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Process control for energy efficient operation of reactive dividing wall columns
Accepted Manuscript Title: Process control for energy efficient operation of reactive dividing wall columns Authors: Lisa S. Egger, Georg Fieg PII: DOI: Reference:
S0263-8762(19)30073-5 https://doi.org/10.1016/j.cherd.2019.02.026 CHERD 3536
To appear in: Received date: Revised date: Accepted date:
30 November 2018 4 February 2019 15 February 2019
Please cite this article as: Egger LS, Fieg G, Process control for energy efficient operation of reactive dividing wall columns, Chemical Engineering Research and Design (2019), https://doi.org/10.1016/j.cherd.2019.02.026 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Process Control for Energy Efficient Operation of Reactive Dividing Wall Columns
Lisa S. Egger1*, Georg Fieg1
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Hamburg University of Technology, Institute of Process and Plant Engineering
E-mail address: [email protected]
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* Tel.: +49 40 428 78 6112
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Am Schwarzenberg-Campus 4, 21073 Hamburg, Germany
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The highly complex RDWC can be controlled by decentralized concentration control Energy efficient control can be facilitated by using the liquid split Liquid split directly influences the component distribution of the RDWC Disturbances in feed flow and feed composition are handled with high accuracy
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Highlights:
Abstract
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Process integration offers great potential, both economically as well as ecologically, mainly due to high possible energy savings. However, the design and operation of integrated processes is impeded by complex process dynamics introduced by the integration of multiple
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unit steps in one apparatus. A prime example, the reactive dividing wall column combines a reaction and several separation steps in one column shell. The high degree of integration results in an especially challenging operation of this apparatus. Reliable design methods and efficient control structures need to be provided in order to enable the industrial application of
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reactive dividing wall columns. A key challenge is to ensure an energy efficient operation in case of disturbances. This study aims at understanding the underlying dependencies of the most important variables, i.e. the liquid split, the energy demand and the component distribution, regarding energy efficient control. Based on these findings a straightforward decentralized control structure is presented and analysed. The structure utilizes the liquid split as manipulated variable in order to control the component composition on the top stage
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of the prefractionator. The evaluation of the structure regarding energy efficient process control is carried out by comparison to a structure with fixed liquid split.
Keywords: Distillation, Process Control, Process Intensification, Reactive Dividing Wall Column. Energy
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Nomenclature a activity i component index ki specific reaction rate constant L molar liquid flow mcat mass of catalyst on a stage N molar flow nR reaction counter ri reaction rate V vapour flow X molar fraction of component i in liquid phase y molar fraction of component i in vapour phase
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Efficiency
1. Introduction
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Process integration enables simultaneous execution of two or more unit operations in one single apparatus. In terms of distillations processes the dividing wall column (DWC) and the
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reactive distillation column (RDC) represent so called first-level integration processes. Both processes are well established alternatives to conventional distillation sequences. It is stated that in 2010 more than 125 dividing wall columns and more than 200 reactive distillation columns were operated worldwide (Kiss, 2014). In contrast the reactive dividing wall column
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(RDWC) is not yet applied in industry, even though Kaibel (1986) already proposed the integration of a reaction and several separation steps in a column in a patent more than 30 years ago. Since the RDWC can be interpreted as the synthesis of DWC and RDC, it is also referred to as second-level process integration (Weinfeld et al., 2018). The high degree of integration poses a challenge when it comes to understanding the chemical and physical interactions in the column. The very high complexity impedes the design and the operation of RDWCs. However, theoretical studies state that high potential savings can be achieved by 2
employing the RDWC. Based on the work of Halvorsen and Skogestad (2003) about energy efficiency of DWCs, Schröder et al. (2016) found that by using RDWCs investment costs and energy demand can be further reduced by 30 % and 15 % respectively in comparison to firstlevel integration processes. 1.1 Control of Reactive Dividing Wall Columns In addition to design and start-up, process control is the third crucial aspect of RDWC
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operation. Process control ensures constant product qualities and plant safety even in case of disturbances. Especially in case of highly integrated apparatuses, it is further important to
maintain the energy efficiency during operation. Although it could be shown that the RDWC
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enables significant savings, the energy demand can exceed that of a conventional distillation
sequence if operated incorrectly. Therefore, an efficient control structure has to be established.
Regarding energy efficient operation Schröder et al. (2016) stated that the component
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distribution in the column shows a significant influence on the required heat duty. It was
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further stated that the liquid split, which is the distribution of the reflux onto both sides of the dividing wall, directly affects the component distribution. For non-reactive dividing wall
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columns it was already shown that the liquid split is an important manipulated variable when
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it comes to energy efficient operation (Halvorsen and Skogestad, 1997). Yildirim et al. (2011) give an overview about control structures for DWCs including schemes that target energy efficiency. Despite affirming results for the DWC to date no studies are known which focus on
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energy efficient control of RDWCs. Weinfeld et al. (2018) present a comprehensive overview about studies on the RDWC including all investigated control structures. Most of the
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published studies deal with decentralized control structures that target the maintenance of product specifications as well as good dynamic performance. Qian et al. (2016) also present a first study on model predictive control (MPC). In their study they apply a fixed liquid split,
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which was optimised for the operation point beforehand. They report a good performance of the applied MPC. However, they also state that, especially in case of feed composition disturbances, the control structure might not depict an optimum control due to the fixed liquid split. Egger and Fieg (2017a) presented first experimental results of a basic decentralized
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control structure, also with a fixed liquid split, of a pilot plant. 2. Model Description In order to investigate and predict the complex physical and chemical interactions in the RDWC a reliable process model is required. Therefore, a fully predictive simulation model, that allows steady state as well as dynamic simulations, serves as base in this study. The applied, rigorous model was developed at the Institute of Process and Plant Engineering 3
(Ehlers, 2016). Comprehensive experimental data of a pilot plant enabled a profound validation of the RDWC model (Egger and Fieg, 2017b, 2018). The model is implemented in Aspen Custom Modeler V.8.4 (ACM) of Aspen Tech®. The simulation platform allows great flexibility while modelling and enables specific adjustments in order to depict highly integrated apparatuses like the RDWC. The property data are sourced from Aspen Properties V.8.4.
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2.1. RDWC Simulation Model The developed RDWC model is based on a modular approach. Thereby the modelling of different column configurations and scales is facilitated. The model consists of a module for
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the column shell which contains sub models for the different column sections (i.e. reaction or
separation sections) and the collector/distributors as well as modules for reboiler, condenser, distillate vessel and feed vessel. A scheme of these components used in the model can be found in Figure 1.
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The well-known equilibrium stage approach is used for the calculation of the phase equilibria
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on each theoretical stage. The Antoine equation is employed for the calculation of the vapour pressure of the pure components, the UNIQUAC model is used for the description of the
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equations as exemplary shown in Eq. (1).
