ARTICLE pubs.acs.org/IECR
Validation of Dividing-Wall Columns Based on Experimental Data and Dynamic Simulations: Pilot-Plant and Production-Scale Columns Gerit Niggemann*,† and Georg Fieg Institute of Process and Plant Engineering, Hamburg University of Technology, Schwarzenbergstraße 95c, 21073 Hamburg, Germany ABSTRACT: Dividing-wall columns are a promising separation device in terms of energy efficiency because they are suitable for the complete separation of ternary mixtures. The increased application of this progressive technology is based on reduced capital and operating costs. Although several articles have already been published in the field of dividing-wall columns, reliable knowledge is still missing concerning the dynamics of this separation process. To bridge this gap, experimental data on the process dynamics of dividing-wall columns around a steady-state operating point are presented herein, together with extensive model validation of the transient behavior of dividing-wall columns. Furthermore, the process model was successfully compared with experimental data from a production-scale dividing-wall column, which emphasizes the validity of the model. Thus, the validated process model serves as a virtual plant and can be used to develop and test future process control structures. Moreover, the knowledge gained from this study will help to increase the acceptance of dividing-wall columns in industry.
1. INTRODUCTION Distillation is still the most common thermal separation technique used in the chemical industry. For example, distillation columns accounted for about 13% of the total primary industrial energy consumption in Great Britain.1 Growing markets, increasing competition, and increasing energy costs are slowly increasing the replacement of energy-intensive conventional distillations column with thermally coupled distillation processes. The development of efficient thermal processes for separating multicomponent mixtures into products with high purities resulted in dividing-wall columns, which represent a promising alternative to sequentially linked distillation columns. Because dividing-wall columns are manufactured as one column and feature higher thermodynamic efficiency, the investment and operating costs can be reduced by up to 30%.2,3 Several articles about dividing-wall columns have been published in the open literature. Initial studies considered the design of dividing-wall columns to determine the minimum energy demand by means of modeling and optimization studies.48 In addition, several studies have focused on theoretical examinations aiming at both energy-efficient steady-state operation and control of dividing-wall columns.912 Further theoretical studies addressed the detailed development of process control concepts and examined disturbance reactions and set-point tracking.1316 Comparisons of dividing-wall columns and conventional distillation column sequences, heat-integrated column systems, and other thermally coupled column setups with respect to energy consumption and controllability indices have also been published, where the behavior in closed control loops was analyzed.1723 Current publications make clear that dividingwall columns are state-of-the-art technology and contribute to process intensification in the chemical industry.2426 Most recently, Dejanovic et al.27,28 introduced a dimensioning method for packed dividing-wall columns27 that was used to evaluate the technical feasibility and industrial viability of four-product columns,28 which require implementation of multiple partition r 2011 American Chemical Society
walls to maximize the efficiency and sustainability of the operation. Only a few publications have reported experimental studies of dividing-wall columns, even though only experiments can guarantee an advanced understanding of the process. The first experimental examinations were published by Abdul Mutalib et al.,29 who studied the separation of a ternary mixture of methanol, isopropanol, and n-butanol. In another study, Adrian et al.30 compared a decentralized temperature control concept with a model predictive controller of a dividing-wall column. Strandberg and Skogestad31 reported the first explorations of a laboratoryscale column featuring two side streams for the separation of a four-component mixture consisting of methanol, ethanol, n-propanol, and n-butanol. Sander et al.32 examined the heterogeneously catalyzed hydrolysis of methylacetate in a reactive dividing-wall column. Niggemann et al.33 realized the separation of a ternary mixture of hexanol, octanol, and decanol into products with purities of 99 wt %. The presented column was run at different steady-state operating points, and the gained experimental data were the basis for an extensive model validation. Hiller et al.34 used the multiplicity of experiments to successfully validate a nonequilibrium-stage model. Buck et al.35 reported the systematic development and testing of a decentralized temperature control concept based on simulation and experimental studies. This overview of the studies performed on the process dynamics of dividing-wall columns clarifies that the previous examinations were theoretical in nature and, in most cases, were directly aimed at developing, testing, and evaluating process control concepts (see, e.g., Wolff and Skogestad,13 Serra et al.,10 Segovia-Hernandez et al.,22 Niggemann and Fieg,36 and Ling and Luyben15). The main reason for this situation is that such studies Received: July 23, 2011 Accepted: November 22, 2011 Revised: November 5, 2011 Published: November 22, 2011 931
