DEVELOPMENT AND APPLICATION OF A TOOL FOR AUTOMATIC ... · 2.1. Construction Of The Gain Matrix The...

DEVELOPMENT AND APPLICATION OF A TOOL FOR AUTOMATIC SELECTION OF CONTROL STRUCTURES FOR

PROCESS PLANTS

TEIXEIRA, C. G. HEBERT 1; SILVA, SIDINEI K.2; DE ARAÚJO, ANTONIO C. B.2

1GERÊNCIA DE AUTOMAÇÃO E OTIMIZAÇÃO DE PROCESSOS, ENGENHARIA BÁSICA, CENPES/PETROBRAS AV. JEQUITIBÁ , 950 – CIDADE UNIVERSITÁRIA – ILHA DO FUNDÃO. RIO DE JANEIRO - RJ, BRAZIL

2DEPARTMENT OF CHEMICAL ENGINEERING, FEDERAL UNIVERSITY OF CAMPINA GRANDE AV. APRIGIO VELOSO, 882. CEP 58.429-900. CAMPINA GRANDE - PB, BRAZIL

E-MAILS:[email protected]

Abstract The self-optimizing control structure method procedure can be applied with the aid of commercial simulators, con-verging or results obtained more suitable if compared with other methodologies. In this way, motivated by the context, this work addresses the construction and application of an Applied Programming Interface tool in VBA language within Excel® like an add-in. The goal is to provide a tool to facilitate the way to obtaining the possible arrangements of controlled variables based on the self-optimizing control procedures for processes simulated in commercials softwares. The tool communicates through Com-ponent Object Model technology from Windows® system with the stationary chemical process simulator PRO/IITM. The array gain, as well as the calculation of the Hessian matrix, which are necessary for the application of the method, is calculated through the use of the Akima Spline method. To obtain the best sets of variables, after construction of the array gain, based on local minimum singular value method, the branch-and-bound optimization procedure is applied. The results indicate the feasibil-ity of application of complex theoretical contents in a simple manipulation tool for the user, which will be obtained the best sets of variables.

Keywords Plantwide controle, VBA tools, Akima Cubic Spline, Minimum singular value, PRO/II.

1. Introduction

The experience of the engineer is the key to the implementation of a good structure; however, it needs some technique to try controlling the plant under his supervision. Thus, the need for a well-structured control loop as described in Downs and Skogestad (2011) is inherent in academic research being essential to control problems solutions of chemical processes such as the application of proce-dures for obtaining control structures "self-optimizing" described by Skogestad (2000, 2004) on case studies of actual problems seen in the industry as shown in Araujo et al. (2009).

Different case studies were also performed such as: Gera et al. (2011) with the process of manufac-ture of cumene; in Araujo and Shang (2009) with improvements in an "off-gas" casting system or Govatsmark and Skogestad (2001) for an evapora-tion process by applying the methodology of self-optimizing control (Skogestad, 2000, 2004). For all processes, robust control structures, amenable to practical implementation and presenting the lowest losses for the considered conditions were found.

Araujo et al. (2007), Araujo & Shang (2009) and Baldea et al. (2008) conducted case studies of differ-ent chemical plants using a process simulator in steady state (AspenPlus from AspenTech®) in which writing were executed and obtained reading data through OLE comunication (Object Linking and Embedding, Microsoft®) by Excel®. The data ob-tained (gain matrix) were treated using the MatLab® for the Hessian matrix calculation and selection of the best sets of controlled variables (Skogestad, 2000, 2004) through the Technical Branch and Bound (Cao & Saha, 2005).

The difficulty, for the cases presented in the pre-vious paragraph, is that the procedure is not automat-ed, i.e., there is no graphical interface to assist in the construction of links between the model and the software for the calculation of plantwide procedure, being required the interaction of the user in activa-tion of each OLE link needed.

This work aims the development and implemen-tation of a tool to assist construction of control struc-tures based on the method of self-optimizing control.

2. Plantwide Control And Mathematical Proce-dures

For this article, we do not present a full review about plantwide control. This not is our objective because the procedure used here is described exten-sively in literature for different size of processes, e.g. Skogestad (2000, 2004), detailed by Halvorsen et al. (2003) and applied to several examples of processes by Araujo et al. (2007, 2009).

Details of procedure should be search in Skoges-tad (2000, 2004) and Halvorsen et al. (2003). The main critical steps on this procedure, is the calcula-tion of the best set of controlled variables (step 3 of the method) case the engineer or scientist get a full plant with many thousands of variables.

