Optimal Product Quality Control in a Hydrocracking ... - ACS Publications

Article pubs.acs.org/IECR

Optimal Product Quality Control in a Hydrocracking Fractionator with Process Simulation Approaches Qingyin Jiang, Yi Cai, Shi Jia, Zhikai Cao,* Binghui Chen, and Hua Zhou* Department of Chemical and Biochemical Engineering, National Engineering Laboratory for Green Chemical Productions of Alcohols−Ethers−Esters, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China ABSTRACT: Steady-state and dynamic simulations of a hydrocracking fractionator are carried out using the process simulator ASPEN PLUS with industrial process data. The main products of fractionation include naphtha, diesel, and tail oil. To obtain more economic benefits, more naphtha must be produced in a refinery because naphtha is more profitable than other products. Thus, optimizing the hydrocracking process is important. Optimization is often challenging to implement because the product quality of naphtha (dry point) is difficult to measure online by sensors. The product quality of naphtha is sampled and analyzed by experimental ASTM D86 curves in the laboratory, so the measured value will be delayed. To solve this problem, a model of naphtha dry point (NDP) is established by artificial neural networks using simulation results. This NDP model is then used as a soft sensor and applied in an optimal quality control strategy. The online soft sensor and optimal quality control strategy are integrated by MATLAB CAPE-OPEN and ASPEN PLUS with an OLE for Process Control server. The increase of naphtha yield is obvious with the use of the proposed method. Several key factors influencing naphtha yield are investigated using the optimal quality control strategy by dynamic simulation, and the results show excellent system robustness.

1. INTRODUCTION Despite intensive efforts having been made to develop and distribute alternative or renewable energy sources, the world energy supplies will still heavily rely on fossil fuel resources in the next several decades. Industrial forecasts predict that oil energy will account for 30% of global energy needs by 2030.1 Unfortunately, while the demand for petroleum products has continued to increase, sweet crude oil reserves are closer to depletion than ever before.1 Hence, oil refineries face several challenges. To satisfy the growing demand for middle distillates, lower quality feeds must be processed as conventional light crudes are depleted. As well, more stringent fuel quality specifications (e.g., sulfur and aromatic contents in diesel) and environmental concerns must be addressed. Hydrocracking technologies have rapidly developed because the process can effectively and cleanly crack low-quality oil into more valuable light products, such as naphtha, diesel, and jet kerosene, that meet the strict demands of environmental laws. Hydrocracking is also capable of processing a wide range of feedstock with different characteristics into a broad range of products. Industrial hydrocracking is normally carried out in two packed-bed catalytic reactors. The first reactor, which decomposes sulfur- and nitrogen-containing compounds, is called the hydrotreater (HT). The liquid fraction from the HT is hydro-isomerized and hydrocracked in the second reactor, called the hydrocracker (HC). Obviously, the HC is the key component of hydrocracking equipment. To obtain optimal operating conditions for commercial HT and HC systems, novel methods including kinetic modeling and optimization approaches have been developed according to the characteristics of the hydrocracking process.2 After passing through the HC, noncondensable gases such as NH3, H2, and H2S are removed, the stream is sent to the fractionator, and products are obtained by separation. If the fractionator is not operated under optimal conditions, optimal HC and HT results are not achieved. © 2015 American Chemical Society

Optimizing fractionators is generally difficult because parameters of product quality, such as naphtha dry point (NDP), pour point, and diesel flash point, cannot be measured online. In industrial processes, the product quality is analyzed off-line by laboratory instruments. However, analytical results may suffer from long measurement delays, and online control or optimization schemes cannot be implemented. Hence, conservative operations are undertaken to ensure that product quality exceeds minimal requirements and that the equipment runs at optimum levels. Online analyzers (e.g., gas chromatographs) can also be used to obtain exact quality index data from products.3 Relative densities, distillation curves, and chemical compositions can be estimated by near-infrared spectroscopy.4 However, the poor reliability of these instruments and long measurement delays of online analyzers render them unsuitable for most industrial processes.3 The problems described above can be resolved by using soft sensors. Soft sensors have faster response speeds than online analyzers because real-time process data are used as inputs in the models.5 Given the uncertainty, complexity, and nonlinearity of industrial processes, mechanical models are often unavailable. Therefore, data-driven empirical models are useful alternatives.6 An online calibrating software model of a fluid catalytic cracking separating column was previously established using linear regression based on test data obtained from field observations.7 Nonlinear methods are often used to build soft sensors. Partial least-squares regression8 and fuzzy models9−11 have been adopted as soft sensor models to measure the properties of petroleum products. Considering their powerful Received: Revised: Accepted: Published: 4805