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can be performed efficiently and expediently by means of a rigorous process model; for such studies, there is no need to analysis the process dynamics. However, when process control concepts are applied to real plants, a previously conducted analysis of the process dynamics is indispensable. Only a few publications have considered the process dynamics in open-loop mode, and these works are limited to simulation studies.17,21,37 This literature overview shows that a balanced combination of experimental and theoretical investigations of the dynamic process behavior of dividing-wall columns (e.g., by means of extensive model validation) is not available. Therefore, a dynamic validation study is an essential contribution to reduce the resistance and hesitancy of industry to build and operate dividing-wall columns. Furthermore, exact knowledge of the process behavior is fundamental for successfully operating a plant and also for developing process control concepts. The fact that dividing-wall columns have more degrees of freedom than conventional columns complicates not only the design of the column but also the analysis of the process dynamics. In particular, dividing-wall columns basically consist of two columns in the same shell with open communication at the upper and lower ends of the partition wall. This leads to further difficulties and results in two process variables: the liquid split above the dividing wall and the vapor split below the dividing wall. In 2010, we published a first example of an integral approach considering experimental data and simulation results for the steady-state operation of a dividing-wall distillation column.33 Extending this integral approach by considering the process dynamics, as is done in this work, is the logical continuation of that earlier work. The important step of validating the process model requires experimental data, which are not available in literature. It has to be mentioned that modern measuring and control technology is required to conduct a detailed experimental examination of the process dynamics. This study aims at analyzing the process dynamics around a steady-state operating point of dividing-wall columns. The focus is on experimental evaluation of the transient process behavior and model validation. In particular, this work encompasses the comparison of experimental results and simulations after the introduction of changes in the manipulated variables or disturbances in open-loop mode. In addition to several experiments that were performed at our own pilot plant, some operating data from a production-scale column were also examined. This publication sets the basis for developing and implementing process control structures with a validated process model. This approach enables the rapid transfer of the developed concepts to corresponding laboratory- or production-scale plants and ultimately could help to accelerate the commissioning of such plants.
modeled as well. For initialization, the process model design parameters (column arrangement, internal configuration, and geometric data) and a consistent set of operating data are required. The process model is even able to simulate the startup of dividingwall columns from ambient conditions to the steady-state operating point.39 Furthermore, the extensive validation of the process model for several steady-state operating points stresses the significance of the obtained simulations results.33 Based on the steady-state validation of the process model, the dynamic validation for the transient process behavior is presented in this publication. The total and component mass balances are given by the equations dðHUi Þ ¼ Li1 þ Viþ1 Li Vi dt
ð1Þ
dðHUi xi, j Þ ¼ Li1 xi1, j þ Viþ1 yiþ1, j Li xi, j Vi yi, j dt
ð2Þ
where the index i denotes the theoretical stage, numbered in increasing order from the top to the bottom of the column. Because the simulations consider transient behavior, some modeling equations and parameters that did not have to be considered in our previously reported studies of steady-state and startup operation are of interest here. These include the overflow condition of the internal liquid streams, Li, and the residence time, τliq. In reality, a change in the internal liquid flows is coupled with a specific time delay along the column. Accordingly, an increase in the reflux stream at the top of the column, with all other manipulated variables held constant, affects the bottom stream with a specific time delay. The Francis weir equation is usually used for tray columns to describe the delay.40 This equation considers the geometry of each stage in terms of weir height, weir length, and specific volume. Additional parameters for this equation exist that can be used for fitting the liquid residence time behavior. Herein, an approach with fictive weir heights for the examined packing column was not pursued. Instead, the following overflow condition for the internal liquid flows is used Li ¼
HUi HUmax, i τliq, i
ð3Þ