2.1. Construction Of The Gain Matrix

The gain can be easily calculated from the dif-ference of the function in question after an increase and its value in a nominal point, divided by the value of Δuj increment applied to the function. Equation 1 summarizes the idea.

Anais do XX Congresso Brasileiro de Automática Belo Horizonte, MG, 20 a 24 de Setembro de 2014

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,

j i

j j

i j i opt i opt ii j

j opt j opt j

f u f u c c cG

u u u u u

(1)

Where ioptc is the nominal value resulting from

the model used (g1) in operating optimal, ci is the resulting value of output variable (a measure or con-trolled variable) i after the increment Δuj, uj is the

new input value applied to the model (g1) and ioptu

is the nominal input value used in the optimal operat-ing for the model.

Equation (1) has a significant advantage: the re-alization of a single increment for obtaining gain value Gi,j. With the objective to achieve greater accu-racy in the calculation of G and fG still get a function that can be used for the calculation of Gi,j. and the Hessian matrix, Akima cubic spline was applied.

2.2. Akima Cubic Spline

The advantage of employing in place of cubic spline polynomial functions of higher degree may be seen in certain applications, e.g. when a function is smooth and undergoes an abrupt change somewhere in the region of interest. Another important factor is the continuity in the first and second derivatives (grade ≥ 2). Akima (1969) presented his study of new techniques of interpolation using polynomials of de-gree three in the points.

In the method presented by Akima, the coeffi-cients are adjusted so "local", i.e., this method re-quires information about the points in the vicinity of the range of interpolation to define the coefficients of cubic polynomial (Spline3, 2012).

2.3 Technical Branch-and-Bound for Selection of Variables

The assessment, depending on the number of variables that must be selected from a set of candi-dates, can be extremely difficult due to the number of

possible combinations of subsets!/ ( )! !n

SC S S n n .

Where S is the total number of elements in a set and n is the number of elements in a subset, with S ≥ n.

The technique of global optimization “Branch-and-Bound” (or branching and pruning) can solve combinatorial problems of selecting subsets of varia-bles without the need for thorough evaluation of all existing subsets in the problem.

2.3.1 Principle of the Method Branch-and-Bound for Selecting Subsets

Being 1, ,S SX x x

is a set of S elements and a subset Xn of n elements selected from XS. Thus

there are !/ ( )! !n

SC S S n n possible combinations of

Xn ⊂ XS for selection. Take up Γ as the criterion for

being used during the selection procedure function. Thus, there is a subset of n elements, which satisfies the following equality:

* maxn S

n nX X

X X

(2)

Taking *nX

as the optimal subset. The result of Equation (2) is the subset of n elements globally op-timal. It is assumed that the criterion function Γ satis-fies the monotonicity property:

se n S n SX X X X

(3)

Assume the characteristic of monotonicity means that a subset with fewer variables cannot be better than any large set containing this subset (Saha & Cao, 2003).

To obtain the best sets of CVs, using the technique of branch-and-bound, the VBA code was based on work by Cao and Kariwala (2008) and Kariwala and Cao (2009), where the original application, devel-oped in Matlab® can be found easily* on the Math-Works Web site. The code was wrote in VBA lan-guage, and adapted to function as one of the subrou-tines needed to run the tool.

2.3.4 Minimum Singular Value: Monotonicity

To use the BAB method, described in the previ-ous section, aiming at the selection of control struc-tures, the only limitation is that the criterion function can satisfy the condition of monotonicity according to Equation (3). Take up the matrix G as the system gain at steady state with all the candidates for the controlled variables. Select a subset of these varia-bles corresponds to the selection of a line of gain

matrix, G. Assuming 1, ,T

Sg g and denoting

1,T

i i iG G g , where i = n,..., S. Then the monotonici-ty of the minimum singular value means:

1r r rn n SG G G

(4)

The minimum singular value is obtained by sin-gular value decomposition (SVD).

3. Development

Software of stationary and dynamic simulation have been used in literature for building control plantwide structures. Examples of the use of such software can be found in Araujo (2007).

For this work, the Excel® (Microsoft Office 2010) was chosen because of its easy access (almost

* The complete code for this method can be found in www.matworks.com or at the following address: http://www.mathworks.com/matlabcentral/fileexchange/17480-bidirectional-branch-and-bound-minimum-singular-value-solver-v2


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all computers with Windows® operating system in-clude an Office® package) and handling (is one of the most used tools of calculus in engineering). This tool has an integrated developer, Visual Basic® Edi-tor (VBE), where applications with Visual Basic for Applications (VBA) can be developed.