December 30, 2014 March 26, 2015 April 13, 2015 April 13, 2015 DOI: 10.1021/ie5050617 Ind. Eng. Chem. Res. 2015, 54, 4805−4814

Article

Industrial & Engineering Chemistry Research

Figure 1. Brief process flow diagram of hydrocracking unit main fractionation column.

function approximation12 and self-study abilities, artificial neural networks (ANNs) have also been used to establish soft sensor models. Multilayer perceptron and radial basis function networks feature high accuracy for predicting the properties and key compositions of petroleum products.3,13−15 Back propagation (BP) networks have been used to establish soft sensor models because of their usability, stringency, and powerful nonlinear computing ability.16−18 Several researchers have integrated different methods to build relevant prediction models.19,20 Steps to develop soft sensors for determining petroleum product properties have been undertaken. The dry point of gasoline and diesel are modeled using back-propagation (BP) networks.21,22 To optimize an industrial hydrocracking process, a soft sensor for the flash point of kerosene is established, and the soft sensor is based on lab assay analysis.23 However, lab assay analysis will be delayed for online optimization. Furthermore, steady-state optimization is implemented in the literature.23 To investigate the control strategy for product quality, steady-state and dynamic simulation of crude oil distillation are provided by Haydary.24 In ref 24, the controlled variable was the temperature of 95% of the ASTM D86 curve. In practice, it is difficult to measure the temperature of 95% of the ASTM D86 curve online. To optimize an industrial hydrocracking process, this study identifies the optimal quality control applications of a novel soft sensor and evaluates the potential of soft sensor development in terms of product quality according to the simulation results. In ref 23, the soft sensor is modeled by 95% of the boiling points of kerosene and diesel, therefore, 95% of the boiling points of kerosene and diesel should be obtained by ASTM D86 curves in the laboratory, so the results would be delayed. The novel soft sensor in this paper is constructed by operating parameters of the hydrocracking fractionator. The rest of this paper is structured as follows: A detailed process description is introduced in section 2, and a discussion on steady-state and dynamic simulations of a hydrocracking

fractionator unit is provided in section 3. In section 4, a soft sensor is established and optimization strategies are introduced by integration of this soft sensor; several influencing factors are considered to test the performance of optimal control strategy. Finally, the results and contributions of this work are summarized in section 5.

2. PROCESS DESCRIPTION The main fractionation column of a hydrocracking unit in SINOPEC with dimensions of Φ5400/3000 × 24/16 × 40464 mm is investigated. A brief process flow diagram is shown in Figure 1. The fractionator comprises a feed-heated furnace (F02001), a fractionation column (C02002), and a stripper (C02003) as the main equipment. The fractionation column is integrated with an overhead condenser and reflux accumulator. A reboiler is not required at the bottom of the column. A pump-around in the middle section of the column is also provided. The feedstock of the fractionator is obtained from the HC after removal of impurities. The feedstock is first heated to an appropriate temperature in F02001 and then sent to the fractionation column C02002. The feedstock of the stripper (C02003) is taken out from the middle section of the fractionation column C02002, and the vapor from the top of C02003 is sent back to C02002. At the bottom of C02002 and C02003, the stream is reboiled by the overheated steam. The liquid stream is extracted from C02002 by a pump as hot stream for Ex1 in the pump-around and then used as the heat source of the naphtha distillation column. After heat exchange, the stream is sent back to C02002. After separation by the fractionator, three products are obtained, namely, naphtha, diesel, and tail oil. Relevant stream information and stage information in the main fractionation column C02002 is presented in Table 1. In Figure 1, TIC, FIC, and LIC denote the temperature controller, flow rate controller, and liquid level controller, respectively. In industrial applications, two cascade control schemes are employed: (1) the reflux controller (FIC2202) of 4806

DOI: 10.1021/ie5050617 Ind. Eng. Chem. Res. 2015, 54, 4805−4814

Article


fractionation column and stripper is set to 70%. In this study, the overall efficiency is not a direct input of the model; instead, it sets the number of stages in the RadFrac block. The stage positions and numbers of stages are presented in Table 1. The properties of the products (i.e., naphtha, diesel, and tail oil) may be obtained by the Engler curve (ASTM) and specific density. The ASTM data of the products were directly obtained from industrial refineries and are listed in Table 3; the predicted

