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Research article
Design and implementation of a control structure for quality products
in a crude oil atmospheric distilla...
accomplishes the separation of hydrocarbons in the refining pro-
cess. This is possible since crude products have different...
(gap), there is a good separation between adjacent products. On
the other hand, if the difference is negative (overlap), t...
3.2. Separation process
3.2.1. Preflash stage
The first stage in refining crude oil corresponds to the preflash
column, operat...
( )=
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Volume
Total outflow Residence time
Liquid level 6
Tables 3 and 4 show the geometric parameters to be introduced
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Hence, PID controllers FIC-1000 and FIC-1002 regulate the liquid
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The resulting static gain matrix Gp is showed in (16).
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Fig. 10. Atmospheric column final control structure.
Fig. 11. Control process flowsheet.
D. Sotelo et al. / ISA Transactions...
5. Simulation modeling and results
The purpose of this section is to present simulation modeling
and results obtained afte...
analyzing the minimum and maximum overshoot, with values of
5.8 % and 6.2 %, respectively.
5.2.2. Pressure profile
Although...
[23] Towler G, Sinnott R. Chemical engineering design. Principles, practice and
economomics of plant and process design. E...
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Design and implementation of a control structure for quality products in a crude oil atmospheric distillation column

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In recent years, interest for petrochemical processes has been increasing, especially in refinement area. However, the high variability in the dynamic characteristics present in the atmospheric distillation column poses a challenge to obtain quality products. To improve distillates quality in spite of the changes in the input crude oil composition, this paper details a new design of a control strategy in a conventional crude oil distillation plant defined using formal interaction analysis tools. The process dynamic and its control are simulated on Aspen HYSYS dynamic environment under real operating conditions. The simulation results are compared against a typical control strategy commonly used in crude oil atmospheric distillation columns.

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Design and implementation of a control structure for quality products in a crude oil atmospheric distillation column

  1. 1. Research article Design and implementation of a control structure for quality products in a crude oil atmospheric distillation column David Sotelo a , Antonio Favela-Contreras a,n , Carlos Sotelo a , Guillermo Jiménez b , Luis Gallegos-Canales c a Tecnologico de Monterrey, Department of Mechatronics and Automation, Monterrey, Mexico b Tecnologico de Monterrey, Department of Chemical Engineering, Monterrey, Mexico c Tecnologico de Monterrey, Department of Electrical and Computer Engineering, Monterrey, Mexico a r t i c l e i n f o Article history: Received 14 April 2016 Received in revised form 16 June 2017 Accepted 8 August 2017 Available online 23 August 2017 Keywords: Distillation process control Crude oil atmospheric distillation process Interaction analysis Process modeling a b s t r a c t In recent years, interest for petrochemical processes has been increasing, especially in refinement area. However, the high variability in the dynamic characteristics present in the atmospheric distillation column poses a challenge to obtain quality products. To improve distillates quality in spite of the changes in the input crude oil composition, this paper details a new design of a control strategy in a conventional crude oil distillation plant defined using formal interaction analysis tools. The process dynamic and its control are simulated on Aspen ® HYSYS dynamic environment under real operating conditions. The si- mulation results are compared against a typical control strategy commonly used in crude oil atmospheric distillation columns. & 2017 ISA. Published by Elsevier Ltd. All rights reserved. 1. Introduction Oil is the most important primary energy source worldwide. As such, the exhaustion of this raw material serves as a motivation for the oil industry to optimize its processes [1] and to obtain fossil fuel products of higher quality. In a petroleum refining plant (Fig. 1), crude oil components are separated by the process known as fractional distillation [2]. The process begins when crude oil enters into the atmospheric column, where its products can be obtained in different fractions by in- creasing the temperature. This is possible due to the fact that each derivative product has a specific boiling point, classified in des- cending order according to their volatility. In this article, multi-loop PI/PID controllers are implemented in an atmospheric distillation column. As in [3–9], the control strategy is proposed considering PI/ PID controllers in order to increase its feasibility to be implemented in refining processes. Each controller is tuned in terms of the de- sired response to the process specifications, using the Control ® Station design tools based on the process open loop step response. Crude distillates are obtained under quality