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liquid phase non-idealities. In the reactive sections a reaction term is included in the MESH
𝑛𝑅
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𝑑𝑁𝑥𝑖 = L𝑖𝑛 𝑥𝑖𝑛,𝑖 + V𝑖𝑛 𝑦𝑖𝑛,𝑖 − L𝑜𝑢𝑡 𝑥𝑜𝑢𝑡,𝑖 − 𝑉𝑜𝑢𝑡 𝑦𝑜𝑢𝑡,𝑖 + 𝑚𝑐𝑎𝑡 ∑ 𝑟𝑖,𝑗 𝑑𝑡
(1)
𝑗=1
The enthalpy balance includes heat transfer from each stage to the column’s steel as well as
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heat loss to the surroundings. Latter is especially significant for small diameter columns like pilot plants but might be neglected for larger scale columns applied in industry. The heat
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transfer is especially relevant for investigation of the dynamic behaviour, such as start-up or during disturbances, when significant changes of the temperature profile occur in the column. As studies have shown, the distribution of the vapour to both sides of the dividing wall, the so called vapour split, plays an important role when it comes to energy efficient operation of the
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RDWC. Although solutions for an active vapour split control for RDWCs (Harvianto et al., 2018) as well as for DWCs (Dwivedi et al., 2012) were proposed, until today no active vapour split control is known to be implemented in industrial dividing wall columns. Generally, the vapour flow adjusts automatically to equalize the pressure drop in the prefractionator and the main column. This so-called self-adjusting vapour split is considered in the simulation model by corresponding correlations for the calculations of the pressure drop. The vapour split is iteratively adjusted to ensure equal pressure drop on both side of the dividing wall. 4
2.2. Reaction System and Operation Point The applied reference reaction system in this study is the transesterification of 1-butanol and n-hexyl acetate as stated in Eq. (2). 𝐶4 𝐻10 O + 𝐶8 𝐻16 𝑂2 ⇋ 𝐶6 𝐻12 𝑂2 + 𝐶6 𝐻14 O
(2)
The reaction kinetics are modelled via a second order power law approach, taking into
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account the product of the activities of all involved components, as shown in Eq. (3). 𝑛
𝑟𝑖 (𝑇) = 𝑘𝑖 ∙ ∏ 𝑎𝑗
(3)
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𝑗=1
The reaction is assumed to take place in the liquid phase only. Parameters for the phase equilibria and reaction kinetics of the specific system are retrieved from Egger and Fieg (2017b).
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The operation point that serves as base case was chosen according to the possible operation range of the pilot plant. Thus, a subsequent experimental validation with the pilot
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plant is enabled. The employed key variables of the base case are shown in Table 1. 3. Sensitivity Analyses and Control Structure Setup
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3.1. Sensitivity Analyses
To evaluate the direct influence of the liquid split onto the energy demand of the column
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sensitivity analyses are conducted. Therefore, several disturbances in the feed flow and the feed composition are investigated. The four employed disturbance cases are shown in Table
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2. The results give insights about the dependency of the energy demand on the liquid split and allow the calculation of the theoretical minimal heat duty for each disturbance case. In steady state simulations the main components of the three product streams (butyl acetate in
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the distillate stream, butanol in the side stream and hexanol in the bottom stream) are set to a fixed value in order to ensure a constant product quality. The base case is modified by changing the respective disturbance variable. The liquid split is then varied for each of the
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disturbance cases in a range of 0.2 to 0.6 by steps of 0.01. The required heat duty to maintain the product specifications is calculated for each step by the simulation model. The boundaries for the liquid split originate from previous studies, where it was found that values smaller 0.2 might lead to unfeasible internal flows, whereas values greater 0.6 lead to monotonously rising heat duties. The results of the sensitivity analyses are shown in Figure 2. The results can be divided into different classes. Increasing the feed flow or the share of high boiling component in the feed flow leads to an overall increased heat demand. The curves for 5
disturbances of +10 % hold a local maximum and two local minima each in the presented range. This indicates that two theoretical values for the minimal heat duty can be reached, depending on the initial state and the settling course of the liquid split in the dynamic simulation. Decreasing the feed flow or the high boiling feed component leads to an overall lower heat duty. No local maxima can be detected for these cases. However, reducing the feed flow leads to a local minimum, whereas decreasing the high boiling feed component leads to a monotonously falling function. Furthermore, the sensitivity of the heat duty towards
presents the theoretical minimal heat duties for all investigated disturbances.
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the liquid split is significantly smaller than for disturbances with increased values. Table 2
The analyses show the necessity of adjusting the liquid split in a range of 0.2 to 0.38 to
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maintain energy efficient operation in case of all investigated disturbances. This indicates the importance of the liquid split as a manipulated variable for process control of the RDWC. 3.2. Evaluation of Suited Control Variables
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While the liquid split directly influences the component distribution and can thus serve as
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manipulated variable, a suited control variable needs to be selected. This control variable has to meet two main criteria. First, the control variable has to be sensitive towards changes
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in the manipulated variable Secondly the control variable must represent the component
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distribution and especially the energy minimal operation in the column. Therefore, the value of the control variable should not differ considerably for all energy minima found in the sensitivity analyses. If a control structure with set point adaptation is applied, a variation of
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the control value would be possible. However, a simulation model is needed to calculate the adapted set points before or during operation. Since this study aims at providing a
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straightforward control structure that can be used even if no simulation model is available, set point adaption will not be considered in the following. Ling and Luyben (2009) examine a control variable for energy efficient control for non-
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reactive dividing wall columns. They suggest controlling the concentration of the high boiling component on the top stage of the prefractionator. This strategy prevents an accumulation of the high boiling component in the top of the column and thus maintains a feasible distribution. Furthermore, high boiling components do not rise above the dividing wall and thereby
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impurities in the side stream are avoided. However, their approach applies for a dividing wall column with three components to be separated. In this study a RDWC with two reactants and two products, i.e. four components, is examined. This raises the question if the highest boiling or another high boiling component should be controlled. Figure 3 shows the mass fractions of all four components on the top stage of the prefractionator at the four selected disturbances cases for the minimal heat duty. As stated above, the concentration should be as close to the base case as possible. The two reactants hexyl acetate and butanol, 6
representing the middle boiling components, show only small deviations and are thus suited control variables. Sticking to the concept of Ling and Luyben (2009), the higher boiling component of these two, hexyl acetate is chosen as control variable. 3.3. Decentralized Control Structure By proving the effective controllability of a highly complex apparatus utilizing a rather “simple” decentralized control structure, the reluctance to operate RDWC is to be reduced. Therefore,
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two concentration control concepts will be investigated and compared. One control structure (CS1) includes a control loop for concentration control of the high boiling reactant on the top stage of the prefractionator by the liquid split. For the second
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structure (CS2) the liquid split is set to a fixed value at all times. The direct comparison enables the evaluation of the energy efficient operation caused by the adjustment of the liquid split. In Figure 4 both control structures are shown. They contain two level control loops for maintaining the level of the reboiler and the distillate vessel. The key components in the
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three product streams are controlled by a concentration control loop each. In this study the
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focus is on maintaining the concentration of the key components, while the composition of impurities might vary. However, control of the impurities is also possible. The control variable
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can be chosen according to the desired specification. CS1 contains a fourth concentration
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control loop aiming at energy efficient operation.