Table 1. Related Stage Position and Stage Number of Column and Stripper

overhead return stage for stripper liquid draw stage for stripper return stage for pump-around draw stage for pump-around feed stage stage number of main column stage number of stripper

actual location

location after efficiency revision

16

11

19 19 21 37 46 10

13 13 15 27 32 7

Table 3. Property Data of Premix Feed

C02002 and the top temperature controller (TIC2207) of C02002 and (2) the tail oil flow controller (FIC2301) of C02002 and the bottom liquid level controller (LIC2201) of C02002. As a fractionator product, tail oil is used in the ethylene pyrolyzer unit; the flow rate of tail oil is determined by the demand of the pyrolyzer unit. Diesel is usually utilized as fuel in this setup. Naphtha may be separated into light and heavy naphtha in the naphtha distillation column; among the fractionator products, light and heavy naphtha are the most important because they are highly profitable. In industrial applications, the end (final) boiling point of ASTM D86 distillation, also called the dry point, is used as a quality index for naphtha. In general, NDP, which is analyzed off-line in the laboratory, must be lower than 174 °C to indicate product quality. Analysis results are usually used only as a reference for operators because the results are often delayed; thus, optimal quality control cannot be implemented. The three-year industrial data presented in Table 2 reflect this issue.

count

percent

[131.5, 150] (150, 155] (155, 160] (160, 165] (165, 170] (170, 174] (174, 178.1]

8 3 48 118 148 50 15

2.05% 0.77% 12.31% 30.26% 37.95% 12.82% 3.85%

naphtha

diesel

tail oil

90.2

182.2

107.8

195.4

123.8

229.4

148.9

279.2 289.2 298.6 821.1

256 361.2 372.2 384.2 397.8 406.6 423.6 435.2 451.4 466.8 493.2 519.2 543 840.3

161.2 759.5

Table 4. True Boiling Point of Products for Steady-State Simulation true boiling point (TBP)/°C

Table 2. Distribution of NDP by the Analysis of Industrial Data during 2010−2012 range of NDP (°C)

vol % distilled 0 (°C) 5 (°C) 10 (°C) 20 (°C) 30 (°C) 40 (°C) 50 (°C) 60 (°C) 70 (°C) 80 (°C) 90 (°C) 95 (°C) 100 (°C) density (20°C, kg/m3)

vol % distilled

naphtha

diesel

tail oil

0 5 10 30 50 70 90 95 100

62.54442 80.60517 91.79056 112.4522 122.6568 135.5396 156.6461 164.8533 171.5212

149.8795 164.664 173.9278 204.8171 230.3271 258.8688 294.0971 304.6304 313.1239

226.7777 313.2985 364.3503 406.6034 442.8124 478.6147 528.3758 556.2802 580.2413

true boiling points of the products are presented in Table 4. Information on feedstock properties is lacking because the feedstock of the fractionator is obtained from the HC and cannot be directly measured. According to the concept of conservation of material balance, the feedstock can be described as a mixture of naphtha, diesel, and tail oil. Hence, the properties of the feedstock provided in Table 4 were calculated from the properties of the products. Separation studies considering every individual component may complicate simulations because of the large number of hydrocarbons involved in the process. To simplify this problem, a selected lumping technique is applied to divide the feedstock and products into pseudocomponents. On the basis of the selected lumping technique, 33 pseudocomponents are proposed as products and feedstock in this study. To obtain the properties of these pseudocomponents, molecular weights, critical temperatures, critical pressures, and so on are calculated from the ASTM data using ASPEN PLUS and listed in Table 5. Other operational conditions of the fractionation process are listed in Table 6.

Qualified products of NDP are mainly distributed in between 160 and 170 °C and about 3% of the product are unqualified from Table 2.

3. STEADY-STATE AND DYNAMIC FRACTIONATOR SIMULATIONS The commercial simulation platform ASPEN PLUS is adopted as the steady-state fractionator simulation tool, and ASPEN DYNAMICS is used as the dynamic fractionator simulation tool. The PetroFrac block is selected as the fractionation model because this block is a rigorous model designed for simulating complex vapor liquid fractionation operations in the petroleum refining industry.25 In this block, any number of pump-arounds and strippers can be simulated. BK10 is used as the property method in the simulation; the k value of this method is calculated by the Braun-K 10 method.26 In industrial applications, the efficiency of actual trays is different from that of theoretical plates. To solve this problem, the overall efficiency of the 4807


Article

Industrial & Engineering Chemistry Research Table 5. Properties of Pseudocomponents for Steady-State Simulation pseudocomponents

TBP (°C)

API

specific gravity

molecular weight

PC63C PC73C PC87C PC101C PC116C PC128C PC142C PC156C PC169C PC184C PC198C PC211C PC225C PC239C PC253C PC267C PC281C PC295C PC309C PC322C PC336C PC350C PC367C PC378C PC392C PC406C PC419C PC442C PC468C PC495C PC522C PC552C PC576C