standards in spite of variations in the input load's composition. Also, the tower operates at a constant temperature and fixed pressure, which ensure quality oil fractions. The control structure's design is based on input-output variable interactions, represented in a relative gain array (RGA). Industrial processes are constantly subject to unplanned pro- cess transients (e.g. process uncertainties, external disturbances and sudden malfunctions) which can induce strong variations in plant operating conditions [10]. The proposed control structure aims to improve distillates' quality of the atmospheric distillation column, avoiding relying on the expertise and intuition of its op- erators and process engineers when dealing with changes in the input crude oil composition. Input crude oil is composed of a mixture of three types of oils, Maya, Isthmus and Olmec, char- acterized as heavy, medium and light, respectively. The model consists of four stages, preflash, atmospheric, stabilizer and va- cuum where different pieces of equipment, such as condensers, reboilers, heat exchangers, distillation columns and strippers, are simulated in steady state and dynamic mode under real operating conditions using Aspen ® HYSYS software. Finally, to compare the performance of the developed control strategy, the refinery plant is also simulated under actual control structures commonly used in crude oil distillation plants. 2. Process description Crude oil is not directly usable. Therefore, separation of mix- tures and solutions into its components is one of the fundamental operations in the petrochemical industry. Distillation Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/isatrans ISA Transactions http://dx.doi.org/10.1016/j.isatra.2017.08.005 0019-0578/& 2017 ISA. Published by Elsevier Ltd. All rights reserved. n Corresponding author. E-mail address: antonio.favela@itesm.mx (A. Favela-Contreras). ISA Transactions 71 (2017) 573–584
  2. 2. accomplishes the separation of hydrocarbons in the refining pro- cess. This is possible since crude products have different boiling points. In the present research, although the proposed control struc- ture is implemented in the crude oil atmospheric distillation col- umn, to present a more realistic conventional refinery plant, the following stages are simulated: 1. Preflash: the more volatile components of the crude oil are re- moved; this in order to avoid weeping in atmospheric distilla- tion column. 2. Atmospheric: the crude oil is heated up to 338.4 °C by a furnace. The distillation process operates at a constant pressure of 104 kPa, slightly above atmospheric pressure value. The crude oil is fractionated into five cuts, e.g. naphtha, kerosene, diesel, atmospheric gas oil (AGO) and atmospheric residue. The column has physical components called strippers and pumparounds interacting to control the main distillation process variables such as temperature and pressure profiles. 3. Stabilizer: the liquid product (naphtha) is treated through a column to separate gas from oil, operating at a constant pres- sure of 1 030 kPa. At this stage, combustible gas, LPG (liquefied petroleum gas) and naphtha stabilized are obtained. 4. Vacuum distillation: using the non vaporized load of crude oil from the atmospheric stage, the column operates at a constant pressure of 2 kPa. At this complementary operation, the ob- tained products are top, bottom, light liquid and heavy liquid gas oil (LVGO and HVGO). The studied refining plant is shown in Fig. 2. The input stream consists of three kinds of crude oils: light, medium and heavy. The entire plant has four distillation columns (preflash, atmospheric, stabilizer and vacuum), modeled by trays. There are three con- densers, located on top of the preflash, atmospheric and stabilizer columns. In addition, there is a reboiler in the stabilizer column. The strippers located in the atmospheric column are referred to as Product-S while pumparounds are under PA legend. To determine distillation efficiency between two adjacent products, a comparison of ASTM D86 curves is done. The recovery degree of the most volatile components is carried out in the light fraction, whereas the heaviest components are in the heavy frac- tion. The temperature difference in ASTM D86 curves defines the quality of the separation between two consecutive fractions. This is calculated by subtracting the temperature corresponding to 95% ASTM D86 light fraction (LF) of the corresponding to 5% ASTM D86 of the heavy fraction (HF). If the temperature difference is positive Fig. 1. Conventional refinery. Fig. 2. Process flowsheet. D. Sotelo et al. / ISA Transactions 71 (2017) 573–584574