For development of rigorous calculations of mass and energy balances for a wide range of chemi-cal, petrochemical and refining processes was used in the PRO/II 9.1. The choice of PRO/II was due to stable communication with Excel® via COM tech-nology (described in more detail in the next section) in addition to detailed documentation provided by

the PRO/II on models applied by users (Invensys, 2011).

3.1. Communication Between Softwares

Microsoft Excel® and PRO/IITM perform stable communication through (Component Object Model) COM technology from Microsoft Windows®. The COM technology that enables communication be-tween software components is performed using reus-able software. Thus, links are created between the components, creating new applications and taking advantage of services such as the Windows® operat-ing system.

Figure 1 – Worksheet with plantwide control tool after selection of MVs, CVs and disturbances.

All the data necessary are on the Database (file with extension *.prz) is the place where all the "data objects" are stored. To access them, you need to identify the "Class" of the object and its respective "Attribute". For example, the class can be "Stream", which identifies the creation of a "current" object, the reading of "Temperature" attribute results in the temperature of the specified stream. All classes and attributes used in this thesis can be found easily in "COM Server Reference Guide" Invensys (2011).

3.2. Graphical Interface and Handling

After selection, the variables like MVs, CVs and disturbances are written on the sheet where the user could see all work variables, the Figure 1 shows the worksheet with some selected variables. During the execution of the procedure by the tool, a worksheet called “Report_Spl” with results for the increments and disturbances applied to the simulation is created for further analysis of the data by the user.

The results of the gain matrices due to incre-ments and disturbance are rewritten separately in two new worksheets named “Gmatrix_Spl” and “Gdma-trix_Spl” respectively.

4. One Application of the tool

The process studied now is a propylene recovery unit. The propylene present in GLP generated in the fluid catalytic cracking unit (FCCU) may be routed to propylene recovery unit, which provides a product of high purity of 99.5% (Brasil et al., 2011). The recovery of propylene in GLP is done only by FCCU separation processes and caustic treatment (hidden in this model) scheme as shown in Figure 7.

4.1. Description of Propylene Recovery Unit

Lots of other species obtained in FCC and un-converted reactants, through stream 1, feed the


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DEPROPANIZER column, see Figure 7. The work of this column is to remove, in the stream 6A, the heavier chemical species, such as, for example, com-pounds of C4 or higher. As top products, it has spe-cies lighter, C3 or lower, in the stream 4 and decant-ing water in the stream 18.

The stream 4 rich in propylene, propane and ethane is pumped and preheated to feed DEETHANIZER column. The major fraction of the vapors from the top of this column is condensed in the stream 20. The remaining fraction, which con-tains ethane, may be referred for GLP storage. The basic product, stream 10, is rich in propylene and propane. The stream 10 feeds the third column, SPLITER, where the key role this is the separation of propylene as top product and propane and heavier fractions of species the stream 11 as the starting ma-terial. This product, in turn, is cooled and sent to storage as GLP.

Importantly, the process of separation of propyl-ene and propane is considered a low temperature distillation. This is due to the relative volatility be-tween propylene and propane under a given operat-ing pressure. A higher relative volatility reduces the number of stages and the reflux ratio, resulting in a

lower thermal load on the reboiler, or lower energy consumption (Henley & Seader, 1981).

Thus, when the distillate is a good refrigerator, it can be compressed by a compressor and the final product of the compression will have a temperature more than the temperature of evaporation in the products of the base of column SPLITER. When the thermal exchange is performed, a fraction of the dis-tillate is freed as desired product and the cooled remnant fraction returns to the column as reflux. This thermal process is called "heat pump".

In the case of Figure 2, the top product of the column SPLITER, stream 12, feeds a thermal pro-cess mentioned in the previous paragraph. Any frac-tion of the liquid stream 12 is separated in vase V1, the vapor coming from stream S3 coming from the stream S3 is compressed at a given operating pres-sure by the compressor COMP where the thermal load is obtained by compressing greater than that needed to evaporate the basic product of SPLITER column. After thermal exchange in heat exchanger E3, propylene partially condensed by stream S5 feeds the vase V3 for complete condensation of pro-pylene.

Figure 2 - Flowchart of recovery of propylene.

The stream of liquefied propylene, 14, is split, which one fraction becomes the stream of the final product, PROPENE, and the remaining fraction stream S11 feeds to the vase V2 removing any frac-tion of vapor present. The stream of liquid propylene coming from the vase V2 is directed to the SPLITER column as reflux, and the gas fraction in the stream 21 is added to the product stream top 12, where the thermal exchange cycle is restarted.