62.57866 72.94347 86.98299 100.7589 115.5547 127.9493 141.5116 155.7735 168.8873 183.5916 197.7502 211.4905 225.0512 239.2145 252.9927 266.7918 280.7189 294.6595 308.5034 321.9196 336.2617 350.3547 366.8891 378.2309 391.8298 406.473 418.9885 441.9138 467.8754 494.7986 522.0194 551.7736 575.8305

65.36085 63.3757 60.8097 58.41833 55.97739 54.02603 51.98091 50.03938 48.3937 46.62559 44.82216 43.41697 42.2083 40.6821 39.24755 37.9822 36.84148 35.82912 35.01594 47.19909 45.78609 44.44018 42.91189 41.89368 40.70355 39.45779 38.42108 36.5854 34.59908 32.63488 30.74016 28.76561 27.23731

0.718782 0.726104 0.735792 0.745057 0.754758 0.762696 0.771197 0.779445 0.786576 0.794383 0.802508 0.808955 0.814584 0.821804 0.828709 0.834896 0.840553 0.845639 0.849769 0.791834 0.798145 0.804251 0.811298 0.816062 0.821702 0.82769 0.83274 0.841834 0.851901 0.862096 0.872164 0.882909 0.89141

80.36109 86.24473 94.23772 102.1036 110.6096 117.8223 125.8542 134.5713 142.8555 152.4937 162.0297 171.7944 181.9056 192.7139 203.6499 215.1098 227.184 239.7881 252.8802 278.474 293.3823 308.5043 326.8195 339.7257 355.5513 372.9991 388.2281 416.8256 450.2015 485.7794 522.5941 563.6236 597.2707

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

Table 6. Operation Data of Main Fractionator Column flow rate (kg/h)

P MPa feedstock steam of fractionator steam of stripper flow of pumparound pressure at top of column temperature of middle reflux

temp (°C) naphtha 43607.1

diesel 66166.6 1199.3 900.1 165618.5

tail oil 101284.2

351.1 386.7 386.7

0.1175 161.74

Simulation results are presented in Figures 2 and 3 and Table 7. In Figures 2 and 3, industrial data are described by a solid line and simulation results are depicted by a dashed line. The solid lines in Figures 2 and 3 agree very well with the dashed lines. Simulated and industrially measured values are listed in Table 7. All of the simulated values are similar to the industrially measured values, which indicate that the simulation results resemble the industrial process fairly well. The effectiveness of the simulation is verified by these results. Dynamic simulation can be implemented based on steadystate simulations. Prior to dynamic simulation, several column parameters must be added to the steady-state simulation

Figure 2. Comparison between real (solid line) and simulated (dashed line) flow rate of the pseudocomponents of three products.

models in ASPEN PLUS. Steady-state modeling can then be transformed into dynamic simulation modeling as in ref 25. During dynamic simulation, the optimal scheme of the process can be investigated. The optimized dynamic simulation scheme is presented in Figures 4 and 5; proportional-integral-derivative controller (PID) controllers are subsequently chosen. 4808


Article


Table 7. Comparison between Industrial and Simulated Data of Fractionator State Data reflux flow rate (kg/h) temp (°C) overhead condenser top of fractionator column vapor liquid bottom of fractionation column stripper’s top stripper’s bottom

measured

simulated

106096.40

109439.73

55.93

56.20

116.85 135.81 307.95

131.85

192.49 176.62

191.28 176.09

329.46

340°C ≤ Tfeed ≤ 365°C Figure 3. Comparison between real (solid line) and simulated (dashed line) of the Engler distillation curve of three products.

40°C ≤ Tcondenser ≤ 65°C

50000kg/h ≤ Fmpump − around ≤ 300000kg/h Fmreflux ≤ 200000kg/h Fmdiesel ≤ 120000kg/h Figure 4. Diagram of ASPEN DYNAMICS interaction with MATLAB via an OPC server.

where Fmnaphtha, Fmpump‑around, Fmreflux, and Fmdiesel are the flow rates of the naphtha product, pump-around, overhead reflux, and diesel product, respectively. Here, Tcondenser is the temperature of the overhead condenser. During hydrocracking, naphtha is obtained at the top of the column because it is a light component. During separation of the components of the same feedstock, if NDP is higher, more naphtha is obtained. Thus, the optimization objective can be transformed to control the optimal NDP. To achieve this aim, an online measurement approach is needed. During dynamic simulation at the ASPEN platform, NDP can be obtained from pseudocomponent information. However, measuring NDP

4. OPTIMIZED CONTROL OF NAPHTHA FLOW RATE Naphtha is separated into light and heavy naphtha in the naphtha distillation column. The optimization strategy for naphtha is investigated, and the optimization objective is Max Fmnaphtha To satisfy the design specifications or physical operating limits, certain constraints are set:

NDP ≤ 174°C

Figure 5. Diagram of novel optimal control strategy and how it works. 4809


Article


To establish the NDP soft-measure model based on the BP network, a series of data for training and testing must be provided. To ensure that the model can be applied to different feeding conditions, different feedstocks are simulated in ASPEN DYNAMICS. Analysis results of the process show that certain variables affect NDP and that these variables include Tstage1 (temperature of the top of the fractionation column), Tcondenser, Tfeed, Tbottom, and Pstage1. NDP is also easily affected by the feedstock properties. However, the properties of feedstock can be directly measured online. The properties of feedstock (e.g., dry point or 95% boiling point) should be analyzed in the laboratory. Thus, feedstock properties are not included in NDP and NDP can be described as follows:

Table 8. Factor-Level Table of Fractionator for Simulation temperature (°C)

kg/h

level

feed

condenser

stage 1

pumparound return

1 2 3 4 5 6 7 8 9

346 348 350 350.5 351 351.5 352 356 360

52 54 55 56 57 58 59 60 62

130 133 136 139 141 143 144 145 146

159 159.5 160 160.5 161 161.5 162 162.5 163.5

pumparound flow

diesel yield

120000 140000 150000 160000 165000 170000 180000 190000 200000

40000 60000 65000 70000 75000 80000 85000 90000 100000

NDP = f (Tfeed , Tcondenser , Tstage1, Tbottom , Pstage1)

To obtain more basic data with different factors, an orthogonal simulation experiment with six factors and nine levels is designed. The factor-level table of the fractionator for simulation is listed in Table 8. Another eight steady state and dynamic simulations with eight different sets of industrial data are built to obtain more experiment data, which will be used for NDP predicted model’s establishment, by using the orthogonal experiment. Then 9·81 sets of experiment data are obtained. A part of the data of six dynamic simulations is used for modeling of the NDP by the BP network training, and the rest of these simulations’ data are used for testing group 1 to test the model. The data of the remainder of the simulations are used for testing group 2. BP network training and model testing are carried out using the MATLAB toolbox. To validate the modeling results, the test data are divided into two groups. The training and testing results of the BP model for NDP are

Figure 6. Control block diagram of novel optimal control strategy.

online is impossible in most industrial processes. Hence, an NDP soft sensor must be established. 4.1. Establishment of the NDP Soft-Measure Model. Error BP is the most popular learning algorithm multilayer feed-forward ANN because of its simplicity, power to extract useful information from examples, and capability of storing information implicitly in connecting links in the form of weights.27 In this paper, a single hidden BP network layer is selected to establish the soft-measure model of NDP.

Table 9. Errora Distributions and Maximum and Minimum Error of NDP Prediction Model training first testing group second testing group a

error

0 ≤ |e| < 1

1 ≤ |e| < 2

2 ≤ |e| < 3

3 ≤ |e| < 4

4 ≤ |e|

count percent count percent count percent

181 60.33% 116 62.37% 122 50.21%

70 23.33% 50 26.88% 72 29.63%

42 14.00% 19 10.22% 35 14.40%

7 2.33% 1 0.54% 13 5.35%

0 0.00% 0 0 1 0.41%

max error

min error

3.28

−3.45

3.63

−2.53

4.38

−3.53

Error: e = true value − predicted value.

Figure 7. Dynamic behaviors of the processes with novel optimal control strategy. 4810


Article


points, and then the flash points of kerosene and diesel are correlated using 95% of the boiling points of kerosene and diesel. In practice, 95% of the boiling points of kerosene and diesel are analyzed by ASTM D86 curves in the laboratory. 4.2. NDP Optimization Control. NDP prediction and dynamic simulation of the hydrocracking process are carried out in MATLAB and ASPEN DYNAMCS, respectively. An OLE for Process Control (OPC) server is used as the medium for data exchange between the software. The data exchange strategy is shown in Figure 4. In industrial processes, NDP is indirectly controlled by the temperature at the top of the fractionation column. In the optimal control strategy for the fractionator, an NDP soft sensor can be used, and the NDP value is considered the input

presented in Table 9; here, the predicted NDP of the model is nearly identical to the real value. The provided method for a soft sensor is different with the method of literature.23 In literature,23 the soft sensor is constructed by 95% of the boiling Table 10. Data Acquired before and after the Optimal Control Strategy Is Carried Out

yield of naphtha (kg/h) real NDP (°C) soft NDP (°C) NDP error

before NDP control

after NDP control

variation

changing ratio

43686.2

46030.4

2344.2

+5.37%

167.73 167.21 0.5

170.70 171.00 −0.3

2.97 3.79

Figure 8. Comparisons of dynamic responses of the processes under disturbance. Simulation results of dynamic responses (a) without NDPC and (b) with NDPC. 4811