  3. 3. (gap), there is a good separation between adjacent products. On the other hand, if the difference is negative (overlap), the se- paration between consecutive fractions is poor [11]. In Fig. 3, ASTM D86 volatility curves of extracted products in the atmospheric column are shown. Volatility curves are useful for defining the Gap/Overlap between continuous products. The Gap/Overlap values obtained in the simulation and their corre- sponding limits in real processes are shown in Table 1. As it can be seen, based on the ranges specified in [12,13], the results do not exceed the limits; therefore, the separation of consecutive pro- ducts is considered efficient. 3. Modeling and simulation The refining plant is simulated using Aspen ® HYSYS , based on [14]. The oil supply modeling is described in Section 3.1. The se- paration process is presented in Section 3.2. Separation of the more volatile components takes place in the preflash column, described in Section 3.2.1. The distillation process of studied sub- products is conducted by the atmospheric column, described in Section 3.2.2. LPG and stabilized naphtha are obtained by the stabilizer column, presented in Section 3.2.3. The separation of heavier components is performed in the vacuum column, de- scribed in Section 3.2.4. Finally, geometric parameters to be in- troduced in the model are described in Section 3.3. 3.1. Crude feed Crude oil is the raw material for the distillation process; therefore, its physical and chemical characteristics [15] must be established. In crude oil modeling, characteristic data, mixture proportions and oil pseudo components must be defined. In Aspen ® HYSYS , modeling of the oil pseudo components is based on the API (American Petroleum Institute) methodology, where their density curve and volatility are required. The modeled refining process receives a mixture composed of three kind of oils, denominated Light Crude, Medium Crude and Heavy Crude. Based on its API gravity and volatility curve, raw oil mined in Mexico corresponds to Olmec, Isthmus and Maya re- spectively. The input data [16] for crude feed modeling is: dis- tillation curve, light analysis, density curve, API density and mass fractions. Fig. 4 shows the load's TBP (True Boling Point) curves [17] of the process. As shown, the Maya Crude with a FBP (Final Boiling Point) of 947 °C corresponds to the heavier crude which means that its density is higher than the others; the Isthmus and Olmec Crudes have an FBP of 934 °C and 896 °C, respectively. The raw oil mix composition defines the crude feed to the preflash column with a new TBP distillation curve. The new FBP is at 931 °C, which was expected, since the value of the final boiling point must be within the 896–947 °C range. Once the characterization of the three types of crude oils is done, the pseudo components of the crude oil load are calculated automatically by the Aspen ® HYSYS software. Thus, based on Eq. (1) and setting the inflow, the amount of naphtha, kerosene, die- sel, AGO and residue to distill can be estimated. Fig. 5 shows the mixed oil cut distribution presented in Table 2, where the temperature at which each cut starts to boil is refered to as the IBP (Initial Boiling Point), and the temperatures at which each cut boils off completely is refered to as FBP (Final Boiling Point). In the present research, the input oil is at a temperature of 232.1 °C and at a pressure of 517 kPa, considering an input stream operating point of 99 000 barrels/day. = · ( )Q f Q 1distilled r in where Qdistilled indicates the liquid volumetric flow distilled ⎡⎣ ⎤⎦barrels day/ , fr stands for the liquid volumetric fraction of total oil and Qin indicates the input liquid volumetric flow ⎡⎣ ⎤⎦barrels day/ . Fig. 3. ASTM D86 volatility curves. Table 1 Gap/Overlap evaluation. Admissible Simulation Gap/Overlap Naphtha-Kerosene ≥ °16.7 C °28.2 C Kerosene-Diesel − °11 C a + °28 C °8.7 C Diesel-AGO − °20 C a °0 C − °2.6 C AGO-Residue − °30 C a − °10 C − °30.0 C Fig. 4. Crude feed TBP curves. Fig. 5. Cut distribution of crude oil. D. Sotelo et al. / ISA Transactions 71 (2017) 573–584 575
  4. 4. 3.2. Separation process 3.2.1. Preflash stage The first stage in refining crude oil corresponds to the preflash column, operating at a pressure of 167 kPa and a temperature of 42.7 °C. At this stage, the lighter components called PreFlash-Vap are removed, producing Light Naphtha at the top and PreFlash-Liq at the bottom. The condensing section is modeled by a partial condenser that removes lighter components. In real plants, in order to reduce the condenser temperature, a heat exchanger network is im- plemented; however, this is modeled by a single exchanger (Q-1). 3.2.2. Atmospheric stage A key stage in the refining process is the atmospheric distilla- tion [18], operating at a pressure of 104 kPa and at a temperature of 76.9 °C. At this stage, five extractions are performed by loading crude treated in the bottom section of the preflash column: heavy naphtha (labeled simply Naphtha), kerosene, diesel, atmospheric gas-oil (AGO) and atmospheric residue (Residue). A furnace (Heater ATM) is implemented, which receives refined light oil at 228.4 °C to heat it up to 338.4 °C. This is possible re- gards to −Q 3, which provides the amount of heat required. A partial condenser at the top of the column is also implemented, accumulating naphtha and processing water. Similar to the pre- flash column, a single exchanger removes heat ( −Q 2) to reduce the condenser temperature. The four pumparounds (PA-1, PA-2, PA-3 and PA-4) are modeled as heat exchangers. 3.2.3. Stabilizing stage In order to stabilize naphtha, a stabilizing column is added, op- erating at a pressure of 1 030 kPa and at a temperature of 159.1 °C. At this stage, using a pump (P-101), naphtha pressure increases from 104 to 1 825 kPa, and by means of a heat exchanger network (HEN-1), naphtha temperature increases from 77.6 to 250 °C. The stabilizer column works at a higher pressure than the rest of the columns, and it receives 20,000 barrels/day of naphtha. At the condensing section, there is a total condenser, producing LPG. As in the previous columns, a heat exchanger Q-5 removes the excess temperature. The reboiler is known as Kettle reboiler (Once- through), where the liquid in the last tray is passed through a heat exchanger. The heat injected by Q-6 generates vapors which are returned into the bottom of the column. No evaporated product is called Stabilized Naphtha. 