control of reactive distillation column: a review

INTERNATIONAL JOURNAL OF CHEMICAL

REACTOR ENGINEERING

Volume 8 2010 Review R5

Control of Reactive Distillation Column: AReview

Neha Sharma∗ Kailash Singh†

∗Malaviya National Institute of Technology Jaipur, [email protected]†Malaviya National Institute of Technology Jaipur, [email protected]

ISSN 1542-6580

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Control of Reactive Distillation Column: A Review

Neha Sharma and Kailash Singh

Abstract

The objective of this paper is to give a critical survey of the present statuswithin the field of control of a reactive distillation column. Control of a reactivedistillation column is a challenging task due to process nonlinearity and complexinteractions between the vapor-liquid equilibrium and chemical reactions. Thereare different types of control methodologies, which have been studied in the re-active distillation, ranging from a simple proportional-integral (PI) controller toadvanced model predictive controllers (MPC) such as dynamic matrix control(DMC), quadratic dynamic matrix control (QDMC), robust multivariable pre-dictive control technology (RMPCT), generalized predictive control (GPC), andother advanced control techniques. With the goals of optimal performance, energyconservation and cost effectiveness of process operations in industries, the designof optimal controllers and controller performance assessment have received greatattention in both industries and academia. The main objective of control is tomaintain the product purity within the desired range.

KEYWORDS: reactive distillation column, PID control, model predictive con-trol, advanced control techniques, reactive dividing wall distillation column



1. Introduction

Nowadays, process intensification (PI) has become a thrust area of research in chemical engineering and related disciplines because it offers many potential advantages, such as reduction in equipment and plant size (low capital investment), improvements in process efficiency and safety, and decreased energy consumption (low operating cost). This goal is achieved by developing a novel apparatus or technique and combining different mechanisms like reaction and separation in a single unit, to bring dramatic improvements in manufacturing and processing, substantially decreasing equipment size/production-capacity ratio, energy consumption, or waste production. Reactive distillation (RD) is an important example of process intensification.

Reactive distillation is a combination of chemical reaction and multi-component distillation in a counter-current column. There were a total of 562 publications for the period of 1970-1999 (Malone 2000). In the next five-year period (2000-2004), there were another 253 publications (Zeng 2006) in the research area of reactive distillation and from year 2005 till date, the number of applications are approximately 373 (Source: Google Scholar). These processes as a whole are not a new concept as the first patent dates back to the 1920’s .The reaction can be catalyzed either homogenously or heterogeneously. The most important advantage in use of RD for equilibrium-controlled reactions is the elimination of conversion limitations by continuous removal of products from the reaction zone. The use of RD process can offer several advantages (Hiwale et al. 2004) such as reduced downstream processing, utilization of heat of reaction for evaporation of liquid phase, simple temperature control of reactor, possibility of influencing chemical equilibria by removal of products and limitations imposed by azeotropic mixture. All these factors contribute to the growing commercial importance of reactive distillation column (RDC). Thus it is also necessary to discuss about the reactive azeotrope both from theoretical and practical standpoint. At a reactive azeotrope, the change in composition due to reaction is compensated by the change in composition due to phase equilibrium and so a constant boiling mixture is achieved. They limit the products of a reactive distillation in exactly the same way that ordinary azeotropes limit the products of non-reactive distillation. Like in a non-reactive column these azeotropes cannot be crossed at steady state in a fully reactive system. In the case of reactive systems, azeotropes may also prove to be beneficial, for example by preventing an adverse back reaction (Ung & Doherty 1995). Furthermore, the location of reactive azeotropes is needed in the construction of residue curve maps for the synthesis and design of reactive separation operations.

Reactive distillation is ideal for reactions that are difficult to drive to completion without separation of one of the products. Such reactions are called

1Sharma and Singh: Control of Reactive Distillation Column: A Review



'equilibrium limited' reactions such as esterification, ester hydrolysis and transesterification; this offers distinct advantages over the conventional sequential approach. In a reactor, the principal process objective, which maximizes profitability, is conversion of the limiting reactant, whereas in distillation, it is separation (or fractionation) which is normally measured by the product purity. A typical set-up used for reactive distillation is shown in Figure 1.

Due to the combination of reaction and separation, reactive distillation exhibits complex behaviors (Khaledi & Young 2005), such as steady state multiplicity, process gain sign changes (bi-directionality) and strong interactions between process variables. Hence, reactive distillation processes are highly nonlinear in nature. These complexities make reactive distillation control extremely difficult.