For the propylene recovery unit, the following data are presented nominal configuration:

Stream 1: Composition of the feed stream (molar frac-

tion): o the water - 1.47x10-03; o the Ethane - 0.0342; o the Propylene - 0.4096; o Propane - 0.0985; o The Heavy (C4 - C6) - 0.4577;

Feed Flow (F): 1300 kmol/h;

Temperature of the supply stream: 66 ° C;

Column top pressure: 17.9 kg/cm2; Column DEPROPANIZER Number of stages: 34 (including the reboiler

and condenser);

Feed Stage: 09 (top as reference);

Operating pressure: 16.5 kg/cm2; Column DEETHANIZER Number of stages: 51 (including the reboiler

and condenser);


Operating pressure: 30.4 kg/cm2; Column SPLITER Number of stages: 152 (including reboiler

and condenser);


Operating pressure: 9.0 kg/cm2;


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Exchanger E1 Exit temperature (stream 5): 36.3ºC;

Pump P1 Discharge pressure: 34.6 kg/cm2;

Compressor COMP Discharge pressure: 14.3 kg/cm2;

Spliter SP1 Ratio of streams 15/14 = 0.08;

Spliter SP2

Ratio of streams S8/13 = 0.13;

4.2. Application of Plantwide Control Method

Following the steps of the method of construc-tion of control structures using the tool as discussed, based on the methodology Skogestad (2000, 2004).

Table 1 - Degrees of freedom for the equipment shown in Figure 7.

Process Unit Dynamical Degrees of Freedom

Stationary Degrees of Freedom

Detailing of Consumption of the Degrees of Freedom

Column – DEPROPANIZER 7 2 Liquid level of the base + liquid level of the reflux vase + column pressure + power flow upstream + water stream

Column – DEETHANIZER 7 2 Liquid level of the base + liquid level of the reflux vase + column pressure + power flow upstream + water stream

Column – SPLITER 5 1 Liquid level of the base + liquid level in Vase V2+ col-umn pressure+ power flow upstream

Pump – P1 1 0 DEETHANIZER pressure column Heat exchanger – E1 1 1 --- Heat exchanger – E2 1 0 Temperature of the stream S10 at 35 ° C Heat exchanger – E3 1 0 E3 exchanger is part of spliter column (reboiler) Heat exchanger – E4 1 0 The temperature control is made for storage Compressor – COMP 1 1 --- Vase – V1 1 0 Adiabatic and no pressure drop Vase – V2 1 0 Adiabatic and no pressure drop Vase – V3 1 0 Adiabatic and no pressure drop Divisor – SP1 1 1 --- Divisor – SP2 1 1 --- Mixer – M1 0 0 No manipulated variables Mixer – M2 0 0 No manipulated variables

Total Degrees of Freedom 30 9 ---

4.2.1. Step 1: Analysis Of The Degree Of Freedom

The analysis of the number of degrees of free-dom (dynamic) for the found equipment in Figure 7 are presented in Table 1. In this table, the results of

stationary degrees of freedom from a first analysis of the consumption of degrees of freedom only for vari-ables used in dynamic or previously specified state are also presented. Further details are in the column entitled: Detailing of Consumption of the Degrees of Freedom in Table 1.

Table 2 - List of manipulated variables available before the optimization process.

Equipment Manipulated Variable Nomenclature Column – DEPROPANIZER Leakage stream 6A

Reflux reason u1 u2

Column – DEETHANIZER Leakage stream 10 Reflux reason

u3 u4

Column – SPLITER Leakage stream 11 u5 Heat exchanger – E1 Temperature of the stream 5 u6 Compressor – COMP Discharge pressure u7 Divisor – SP1 Fraction of the stream 13 directed to S7 u8 Divisor – SP2 Fraction of the stream 14 directed to 15 u9

The manipulated variables, 9 degrees of freedom shown in Table 1 are listed in Table 2. After optimi-zation of the process, a few degrees of freedom can

be consumed to maintain active constraints at speci-fied values.

In this Example, were considered of MVs three subgroups: u1 = [6A, R.R.Col1], u2 = [10, R.R.Col2] e u3 = [11] for DEPROPANIZER, DEETHANIZER

and SPLITER columns respectively. Where R.R. is the reflux ration and “Col + number” is the column used. The use of other sets of MVs may be consid-ered if the precedent set does not present satisfactory results.