Appropriate correlations were used to calculate the maximum liquid holdup according to Mackowiak,41 and the hydraulic time constant τliq,i was fitted to describe the residence time behavior of one theoretical stage completely filled with liquid HUmax, i ¼ f ðgeometry; physical properties; tray hydraulicsÞ ð4Þ The liquid outlet streams of collectors and distributors are also described by eq 3. The liquid volumes of collecting and distributing elements were determined before the column was set up. To determine τliq, two different experiments were conducted in which pure liquid was fed to the empty column and the variation of the liquid level was recorded. In the first experiment, the feed entered in the middle segment of the column, and in the second experiment, the liquid was charged in the distillate vessel and fed to the top of the column. In both experiments, the feed flow rate was varied, and the change of the liquid level was recorded. The parameter τliq,stage was chosen as 0.075 min to optimally describe the experimental
2. DYNAMIC MODELING The rigorous process model used in this study is based on the MESH (Material balance; Equilibrium Condition; Summation Condition; Heat balance) and can be used for both steady-state and dynamic simulation studies of dividing-wall columns.38 Because of the modular structure of the process model, several arbitrary column arrangements can be simulated. The column configurations are not limited to dividingwall columns, but rather, thermally coupled columns can be 932
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results. For collectors and distributors, a residence time, τliq,cd, of 0.225 min was set corresponding to their liquid holdup. Based on the experimentally fitted parameter τliq, the process model is able to dynamically describe the internal liquid flows of the column. This was confirmed by means of a dynamic experiment validation, as described in the next section. Important process variables for the dividing-wall column, such as the heat transfer across the dividing wall and the self-adjusting vapor split, are also considered by the model. The vapor split, VR*, is the only operating parameter that cannot be determined experimentally. To obtain this parameter, the self-adjusting vapor split is calculated by the model according to the condition of equal pressure drops on the two sides of the dividing wall. Therefore, the proper calculation of the pressure drop is an important aspect of the model. According to earlier studies, the vapor split VR* is defined as the ratio between the vapor stream to the prefractionator and the total vapor stream at the lower end of the dividing wall VR ¼
Vprefrac Vprefrac þ Vmain col
Table 1. Location of Pilot-Plant Temperature Sensors Correlated with Equilibrium Stages of the Simulation Modela prefractionator pilot plant
simulation model
main column pilot plant TIR27
simulation model 0 1
TIR26 TIR25 TIR24
2 3 4 5
6f
6s
TIR17
7f
TIR21
7s
TIR16
8f
TIR20
8s
TIR15
9f
TIR19
9s
10f 11f
ð5Þ
Thus, a split ratio of 1 means that the entire stream is fed to the prefractionator, whereas a value of 0 means that the total flow enters the side-stream section.
10s 11s
TIR9
12f
TIR13
12s
TIR8
13f
TIR12
13s
TIR7
14f
TIR11
14s 15 16
3. PILOT PLANT ON THE LABORATORY SCALE All experiments were performed at a pilot-plant dividing-wall distillation column with a welded vertical partition in the middle of the column (see the Appendix). The column had a diameter of 68 mm and a total height of about 12 m, including the reboiler and total condenser. Four column segments were equipped with about 1 m of Montz B1-500 structured packing. The measured variables were the top pressure, differential pressures, temperatures, heat duty, liquid levels, and mass flows, which were all recorded by the process control system LabVIEW (National Instruments). The feed and reflux streams were heated by means of microstructured heat exchangers. Liquid samples were taken from the feed and from all product streams. The samples were analyzed by gas chromatography. The product streams were measured and controlled by Coriolis mass flow controllers ensuring high accuracy, which is essential for a meaningful evaluation of the experiments. The liquid level was measured using sensors based on high-frequency microwave pulses whereby the pulses are reflected by the product surface and the time from emission to reception of the signals is proportional to the liquid level in the corresponding vessel. The column could be operated under atmospheric conditions but also under vacuum at a minimum top pressure of 3 mbar. A detailed description of the column setup, process measuring, and control technology and a process and instrumentation diagram are provided in Niggemann et al.33 The inventory control of the dividing-wall column in this study was as follows: The liquid level in the distillate vessel was controlled by the reflux stream, and the bottom stream was chosen as the manipulated variable to control the bottom level. A fatty alcohol mixture consisting of n-hexanol (C6), n-octanol (C8), and n-decanol (C10) served as the reference system. According to their normal boiling points in ascending order, n-hexanol was obtained as the top product, n-octanol was obtained as the side-stream product, and n-decanol was obtained as the bottom product with purities of ∼99 wt %.
TIR5
17
TIR4 TIR3
18 19 20
TIR2 a
21 (reboiler)
According to the HETP value provided by the supplier.