Most of the publications of reactive distillation column deal with the research on various aspects, such as modeling and simulation, process synthesis, column hardware, nonlinear dynamics and control, etc. Most of the papers are basically based on steady state conditions including process design and the analysis of multiple steady states (Pisarenko et al. 1988; Ciric & Miao 1994; Sneesby et al. 1997; Sneesby et al. 1998; Gehrke & Marquardt 1997; Güttinger & Morari 1997) but a relatively small amount of research work has been reported on the control of RD columns.

Fig. 1 Reactive Distillation Column

Catalyst (Reaction zone)

Top product

Bottom Product

Feed

Reflux

Reboiler

Rectifying Section

Stripping Section

Condenser + decanter

2 International Journal of Chemical Reactor Engineering Vol. 8 [2010], Review R5



Research and development of reactive distillation has been comprehensively reviewed by Doherty and Buzad (1992) and Taylor and Krishna (2000). The control papers up to 1999 were discussed by Al-Arfaj (2000). Sneesby, Tade and Smith (2000) studied three control structures for a single-feed ETBE reactive distillation. Monroy-Loperens et al. (2000) studied the control of ethylene glycol reactive distillation using nonlinear control. Luyben (2000) studied the economics and control effects of using excess reactant. The books by Doherty and Malone (2001) , Sundmacher and Kienle (2003), Luyben & Yu (2008), Stichlmair and Fair (1998) give updated summaries of modeling, simulation and control of reactive distillation.

Many Chemical systems are studied by Al-Arfaz and Luyben. In the ETBE system (Al-Arfaj 2002c), there are two reactants, one product and one inert. They studied single-feed and double-feed designs and several control schemes were examined. The ethylene glycol system (Al-Arfaj 2002b) has two feeds but only one product. A control scheme where a temperature in the stripping section is controlled by the heat input was found to be effective. Olefin metathesis (Al-Arfaj 2002d) has only one reactant and two products. A temperature in the stripping section is controlled by the heat input and another temperature in the rectifying section is controlled by the reflux rate.

Most of the publications on closed-loop control of RD columns deal with the linear control schemes including conventional proportional integral (PI) controller (Monroy-Loperens 2000; Al-Arfaj 2002c; Al-Arfaj 2002b; Al-Arfaj 2002d; Al-Arfaj 2002a; Kaymak & Luyben 2005; Kaymak & Luyben 2006) and model predictive controller (MPC) (Balasubramhanya & Doyle III 2000; Dwivedi & Kaistha 2008; Kawathekar 2004; Olanrewaju & Al-Arfaj 2005; Qin & Badgwell 2003; Silva & Kwong 1999; Kumar & Daoutidis 1999) . Roat et al. (1986) presented the inadequacies of conventional linear multi-loop controllers and highlighted the need for more advanced controllers designed within the framework of nonlinear control science. A limited number of papers dealing with the advanced nonlinear control of continuous reactive distillation have appeared in open literature (Jana & Adari 2009).

In recent years, many articles have appeared in the literature on the control of RD columns. Kumar and Kaistha published a series of articles on RD control (Singh et al. 2007; Kumar & Kaistha 2008d; 2008a; 2008e; 2008b; 2008c; 2009b; 2009a). Their work offers much insight on key issues that a RD column control system must address. The steady-state simulation of reactive distillation columns for methyl acetate and MTBE systems using the conventional Naphtali-Sandholm (NS) method is studied in Singh et al. (2007). The authors show that controlling a reactive tray temperature with acceptable sensitivity but larger rangeability gives better robustness. They also showed that controlling the difference in the temperature of two suitably chosen reactive trays further improves robustness of




both the structures as input multiplicity is avoided (Kumar & Kaistha 2008d). The authors also focused their work on the controllability issues at the design stage.

Effect of feed tray location on the controllability of double feed reactive distillation (RD) columns is evaluated in Kumar and Kaistha (2008b). They also studied the effect of internal heat integration by catalyst redistribution on the controllability of an ideal and a methyl acetate reactive distillation (RD) column in Kumar and Kaistha (2008c). A two-temperature control structure for a methyl acetate reactive distillation (RD) column on the basis of ratio control scheme is evaluated in Kumar and Kaistha (2009a).