4.2.2. Step 2: Cost Function and Restrictions

In this example the compositions are considered as reference values for the calculation of the objec-


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tive function. The objective is to minimize the devia-tion function setpoint of Equation (5).

The objective function is based on deviations from the nominal values of the mole fractions in each predetermined stream in each column of the propyl-ene recovery unit.

3 3 3 3 3 3

33 3

3 3 3 3 2 2

23 3

3 3

3

2 2

6 6 , 4 4,

4,6 ,

2 2

10 10, 8 8,

8,10,

2

11 11,

11,

min

C C C C C CA A s s

CC CsA s

C C C C C Cs s

CC Css

C C

s

C

s

x x x xJ

xx

x x x x

xx

x x

x

3 3

2

,

,

C C

PROPENE PROPENE s

LPROPENE s

x x

x

(5)

Where x is the molar fraction of a given chemi-

cal species, and the exponents 3C

and 3Crefer to

propylene and propane respectively. To 4C has the summation of molar fractions of the heavier com-

pounds such as C4 to C6. The 2C is ethane and the

index 4, 6A, 8, 10, 11 and PROPENE indicate the streams which the compositions are being read. The index “s” refers to the value of the fraction of chem-ical species in its nominal value.

The cost function was inserted into the model PRO/IITM using a calculator (CA1) with an optimizer (OP1) which is assigned to the objectives restrictions

and specifications. The restrictions to be followed are listed below:

Mass fraction of propylene + propane at 6A stream is ≤ 1.22%;

Sum of the molar fractions of the ibutano to hexane the stream 4 ≤ 0.1;

Temperature of the stage 1 of DEETHANIZER column = 35°C ≤ T ≤ 40°C;

Molar composition of the ethylene in the stream 10 = 120 ppm;

Mole fraction of propylene in the stream 11 ≤ 0.05;

Mole fraction of propylene in the stream propene ≥ 0.995;

Pressure compressor discharge: 14.3 kgf/cm2g ≤ P ≤ 16.5 kgf/cm2g.

The values of the restrictions are due to the product specifications in the market demand and economic factors of the process. If the market is "hot", a greater demand for products under the given specifications must be satisfied, leading to the plant to its maximum operating capacity. If product de-mand is minimized, costs relating to utilities should be taken into consideration for a production at lower costs.

Table 3 - Nominal values and values obtained after optimization of the propylene recovery unit.

Specification Nominal Optimal d1 d2 d3 Mass fraction of propylene + propane at 6A stream is ≤ 1.22%;

1.22 1.22 1.22 1.22 1.22

Sum of the molar fractions of the ibu-tano to hexane ≤ 0.1%

0.1 0.0999 0.0999 0.0999 0.0999

Temperature of the stage 1 of DEETHANIZER column = 35 ° C ≤ T ≤ 40°C

38 38.6 38.6 38.6 38.6

Composition of the ethane in the stream 10 = ≤ 120ppm

120 120 120 120 120

Mole fraction of ethylene in the stream 11≤ 0.05

0.0775 0.04998 0.05 0.05 0.05

Mole fraction of propylene in the stream propene ≥ 0.995

0.995 0.996 0.9961 0.9958 0.9935

Compressor pressure discharge 14.3 kgf/cm2g ≤ P ≤ 16.5 kgf/cm2g

16.2 14.3 infeasible 14.3 14.3

4.2.3. Step 3: Identifying the Most Important Dis-turbance

The recovery unit evaluated propylene does not expose the main variables of disturbances to the pro-cess. Especially in chemical plants, is very common variation of flow rate, composition and temperature of the feed stream system. Thus, as in Example 1, the following disturbances are considered:

d1: variation of the feed rate in 10%;

d2: variation in the composition of pro-pylene feed +10%;

d3: temperature variation of Feed by +10%.

4.2.4. Step 4: Optimization Model

Observed after optimization of columns, Table 3, which, with the exception of the stage 1 tempera-ture of DEETHANIZER column and the mole frac-tion of propylene in the stream PROPENE. And the


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discharge pressure of the compressor, all other re-strictions are active. With this, four degrees of free-dom are consumed.

Thus, the number of manipulated variables, after removal of the variables only influence on the dy-namic state and the necessary variables to control the active specifications, it has that Nu = 9-4 = 5.

The four needed variables to control the active restrictions refer to the variables available in the three columns on which the compositions were speci-fied.

