue Curve Map for Trimethyl Borate-Methanol-DMSO (101.3 kPa) 238x138mm (96 x 96 DPI)



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Figure 3. Non-Optimized Flowsheet for the Process 609x410mm (96 x 96 DPI)


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Figure 4(a). Concentration of Components in D1 at Different RR1s and Ss 133x101mm (96 x 96 DPI)



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Figure 4(b). Concentration of Components in D1 at Different RR1s and Ss 126x106mm (96 x 96 DPI)


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Figure 4(c). Concentration of Components in D1 at Different RR1s and Ss 129x106mm (96 x 96 DPI)



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Figure 5. Optimization Procedure Sequence 262x803mm (96 x 96 DPI)


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Figure 6(a). Correlation between Product Purity and NT1 34x25mm (600 x 600 DPI)



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Figure 6(b). Correlation between Product Purity and NF1 125x111mm (96 x 96 DPI)


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Figure 6(c). Correlation between Product Purity and NFS 122x111mm (96 x 96 DPI)



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

Figure 6(d). Correlation between Product Purity and NT2 119x111mm (96 x 96 DPI)


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Figure 6(e). Correlation between Product Purity and NF2 114x112mm (96 x 96 DPI)



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

Figure 7. Optimal Design Flowsheet 784x531mm (96 x 96 DPI)


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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


Figure 8(a). Temperature and Liquid Concentration Profiles for C1 125x93mm (96 x 96 DPI)



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

Figure 8(b). Temperature and Liquid Concentration Profiles for C2 125x95mm (96 x 96 DPI)


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Figure 9(a). Ultimate Control Structure Scheme with Fixed RRs 595x445mm (96 x 96 DPI)



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

Figure 9(b). Ultimate Control Structure Scheme with Fixed RRs 203x103mm (96 x 96 DPI)


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Figure 10(a). Dynamic Responses of Feed Flow Rate Disturbances for Fixed RRs Scheme 82x67mm (96 x 96 DPI)



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

Figure 10(b). Dynamic Responses of Feed Flow Rate Disturbances for Fixed RRs Scheme 83x64mm (96 x 96 DPI)


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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


Figure 10(c). Dynamic Responses of Feed Flow Rate Disturbances for Fixed RRs Scheme 81x63mm (96 x 96 DPI)



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

Figure 10(d). Dynamic Responses of Feed Flow Rate Disturbances for Fixed RRs Scheme 81x60mm (96 x 96 DPI)


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Figure 11. Ultimate Control Structure Scheme with Fixed R/F Ratio 597x455mm (96 x 96 DPI)



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

Figure 12(a). Dynamic Responses of Feed Flow Rate Disturbances for Fixed R/F Ratio Scheme 112x69mm (96 x 96 DPI)


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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


Figure 12(b). Dynamic Responses of Feed Flow Rate Disturbances for Fixed R/F Ratio Scheme 112x69mm (96 x 96 DPI)



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

Figure 12(c). Dynamic Responses of Feed Flow Rate Disturbances for Fixed R/F Ratio Scheme 89x62mm (96 x 96 DPI)


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Figure 12(d). Dynamic Responses of Feed Flow Rate Disturbances for Fixed R/F Ratio Scheme 96x64mm (96 x 96 DPI)



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

Figure 13(a). Dynamic Responses of Composition Disturbances for Fixed R/F Ratio Scheme 121x71mm (96 x 96 DPI)


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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


Figure 13(b). Dynamic Responses of Composition Disturbances for Fixed R/F Ratio Scheme 91x63mm (96 x 96 DPI)



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

Figure 13(c). Dynamic Responses of Composition Disturbances for Fixed R/F Ratio Scheme 90x61mm (96 x 96 DPI)


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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


Figure 13(d). Dynamic Responses of Composition Disturbances for Fixed R/F Ratio Scheme 88x58mm (96 x 96 DPI)



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

Figure 14. Control Structure Scheme with Fixed QR/F Ratios 601x458mm (96 x 96 DPI)


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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


Figure 15(a). Dynamic Responses of Feed Flow Rate Disturbances for Fixed QR/F Ratios Scheme 96x66mm (96 x 96 DPI)



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

Figure 15(b). Dynamic Responses of Feed Flow Rate Disturbances for Fixed QR/F Ratios Scheme 85x61mm (96 x 96 DPI)


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Figure 15(c). Dynamic Responses of Feed Flow Rate Disturbances for Fixed QR/F Ratios Scheme 83x56mm (96 x 96 DPI)



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

Figure 15(d). Dynamic Responses of Feed Flow Rate Disturbances for Fixed QR/F Ratios Scheme 94x61mm (96 x 96 DPI)


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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


Figure 16(a). Dynamic Responses of Composition Disturbances for Fixed QR/F Ratios Scheme 83x67mm (96 x 96 DPI)



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

Figure 16(b). Dynamic Responses of Composition Disturbances for Fixed QR/F Ratios Scheme 83x61mm (96 x 96 DPI)


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Figure 16(c). Dynamic Responses of Composition Disturbances for Fixed QR/F Ratios Scheme 90x63mm (96 x 96 DPI)



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

Figure 16(d). Dynamic Responses of Composition Disturbances for Fixed QR/F Ratios Scheme 85x67mm (96 x 96 DPI)


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Figure 17(a). Control Structure Scheme with Improved R/F Ratios 593x455mm (96 x 96 DPI)



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

Figure 17(b). Control Panel with Improved R/F Ratios 203x102mm (96 x 96 DPI)


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Figure 18(a). Dynamic Responses of Feed Flow Rate Disturbances for Improved R/F Ratios Scheme 111x67mm (96 x 96 DPI)



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

Figure 18(b). Dynamic Responses of Feed Flow Rate Disturbances for Improved R/F Ratios Scheme 31x22mm (600 x 600 DPI)


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Figure 18(c). Dynamic Responses of Feed Flow Rate Disturbances for Improved R/F Ratios Scheme 90x53mm (96 x 96 DPI)



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

Figure 18(d). Dynamic Responses of Feed Flow Rate Disturbances for Improved R/F Ratios Scheme 94x65mm (96 x 96 DPI)


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Figure 19(a). Dynamic Responses of Composition Disturbances for Improved R/F Ratios Scheme 88x67mm (96 x 96 DPI)



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

Figure 19(b). Dynamic Responses of Composition Disturbances for Improved R/F Ratios Scheme 91x62mm (96 x 96 DPI)


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Figure 19(c). Dynamic Responses of Composition Disturbances for Improved R/F Ratios Scheme 85x62mm (96 x 96 DPI)



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

Figure 19(d). Dynamic Responses of Composition Disturbances for Improved R/F Ratios Scheme 89x57mm (96 x 96 DPI)


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Design, Optimization and Control of Extractive Distillation for the

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