Other authors have also studied the control of RD columns. Jana and Adari (2009) discussed the advanced adaptive control, i.e. generic model controller (GMC) and an adaptive state estimator (ASE) for nonlinear process, of a batch reactive distillation (BRD) column. Wang et al. (2008) investigate the design and control strategies of a reactive distillation process with partially thermal coupling for the production of methanol and n-butyl acetate by transesterification reaction of methyl acetate and n-butanol. Adams and Seider (2009b) introduced a semi-continuous process that alternates between reactive extraction and reactive distillation in a single packed column. They have shown a different control scheme with different tuning parameters, which triggers for the various process functionalities. Wang et al. (2010) investigate the control strategies of the reactive distillation with thermally coupled extractive distillation process for the production of dimethyl carbonate (DMC) and ethylene glycol. Dynamic simulation results reveal that designed temperature control strategy can maintain reactant inventory in the RDC and product purities at or around their desired values under external and internal disturbances. 2. Control Structure

A “control structure” as defined by Singh et al. (2005) refers to the number of control loops and the specific input-output pairing used in the loops. Potential input variables are the reflux rate, reflux ratio, reboiler duty, reboiler ratio, distillate rate, bottoms rate, and the fresh feeds. Potential output variables are easily measurable variables such as tray / stream temperatures and compositions. There are thus several possible input (manipulated) variables and output (controlled) variables even in a simple RD column.

Three types of control structures are evaluated by Kawathekar (2004) for the reactive distillation systems:

1. In the first control structure, the top and bottom purities are controlled by adjusting reflux rate and reboiler heat duty, respectively. The composition inside




the reactive zone of the column is measured and controlled by manipulating one of the fresh feeds.

2.In the second control structure, only the column internal composition is controlled and a temperature is controlled in the stripping section in order to maintain bottom purity at a specified value. Distillate purity is not controlled but the reflux ratio is held constant.

3.The third control structure uses two temperatures that manipulate the two fresh feeds. Reboiler heat input is flow controlled and serves as a production rate handle while the reflux ratio is held constant.

Several different control structures have been proposed for reactive distillation columns. Al-Arfaz (2002a), Kayamak and Luyben (2005), Sneesby et al. (1999), and Sneesby et al. (1997) investigated a suitable control structure (i.e. the choice of manipulated variables and controlled variables) for reactive distillation processes. The appropriate control structure depends on the flowsheet and on the type of reactions occurring in the column (Luyben & Yu 2008). For example, some of the common types of reactions are the following:

1. Reactions with two reactants and two products 2. Reactions with two reactants and one product 3. Reactions with one reactant and two products

Two different control structures are explored for the single reactive distillation column process. The first is the two temperature control structure, which uses only temperatures and does not require a direct composition measurement. The second control structure uses a composition analyzer to measure a composition of one of the reactants on a selected tray in the column to adjust one of the fresh feeds.

If two reactants are involved and if it is desirable to operate the process without any excess of reactant, it is necessary to manage the fresh feedstreams so that the stoichiometry is exactly balanced. A composition analyzer that measures an internal composition in the column is sometimes required. However, if two products are produced, it may be possible to avoid the use of an analyzer by using two temperatures in the column to adjust the two feedstreams. This type of structure was proposed by Roat et al. (1986) for the ideal reaction A + B ↔ C + D in one of the earliest articles dealing with reactive distillation control. We call this control structure the “Eastman structure” (Luyben & Yu 2008).

A control structure that maintains a tray temperature in both the reactive and stripping sections using the fresh acetic acid and methanol feeds respectively provides the best control of all the control structures studied. The column is




operated at fixed reflux ratio and the reboiler duty is the production rate handle. It is worth mentioning that this is the control structure implemented at Eastman.

There are three basic methods for monitoring product composition (Sneesby et al. 1997):

(a) Directly, with one or more online analyzers (GC, NIR); (b) Indirectly, using a temperature or pressure corrected temperature to infer composition; and (c) Externally, using process samples taken at regular intervals and appropriate laboratory equipment.