In order to conduct dynamic simulations the presented control structures are implemented in
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the simulation model. Therefore control models provided by ACM are employed. PIDIncr model, which calculates changes in the output as a function of the error, is employed as PID controller model using an ideal algorithm. Concentration control is modelled via proportional
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integral controllers, while level control is modelled via proportional controllers only. To take into account delays in the control loops introduced by concentration measurements, dead
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times are added to all concentrations control loops. Tuning is carried out with the closed loop ATV test provided in ACM. The tuning parameters are calculated via the ultimate gain and ultimate period applying Tyreus-Luyben’s method (Tyreus, 1992). The tuning parameters for
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the control loops are shown in Table 3. 4. Simulation Results Dynamic simulations were conducted for each disturbance case and both control structures. Each simulation run starts with the base case set up. Process control is then set to automatic mode. After 15 minutes simulation time, the disturbance is introduced to the system by changing the disturbance variable. The dynamic changes of all variables are recorded for a simulation time of 15 hours in intervals of 0.01 hours. The evaluation of the simulated values 7
is done in two steps. First, the dynamic performance of both structures is examined. Therefore, the accuracy of the concentration control, determined by the settling course and the settling time, is evaluated. In a second step, the energy efficiency is assessed by comparison to the theoretical minimal heat duty found in the previous sensitivity analyses. 4.1. Dynamic Performance The evaluation of the dynamic performance is carried out based on the results for the key
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components of the three product streams. In Figure 5 the courses of the key components’ concentrations are shown for each disturbance case. Each graph depicts the direct comparison of CS1 (red lines) and CS2 (black lines). At a first glance it can be seen that the
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settling courses of both structures are similar. Feed flow disturbances can be handled by both structures in about 5 hours. During the settling time, products obtained at the reboiler stream and distillate stream show small deviations of max. 0.5 mass%. A larger effect is observed for the side stream concentrations. A maximum deviation of up to 4 mass% can be
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detected for short time periods. Moreover, the dynamic course of the side stream concentration shows oscillations when CS1 is applied. These oscillations are imposed by the
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fourth additional control loop. The liquid split directly influences the concentration on both
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sides of the partition wall and therefore interacts with the control loop for the side stream concentration.
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At disturbances in the feed composition, the concentrations in the reboiler and distillate stream show again only small deviations up to 0.5 mass% during the settling course. The
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side stream concentration is the most affected with deviations of up to 3 mass%. Interestingly, in case of an increase of the high boiling reactant, CS2 displays oscillations in
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the side stream concentrations. Deviations up to 0.02 mass% can still be observed after 15 h simulation time. However the oscillation fades and deviations get negligibly small, so that after 24 h deviations are smaller than 0.01 mass% and after 30 h smaller than 0.005 mass%.
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For all other cases both control structures reach a new steady state. In Figure 6 the accuracy of the concentration control of the products is assessed. The relative deviation from the set point is shown for all components at the newly adapted steady states. For each type of disturbance, i.e. feed flow and feed composition, the mean deviation is calculated. The
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accuracy is very high for both structures. Maximal relative deviations of 0.06% prove the effective functionality of the control structures. 4.2. Energy Efficient Control The evaluation of the dynamic performance shows that both control structures can efficiently handle disturbances in feed flow and feed composition. However, the evaluation only focuses on the outlet concentrations. In a second step the energy duty required to maintain these 8
concentrations is analysed. For each disturbance case the heat demand adjusted at the new steady state is identified. The sensitivity analyses in chapter 3.1. give insights about the theoretical minimal heat duty for each of these disturbance case. The minimal values from Table 2 are then compared to the results of the dynamic simulation study. In order to evaluate the differences between CS1 and CS2, the values are normalised to the minimal heat duty. Therefore, the relative deviations from the minimal heat duty are presented in Figure 7.
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The heat duty adjusted by CS1 is very close to the minimal heat duty for all disturbance cases. Deviations are always smaller than 1 %. In case of feed flow disturbances, CS2
shows a good performance, too, with overall deviations below 2 %. As shown in the
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sensitivity analyses, the heat duty is moderately sensitive towards the liquid split when the feed flow is changed. Therefore, the difference between a fixed liquid split value and an
adjusted liquid split is expectedly small. In contrast, the high sensitivity towards feed composition disturbances leads to big differences in the heat duty for both control structures.
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Whereas CS1 is able to maintain energy minimal operation, CS2 leads to a new steady state with a heat duty that is up to 20 % above the theoretical minimum. These findings are also
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supported by the adjusted liquid split values shown in Table 4. In three cases the value of the
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adjusted liquid split of CS1 is closer to the liquid split at minimal heat duty than the fixed
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liquid split of CS2. In these cases the heat duty is always lower for CS1. In the case of an increase of the feed flow by +10 % the adjusted liquid split by CS1 is 0.33 whereas the minimal value as well as the fixed value is 0.3. However, due to the low sensitivity in this
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case no significant deviation in the heat duty can be observed. In order to further assess the choice of the selected control variable, the composition on the
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top stage of the prefractionator is examined for an exemplary disturbance case. Figure 8 shows the concentrations of all four components for an increase of the high boiling reactant in the feed flow of +10% for the energy minimal case as well as the results of CS1 and CS2.
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It can be seen that even though only one concentration is controlled by the liquid split, the overall composition on the stage with CS1 resembles the composition in the energy minimal case. On the other hand, the composition obtained by CS2 differs strongly from the minimal values. Due to the fixed liquid split, the composition changes and an increase of the heaviest
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key is not compensated by the control structure. 5. Discussion As shown in the previous chapter the amount of energy savings that can be achieved by controlling the liquid split is dependent on the sensitivity of the heat duty towards the different disturbance cases. Therefore, in some cases a fixed liquid split can be sufficient while in other cases an adjustment of the liquid split is highly recommended. Hence, a sensitivity 9
study regarding the most common disturbances for the desired operation point is useful to quantify possible savings. Since the liquid split is a fixed variable in CS2, two structures with different degrees of freedom are compared. Therefore, the impurities in the product streams are allowed to vary. Depending on the goal that is to be achieved by the control structure, it is necessary to elaborate which variables should be controlled and which may fluctuate. In this study the main task is to ensure a certain concentration for all key components.
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In industrial applications temperature controllers are preferred to concentration control as they are less expensive and provide faster dynamics, due to lack of dead times. However,
the temperature only represents the composition in the column indirectly. The highly complex
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interactions of reaction and separation in the RDWC might lead to ambiguous representation
of the concentrations by the temperature (Harbou et al., 2017). On behalf of validating the theoretical findings about the influence of the liquid split on the component distribution and to eliminate potential errors due to ambiguous representation a direct measurement of the
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control variable is favoured in this study. The validation of the general feasibility of energy efficient control forms the base for further investigations. As a next step use of temperature
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controllers is highly recommended in order to improve the dynamics of the control structure.
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The allocation of control and manipulated variables in this study was decided according to
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the proximity of both variables. Depending on the algorithm selecting the control variables different control structures might arise. Furthermore, it might be also possible, analogue to considerations of Ling and Luyben (2009), to control the light boiling reactant on the lowest
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stage of the prefractionator.
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6. Conclusions
In this paper the energy efficient control of a RDWC is assessed. A developed control structure is presented, evaluated for different disturbances and the underlying mechanisms
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are analysed. Sensitivity analyses show that the energy demand of the RDWC is highly dependent on the liquid split. Depending on the applied disturbance, one or two energy minimal operation points can be identified. In the proposed control structure the liquid split is employed to control the heavy reactant concentration on the top stage of the prefractionator.
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Disturbances in the feed flow and feed composition can be handled successfully with a high accuracy regarding the product specifications. The evaluation of the energy efficiency, by comparison to a control structure with fixed liquid split, shows that significant savings in the energy demand are possible. Without adjusting the liquid split the energy demand exceeds the energy minimal demand in one of the applied disturbances by 22 %. As expected, the saving potential is highly dependent on the disturbance type. The sensitivity analyses confirm that changes in the feed composition strongly affect the dependency of the energy demand 10
on the liquid split. In case of a feed flow disturbance the sensitivity is relatively low. Since the liquid split control loop does not affect the dynamic performance of the control structure, it is
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highly recommended to always apply the liquid split for RDWC control.