A reliable set of process variables to be specified in the model is of special interest for a successful validation: These process variables consist of both design parameters, which are given by the column setup, and operating parameters, which are determined by measurement and control technology. Furthermore, for steady-state and dynamic simulations, reliable correlations for hydrodynamic and heat-transport phenomena are indispensable. Corresponding equations for determining the pressure drop, heat loss, and heat transfer across the dividing-wall for this pilot plant were already identified by Niggemann et al.33 by means of a steady-state validation. For the dynamic validation, additional correlations mentioned in the previous section were considered to describe the dynamic process behavior realistically. The simulation model was discretized into equilibrium stages based on the value of the height equivalent to a theoretical plate (HETP) provided by the supplier of the packing, which resulted in five theoretical stages per meter of bed height. For the packings employed in our pilot plant, the efficiency is independent of the F factor. This means that equal performance can be expected over the range of vapor loads encountered in this study (0.61.7 Pa0.5). The measured temperatures on given locations are of crucial importance for the comparison of experimental data against simulation results. The corresponding temperature sensors of the pilot plant used for the validation are listed in Table 1 and correlated with the equilibrium stages of the simulation model. 3.1. Transient Behavior. The transient process behavior describes the dynamic period of a chemical plant from one steady-state operating point to another when the plant has been subjected to a disturbance. This means that the dynamic 933
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Figure 1. Concentrations of key components after (a) changing the side stream flow rate and (b) reducing the reboiler heat duty.
side-product flow rate (a) and reboiler duty (b). The other manipulated variables remained unchanged. In the first case, the transient period required 15 h, and in the second case, it took 20 h before the steady-state operating point was reached. Furthermore, the amplitude of the disturbance has to be large enough so that the effects of the triggered disturbance can be observed. This means that the disturbance must be distinguishable from measurement noise and “unwanted” disturbances. Based on simulation studies, appropriate disturbance scenarios for the pilot plant were identified. Most authors limit a validation study to one disturbance scenario. However, it has to be considered that high-purity dividing-wall distillation columns
response of the process variables is described in open-loop mode after the manipulated variables have been changed. To probe the transient behavior, appropriate disturbance scenarios have to be chosen that can be carried out at the pilot plant. In particular, the expected time needed to reach the new steady-state operating point limits the choice of possible disturbance scenarios. For example, experiments resulting in a decrease in the bottom product concentration require a long experimental time, making these types of disturbance scenarios infeasible. As an example, Figure 1 shows the mass fractions of the key components as a function of the time elapsed upon the introduction of disturbances involving 10% reductions in the 934
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Table 2. Experimentally Examined and Validated Disturbance Scenarios disturbance scenario
distillate
side stream
A
9.2%
+11.3%
B
+11.5%
+15.4%
Table 3. Steady-State Operating Parameters and Results from Experiment and Simulation (Disturbance Scenario A) scenario (time) A1 (0 min) exp
sim
A2 (400 min) exp
sim
feed (kg/h)
3.02
3.03
wF,C6 (wt %)
33.4
33.2
wF,C8 (wt %) wF,C10 (wt %)
36.2 30.4
36.5 30.3
distillate (kg/h)
1.00
0.91
wD,C6 (wt %)
99.8
99.9
99.8
100
wD,C8 (wt %)
0.2
0.1
0.2
0
side stream (kg/h)
1.10
Figure 2. Experimental and simulated steady-state temperature profiles for scenario A, (left) before and (right) after the disturbance.
1.22
wS,C6 (wt %)
0.7
0.7
8.1
8.0
wS,C8 (wt %)
99.0
99.2
90.5
90.6
wS,C10 (wt %) bottom stream (kg/h)
0.3 0.92
0.1
1.4 0.90
1.4
wB,C8 (wt %)
0.8
0.5
0.1
0.1
wB,C10 (wt %)
99.2
99.5
99.9
99.9
ptop (kPa)
8
8
liquid split (s:s)
3:3
3:3
reflux ratio
3.3
4.0
Qreboiler (kW)
1.33
1.33
Δp33 (kPa) Δp34 (kPa)
0.54 0.75
vapor split
As discussed in detail by Niggemann et al.,33 the crucial indicators and criteria for an experiment being called steady state are constant column temperatures, constant pressure drops, constant product qualities, and a successful data reconciliation of the component and total mass balances. This approach has been verified and was further applied in this study. Furthermore, mass flows and concentrations determined by data reconciliation were used to initialize the process model. 3.2.1. Steady-State Observations. Table 3 reports experimental operating parameters, including the “reconciled” mass flows and concentrations and the pressure drops, for the steady-state operating points A1 at 0 min (before the disturbance had been triggered) and A2 at 400 min (at the end of the disturbance). Starting from the steady-state operating point with product purities of about 99 wt %, the distillate stream was reduced by 9.2% to 0.91 kg/h, and the side stream was increased by 11.3% to 1.22 kg/h. The steady-state operating points showed very good agreement between experiment and simulation (see Table 3 for product purities and pressure drops). This confirms the very good results of our previous publication.33 As a consequence of the disturbance, the side stream contained a tremendous amount of low-boiling component, and the distillate and bottom streams were obtained with increased purities. As a consequence of the observed composition change, the temperature profile of the column changed accordingly (see profiles A1 and A2 in Figure 2). Whereas the lower column segment was characterized by increasing temperatures, the temperatures in the upper column segment decreased until the boiling temperature of pure hexanol was reached. A significant temperature decrease occurred in the dividing-wall segment for both the upper segment of the main column and the upper feed stream segment. Based on the temperature gradient profile, it appears that the separation of the low- and middle-boiling components originally occurred in the upper column segment and the separation of middle- and high-boiling components occurred in the lower column segment. Because of the variation of the product streams, the locations of the high temperature gradients were shifted into the partitioned section of the column (stages 615). As is illustrated by the profiles of A1 and A2, the experimental and simulated temperatures agreed very well.