3.2.4. Vacuum stage The vacuum stage receives residue (bottom product) from the atmospheric column, operating at a pressure of 2 kPa and at a temperature of 112 °C. At this stage, evaporation of the heavier components is achieved, which, at the same temperature but at atmospheric pressure, had not been able to be removed [19]. Through a pump (P-102) and a heater (Heater Vac), atmospheric residue (Residue) is extracted and maintained at the desired temperature. The vacuum column receives approximately 38,210 barrels/day of Residue at a pressure of 234 kPa, and through a heater called Heater Vac the temperature is mantained at 408.5 °C, avoiding thermal cracking. The purpose is to carry out the distillation without producing coke because the trays could get blocked up. Considering the upper and lower part, four extractions (VacOver- head, LVGO, HVGO and VacResidue) are performed. Pumparounds PA-5 and PA-6 are modeled as heat exchangers [20,21]. 3.3. Geometric parameters The geometric parameters to be introduced in each of the main columns (Preflash, Atmospheric, Stabilizer and Vacuum), and strippers (Kero-S, Diesel-S and AGO-S) are described in Section 3.3.1. Finally, geometric parameters for vessels ( Cond Cond Cond, ,Pfl Atm Stab and RebStab) are described in Section 3.3.2. 3.3.1. Columns and strippers In order to size a column [22], different volumetric flows must be considered for each of the zones in the column; therefore, As- pen ® HYSYS recommends dividing the column by sections. As part of the design, the number of internal flow paths must be proposed. The column diameter corresponds to that of the largest of the different sections. Both, the tray spacing and the height of the weir are appointed from automatic calculations performed by the software settings. The weir length is obtained by dividing the total weir length (given by HYSYS) by the numbers of internal flow paths Eq. (2). The pressure along the column is obtained by mul- tiplying the maximum pressure increase per tray by the total number of trays Eq. (3). Finally, adding the top pressure and the pressure increase along the column, the bottom pressure is ob- tained Eq. (4). Fig. 6 shows the internal parameters. = ( ) Weir length Total weir length No. of internal flow paths 2 ( )( )Δ = Δ ( )Total P Max P/Tray No. of trays 3 = + Δ ( )Bottom pressure Top pressure Total P 4 3.3.2. Condensers and reboilers The total outflow is the sum of the liquid flows with zero phase steam leaving the vessel Eq. (5). The residence time is an esti- mating time required for material flow, stay and exit the vessel. Considering these parameters and an approximated liquid level [22], the vessel volume can be calculated using Eq. (6). Fig. 7 shows the parameters to be considered. = + ( )Total outflow flow 1 flow 2 5 Table 2 Crude oil fractions to be distilled. IBP FBP Fraction Production [ ]°C [ ]°C ⎡⎣ ⎤⎦barrels day/ Naphtha 70 180 0.157 15,543 Kerosene 180 240 0.105 10,395 Light Diesel 240 290 0.087 8613 Heavy Diesel 290 340 0.083 8217 AGO 340 370 0.048 4752 Residue 370 932 0.457 45,243 Fig. 6. Column parameters. D. Sotelo et al. / ISA Transactions 71 (2017) 573–584576
  5. 5. ( )= ( ) Volume Total outflow Residence time Liquid level 6 Tables 3 and 4 show the geometric parameters to be introduced in the model for columns (both main and strippers) and vessels, respectively. The results presented in Table 3 are reasonable. This can be seen in the values obtained for the weir length for the different columns, since according to [23], these must be between 60–85% of the diameter of the main column section. Furthermore, the volumes specified in Table 4 are close to those in real plants; e.g., typical atmospheric condenser volume is 90 m3 [23]. Table 5 shows the top pressures for which the process is si- mulated, and the theoretical values consulted in piping diagrams. As it can be seen, these pressures are not far from those in an actual process; the minimal difference is due to simplifications in modeling the condensing part. Another important result reported in Table 5 is the weir length, which, according to consulted sources [23], should be in a range defined by the diameter of the main section. Although only the required specifications in dynamic mode to run the simulation are presented, the crude oil units were carefully designed based on a deep literature review where geo- metric parameters, pressures, temperatures and numbers of trays are specified. More detailed explanation about dynamic simulation setup is reported in [24]. 4. Control structure 4.1. Variable pairing selection System control design in crude oil distillation processes de- pends on plant conditions. Consequently, control strategies exhibit notable differences as much in complexity as in cost, and their development relies on the relationship between manipulated and process variables. Inappropriate selection of the manipulated or controlled variables causes an inefficient control [25], even if an advanced control strategy is implemented [26]. In this section, with the purpose of designing an efficient control system, a structured method for selecting manipulated and controlled vari- ables is presented. 