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References
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Dwivedi, D., Strandberg, J.P., Halvorsen, I.J., Preisig, H.A., Skogestad, S., 2012. Active Vapor Split Control for Dividing-Wall Columns. Industrial & Engineering Chemistry Research 51 (46), 15176–15183. 10.1021/ie3014346. Egger, L.S., Fieg, G., 2017a. Decentralized Process Control of Reactive Dividing Wall Columns. Chemical Engineering Transactions (57), 1693–1698. 10.3303/CET1757283. Egger, T., Fieg, G., 2017b. Enzymatic catalyzed reactive dividing wall column: Experiments and model validation. AIChE Journal 63 (6), 2198–2211. 10.1002/aic.15598. Egger, T., Fieg, G., 2018. Dynamic process behavior and model validation of reactive dividing wall columns. Chemical Engineering Science 179, 284–295. 10.1016/j.ces.2017.12.011. Ehlers, C., 2016. Experimentelle und simulative Analyse integrierter Rektifikationsapparate am Beispiel der reaktiven Trennwandkolonne. Dissertation, Hamburg. Halvorsen, I.J., Skogestad, S., 1997. Optimizing Control of Petlyuk Distillation: Understanding the Steady-State Behavior. Computers & Chemical Engineering (21), 249–254. 10.1016/S0098-1354(97)87510-2. Halvorsen, I.J., Skogestad, S., 2003. Minimum Energy Consumption in Multicomponent Distillation. 2. ThreeProduct Petlyuk Arrangements. Industrial & Engineering Chemistry Research 42 (3), 605–615. 10.1021/ie0108649. Harbou, E. von, Ryll, O., Schrabback, M., Bortz, M., Hasse, H., 2017. Reactive Distillation in a Dividing-Wall Column: Model Development, Simulation, and Error Analysis. Chemie Ingenieur Technik 89 (10), 1315–1324. 10.1002/cite.201700068. Harvianto, G.R., Kim, K.-H., Kang, K.-J., Lee, M., 2018. Optimal Operation of Dividing Wall Column using EnhancedActive Vapor Distributor. Chemical Engineering Transactions 2018 (69). Kaibel, G., 1986. Verfahren zur Durchführung von chemischen Reaktionen und gleichzeitiger destillativer Zerlegung eines Produktgemisches in mehrere Fraktionen mittels einer Destillationskolonne - European Patent Office - EP 0126288 B1, 5 pp. Kiss, A.A., 2014. Distillation technology - still young and full of breakthrough opportunities. Journal of Chemical Technology & Biotechnology 89 (4), 479–498. 10.1002/jctb.4262. Ling, H., Luyben, W.L., 2009. New Control Structure for Divided-Wall Columns. Industrial & Engineering Chemistry Research 48 (13), 6034–6049. 10.1021/ie801373b. Qian, X., Jia, S., Skogestad, S., Yuan, X., Luo, Y., 2016. Model Predictive Control of Reactive Dividing Wall Column for the Selective Hydrogenation and Separation of a C3 Stream in an Ethylene Plant. Industrial & Engineering Chemistry Research 55 (36), 9738–9748. 10.1021/acs.iecr.6b02112. Schröder, M., Ehlers, C., Fieg, G., 2016. A Comprehensive Analysis on the Reactive Dividing-Wall Column, its Minimum Energy Demand, and Energy-Saving Potential. Chemical Engineering & Technology 39 (12), 2323– 2338. 10.1002/ceat.201500722. Tyreus, 1992. Tuning PI Controllers for Integrator/Dead Time Processes. Industrial & Engineering Chemistry Research (31), 2625–2628. Weinfeld, J.A., Owens, S.A., Eldridge, R., 2018. Reactive dividing wall columns: A comprehensive review. Chemical Engineering and Processing - Process Intensification 123, 20–33. 10.1016/j.cep.2017.10.019. Yildirim, Ö., Kiss, A.A., Kenig, E.Y., 2011. Dividing wall columns in chemical process industry: A review on current activities. Separation and Purification Technology 80 (3), 403–417. 10.1016/j.seppur.2011.05.009.
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List of Figures: Figure 1: Schematic depiction of RDWC main components for modelling in Aspen Custom
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Modeler
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Figure 2: Sensitivity analyses of the influence of the liquid split on the heat duty at different
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disturbance cases
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Figure 3: Concentration of all components on top stage of the prefractionator at four
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disturbance cases
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Figure 4: Control structures CS1 with adjustable liquid split and CS2 with fixed liquid split
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Figure 5: Dynamic course of the three key product components (distillate stream: butyl acetate, bottom stream: hexanol, side stream: butanol) after feed composition disturbances by -10 % (a) and +10 % hexyl acetate (b) and feed flow disturbances by -10 % (c) and +10 %
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(d)
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Figure 6: Relative deviation from set points of the three key components after feed flow
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disturbances (a) and feed composition disturbances (b)
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Figure 7: Adjusted heat duties in relation to theoretical minimal heat duty for the investigated
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disturbance cases
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Figure 8: Concentration on top stage of prefractionator after a disturbance of -10 mass% hexyl acetate in the feed flow for the energy minimal case Q_min, CS1 (LS free) and CS2
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List of Tables:
Symbol
Value
Unit
Feed stream
F
2.3
kg/h
Feed fraction hexyl acetate
w_HeAc
0.35
kg/kg
Feed fraction butanol
w_BuOH
0.65
kg/kg
Reflux stream
R
4.35
kg/h
Distillate stream
D
1.0
kg/h
Side stream
S
0.75
Bottom stream
B
0.55
Reboiler duty
Q
0.92
Condenser pressure
P
3.0
Liquid split
LS
Conversion of hexyl acetate
X_HeAc
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Variable
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Table 1: Base case data
kg/h kg/h kW
kPa -
95.64
%
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A
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0.3
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Table 2: Investigated disturbance cases with corresponding minimal heat duty and liquid split Intensity
variable
1st minimum
2nd minimum
Qmin
liquid split
Qmin
liquid split
[kW]
[-]
[kW]
[-]
F
- 10%
0.753
0.25
2
F
+10%
1.113
0.3
3
w_HeAc
-10 mass%
0.715
0.2
4
w_HeAc
+10 mass%
1.209
0.2
1.241
1.238
0.2
0.38
A
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ED
M
A
N
U
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1
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Case Disturbance
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Table 3: Tuning parameters of all control loops of CS1 and CS2 QC1
QC2
QC3
QC4
LC1
LC2
Kp [%/%]
12.8
8
121.6
0.73
28.6
133.3
Ti [min]
145.2
66
118.8
39.6
-
-
Action
direct
direct
reverse
direct
direct
direct
A
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ED
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A
N
U
SC R
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Controller
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Table 4: Liquid split values for each investigated disturbance case at minimal heat duty and as adjusted by CS1 (LS free) and CS2 (LS fixed) Disturbance
Intensity
variable
LS at Qmin
LS free
LS fixed
[-]
[-]
[-]
- 10%
0.25
0.27
0.3
F
+10%
0.3
0.33
0.3
w_HeAc
-10 mass%
0.38
0.39
0.3
w_HeAc
+10 mass%
0.2
0.2
0.3
A
CC E
PT
ED
M
A
N
U
SC R
IP T
F
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S0263-8762(19)30073-5 https://doi.org/10.1016/j.cherd.2019.02.026 CHERD 3536
To appear in: Received date: Revised date: Accepted date:
30 November 2018 4 February 2019 15 February 2019
Please cite this article as: Egger LS, Fieg G, Process control for energy efficient operation of reactive dividing wall columns, Chemical Engineering Research and Design (2019), https://doi.org/10.1016/j.cherd.2019.02.026 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Process Control for Energy Efficient Operation of Reactive Dividing Wall Columns
Lisa S. Egger1*, Georg Fieg1
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Hamburg University of Technology, Institute of Process and Plant Engineering
E-mail address: [email protected]
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* Tel.: +49 40 428 78 6112
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Am Schwarzenberg-Campus 4, 21073 Hamburg, Germany
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The highly complex RDWC can be controlled by decentralized concentration control Energy efficient control can be facilitated by using the liquid split Liquid split directly influences the component distribution of the RDWC Disturbances in feed flow and feed composition are handled with high accuracy
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Highlights:
Abstract
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Process integration offers great potential, both economically as well as ecologically, mainly due to high possible energy savings. However, the design and operation of integrated processes is impeded by complex process dynamics introduced by the integration of multiple
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unit steps in one apparatus. A prime example, the reactive dividing wall column combines a reaction and several separation steps in one column shell. The high degree of integration results in an especially challenging operation of this apparatus. Reliable design methods and efficient control structures need to be provided in order to enable the industrial application of
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reactive dividing wall columns. A key challenge is to ensure an energy efficient operation in case of disturbances. This study aims at understanding the underlying dependencies of the most important variables, i.e. the liquid split, the energy demand and the component distribution, regarding energy efficient control. Based on these findings a straightforward decentralized control structure is presented and analysed. The structure utilizes the liquid split as manipulated variable in order to control the component composition on the top stage
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of the prefractionator. The evaluation of the structure regarding energy efficient process control is carried out by comparison to a structure with fixed liquid split.