0.52 0.79 0.42
0.43 0.68
0.46 0.75 0.20
show an extremely nonlinear behavior. This requires a validation study that comprises more than one disturbance scenario. As such, our model was experimentally validated for two disturbance scenarios that affected the temperature profile in opposite ways. These disturbance scenarios are listed in Table 2 and are characterized by two important features: a rapid approach to the steady-state operating point 67 h after the disturbance was triggered and a sufficiently large disturbance amplitude with changes in the manipulated variables of about 10%. Both disturbance scenarios were examined at the pilot plant starting from a steady-state operating point. In scenario A, the distillate stream was decreased, and the side stream was increased. In contrast, in scenario B, both the distillate and side streams were increased. In the following sections, these disturbance scenarios are discussed in detail. 3.2. Validation of Disturbance Scenario A. A successful dynamic validation means both good agreement between the experimental and simulated transient responses and the successful steady-state validation of the corresponding operating points at the beginning and end of the dynamic response. 935
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Figure 3. Experimental and simulated temperatures for disturbance scenario A.
Additionally, Table 3 reports the simulated values for the selfadjusting vapor splits. These results indicate that the vapor split of steady-state operating point A2 deviated significantly from the value of A1. Based on these results, only 20% of the vapor reached the prefractionator, whereas the majority of the main vapor stream ascended through the main column. This difference is based on the fact that, because of the variation of the product streams, the composition and consequently the temperature profile of the column were significantly shifted. This strongly influenced the heat transfer across the dividing wall. Niggemann et al.33 previously showed that the heat transfer across the dividing wall significantly influences the vapor split through vaporization and condensation effects, which was not expected beforehand. To summarize, the successful steady-state validation of the model at the beginning and end of the disturbance serves as a promising basis for the validation of the dynamic period between the two operating points. 3.2.2. Dynamic Observations. The operating conditions of the plant changed after t > 0 min because of the variation of the manipulated variables. At first, the internal liquid flows were affected, and the concentration and temperature profiles of the column shifted. About 4 h after the disturbance had been introduced, the process started to approach the new steadystate operating point, which was reached after 6.5 h (see Figures 35). The dynamic responses of the temperatures, reflux flow rate at the top, and product purities were examined for dynamic validation. In the following figures, t = 0 min corresponds to the change of the manipulated variables. Figure 3 compares the experimental and simulated timedependent temperature courses. In the upper column segment (stages 15), the disturbance resulted in a temperature decrease within the first 120 min. Because of the reduced distillate stream, the hexanol concentration increased, as indicated by temperatures approaching its boiling point. The “step response” corresponds to a first-order system. The dynamic temperature profile courses of the upper column segment are perfectly described by
the simulation. The direction, amplitude, and time-dependent profile pattern agree very well with the experimental results. In the upper segments of the main column (stages 6s10s) and prefractionator (stages 6f10f), the temperatures decreased considerably. Thus, the amplitude of the temperature change in the column profile decreased from top to bottom. The step response in these segments corresponds to a second-order system, namely, a small temperature change within 60 min after the disturbance had been introduced and the maximum temperature slope after 100 min. Then, the temperature slope flattened, and after about 4 h, the steady-state operating point was obtained. This complicated dynamic behavior is described by the simulation very well. The shape and dynamics of the temperature response show very good agreement between the experimental and simulation results. In the lower segments of the main column (stages 11s15s) and prefractionator (stages 11f15f), the impacts of the disturbance were visible for a long period. Compared to the previously discussed segments, the amplitude of the temperature change was small. Whereas the temperatures in the main column increased by 15 K, a temperature decrease of 13 K was observed in the prefractionator. In the period 46 h after the disturbance was triggered, the measured temperatures showed some oscillation, which was caused by superheating in the reboiler. In the lower conventional column section (stages 1620), increasing temperatures were observed, as decanol accumulated in this section because of the increased side stream. Whereas the temperature in the reboiler remained almost constant, the temperature on stage 17 increased by 7 K. Overall, the amplitude and dynamics of the disturbance were described very well by the model. The temperature deviations at the steady-state operating points and during the dynamic response were always less than 2 K. As the distillate stream decreased and the reboiler heat duty was kept constant, the reflux stream increased. For a short time, the overall condensate stream increased in parallel, as the internal circulating condensate stream in the top segment increased. This dynamic effect was described very well by the process model. 936
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Figure 4. Experimental and simulated reflux flow rate for disturbance scenario A.