4.1.1. Static gain matrix Proper selection of the control variables, as well adequate in- stallation of the sensors, is fundamental in controlling a crude oil distillation column [27]; however, many times in the industry, variables are ignored during control strategy design, therefore operational problems appear and its development is affected. The loop sensitivity, showed in Eq. (7), measures how the sensor signal responds in stationary state to changes in each manipulated variable [28]. = ∂ ∂ ( ) LS S M 7 i j where LS stands for loop sensitivity, Si indicates the controlled variable i expressed in percentage of the maximum value, Mj stands for the manipulated variable j measured in percentage of the maximum value. Taking into account Eq. (7), static gain matrix G, showed in Eq. (8), can be obtained by setting the column sen- sitivity with respect to the boiling temperature ( =T Si i), at 5 % and 95 %, of each distillated product. The sensitivity is determined by opening the valves corresponding to side extractions flow and steam supply to strippers. The static gain matrix G (Eq. (8)) describes the influence of each opened valve on each boiling temperature, thus a control strategy capable of reducing the effect that disturbances have on product quality can be defined. Fig. 7. Condenser parameters. Table 3 Geometric parameters for the columns. Diameter Tray spacing Weir height Weir length Bottom pressure [ ]m [ ]m [ ]m [ ]m ⎡⎣ ⎤⎦kPa Preflash 3.200 0.6096 0.0508 2.5575 415.79 Atmospheric 6.248 0.6096 0.0508 4.9230 198.54 Kero-S 1.829 0.6096 0.0508 1.5335 185.95 Diesel-S 1.981 0.6096 0.0508 1.6930 187.02 AGO-S 1.219 0.6096 0.0508 0.9618 193.46 Stabilizer 2.591 0.6096 0.0508 2.0916 1 043.752 Vacuum 12.95 0.6096 0.0508 8.5200 16.21 Table 4 Geometric parameters for the condensers and reboilers. Total outflow Geometry Orientation Volume Diameter Height/ Length ⎡ ⎣ ⎤ ⎦m h/3 ⎡ ⎣ ⎤ ⎦m3 [ ]m [ ]m CondPfl 73.16 Cylindrical Vertical 24.39 2.746 4.119 CondAtm 278.476 Cylindrical Vertical 92.82 4.287 6.431 CondStab 91.08 Cylindrical Vertical 30.36 2.954 4.431 RebStab 96.06 Cylindrical Horizontal 32.02 3.007 4.510 Table 5 Comparison between simulation and real data. Simulation Real Top pressure [ ]kPa Preflash 167 170 Atmospheric 104 105 Stabilizer 1 030 1 050 Vacuum 2 2 Weir length [ ]m Preflash 2.56 1.9–2.7 Atmospheric 4.92 3.8–5.3 Kero-S 1.53 1.1–1.6 Diesel-S 1.69 1.2–1.7 AGO-S 0.96 0.7–1.0 Stabilizer 2.09 1.6–2.2 Vacuum 8.52 7.8–11.0 D. Sotelo et al. / ISA Transactions 71 (2017) 573–584 577
  6. 6. ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ = ∂ ∂ ⋯ ∂ ∂ ⋯ ∂ ∂ ⋮ ⋮ ⋮ ∂ ∂ ⋯ ∂ ∂ ⋯ ∂ ∂ ⋮ ⋮ ⋮ ∂ ∂ ⋯ ∂ ∂ ⋯ ∂ ∂ ( ) G T M T M T M T M T M T M T M T M T M 8 j m i i j i m n n j n m 1 1 1 1 1 1 where G indicates the static gain matrix containing n boiling temperatures from m manipulated variables and Ti stands for the boiling temperature i of distilled product at 5% and 95%, measured in percentage. 4.1.2. Singular value decomposition Singular value decomposition (SVD) of the static gain matrix, presented in Eq. (8), is a useful technique to analyze multivariable interactions. When applying this technique, the static gain matrix is decomposed into three matrices, as it is showed in Eq. (9). Σ= ( )G U V 9T where U is an ortonormal matrix with dimension ×n n, its col- umns are left singular vectors, Σ is a diagonal matrix with di- mension ×n m, singular values, s.t. σ σ σ≥ ≥ ≥ 0m1 2 and V is an ortonormal matrix with dimension ×m m, its columns are right singular vectors. 4.1.3. Stability and decoupling condition In pairing variables, the strongest interactions between input- output variables should be considered. Additionally, the Nie- derlinski Index is defined in Eq. (10), where negative values in- dicate the existence of paired variables that affect system stability, regardless of controller tuning [28]. = ∏ ( )= NI G g 10i n ii1 where NI is the Niederlinski index, >NI 0 as a necessary and sufficient condition for stability, G is the determinant of static gain matrix and gii are the diagonal elements of matrix G. Moreover, singular value decomposition shows us the degrees of freedom of the control strategy. In the diagonal matrix of sin- gular values Σ, smaller values indicate a difficulty in control. The Conditional Number, presented in Eq. (11), defines the degrees of interaction among control loops, and it is set as the ratio between the maximum and minimum singular values [28]. σ σ = ( ) CN 11 max min where CN is the conditional number, σmax indicates the maximum singular value and σmin indicates the minimum singular value. Therefore, the CN can be defined as a numerical index of the system sensitivity. The higher the CN value, the more difficult it is to decouple the interactions of the control loops. Based on [29], if ≥CN 20, the system is almost singular and decoupling is not feasible. 4.1.4. Relative gain array Relative gain array (RGA) is a mathematical tool useful for identifying the strongest interaction between manipulated and controlled variables. In this article, RGA is applied in order to de- fine a control structure [30]. RGA is showed in Eq. (12): ⎡ ⎣ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ Λ λ λ λ λ = … ⋮ ⋱ ⋮ … ( )12 n n nn 11 1 1 where each element is obtained by means of Eq. (13) [31]. λ λ = ( ) ′ ( ) = ′ = … ( ) → − → − G s G s g g i j n lim lim , 1, , 13 ij s ij All control loops opened s ij Remaining control loops closed ij ij ij 0 0 An alternative way to obtain the RGA is through Eq. (14). ⎡⎣ ⎤⎦( )Λ λ= = ⊗ ( ) − G G G 14ij T where ⊗ stands for Hadamard Product and − G T corresponds to the transpose of the inverse matrix of G. The RGA pairing criterion relies on: The sum of the elements in each row or column is 1. The control loops should be defined by relative static gains closer to 1. Control loops with relative static gains tending to infinity or zero should not be defined [32]. 