Keywords: Distillation, Process Control, Process Intensification, Reactive Dividing Wall Column. Energy
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Nomenclature a activity i component index ki specific reaction rate constant L molar liquid flow mcat mass of catalyst on a stage N molar flow nR reaction counter ri reaction rate V vapour flow X molar fraction of component i in liquid phase y molar fraction of component i in vapour phase
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Efficiency
1. Introduction
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Process integration enables simultaneous execution of two or more unit operations in one single apparatus. In terms of distillations processes the dividing wall column (DWC) and the
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reactive distillation column (RDC) represent so called first-level integration processes. Both processes are well established alternatives to conventional distillation sequences. It is stated that in 2010 more than 125 dividing wall columns and more than 200 reactive distillation columns were operated worldwide (Kiss, 2014). In contrast the reactive dividing wall column
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(RDWC) is not yet applied in industry, even though Kaibel (1986) already proposed the integration of a reaction and several separation steps in a column in a patent more than 30 years ago. Since the RDWC can be interpreted as the synthesis of DWC and RDC, it is also referred to as second-level process integration (Weinfeld et al., 2018). The high degree of integration poses a challenge when it comes to understanding the chemical and physical interactions in the column. The very high complexity impedes the design and the operation of RDWCs. However, theoretical studies state that high potential savings can be achieved by 2
employing the RDWC. Based on the work of Halvorsen and Skogestad (2003) about energy efficiency of DWCs, Schröder et al. (2016) found that by using RDWCs investment costs and energy demand can be further reduced by 30 % and 15 % respectively in comparison to firstlevel integration processes. 1.1 Control of Reactive Dividing Wall Columns In addition to design and start-up, process control is the third crucial aspect of RDWC
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operation. Process control ensures constant product qualities and plant safety even in case of disturbances. Especially in case of highly integrated apparatuses, it is further important to
maintain the energy efficiency during operation. Although it could be shown that the RDWC
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enables significant savings, the energy demand can exceed that of a conventional distillation
sequence if operated incorrectly. Therefore, an efficient control structure has to be established.
Regarding energy efficient operation Schröder et al. (2016) stated that the component
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distribution in the column shows a significant influence on the required heat duty. It was
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further stated that the liquid split, which is the distribution of the reflux onto both sides of the dividing wall, directly affects the component distribution. For non-reactive dividing wall
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columns it was already shown that the liquid split is an important manipulated variable when
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it comes to energy efficient operation (Halvorsen and Skogestad, 1997). Yildirim et al. (2011) give an overview about control structures for DWCs including schemes that target energy efficiency. Despite affirming results for the DWC to date no studies are known which focus on
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energy efficient control of RDWCs. Weinfeld et al. (2018) present a comprehensive overview about studies on the RDWC including all investigated control structures. Most of the
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published studies deal with decentralized control structures that target the maintenance of product specifications as well as good dynamic performance. Qian et al. (2016) also present a first study on model predictive control (MPC). In their study they apply a fixed liquid split,
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which was optimised for the operation point beforehand. They report a good performance of the applied MPC. However, they also state that, especially in case of feed composition disturbances, the control structure might not depict an optimum control due to the fixed liquid split. Egger and Fieg (2017a) presented first experimental results of a basic decentralized
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control structure, also with a fixed liquid split, of a pilot plant. 2. Model Description In order to investigate and predict the complex physical and chemical interactions in the RDWC a reliable process model is required. Therefore, a fully predictive simulation model, that allows steady state as well as dynamic simulations, serves as base in this study. The applied, rigorous model was developed at the Institute of Process and Plant Engineering 3
(Ehlers, 2016). Comprehensive experimental data of a pilot plant enabled a profound validation of the RDWC model (Egger and Fieg, 2017b, 2018). The model is implemented in Aspen Custom Modeler V.8.4 (ACM) of Aspen Tech®. The simulation platform allows great flexibility while modelling and enables specific adjustments in order to depict highly integrated apparatuses like the RDWC. The property data are sourced from Aspen Properties V.8.4.
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2.1. RDWC Simulation Model The developed RDWC model is based on a modular approach. Thereby the modelling of different column configurations and scales is facilitated. The model consists of a module for
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the column shell which contains sub models for the different column sections (i.e. reaction or
separation sections) and the collector/distributors as well as modules for reboiler, condenser, distillate vessel and feed vessel. A scheme of these components used in the model can be found in Figure 1.
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The well-known equilibrium stage approach is used for the calculation of the phase equilibria
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on each theoretical stage. The Antoine equation is employed for the calculation of the vapour pressure of the pure components, the UNIQUAC model is used for the description of the
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equations as exemplary shown in Eq. (1).