4.2.5. Step 5: Identification of the Candidate to Con-trolled Variables

For the propylene recovery unit, Figure 6, the controlled variables were chosen according to the availability of the measurement process. The set of CVs, candidate to controlled variables are summa-rized in Table 4. For the case studied, in the three columns, will be selected secondary variables, held constant, will serve as a means of controlling the compositions (active restrictions) of their respective streams. In the case of selection of temperatures, the tool can identify which of all the best stage s in which there may be control. Above all, the best tem-perature of control (top stage) are those with greater temperature variation in the profile presented by the stages of the column.

Table 4 - List of candidate to controlled variables.

Equipment or streams Identification of variable

DEPROPANIZER column

Reflux flow Reflux reason Temperature of stage 1 Temperature of stage 25 Temperature of stage 29

DEETHANIZER column

Reflux flow Reflux reason Temperature of stage 1 Temperature of stage 30 Temperature of stage 36

SPLITER column

Temperature of stage 36 Temperature of stage 144 Temperature of stage 151

Stream S5 Temperature

Stream S11 Temperature

Stream PROPENE Propylene mole fraction

According to Hori and Skogestad (2007), that is because controlling for control purposes, the gain is related to the temperature difference in the stages should be large which results in improved dynamic response of the control system applied to these vari-ables. In this case, it follows that the number of pos-

sible combinations is equal 16! 9!7! 11.440C poten-tial candidate choices from the controlled variables.

Table 5 - Minimum singular values and best sets of CVs calculated by the tool.

Minimum singular

value( ) Sets of CVs

0.014900243 A.Rrate A.T25 B.RRate B.T1 B.T30 C.T36 C.T144 S5.T S11.T 0.014900239 A.Rrate A.T25 B.RRate B.T1 B.T36 C.T36 C.T144 S5.T S11.T 0.014900200 A.Rrate A.T25 B.RRate B.RR B.T36 C.T36 C.T144 S5.T S11.T 0.014900179 A.Rrate A.T25 B.RRate B.RR B.T30 C.T36 C.T144 S5.T S11.T 0.014900076 A.Rrate A.T29 B.RRate B.T1 B.T30 C.T36 C.T144 S5.T S11.T 0.014900074 A.Rrate A.T29 B.RRate B.T1 B.T36 C.T36 C.T144 S5.T S11.T 0.014900034 A.Rrate A.T29 B.RRate B.RR B.T36 C.T36 C.T144 S5.T S11.T

4.3. Results Obtained By the Tool

For the set of MVs, the rank was full for the gain ma-trix obtained for selected MVs, i.e., all selected MVs have influence on the CVs selected as the best candidates. With respect to sets of controlled variables, Table 5 shows a summary of the top twenty sets along with their respective minimum singular values obtained by the tool.

Where A, B and C represent the DEPROPANIZER, DEETHANIZER and SPLITER columns respectively;

R.R. is the reflux ratio, R.Rate is the reflux rate, S5 and S11 are the names of streams and T is the temperature in a given stage (+stage number).

Note that the minimum singular values have almost the same results for the first ten sets calculated by the tool. However, it is important to remember that even with close values, the tool lists the best sets of CVs in descending order. Thus, tests for application of structures can be started from the first set presented in Table 5.


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Importantly, the pairing of variables is part of the ascending analysis ("bottom-up") of the control sys-tem starting with the stabilizing control layer (steps 5-8 in Table 2.1) (Skogestad, 2000).

Conclusions

This work aimed at the development and appli-cation of an auxiliary tool for construction of control structures “plantwide” taking as a basis the proce-dures described by Skogestad (2000, 2004). The ini-tiative was inspired by previous work (Araujo et al., 2009; Araujo & Shang, 2009; Araujo et al., 2007) that, by applying the technique of "self-optimizing control" in simulations of chemical plants, confirmed the efficiency of the method Skogestad of using soft-ware such as, for example, Matlab®, AspenPlus® and Microsoft Excel®, for best set of controlled variables.

The procedures for obtaining plantwide control structures, that may present lower losses, occur when disturbances have been fairly investigated and ap-plied in recent years. The efficiency of the method, inspiration for this work, made it developed a way for facilitating the use of the self-optimizing control pro-cedure.

For the control structure proposed by the tool presents operational viability of the process. More accurate results may be exposed after test of robust-ness of suggested loop in a dynamic model, e.g. with Dynsim (Invensys®) for the propylene recovery unit or comparing the results with existing processes in practice.

Acknowledgment

The authors acknowledge the Coordination of Improvement of Higher Education Personnel (CAPES) and PETROBRAS for its support and en-couragement to research and technological develop-ment.

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