Article

Industrial & Engineering Chemistry Research Table 11. Results of Step Response for NDPC original value overhead condenser temp

55.93 °C

diesel flow rate

66166.6 kg/h

pump-around flow rate

165618.5 kg/h

feed temp

351.08 °C

variation value

real NDP before change (°C)

real NDP after change (°C)

variational range of real NDP (°C)

variational range of soft NDP (°C)

yield increment of naphtha (kg/h)

+4 °C −4 °C +20000 kg/h −20000 kg/h +40000 kg/h −40000 kg/h +10 °C −10 °C

170.70 170.70 170.70 170.70 170.70 170.70 170.70 170.70

170.64 170.80 170.77 170.68 170.79 170.63 170.64 170.78

170.60−170.70 170.70−170.85 170.68−170.88 170.59−170.72 170.69−170.87 170.53−170.72 170.60−170.70 170.68−170.78

170.72−171.03 170.97−171.20 170.92−171.27 170.51−171.06 170.87−171.48 169.86−171.28 170.68−171.03 170.96−171.30

−127.9 211.5 −87.3 323.5 75.1 −22.1 −128.4 −101.0

Table 12. Results of NDPC Antidisturbance from Feedstock Rate Change

feed flow rate

original value (kg/h)

changing ratio

real NDP before change (°C)

real NDP after change (°C)




211057

+10% −10%

170.70 170.70

170.67 170.83

170.48−170.70 170.70−170.83

170.07−171.08 170.85−171.68

4436.6 −4273.9

Table 13. Results of NDPC Antidisturbance from Feedstock Property Change composition of feedstock

original feedstock Scheme 1 Scheme 2 Scheme 3 Scheme 4

naphtha (kg/h)

diesel (kg/h)

tail oil (kg/h)

real NDP (°C)




43607 53607 33607 53607 33607

66166 61166 71166 56166 76166

101284 96284 116284 101284 101284

170.70 170.94 170.46 171.00 170.34

170.49−170.99 170.46−171.09 170.54−171.01 170.34−170.99

169.26−171.25 170.53−171.85 169.48−171.29 170.46−171.76

10316.4 −10179.3 10368.5 −10207.7

signal. The novel optimal control strategy is shown in Figures 5 and 6. To determine a quality product, the NDP must be less than 174 °C. Simulation results show that errors in predicted values of the soft sensor are distributed between −3.5 and 4.4 °C. Thus, the set point of NDPC is 171 °C and PID controller is adopted in NDPC. In Figure 7, SP denotes the set point of the controller and PV is the process value. The soft-measured value of NDP is described as sensor, and stage 1 temperature is the temperature at the top of the main column. SP for NDP is the set point of the naphtha quality controller, PV for NDP is the real NDP that is detected from the virtual NDP sensor which is set at the naphtha stream of dynamic simulation, and the sensor of NDP is calculated by a soft sensor. The values of PID are 0.5, 1.5, and 0, respectively. Rapid response can be achieved by the optimal control strategy in Figure 7. In this optimal control strategy, about 12000 kg/h flow should be increased instantly for naphtha. The duty of the pump can be satisfied in industry because flow may be increased to 3.33 kg/sec instantly. To compare the effectiveness of this strategy with industrial data, the system is simulated as 1 h industrial processes. The system is then simulated under the optimal control strategy for 1 h, and the NDPC SP is set to 171 °C. Comparison results are listed in Table 10. The naphtha flow rate increases to 2344.2 kg/h under the NDP optimal control strategy. 4.3. Stability of the Optimal Control Strategy with Different Disturbance. The fractionator features many controllers that promote normal operation of the system. These controllers include TIC2208, FIC2401, FIC2303, TIC2106, and TIC2207, as shown in Figure 5. Different control loops may interact with one another, and the NDPC performance may be affected by the flow rate and feedstock properties. To test the