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liquid phase non-idealities. In the reactive sections a reaction term is included in the MESH
𝑛𝑅
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𝑑𝑁𝑥𝑖 = L𝑖𝑛 𝑥𝑖𝑛,𝑖 + V𝑖𝑛 𝑦𝑖𝑛,𝑖 − L𝑜𝑢𝑡 𝑥𝑜𝑢𝑡,𝑖 − 𝑉𝑜𝑢𝑡 𝑦𝑜𝑢𝑡,𝑖 + 𝑚𝑐𝑎𝑡 ∑ 𝑟𝑖,𝑗 𝑑𝑡
(1)
𝑗=1
The enthalpy balance includes heat transfer from each stage to the column’s steel as well as
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heat loss to the surroundings. Latter is especially significant for small diameter columns like pilot plants but might be neglected for larger scale columns applied in industry. The heat
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transfer is especially relevant for investigation of the dynamic behaviour, such as start-up or during disturbances, when significant changes of the temperature profile occur in the column. As studies have shown, the distribution of the vapour to both sides of the dividing wall, the so called vapour split, plays an important role when it comes to energy efficient operation of the
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RDWC. Although solutions for an active vapour split control for RDWCs (Harvianto et al., 2018) as well as for DWCs (Dwivedi et al., 2012) were proposed, until today no active vapour split control is known to be implemented in industrial dividing wall columns. Generally, the vapour flow adjusts automatically to equalize the pressure drop in the prefractionator and the main column. This so-called self-adjusting vapour split is considered in the simulation model by corresponding correlations for the calculations of the pressure drop. The vapour split is iteratively adjusted to ensure equal pressure drop on both side of the dividing wall. 4
2.2. Reaction System and Operation Point The applied reference reaction system in this study is the transesterification of 1-butanol and n-hexyl acetate as stated in Eq. (2). 𝐶4 𝐻10 O + 𝐶8 𝐻16 𝑂2 ⇋ 𝐶6 𝐻12 𝑂2 + 𝐶6 𝐻14 O
(2)
The reaction kinetics are modelled via a second order power law approach, taking into
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account the product of the activities of all involved components, as shown in Eq. (3). 𝑛
𝑟𝑖 (𝑇) = 𝑘𝑖 ∙ ∏ 𝑎𝑗
(3)
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𝑗=1
The reaction is assumed to take place in the liquid phase only. Parameters for the phase equilibria and reaction kinetics of the specific system are retrieved from Egger and Fieg (2017b).
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The operation point that serves as base case was chosen according to the possible operation range of the pilot plant. Thus, a subsequent experimental validation with the pilot
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plant is enabled. The employed key variables of the base case are shown in Table 1. 3. Sensitivity Analyses and Control Structure Setup
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3.1. Sensitivity Analyses
To evaluate the direct influence of the liquid split onto the energy demand of the column
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sensitivity analyses are conducted. Therefore, several disturbances in the feed flow and the feed composition are investigated. The four employed disturbance cases are shown in Table
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2. The results give insights about the dependency of the energy demand on the liquid split and allow the calculation of the theoretical minimal heat duty for each disturbance case. In steady state simulations the main components of the three product streams (butyl acetate in
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the distillate stream, butanol in the side stream and hexanol in the bottom stream) are set to a fixed value in order to ensure a constant product quality. The base case is modified by changing the respective disturbance variable. The liquid split is then varied for each of the
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disturbance cases in a range of 0.2 to 0.6 by steps of 0.01. The required heat duty to maintain the product specifications is calculated for each step by the simulation model. The boundaries for the liquid split originate from previous studies, where it was found that values smaller 0.2 might lead to unfeasible internal flows, whereas values greater 0.6 lead to monotonously rising heat duties. The results of the sensitivity analyses are shown in Figure 2. The results can be divided into different classes. Increasing the feed flow or the share of high boiling component in the feed flow leads to an overall increased heat demand. The curves for 5
disturbances of +10 % hold a local maximum and two local minima each in the presented range. This indicates that two theoretical values for the minimal heat duty can be reached, depending on the initial state and the settling course of the liquid split in the dynamic simulation. Decreasing the feed flow or the high boiling feed component leads to an overall lower heat duty. No local maxima can be detected for these cases. However, reducing the feed flow leads to a local minimum, whereas decreasing the high boiling feed component leads to a monotonously falling function. Furthermore, the sensitivity of the heat duty towards
presents the theoretical minimal heat duties for all investigated disturbances.
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the liquid split is significantly smaller than for disturbances with increased values. Table 2
The analyses show the necessity of adjusting the liquid split in a range of 0.2 to 0.38 to
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maintain energy efficient operation in case of all investigated disturbances. This indicates the importance of the liquid split as a manipulated variable for process control of the RDWC. 3.2. Evaluation of Suited Control Variables
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While the liquid split directly influences the component distribution and can thus serve as
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manipulated variable, a suited control variable needs to be selected. This control variable has to meet two main criteria. First, the control variable has to be sensitive towards changes
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in the manipulated variable Secondly the control variable must represent the component
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distribution and especially the energy minimal operation in the column. Therefore, the value of the control variable should not differ considerably for all energy minima found in the sensitivity analyses. If a control structure with set point adaptation is applied, a variation of
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the control value would be possible. However, a simulation model is needed to calculate the adapted set points before or during operation. Since this study aims at providing a
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straightforward control structure that can be used even if no simulation model is available, set point adaption will not be considered in the following. Ling and Luyben (2009) examine a control variable for energy efficient control for non-
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reactive dividing wall columns. They suggest controlling the concentration of the high boiling component on the top stage of the prefractionator. This strategy prevents an accumulation of the high boiling component in the top of the column and thus maintains a feasible distribution. Furthermore, high boiling components do not rise above the dividing wall and thereby
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impurities in the side stream are avoided. However, their approach applies for a dividing wall column with three components to be separated. In this study a RDWC with two reactants and two products, i.e. four components, is examined. This raises the question if the highest boiling or another high boiling component should be controlled. Figure 3 shows the mass fractions of all four components on the top stage of the prefractionator at the four selected disturbances cases for the minimal heat duty. As stated above, the concentration should be as close to the base case as possible. The two reactants hexyl acetate and butanol, 6
representing the middle boiling components, show only small deviations and are thus suited control variables. Sticking to the concept of Ling and Luyben (2009), the higher boiling component of these two, hexyl acetate is chosen as control variable. 3.3. Decentralized Control Structure By proving the effective controllability of a highly complex apparatus utilizing a rather “simple” decentralized control structure, the reluctance to operate RDWC is to be reduced. Therefore,
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two concentration control concepts will be investigated and compared. One control structure (CS1) includes a control loop for concentration control of the high boiling reactant on the top stage of the prefractionator by the liquid split. For the second
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structure (CS2) the liquid split is set to a fixed value at all times. The direct comparison enables the evaluation of the energy efficient operation caused by the adjustment of the liquid split. In Figure 4 both control structures are shown. They contain two level control loops for maintaining the level of the reboiler and the distillate vessel. The key components in the
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three product streams are controlled by a concentration control loop each. In this study the
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focus is on maintaining the concentration of the key components, while the composition of impurities might vary. However, control of the impurities is also possible. The control variable
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can be chosen according to the desired specification. CS1 contains a fourth concentration
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control loop aiming at energy efficient operation.
In order to conduct dynamic simulations the presented control structures are implemented in
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the simulation model. Therefore control models provided by ACM are employed. PIDIncr model, which calculates changes in the output as a function of the error, is employed as PID controller model using an ideal algorithm. Concentration control is modelled via proportional
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integral controllers, while level control is modelled via proportional controllers only. To take into account delays in the control loops introduced by concentration measurements, dead
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times are added to all concentrations control loops. Tuning is carried out with the closed loop ATV test provided in ACM. The tuning parameters are calculated via the ultimate gain and ultimate period applying Tyreus-Luyben’s method (Tyreus, 1992). The tuning parameters for
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the control loops are shown in Table 3. 4. Simulation Results Dynamic simulations were conducted for each disturbance case and both control structures. Each simulation run starts with the base case set up. Process control is then set to automatic mode. After 15 minutes simulation time, the disturbance is introduced to the system by changing the disturbance variable. The dynamic changes of all variables are recorded for a simulation time of 15 hours in intervals of 0.01 hours. The evaluation of the simulated values 7
is done in two steps. First, the dynamic performance of both structures is examined. Therefore, the accuracy of the concentration control, determined by the settling course and the settling time, is evaluated. In a second step, the energy efficiency is assessed by comparison to the theoretical minimal heat duty found in the previous sensitivity analyses. 4.1. Dynamic Performance The evaluation of the dynamic performance is carried out based on the results for the key
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components of the three product streams. In Figure 5 the courses of the key components’ concentrations are shown for each disturbance case. Each graph depicts the direct comparison of CS1 (red lines) and CS2 (black lines). At a first glance it can be seen that the
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settling courses of both structures are similar. Feed flow disturbances can be handled by both structures in about 5 hours. During the settling time, products obtained at the reboiler stream and distillate stream show small deviations of max. 0.5 mass%. A larger effect is observed for the side stream concentrations. A maximum deviation of up to 4 mass% can be
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detected for short time periods. Moreover, the dynamic course of the side stream concentration shows oscillations when CS1 is applied. These oscillations are imposed by the
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fourth additional control loop. The liquid split directly influences the concentration on both
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sides of the partition wall and therefore interacts with the control loop for the side stream concentration.