4.2. Control structure definition In the Section 4.2.1, a control strategy to achieve stability in distillation columns is described. Then, in Section 4.2.2 control strategies to regulate the liquid volumetric flow of distillates in atmospheric distillation columns are presented. Finally, in Section 4.2.3 an efficient method for defining manipulated and controlled variables is presented. 4.2.1. Stability in distillation columns Stability in the pressure on the top of the column is achieved once the liquid level and the pressure into the condenser are regulated. In the atmospheric distillation column (Fig. 8), the li- quid level in the condenser is usually controlled by a PID controller LIC-100 which determines the reflux to the top of the column. This control scheme is commonly used in the direct feed split control scheme, when the heat input is limited or must be fixed and distillate is manipulated directly to control the composition profile [33,34]. In addition, the pressure in the condenser is regulated by PIC-100 which establishes the extracted heat from the vessel [35]. These control strategies were also implemented in preflash and stabilizer columns. For the side extractions, single feedback control loops are commonly used. Hence, PID controllers LIC-1000, LIC- 1001 and LIC-1002 controls the liquid level of strippers Kero-S, Diesel-S and AGO-S to maintain the liquid volumetric flow of Ker- osene, Diesel and AGO respectively. Nowadays, this control strat- egy is commonly used in refinery processes. On the other hand, crude oil temperature, processed by the preflash section, must be kept in a range of 290 to 370 °C, allowing the phase change to occur without thermal cracking. In this case, ATM Crude is set at 338 °C using a PID controller TIC-300 which regulates the input thermal load to the furnace. 4.2.2. Distillates flow in the atmospheric column In order to ensure that sufficient material is leaving the column, PID controllers are implemented (Fig. 9). In a typical operating scenario for the distillation process, a PID, FIC-300, regulates the liquid volumetric flow of distilled naphtha, using the input power of a pump P-100 as manipulated variable. In this work, for the remaining side extractions, single feedback control loops are used. D. Sotelo et al. / ISA Transactions 71 (2017) 573–584578
  7. 7. Hence, PID controllers FIC-1000 and FIC-1002 regulate the liquid volumetric flow of kerosene and AGO respectively, while LIC-1001 controls the liquid level of stripper Diesel-S to maintain the liquid volumetric flow of Diesel. The manipulated valves from these controllers are respectively V-100, V-102 and V-101. Looking for a control of distilled Diesel effectively that rejects disturbances, a cascade control strategy is proposed [36]. The FIC-1001 controller generates the reference for the liquid level control system of the stripper Diesel-S, based on the measurement of the liquid volu- metric flow of Diesel. 4.2.3. Distillates quality in the atmospheric column Control strategy definition for improving distillates quality in the atmospheric column begins with the input-output interaction analysis. The applied method consists of: 1. Compute the static gain matrix G (Eq. (8)) containing the relation between manipulated and controlled variables in the atmospheric distillation column. 2. Verify whether or not the process is stable using the Nie- derlinski Index (Eq. (10)), NI 0. In case this condition stability is not satisfied, reduce the number of pairing variables in the G matrix considering those with lower absolute value. Once this is done, verify the stability condition again. 3. Verify whether or not the process is decoupled based on the Conditional Number (Eq. (11)), CN 20. In case this condition is not satisfied, there exists at least one manipulated variable strongly affecting two or more process variables, thus it must be eliminated from G. 4. Compute the RGA matrix (Eq. (14)). 5. Define for each manipulated variable its corresponding controlled variable in order to set the control loops. This should be done relying on the highest degree interaction. Table 6 shows the manipulated and controlled variables in- itially considered to control the distillates quality [24]. Thus, G0 (15) corresponds to the initial static gain matrix of the atmo- spheric distillation column. Columns of the G0 matrix indicate the manipulated variables ( OPj), while rows stand for the controlled variables (Ti). ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ = − − − − ( ) G 0.0179 0.1711 0.0061 0 0 0.0052 0.0109 0.9812 0.0061 0.0712 0 0.0049 0.0100 1.0600 0.0381 0.0198 0 0.0055 0.0076 0.8195 0.0139 0.1348 0 0.0052 0.0003 0 0.0142 0.0761 0 0.0312 0.0014 0 0 0.0075 0 0.0133 15 0 Before we apply RGA criterion, we must select the most re- presentative variables to control destillates Gap/Overlap. Analyzing the columns of G0 matrix, one can observe that the controlled variables associated with OP102 show a magnitude zero. This implies that these interactions can be neglected. On the other hand, it is observed that OP100 is strongly correlated with more than one controlled variable, TK05, TK95 and TD05. Thus, this ma- nipulated variable should be also neglected to preserve the system stability. By observing the rows of G0 matrix, and considering that Kerosene-Diesel Gap/Overlap can be controlled through TK95 or TD05, one can conclude that TK95 must be neglected since OP105 affects TD05 6.25 times more. Then, considering that Diesel-AGO Gap/Overlap can be controlled through TD95 or TA05, one must ne- glect TA05 because OP101 affects TD95 2.44 times more. Fig. 8. Atmospheric column under original control structure. D. Sotelo et al. / ISA Transactions 71 (2017) 573–584 579