Analyzers have many advantages but are costly, require high maintenance, and introduce dead time into the control loop, it is desirable to use inferential temperature measurements instead of direct composition measurements whenever possible. It is necessary to measure the composition dead time as it affects the effectiveness of the control system and may result in an unstable control system. Sneesby et al. (1997) used inferential controller to monitor product composition. Chiang et al. (2002) assumes a 4 minute analyzer dead time for the composition measurements with a transmitter span of 0.1 mole fraction for the production of amyl acetate. Wang et al. (2003) assumed dead time of 5s for the temperature measurement with a transmitter span of 150 K and dead time of 5 min is assumed for the concentration measurement with a transmitter span of 0.3 for the processing and control of MTBE. Luyben et al. (2004) inserted dead times of 1 min in the column temperature controllers for the production of butyl acetate. Hung et al. (2006) assumed 4 minutes of analyzer dead time for the composition loop. Similarly Tsai et al. (2008) assumed a dead time of 5 minute for composition analyzer for the production of ethyl acetate. So, in most cases dead time of 4-5 minute was considered for composition measurement.

In the Eastman control structure, two PI temperature controllers are used to maintain two tray temperatures in the column by manipulating the two fresh feedstreams. Production rate changes are achieved by changing the vapor boilup. The base level is controlled by manipulating the bottoms flowrate.




(a) (b)

Fig. 2 Two alternatives for Eastman control structure (Luyben & Yu 2008).

There are two alternatives for this control structure: a constant reflux flowrate and a constant reflux ratio. Figure 2a shows the control structure (CS7-R) where the reflux flowrate is fixed and the reflux drum level is controlled by manipulating the distillate flowrate. Figure 2b shows the alternative version (CS7-RR) where the reflux drum level is controlled by the reflux flowrate and the distillate flowrate is adjusted to give a constant reflux ratio.

Both control structures are single-input, single-output structures with PI controllers for temperature control and P controllers for level control. The relay-feedback method is used to obtain the ultimate gains and ultimate periods. The valves are designed to be half open at steady state. Two 60 s first-order measurement lags are used in both temperature loops. The temperature controllers are tuned using the Tyreus–Luyben tuning method (Luyben 2007). The column pressure is assumed to be constant, which is achieved by manipulating the condenser heat duty.

Several different control structures have been proposed for reactive distillation columns by several authors. Al-Arfaz (2002a) evaluated three control structures for both ideal and methyl acetate reactive distillation systems. A control structure with one internal composition controller and one temperature controller provides effective control of both systems for high as well as moderate conversion




designs. A two-temperature control structure is effective when the system is over designed in terms of number of reactive trays, holdup and/or catalyst load. Another author Kaymak and Luyben (2005) compare the effectiveness of two different types of two-temperature control structures for reactive distillation columns operated in the “neat” mode (no excess reactant) with chemical reactions that have two reactants and produce two products. The effectiveness of these control structures was compared in the face of disturbances in the production rate and fresh feed compositions. For both control structures, the process did not require the measurement of an internal composition. For the first control structure (CS7), two tray temperatures were controlled by manipulating the two fresh feed streams. The vapor boilup (or reboiler duty) is the production rate handle. For the second control structure (CS8), two tray temperatures were controlled by manipulating one of the two fresh feed streams and vapor boilup (or reboiler duty). The other fresh feed stream is the production rate handle. Therefore, selection of the manipulated fresh feed stream in the second structure has an important role in the stability of the system. Sequential tuning of the interacting temperature controllers is sometimes necessary.

3. Control Structure of Various Chemical Systems

As mentioned earlier different control structures were proposed for different chemical systems of reactive distillation column. Control system design basically includes the selection of control configuration and control algorithm. Regarding control configuration, cascade inferential control scheme via measurable temperatures is reasonably adopted due to the lack of reliability of composition controls. This is basically consisting of the following steps: (1) setting the control objective, (2) selection of manipulated variables, (3) determination of temperature-control trays, (4) finding controller settings for regulatory control, and (5) providing feed forward compensation for production rate variation. A good control structure is one that rejects disturbances effectively. Below we discuss control structures of some chemical systems:

3.1 Control of MTBE System

Methyl tert-butyl ether (MTBE) is a commonly used antiknock compound added to gasoline to increase its octane number. It has been probably the most studied reacting system in reactive distillation. Reactive distillation was patented for methyl tertiary butyl ether (MTBE) synthesis by L. A. Smith in 1980 (U. S. Pat. No. 4307254).