Figure 5. Experimental and simulated product purities of the key components for disturbance scenario A.
As shown in Figure 4, the reflux stream increased slightly faster in the simulation. The time delay was greatest at about 20 min. The difference is mainly based on the fact that the model does not exactly describe the dynamics of the level controller of the distillate vessel. The transient responses of the key component concentrations in all three product streams (hexanol in the distillate, octanol in the side stream, and decanol in the bottom stream) are depicted in Figure 5. The product purities show very good agreement between experiment and simulation. Because of the applied disturbance scenario, an extreme increase of the low-boiling component in the side stream was observed, and the distillate and bottom streams had permanently high purities.
The vapor split is presented in Figure 6 and exhibits a continuous decrease to the new steady-state value. The fast decrease of VR* from 0 to 100 min corresponds to the significant temperature changes in the partitioned section of the dividing-wall column. 3.3. Validation of Disturbance Scenario B. In the case of disturbance scenario B, the initial point for triggering the disturbance was also a steady-state operating point at the pilot plant (see B1 in Table 4). After data reconciliation at 0 min had been performed successfully, the mass flows of the distillate and side stream were changed and kept constant throughout the experiment. 3.3.1. Steady-State Observations. The distillate stream was increased by 11.5% to 0.68 kg/h, and the side stream was 937
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Figure 6. Simulated vapor splits for disturbance scenario A.
Table 4. Steady-State Operating Parameters and Results from Experiment and Simulation (Disturbance Scenario B) scenario (time) B1 (0 min) exp
sim
B2 (300 min) exp
sim
feed (kg/h)
2.50
2.50
wF,C6 (wt %)
24.4
24.5
wF,C8 (wt %)
37.9
37.9
wF,C10 (wt %)
37.7
37.6
distillate (kg/h) wD,C6 (wt %)
0.61 99.9
99.9
0.68 90.9
90.7
wD,C8 (wt %)
0.1
0.1
9.1
9.3
side stream (kg/h)
0.94
1.09
wS,C6 (wt %)
0.4
0.4
0.2
0.1
wS,C8 (wt %)
99.5
99.5
81.6
81.5
wS,C10 (wt %)
0.1
0.1
18.2
18.4
1.2 98.8
0 100
bottom stream (kg/h)
0.96
wB,C8 (wt %) wB,C10 (wt %)
1.3 98.7
0.74
ptop (kPa)
8
8
liquid split (s:s)
3:3
3:3
0 100
reflux ratio
4.9
4.3
Qreboiler (kW)
1.24
1.24
Δp33 (kPa)
0.30
0.33
0.37
0.39
Δp34 (kPa)
0.53
0.56
0.59
0.62
vapor split
Figure 7. Experimental and simulated steady-state temperature profiles for scenario B, (left) before and (right) after the disturbance.