robustness of the optimal control strategy, certain variations are considered. To illustrate the results, a step response in TIC2008 is adopted as an example. Simulation results of the system without NDPC are presented in Figure 8a, and corresponding results of the system with NDPC are presented Figures 8b. The figures clearly show that the system with NDPC is more robust than that without NDPC. Other comparison results of the effect of different control loops are listed in Table 11. Despite variations in the temperature of the condenser, the naphtha quality remains stable. In all the experiments, soft NDP varied in more wide ranges than real NDPs, meaning that soft NDP is more easily affected by disturbance. But all of the soft NDP can return to the set point quickly after the disturbance appeared, just as Figure 8b shows. As the process value of NDPC, soft NDP can be controlled stably; also the quality of naphtha can meet the requirement. The yields of naphtha in all the experiments have little changes because the disturbance predicted errors after are not the same as before. Variations in flow rate and feedstock properties are investigated further, and the results are presented in Tables 12 and 13. The corresponding adjustment, soft sensor, and product quality curves are illustrated in Figure 9. Despite variations in the system, a stable NDP can be obtained by integrating optimal control from the simulation results. When the feedstock property is varied, the variation range of the predicted NDP is less than 2 °C. Naphtha quality (NDP) is generally less than 1 °C. In Figure 9, the setting point of NDP is 171 °C. Product quality (NDP) is depicted as PV of NDP, and the optimal NDPC output is PV of stage 1 temperature in Figure 9. Results demonstrate that the optimal control strategy can address fluctuations in operating mode. From the above results, the provided product quality control strategy can be implemented in industrial hydrocracking processes as dynamic optimization. 4812


Article


Figure 9. Dynamic behavior of NDP and temperature of fractionation column top.

■

5. CONCLUSION

AUTHOR INFORMATION

Corresponding Authors

For dynamic optimization and control purposes, an integrated soft sensor control strategy is systematically constructed for an industrial hydrocracking fractionator. To obtain a soft sensor, a steady-state simulation of a hydrocracking fractionator is established in ASPEN PLUS with industrial data. The simulation results are in good agreement with actual process results. Dynamic simulation is implemented in ASPEN DYNAMICS based on the steady-state simulation, and a model of the product quality of naphtha (NDP) is built by ANNs in MATLAB using dynamic simulation results. During application of NDP, an optimal control strategy is constructed for the fractionator. NDP is an empirical model and it correlates the dry points with operating parameters of the fractionator. As such it is used to predict the effect of the fractionator operating conditions on the distillation product distribution. The online soft sensor and optimal control strategy are integrated by MATLAB CAPE-OPEN and ASPEN PLUS with an OPC server. The method of modeling the soft sensor has appeared earlier but the provided strategy of dynamic optimization with a soft sensor is new. In addition, the variables of the soft sensor are different with a published soft sensor.23 In ref 23, the soft sensor relies on 95% of the boiling points of kerosene and diesel. Since 95% of the boiling points of kerosene and diesel is obtained by ASTM D86 curves in the laboratory, 95% of the boiling points of kerosene and diesel will be delayed for dynamic optimization. In the novel soft sensor, operating parameters of hydrocracking fractionator are adopted so the novel soft sensor can be implemented online. Using the proposed method, the yield of naphtha obviously increases by simulation results. The robustness of the optimal control system is investigated by step response, and results indicate that this strategy can adequately address fluctuations in operating mode. Through this study, the potential benefits of implementing the dynamic optimization for the hydrocracking fractionator have been obtained.

*E-mail: [email protected]. *E-mail: [email protected]. Tel.: +86-592-2180214. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors would like to thank the National Natural Science Foundation of China (No. 21106120 and No. 61174093) for financial support. The industrial data are provided by a plant of SINOPEC which is also acknowledged.

■

REFERENCES

(1) Paszkiewicz, L.. Extra-Heavy Oils in the World Energy Supply, Total, Paris, France, 2012. (2) Zhou, H.; Lu, J.; Cao, Z.; Shi, J.; Pan, M.; Li, W.; Jiang, Q. Modeling and optimization of an industrial hydrocracking unit to improve the yield of diesel or kerosene. Fuel 2011, 90 (12), 3521− 3530. (3) Ujevic, Z.; Mohler, I.; Bolf, N. Soft sensors for splitter product property estimation in CDU. Chem. Eng. Commun. 2011, 198 (12), 1566−1578. (4) Reboucas, M. V.; Santos, E. C.; Vieira, F. S. Feasibility of quality process control of a naphtha fractioning unit based on near-infrared spectroscopic prediction of physical and chemical properties of medium naphtha streams. Vib. Spectrosc. 2007, 44 (1), 187−191. (5) Bolf, N.; Ivandic, M.; Galinec, G. In Soft Sensors for Diesel Fuel Property Estimation; Petton, R. J., Maquin, D. Eds.; 16th Mediterranean Conference on Control and Automation. IEEE: 2008; p 1804. (6) Luo, J. X.; Shao, H. H. Developing soft sensors using hybrid soft computing methodology: A neurofuzzy system based on rough set theory and genetic algorithms. Soft Comput. 2006, 10 (1), 54−60. (7) Zhang, K. J.; Shen, Y. M.; Yu, J. S. The application of soft measurement in inference control of dry point of rude gasoline in separating column. Process Autom. Instrum. (China) 1999, 11, 20−22. (8) Yan, X. F.; Yu, J.; Qian, F. Development of naphtha dry point soft sensor by adaptive partial least square regression. J. Chem. Ind. Eng. (China) 2005, 08, 1511−1515. 4813