Wang et al. (2003) studied the control strategy of a reactive distillation system that synthesizes MTBE. The main purpose was to show that a simple linear




control strategy would keep the MTBE column operating at high product purity and reactant conversion if a proper input-output pairing can be found, even though the overall process exhibits steady state multiplicities and nonlinear dynamics.

Barlett (1999) studied the control of a methyl tert-butyl ether (MTBE) reactive distillation column. They discussed the several schemes using conventional PI controllers. The selection of an appropriate tray temperature is explored. They recommend the use of a tray in the reactive section instead of the more conventional approach of using a temperature in the stripping section. The authors recognized the importance of maintaining tight control over the feed stoichiometry to avoid an excess of methanol. They first tried to measure the methanol concentration in the overhead and manipulate methanol fresh feed, but found that there was severe interaction between the temperature controller and the methanol composition controller. They finally recommended a feed forward scheme in which the feed composition is measured and used to reset the fresh methanol feed flow rate. Flow measurement inaccuracies and composition analyzer inaccuracies would doom this strategy to failure in a real plant environment unless significant amounts of excess methanol are used.

A number of decentralized control systems can be synthesized and designed for the MTBE decomposition reactive distillation column, including direct composition control, indirect composition (i.e., temperature based) control, and their various alternatives (Al-Arfaj 2000; Wang 2003; Zeng 2006; Kaymak & Luyben 2006). Recently, Huang et al. (2007) studied a reactive distillation column that decomposes methyl tertiary butyl ether (MTBE) into isobutylene (IBUT) and methanol (MEOH) using direct composition control.

)HC-( IBUTOH)MEOH(CHO)H(CMTBE 843125 i

Figure 3 presents a sketch of a control configuration in which the purities of both the top and the bottom products are measured and controlled. In accordance with the relative gain array (RGA) analysis, the composition of the isobutylene component is controlled by manipulating the reflux flow rate in the top control loop. In the bottom control loop, the composition of the methanol component is controlled by manipulating the heat duty of the reboiler. The levels of the reflux drum and the bottom reboiler are controlled by the distillate and bottom-product flow rates, respectively. The dynamics of concentration measurements was assumed to be two first-order lags of 30 s each, in series. The transmitter span of all composition measurements was taken to be 0.12, and all control valves were designed to be half-open at the nominal steady states. Proportional-only controllers were used for all level control loops, and PI controllers were adopted for the top and bottom composition control loops. The PI control systems were tuned in a sequential manner. For each control loop, a relay-feedback test was performed to




obtain the ultimate gain and ultimate frequency. The following equations were then used to calculate the tuning parameters of the PI controllers

3ccuK K 5.0

cuI

P

where Kc and τI represent the proportional gain and integral time, respectively, and Kcu and Pcu are the ultimate gain and ultimate period, respectively.

Fig. 3 Control schemes for the MTBE decomposition reactive distillation column (Huang & Wang 2007).




3.2 Control of ETBE (2-ethoxy 2-methylpropane) System

Ethyl tert-butyl ether (ETBE) is growing in importance as a gasoline oxygenate and octane enhancer. Its gasoline blending properties are superior to methyl tert-butyl ether (MTBE), and its semi renewability is attracting subsidies from government of many countries. Synthesis of ETBE via reactive distillation offers advantages of higher conversion, improved energy efficiency, and lower capital costs.

Sneesby et al. (1997; 1998; 1999; 2000) discuss different designs for the ETBE process using reactive distillation. They also look at some control aspects of this process. In their control work, they studied a reactive column with only nine trays and a small production rate. Their work is limited to the single-feed design, and reaction equilibrium is assumed on all reactive trays. They proposed a two-point control scheme for an ethyl tert-butyl ether (ETBE) reactive distillation column in which both bottoms product purity and conversion are controlled.