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At disturbances in the feed composition, the concentrations in the reboiler and distillate stream show again only small deviations up to 0.5 mass% during the settling course. The
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side stream concentration is the most affected with deviations of up to 3 mass%. Interestingly, in case of an increase of the high boiling reactant, CS2 displays oscillations in
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the side stream concentrations. Deviations up to 0.02 mass% can still be observed after 15 h simulation time. However the oscillation fades and deviations get negligibly small, so that after 24 h deviations are smaller than 0.01 mass% and after 30 h smaller than 0.005 mass%.
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For all other cases both control structures reach a new steady state. In Figure 6 the accuracy of the concentration control of the products is assessed. The relative deviation from the set point is shown for all components at the newly adapted steady states. For each type of disturbance, i.e. feed flow and feed composition, the mean deviation is calculated. The
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accuracy is very high for both structures. Maximal relative deviations of 0.06% prove the effective functionality of the control structures. 4.2. Energy Efficient Control The evaluation of the dynamic performance shows that both control structures can efficiently handle disturbances in feed flow and feed composition. However, the evaluation only focuses on the outlet concentrations. In a second step the energy duty required to maintain these 8
concentrations is analysed. For each disturbance case the heat demand adjusted at the new steady state is identified. The sensitivity analyses in chapter 3.1. give insights about the theoretical minimal heat duty for each of these disturbance case. The minimal values from Table 2 are then compared to the results of the dynamic simulation study. In order to evaluate the differences between CS1 and CS2, the values are normalised to the minimal heat duty. Therefore, the relative deviations from the minimal heat duty are presented in Figure 7.
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The heat duty adjusted by CS1 is very close to the minimal heat duty for all disturbance cases. Deviations are always smaller than 1 %. In case of feed flow disturbances, CS2
shows a good performance, too, with overall deviations below 2 %. As shown in the
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sensitivity analyses, the heat duty is moderately sensitive towards the liquid split when the feed flow is changed. Therefore, the difference between a fixed liquid split value and an
adjusted liquid split is expectedly small. In contrast, the high sensitivity towards feed composition disturbances leads to big differences in the heat duty for both control structures.
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Whereas CS1 is able to maintain energy minimal operation, CS2 leads to a new steady state with a heat duty that is up to 20 % above the theoretical minimum. These findings are also
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supported by the adjusted liquid split values shown in Table 4. In three cases the value of the
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adjusted liquid split of CS1 is closer to the liquid split at minimal heat duty than the fixed
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liquid split of CS2. In these cases the heat duty is always lower for CS1. In the case of an increase of the feed flow by +10 % the adjusted liquid split by CS1 is 0.33 whereas the minimal value as well as the fixed value is 0.3. However, due to the low sensitivity in this
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case no significant deviation in the heat duty can be observed. In order to further assess the choice of the selected control variable, the composition on the
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top stage of the prefractionator is examined for an exemplary disturbance case. Figure 8 shows the concentrations of all four components for an increase of the high boiling reactant in the feed flow of +10% for the energy minimal case as well as the results of CS1 and CS2.
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It can be seen that even though only one concentration is controlled by the liquid split, the overall composition on the stage with CS1 resembles the composition in the energy minimal case. On the other hand, the composition obtained by CS2 differs strongly from the minimal values. Due to the fixed liquid split, the composition changes and an increase of the heaviest
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key is not compensated by the control structure. 5. Discussion As shown in the previous chapter the amount of energy savings that can be achieved by controlling the liquid split is dependent on the sensitivity of the heat duty towards the different disturbance cases. Therefore, in some cases a fixed liquid split can be sufficient while in other cases an adjustment of the liquid split is highly recommended. Hence, a sensitivity 9
study regarding the most common disturbances for the desired operation point is useful to quantify possible savings. Since the liquid split is a fixed variable in CS2, two structures with different degrees of freedom are compared. Therefore, the impurities in the product streams are allowed to vary. Depending on the goal that is to be achieved by the control structure, it is necessary to elaborate which variables should be controlled and which may fluctuate. In this study the main task is to ensure a certain concentration for all key components.
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In industrial applications temperature controllers are preferred to concentration control as they are less expensive and provide faster dynamics, due to lack of dead times. However,
the temperature only represents the composition in the column indirectly. The highly complex
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interactions of reaction and separation in the RDWC might lead to ambiguous representation
of the concentrations by the temperature (Harbou et al., 2017). On behalf of validating the theoretical findings about the influence of the liquid split on the component distribution and to eliminate potential errors due to ambiguous representation a direct measurement of the
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control variable is favoured in this study. The validation of the general feasibility of energy efficient control forms the base for further investigations. As a next step use of temperature
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controllers is highly recommended in order to improve the dynamics of the control structure.
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The allocation of control and manipulated variables in this study was decided according to
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the proximity of both variables. Depending on the algorithm selecting the control variables different control structures might arise. Furthermore, it might be also possible, analogue to considerations of Ling and Luyben (2009), to control the light boiling reactant on the lowest
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stage of the prefractionator.
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6. Conclusions
In this paper the energy efficient control of a RDWC is assessed. A developed control structure is presented, evaluated for different disturbances and the underlying mechanisms
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are analysed. Sensitivity analyses show that the energy demand of the RDWC is highly dependent on the liquid split. Depending on the applied disturbance, one or two energy minimal operation points can be identified. In the proposed control structure the liquid split is employed to control the heavy reactant concentration on the top stage of the prefractionator.
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Disturbances in the feed flow and feed composition can be handled successfully with a high accuracy regarding the product specifications. The evaluation of the energy efficiency, by comparison to a control structure with fixed liquid split, shows that significant savings in the energy demand are possible. Without adjusting the liquid split the energy demand exceeds the energy minimal demand in one of the applied disturbances by 22 %. As expected, the saving potential is highly dependent on the disturbance type. The sensitivity analyses confirm that changes in the feed composition strongly affect the dependency of the energy demand 10
on the liquid split. In case of a feed flow disturbance the sensitivity is relatively low. Since the liquid split control loop does not affect the dynamic performance of the control structure, it is
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highly recommended to always apply the liquid split for RDWC control.