  8. 8. The resulting static gain matrix Gp is showed in (16). ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ = − − ( ) G OP OP OP OP 0.0179 0.0061 0 0.0052 0.0100 0.0381 0.0198 0.0055 0.0076 0.0139 0.1348 0.0052 0.0014 0 0.0075 0.0133 T T T T 16 p K D D A 104 105 101 106 05 05 95 95 With =NI 0.8996 and =CN 10.6, the system is stable and de- coupled; this guarantees there is no impact among unpaired variables. RGA is obtained based on Gp, and it is shown in ( )Λ Gp (17). By this matrix, the input-output variables that present the largest interactions are indicated in bold. ⎡ ⎣ ⎢ ⎢ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ ⎥ ⎥ ( )Λ = − − − − − ( ) G 1.0669 1.1595 1.0404 0.9637 OP OP OP OP 0.0937 0 0.0268 0.0967 0.0495 0.0133 0.0026 0.0658 0.0228 0.0272 0 0.0091 T T T T 17 p K D D A 104 105 101 106 05 05 95 95 Attending this pairing criterion and using PID controllers, the proposed atmospheric column control scheme is shown in Fig. 10. Results show that for controlling the Naphta-Kerosene Gap/Over- lap, the TIC-104 controller manipulates the position of the steam supply valve V-104 (OP104) in order to modify ASTM D86 5% Ker- osene temperature (TK05). Similarly, to maintain in range Kerosene-Diesel Gap/Overlap, the TIC-105 controller is designed, the position of the steam supply valve V-105 ( OP105) modifies ASTM D86 5% Diesel temperature (TD05). The TIC-101 controller is added for the control of Diesel-AGO Gap/Overlap. This controller is different from the previous ones as it generates the reference for the proposed cascade control strat- egy (OP101). Thus, the distilled Diesel modifies the ASTM D86 95% Diesel temperature (TD95). Finally, the TIC-106 is implemented to control AGO-Residue Gap/Overlap. The position of the steam supply valve V-106 (OP106) modifies ASTM D86 95% AGO temperature ( TA95). The im- plemented control structure in the entire refinery process is shown in Fig. 11. Fig. 9. Atmospheric column under proposed control structure. Table 6 Manipulated and controlled variables initially considered. Matrix G0 Variables description Column Manipulated variable 1 OP104 Kero-S steam supply 2 OP100 Distilled Kerosene flow 3 OP105 Diesel-S steam supply 4 OP101 Distilled Diesel flow 5 OP102 Distilled AGO flow 6 OP106 AGO-S steam supply Row Controlled temperature 1 TK05 Kerosene ASTM D86 5% 2 TK95 Kerosene ASTM D86 95% 3 TD05 Diesel ASTM D86 5% 4 TD95 Diesel ASTM D86 95% 5 TA05 AGO ASTM D86 5% 6 TA95 AGO ASTM D86 95% D. Sotelo et al. / ISA Transactions 71 (2017) 573–584580
  9. 9. Fig. 10. Atmospheric column final control structure. Fig. 11. Control process flowsheet. D. Sotelo et al. / ISA Transactions 71 (2017) 573–584 581
  10. 10. 5. Simulation modeling and results The purpose of this section is to present simulation modeling and results obtained after implementing the proposed control structure in the atmospheric column, described in Section 4.2. As soon as the atmospheric column process becomes stable, input flow changes are applied to the system in order to disturb it, and evaluate the proposed control structure. The simulated dis- turbances correspond to a typical composition changes that take place in a refinery of Mexico. The objective of the control scheme is to keep temperature and flow variables in specific reference values which guarantee distillate quality. 5.1. Liquid level in the condensers and pressure on the top of columns Table 7 shows pressure reference values required to keep dis- tillation columns stable. Pressure and level variables in condensers remain constant even though input crude oil changes. 5.2. Atmospheric distillation column 5.2.1. Temperature in ATM Crude In spite of disturbances appearing in the process, crude oil temperature remains stable at its reference value of 338.4 °C, with 336.7 °C as minimum, and 340 °C as maximum, and these tem- peratures do not represent a possible thermal cracking. The con- troller's performance under these circumstances is evaluated by Table 7 Pressure values on condensers of actual distillation columns. Reference value Top pressure [ ]kPa Preflash 167 Atmospheric 104 Stabilizer 1 030 Fig. 12. Simulation results. Table 8 Gap/Overlap evaluation to input crude oil changes. Cut Mean Variance Admissible Gap/Overlap Time out of range ⎡⎣ ⎤⎦h Original Proposed Original Proposed Original Proposed Naphtha-Kerosene 27.6 28.1 4 − e 2 6 − e 5 ≥ °16.7 C 0 0 Kerosene-Diesel 10 8.5 0.61 0.44 − °11 C a + °28 C 0 0 Diesel-AGO À0.02 À2.5 2.75 0.73 − ° °20 C a 0 C 18.8 0.8 AGO-Residue À12.8 À28 18.1 1.01 − °30 C a − °10 C 8.29 2.15 D. Sotelo et al. / ISA Transactions 71 (2017) 573–584582