0.52
Different values for the vapor split were obtained for B1 and B2, in accordance with the observations of the previously simulated disturbance scenario. However, in this case, the change in the vapor split was less pronounced, namely, from 0.52 to 0.4. The changes in the manipulated variables caused the expected decrease of the purities in the distillate and side-stream products, whereas the bottom stream permanently showed high purities. As indicated by profiles B1 and B2 in Figure 7, because of the increase in the distillate and side stream, the temperature profile of the column shifted significantly (Figure 7). Furthermore, it can be seen that the temperature difference between the prefractionator and main column increased significantly for case B2. Thus, a significant temperature increase in the lower-temperature segment and in the upper part of the main column was observed. Furthermore, the temperatures in the lower column
0.40
increased by 15.4% to 1.09 kg/h. The pilot plant reached the new steady-state operating point B2 at 300 min according to the changes in the distillate and side streams. The results of the experiment and simulation agree very well for both steady-state operating points in terms of product purities and pressure drops. 938
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Figure 8. Experimental and simulated temperatures for disturbance scenario B.
Figure 9. Steady-state validation: Temperature profiles of the production-scale column.
segment approach the boiling temperature of pure decanol. The shift of the temperature gradient in the column profile shows that the separation of the low- and middle-boiling components moved to the upper column segment, whereas the separation of middleand high-boiling component shifted from the lower column segment to the middle of the dividing-wall segment. The good agreement between experiment and simulation for both steady-state operating points served as the basis for the subsequent dynamic validation. 3.3.2. Dynamic Observations. A comparison of the experimental and simulated temperatures is shown in Figure 8 and reveals the highest temperature slope in the period from 50 to 75 min. Furthermore, it can be seen that the new steady-state operating
point was reached after 34 h. However, large differences in temperature occurred for different segments of the dividing-wall column. These discrepancies will be discussed later. The significant temperature increase in the upper column segment (stages 15) resulted from increase of the distillate stream of about 10% and the correspondingly increased fraction of octanol in the upper stages. However, in the upper segment of the main column (stages 6s10s), only a slight temperature increase of about 3 K was observed, as this segment before and after the disturbance was dominated by the temperature of the middle-boiling component. In the lower segment of the main column (stages 11s15s) and in the bottom section of the column (stages 1620), the 939
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Figure 10. Sketch of the process variables used for dynamic validation.
effect of the disturbance was observed within 34 h. Large temperature increases below the side drawoff were observed in both segments. Again, the temperature rise was due to the composition change resulting from the increased side stream, which caused an accumulation of the high-boiling component, decanol. The temperature increased by 17 K on stage 12s, by 23 K on stage 14s, and by about 19 K on stage 17. However, the amplitude of the temperature change in the column profile decreased in the direction of the column sump. The temperature in the reboiler slightly increased by about 1.5 K, as the column sump was already dominated by the boiling temperature of the high boiler because of the high decanol purity. It has to be reiterated that the dynamic temperatures were described very well by the simulation. At the steady-state operating points and during the transient behavior, the temperature deviation was always smaller than 23 K. The validation for scenario B was performed based exclusively on the temperatures, after the process model had been extensively validated for scenario A.
4. COLUMN AT PRODUCTION SCALE In addition to the experimental data from our own pilot plant, we also validated the process model with operating data from a production-scale dividing-wall column of our industrial partner. The goal of model validation with data from a production-scale column is to underline the integrity and generality of the process model. However, plant managers are generally reluctant to perform extensive experimental examinations of disturbances in open-loop mode at an industrial column, which implies running the column off-specification for a specific period of time. Nevertheless, we obtained approval from our industrial partner to examine the transient behavior of an industrial dividing-wall column, to be able to generate data required to validate our model properly. The advantage of a dynamically validated process model
Figure 11. Dynamic validation of the (a) bottom concentration and (b, c) temperatures for a production-scale dividing-wall column.
is the fast and reliable development of process control structures. Prior testing of a simulation model dramatically increases the acceptance of implementing the developed concepts at a real plant. Because of secrecy agreements, all of the following data were made anonymous. Thus, dimensionless graphs are presented in all of the following figures. 940
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Figure A1. Experimental setup of the pilot plant.