Article

Industrial & Engineering Chemistry Research (9) Macias-Hernandez, J. J.; Angelov, P.; Zhou, X. W.; Soft sensor for predicting crude oil distillation side streams using evolving TakagiSugeno fuzzy models. In 2007 IEEE International Conference on Systems, Man and Cybernetics; IEEE: New York, 2007; Vols 1−8, pp 2156−2161. (10) Macias, J. J.; Angelov, P.; Zhou, X. W. A Method for Predicting Quality of the Crude Oil Distillation; IEEE: New York, 2006; p 214− 220. (11) Sui, Y. Y., Fuzzy-neural networks based on subtractive clustering and its applications in soft sensor. In Measurement Technology and Its Application; Yarlagadda, P., Kim, Y. H., Eds.; Trans Tech Publications Ltd: Stafa-Zurich, 2013; Vol. 239−240, Parts 1, 2, pp 1516−1521. (12) Hagan, M. T.; Demuth, H. B. In Neural networks for control. Proceedings of the 1999 American Control Conference; IEEE: 1999; p 1642−1656. (13) Rogina, A.; Sisko, I.; Mohler, I.; Ujevic, Z.; Bolf, N. Soft sensor for continuous product quality estimation (in crude distillation unit). Chem. Eng. Res. Des. 2011, 89 (10A), 2070−2077. (14) Fortuna, L.; Graziani, S.; Xibilia, M. G. Soft sensors for product quality monitoring in debutanizer distillation columns. Control Eng. Pract. 2005, 13 (4), 499−508. (15) Bolf, N.; Galinec, G.; Baksa, T. Development of soft sensor for diesel fuel quality estimation. Chem. Eng. Technol. 2010, 33 (3), 405− 413. (16) Fortuna, L.; Rizzo, A.; Sinatra, M.; Xibilia, M. G. Soft analyzers for a sulfur recovery unit. Control Eng. Pract. 2003, 11 (12), 1491− 1500. (17) Motlaghi, S.; Jalali, F.; Ahmadabadi, M. N. An expert system design for a crude oil distillation column with the neural networks model and the process optimization using genetic algorithm framework. Expert Syst. Appl. 2008, 35 (4), 1540−1545. (18) Wang, R., Process simulation, optimization, and property control of FCCU main fractionator. Thesis of Master Degree, China University of Petroleum, 2011. (19) Liu, A. L.; Zhong, W.; Yu, J. S.; Weng, S. X.; Wu, Y. Q.; Liang, W. M. The soft sensor for dry point of jet fuel in hydrocracking fractionator. Autom. Petrochem. Ind. 2000, 02, 39−41. (20) Yan, X. F. Hybrid artificial neural network based on BP-PLSR and its application in development of soft sensors. Chemom. Intell. Lab. Syst. 2010, 103 (2), 152−159. (21) Wadi, I. Neural network model predicts naphtha cut point. Oil Gas J. 1996, 94 (48), 67−70. (22) Wang, R. Process simulation, Optimization, and Property control of FCCU main fractionator. Thesis of Master Degree, China University of Petroleum (East China), 2011 (23) Sildir, H.; Arkun, Y.; Cakal, B.; Gokce, D.; Kuzu, E. Plant-wide hierarchical optimization and control of an industrial hydrocracking process. J. Process Control 2013, 23, 1229−1240. (24) Haydary, J.; Pavlik, T. Steady-state and dynamic simulation of crude oil distillation using ASPEN PLUS and ASPEN DYNAMICS. Pet. Coal 2009, 51 (2), 100−109. (25) Luyben, W. L. Distillation Design and Control Using Aspen Simulation; John Wiley & Sons: 2013. (26) Tarighaleslami, A. H.; Omidkhah, M. R.; Ghannadzadeh, A.; Hesas, R. H. Thermodynamic evaluation of distillation columns using exergy loss profiles: A case study on the crude oil atmospheric distillation column. Clean Technol. Environ. Policy 2012, 14 (3), 381− 387. (27) Sarkar, D. Methods to speed up error back-propagation learning algorithm. ACM Comput. Surveys (CSUR) 1995, 27 (4), 519−544.

4814


Optimal Product Quality Control in a Hydrocracking ... - ACS Publications

Recommend Documents