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References
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Dwivedi, D., Strandberg, J.P., Halvorsen, I.J., Preisig, H.A., Skogestad, S., 2012. Active Vapor Split Control for Dividing-Wall Columns. Industrial & Engineering Chemistry Research 51 (46), 15176–15183. 10.1021/ie3014346. Egger, L.S., Fieg, G., 2017a. Decentralized Process Control of Reactive Dividing Wall Columns. Chemical Engineering Transactions (57), 1693–1698. 10.3303/CET1757283. Egger, T., Fieg, G., 2017b. Enzymatic catalyzed reactive dividing wall column: Experiments and model validation. AIChE Journal 63 (6), 2198–2211. 10.1002/aic.15598. Egger, T., Fieg, G., 2018. Dynamic process behavior and model validation of reactive dividing wall columns. Chemical Engineering Science 179, 284–295. 10.1016/j.ces.2017.12.011. Ehlers, C., 2016. Experimentelle und simulative Analyse integrierter Rektifikationsapparate am Beispiel der reaktiven Trennwandkolonne. Dissertation, Hamburg. Halvorsen, I.J., Skogestad, S., 1997. Optimizing Control of Petlyuk Distillation: Understanding the Steady-State Behavior. Computers & Chemical Engineering (21), 249–254. 10.1016/S0098-1354(97)87510-2. Halvorsen, I.J., Skogestad, S., 2003. Minimum Energy Consumption in Multicomponent Distillation. 2. ThreeProduct Petlyuk Arrangements. Industrial & Engineering Chemistry Research 42 (3), 605–615. 10.1021/ie0108649. Harbou, E. von, Ryll, O., Schrabback, M., Bortz, M., Hasse, H., 2017. Reactive Distillation in a Dividing-Wall Column: Model Development, Simulation, and Error Analysis. Chemie Ingenieur Technik 89 (10), 1315–1324. 10.1002/cite.201700068. Harvianto, G.R., Kim, K.-H., Kang, K.-J., Lee, M., 2018. Optimal Operation of Dividing Wall Column using EnhancedActive Vapor Distributor. Chemical Engineering Transactions 2018 (69). Kaibel, G., 1986. Verfahren zur Durchführung von chemischen Reaktionen und gleichzeitiger destillativer Zerlegung eines Produktgemisches in mehrere Fraktionen mittels einer Destillationskolonne - European Patent Office - EP 0126288 B1, 5 pp. Kiss, A.A., 2014. Distillation technology - still young and full of breakthrough opportunities. Journal of Chemical Technology & Biotechnology 89 (4), 479–498. 10.1002/jctb.4262. Ling, H., Luyben, W.L., 2009. New Control Structure for Divided-Wall Columns. Industrial & Engineering Chemistry Research 48 (13), 6034–6049. 10.1021/ie801373b. Qian, X., Jia, S., Skogestad, S., Yuan, X., Luo, Y., 2016. Model Predictive Control of Reactive Dividing Wall Column for the Selective Hydrogenation and Separation of a C3 Stream in an Ethylene Plant. Industrial & Engineering Chemistry Research 55 (36), 9738–9748. 10.1021/acs.iecr.6b02112. Schröder, M., Ehlers, C., Fieg, G., 2016. A Comprehensive Analysis on the Reactive Dividing-Wall Column, its Minimum Energy Demand, and Energy-Saving Potential. Chemical Engineering & Technology 39 (12), 2323– 2338. 10.1002/ceat.201500722. Tyreus, 1992. Tuning PI Controllers for Integrator/Dead Time Processes. Industrial & Engineering Chemistry Research (31), 2625–2628. Weinfeld, J.A., Owens, S.A., Eldridge, R., 2018. Reactive dividing wall columns: A comprehensive review. Chemical Engineering and Processing - Process Intensification 123, 20–33. 10.1016/j.cep.2017.10.019. Yildirim, Ö., Kiss, A.A., Kenig, E.Y., 2011. Dividing wall columns in chemical process industry: A review on current activities. Separation and Purification Technology 80 (3), 403–417. 10.1016/j.seppur.2011.05.009.
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List of Figures: Figure 1: Schematic depiction of RDWC main components for modelling in Aspen Custom
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Modeler
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Figure 2: Sensitivity analyses of the influence of the liquid split on the heat duty at different
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disturbance cases
14
Figure 3: Concentration of all components on top stage of the prefractionator at four
A
CC E
PT
ED
M
A
N
U
SC R
IP T
disturbance cases
15
A
CC E
PT
ED
M
A
N
U
SC R
IP T
Figure 4: Control structures CS1 with adjustable liquid split and CS2 with fixed liquid split
16
Figure 5: Dynamic course of the three key product components (distillate stream: butyl acetate, bottom stream: hexanol, side stream: butanol) after feed composition disturbances by -10 % (a) and +10 % hexyl acetate (b) and feed flow disturbances by -10 % (c) and +10 %
A
CC E
PT
ED
M
A
N
U
SC R
IP T
(d)
17
Figure 6: Relative deviation from set points of the three key components after feed flow
A
CC E
PT
ED
M
A
N
U
SC R
IP T
disturbances (a) and feed composition disturbances (b)
18
Figure 7: Adjusted heat duties in relation to theoretical minimal heat duty for the investigated
A
CC E
PT
ED
M
A
N
U
SC R
IP T
disturbance cases
19
Figure 8: Concentration on top stage of prefractionator after a disturbance of -10 mass% hexyl acetate in the feed flow for the energy minimal case Q_min, CS1 (LS free) and CS2
A
CC E
PT
ED
M
A
N
U
SC R
IP T
(LS fixed)
20
List of Tables:
Symbol
Value
Unit
Feed stream
F
2.3
kg/h
Feed fraction hexyl acetate
w_HeAc
0.35
kg/kg
Feed fraction butanol
w_BuOH
0.65
kg/kg
Reflux stream
R
4.35
kg/h
Distillate stream
D
1.0
kg/h
Side stream
S
0.75
Bottom stream
B
0.55
Reboiler duty
Q
0.92
Condenser pressure
P
3.0
Liquid split
LS
Conversion of hexyl acetate
X_HeAc
SC R
IP T
Variable
U
Table 1: Base case data
kg/h kg/h kW
kPa -
95.64
%
A
CC E
PT
ED
M
A
N
0.3
21
Table 2: Investigated disturbance cases with corresponding minimal heat duty and liquid split Intensity
variable
1st minimum
2nd minimum
Qmin
liquid split
Qmin
liquid split
[kW]
[-]
[kW]
[-]
F
- 10%
0.753
0.25
2
F
+10%
1.113
0.3
3
w_HeAc
-10 mass%
0.715
0.2
4
w_HeAc
+10 mass%
1.209
0.2
1.241
1.238
0.2
0.38
A
CC E
PT
ED
M
A
N
U
SC R
1
IP T
Case Disturbance
22
Table 3: Tuning parameters of all control loops of CS1 and CS2 QC1
QC2
QC3
QC4
LC1
LC2
Kp [%/%]
12.8
8
121.6
0.73
28.6
133.3
Ti [min]
145.2
66
118.8
39.6
-
-
Action
direct
direct
reverse
direct
direct
direct
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CC E
PT
ED
M
A
N
U
SC R
IP T
Controller
23
Table 4: Liquid split values for each investigated disturbance case at minimal heat duty and as adjusted by CS1 (LS free) and CS2 (LS fixed) Disturbance
Intensity
variable
LS at Qmin
LS free
LS fixed
[-]
[-]
[-]
- 10%
0.25
0.27
0.3
F
+10%
0.3
0.33
0.3
w_HeAc
-10 mass%
0.38
0.39
0.3
w_HeAc
+10 mass%
0.2
0.2
0.3
A
CC E
PT
ED
M
A
N
U
SC R
IP T
F
24