4.1. Steady-State Validation. The first challenge was to adapt the model to the production-scale column setup and to incorporate the corresponding physical property data. This case involved a complex multicomponent mixture, which means that extensive knowledge of all components and their physical data was required. Such data, as well as the results of our industrial tests,
are proprietary knowledge, covered by a secrecy agreement. Therefore, the results shown in the following graphs are presented in dimensionless form. Data from three different steady-state operating points (scenarios IIII) were available for validating the process model. As shown in Figure 9, the steady-state temperature profiles 941
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Industrial & Engineering Chemistry Research differed significantly from each other: The temperatures intersected in the dividing-wall segment for scenario I, whereas no temperature intersections of the prefractionator and main column occurred for scenarios II and III. However, the experimental data for all scenarios agreed very well with the simulated temperature profiles, as is shown by the small relative error [given by (simulation value experimental value)/experimental value] of the temperatures: mean deviation = 0.36 102, standard deviation = 0.53 102, minimum deviation = 0.79 102, maximum deviation = 2.13 102, although it is worth mentioning that, by nature, a production-scale column has fewer temperature measurements than a laboratory-scale column. This is another aspect that makes model validation more difficult. It can be stated that successful validation considerably increases the significance of the simulation model. 4.2. Dynamic Validation. An appropriate disturbance scenario is identified based on a simulation study prior to the field test to realize a successful dynamic validation. The product purities of the key component in the bottom stream and the temperatures of specific measurements of the column were taken for validating the transient behavior of the process (see Figure 10). The chosen disturbance scenario is depicted in Figure 11. It comprised an increase of the feed stream by 5%, whereas the mass flows of the product streams were unchanged. This disturbance caused a decrease of temperatures below the feed location, as the additional amount of light boiler left the column in the bottom stream. Thus, the temperatures above the feed location were not affected and remained constant. Figure 11 shows that the purity of the key component in the bottom decreased because of the increase of the feed flow rate. This aspect is trivial and is based on the fact that the product streams were not adjusted. It can be seen that both effects, the dynamic behavior of the temperatures and the concentration of the key component in the bottom, are described very well by the process model. The relative deviation between the experimental results and simulated data for the key component in the bottom stream was always less than 1.50 102.
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Previously, our studies have dealt with steady-state model validation.33 This study is the natural continuation of our own experimental work. The steady-state and dynamic validation of the process model by means of experimental data from a productionscale column completes the validation and emphasizes the significance and integrity of the simulation tool. These extensive examinations bridge a gap in the open literature; consequently the acceptance of and confidence in the startup strategies and process control concepts resulting from the validated process model will be increased. Results pertaining to last statement will be published in the near future.
’ APPENDIX Figure A1 shows a process and instrumentation diagram of the pilot plant. ’ AUTHOR INFORMATION Corresponding Author
*Tel.: +49 40 42878-3241. Fax: +49 40 42878-2992. E-mail:
[email protected]. Present Addresses †
Evonik Industries AG, Hanau, Germany.
’ ACKNOWLEDGMENT We express our gratitude to Lennart Fries for his contribution to this study. We gratefully acknowledge financial support from the Max-Buchner-Forschungsstiftung (MBFSt 2712). Furthermore, we express our thanks to Julius Montz GmbH (Hilden, Germany) for generously supporting us with our pilot plant. Finally, we thank our industrial partner for providing operating data of a production-scale dividing-wall column. ’ NOMENCLATURE Symbols
HU = holdup (mol or m3) L = molar liquid flow (mol s1) p = pressure (Pa) Q = heat flow rate (J s1) t = time (s) V = molar vapor flow (mol s1) VR* = vapor split w = mass fraction (kg kg1) x = molar liquid fraction (mol mol1) y = molar vapor fraction (mol mol1)
5. CONCLUSIONS For the first time, an extensive dynamic validation of the transient process behavior of dividing-wall columns has been presented. First, the mathematical process model was parametrized for existing dividing-wall columns on the laboratory and production scales. Then, appropriate disturbance scenarios were identified based on systematically conducted simulation studies. Subsequently, these disturbance scenarios were realized on laboratory- and production-scale columns. A comparison of the experimental and simulated results shows that the simulation model describes the experimental disturbance scenario well in all column segments. Different propagation velocities and different amplitudes of temperatures and concentrations were successfully described. Thus, the transient process behavior of the simulation model was shown to be validated. To summarize, the process model is able to describe both the steady-state and transient behavior of dividing-wall columns. The great advantage of this validated model is that it “replaces” a real plant. Hence, several types of analysis can be efficiently conducted by means of the process model, such as developments in the fields of process control, optimization, troubleshooting, and debottlenecking. This contributes to a safe and cost-efficient transfer to a corresponding production-scale column.
Greek Symbols
Δp = pressure drop (Pa) Δp33 = pressure drop without the condenser according to PDIR33 in Figure A1 (Pa) Δp34 = pressure drop without the condenser according to PDIR34 in Figure A1 (Pa) τ = hydraulic time constant (s) Superscripts and Subscripts
B = bottom D = distillate F = feed i = stage index liq = liquid 942
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main col = main column max = maximum prefrac = prefractionator S = side stream
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