  11. 11. analyzing the minimum and maximum overshoot, with values of 5.8 % and 6.2 %, respectively. 5.2.2. Pressure profile Although the atmospheric column is disturbed by composition changes, the liquid level in the condenser, regulated by the ex- traction of distillate crude oil from the accumulator, remains constant. In addition, the pressure on the top of the column is controlled at its reference value of 104 kPa, by removing heat from the condenser, with 104.1 as maximum, and 103.9 kPa as mini- mum. Consequently, the unit's pressure profile, obtained by measuring pressures in the condenser and side extractions (Ker- osene, Diesel and AGO), remains stable. 5.2.3. Temperature profile Since temperature profile stability in the atmospheric column is one of the main control targets in the present work, temperature sensors are located along the unit at the output of their trays. Temperatures in output products of the column vary according to their extraction point; the lower the extraction point, the higher the temperature value, and this relationship is kept continuously, T T T T TNaphtha Kerosene Diesel AGO Residue. This goal is achieved by regulating input steam flow in the strippers and output distillate flow from the column. By doing this, the temperature profile in the atmospheric column remains constant, preserving product composition. 5.3. Gap/Overlap in distillate products In order to define the distillate quality achieved in consecutive products, Gap/Overlap is evaluated in the presence of disturbances. The original and the proposed control structure's performances are observed once input flow composition changes. The simulation results are shown in Fig. 12. The first three graphs show the sequence of changes of flow and composition of the load composed of a mixture of Olmec, Isthmus and Maya crude oils. The following four graphs show the responses of the Gap/Overlap indexes that were obtained respectively for the cuts of: kerosene-naphtha (KER-NAP), diesel-kerosene (DIE-KER), atmospheric gasoil-diesel (AGO-DIE) and residue-atmospheric gasoil (RES-AGO). As it can be seen in Fig. 12, using the original control strategy in the atmospheric distillation column (Fig. 8), distillates quality varies considerably, hence Gap/Overlap AGO-DIE and RES-AGO are out of the admissible range for a certain period. On the other hand, under the proposed control system (Fig. 10), process variability is reduced even when there are abrupt changes in crude oil com- positions. Then, improvement is observed in: Increasing plant stability. Attenuating Gap/Overlap variability. Reducing Gap/Overlap values out of range. In order to compare the performance of the original and proposed control schemes, Table 8 shows a summary of Gap/Overlap once the system is disturbed. Using the proposed control scheme, the reduction of the variance of the process in terms of the settling time is evident, thus quality crude oil distillates is improved. In addition, crude oil temperature, column pressure and temperature profile are maintained at the right values. 6. Conclusion The simulation results shows that the proposed control strat- egy, using PID controllers, increases the distillates quality in a typical refinery plant. Pressure and temperature profiles in dis- tillation columns are maintained close to real operating values, in spite of changes in the input crude oil composition. Comparing the refinery process model of the present research to previous simulations [37–39], the current virtual plant consists of redesigned distillation columns; i.e., preflash, atmospheric, stabilizer and vacuum. In this paper we propose a cascade control strategy to regulate the Diesel flow. Usually, distillates flows are regulated by single feedback control loops; however, considering the time constants of the process, a cascade structure is implemented, improving the control system performance. In addition, by analyzing interactions in the static gain matrix, we identify which variables should be manipulated in order to control those that affect products quality. Thus, measuring performance as a function of Gap/Overlap index, we propose a control structure that ensures the distillates quality. The proposed solution ensures that cut-oils are most of the time inside of the admissible Gap/Overlap margins face to realistic changes in the input crude oil composition. In future work, the RGA analysis with multiple operating fre- quencies will be applied to petrochemical processes in order to improve the actual control system performance. Acknowledgments This work is supported by the Tecnológico de Monterrey and the National Council for Science and Technology (CONACYT), México. The authors also want to show great thanks to the re- search group of Sensors and Devices of the School of Engineering and Sciences for its support for the development of this work. References [1] Bothamley M. Drilling and production. Oil Gas 2004;102(45):47–55. [2] Lawal SA, Zhang J. Actuator fault monitoring and fault tolerant control in distillation columns. Int J Autom Comput 2016;3(11):865–70. [3] Maghadea D, Patre B. Decentralized pi/pid controllers based on gain and phase margin specifications for tito processes. ISA Trans 2012;51(1):550. [4] Ali A, Majhi S. Pid controller tuning for integrating processes. ISA Trans 2010;49(1):70. [5] Yeroglu C, Tan N. Classical controller design techniques for fractional order case. ISA Trans 2011;50(1):461. [6] Das S, Saha S, Das S, Gupta A. 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