Al-Arfaz & Luyben (2002c) extend the work of Sneesby by exploring more control structure alternatives and to study both the design and the control of the double-feed system. They explored four control structures for ethyl tert-butyl ether (ETBE) reactive distillation columns. All of the structures are single-input single-output structures with PI controllers (P only on levels). The controllers are tuned using the Tyreus-Luyben tuning method. They show direct composition control of the bottoms ETBE purity as CS1and temperature control of a tray in the stripping section as CS5. In control structure 1 (Figure 4a), the ETBE purity is controlled in the bottoms by manipulating the reboiler heat input and the ethanol impurity in the top is controlled by manipulating the reflux flow rate. An internal composition is controlled in the reactive zone. There are two alternative cases that depend on which of the two fresh feed streams is flow-controlled to set the production rate and which is manipulated to control the internal column composition. They use the notation CS1-iC4- to indicate a structure in which the butene feed stream is manipulated to control an internal isobutene composition. The other case is labeled CS1-EtOH to denote a scheme in which the ethanol fresh feed stream is manipulated to control an internal ethanol composition. In control structure 2 (Figure 4b), only the ETBE purity in the bottoms is controlled. The distillate composition is not controlled, and the reflux ratio is held constant. In control structure 3 (Figure 4c), a stripping tray temperature is used instead of a bottoms composition analyzer. The reflux ratio is held constant. Control structure 4 (Figure 4d) is similar to the previous one except the ethanol fresh feed is manipulated to control an internal ethanol composition. The interesting feature of this structure is the location of the internal composition (tray 3), which is below the reactive zone. This location is recommended by Singular Value Decomposition (SVD) analysis, looking at the steady-state gain matrix between the ethanol fresh feed and the internal tray compositions.




(a) (b)

(c) (d)

Fig. 4 (a) CS1 (Dual Composition) (b) CS1-RR (double feed) (c) CS5-iC4 - (double feed) (d) CS5-EtOH (double feed). (Al-Arfaj 2002c)




Other authors have also studied the control of ETBE system. Tade et. al.(2003) studied the reactive distillation column for ETBE production (Figure 5). The control scheme consists of two main parts: a nonlinear transformation and a pattern-based predictor (PPC). The PPC system outperforms the standard PI control. They conclude that selection of control schemes including the measurable inferential temperatures has crucial role on the overall control performance. To alleviate the complex model requirement, they proposed a pattern-based predictive control.

Fig. 5 ETBE RD System with the controllers (Tadé et al. 2003)

3.3 Control of TAME (2-methoxy 2-methylbutane) RD System

Tertiary-amyl methyl ether (TAME) is a potential gasoline additive that can be advantageously synthesized using the reactive distillation (RD) Technology. The largest volume component in the past was MTBE, but it is being phased out because of groundwater contamination problems. The TAME reactive distillation is an etherification process that is similar to ETBE. Therefore, TAME is becoming more important. It is produced by the reaction of methanol with unsaturated C5 isoamylenes (2-methyl-1-butene and 2-methyl-2-butene). The liquid-phase reversible reactions considered are

2M2B2M1B

TAMEMeOH2M2B

TAMEMeOH2M1B




It is a widely studied model system to understand the complex behavior of reactive distillation (Katariya et al. 2008). Al-Arfaz and Luyben (2004) studied the plantwide control of TAME reactive distillation column. In this work level controllers are P only and all others are PI controllers. All the controllers are tuned using the Tyres-Luyben tuning method with the ultimate gain and ultimate period calculated using the relay feedback method (Yu 1999). The feeds to the reactor are subcooled methanol and subcooled C5s. As shown in Figure 6, in the TAME system the methanol feed to the reactive column is used to control an internal methanol composition. The C5s feed is used as the production handle. Although both fixed reflux ratio and fixed reflux strategies were found to work. Fixed reflux was selected because it is desirable to take the azeotrope in the distillate to the recovery units and not to recycle it back to the reactive column.

Fig. 6 Control structure proposed by Al-Arfaz and Luyben (2004)




3.4 Control of Ethyl Acetate RD System

Ethyl acetate, one of the most widely used esters, is used as a solvent in a wide range of applications, across many industries including surface coating and thinners, pharmaceuticals, flavors and essences, etc. It is synthesized industrially mainly via the classic Fischer esterification reaction of ethanol and acetic acid. This mixture converts to the ester in about 65% yield at room temperature:

CH3CH2OH + CH3COOH ⇌ CH3COOCH2CH3 + H2O

Reactive distillation (RD) for the production of ethyl acetate (EtAc) has been used from many years. But, relatively very few publications have addressed control issues. Burkett and Rossiter (2000) studied the control strategy of a reactive distillation column with the reaction occurred at the base of a distillation column. The control study was focused on the RD column alone, and the composition of the EtAc product from the top of the column was not pure enough to be of commercial use. Balasubramhanya and Doyle III (2000) applied nonlinear model-based control to a batch reactive distillation column producing ethyl acetate. Vora et al. (2001) studied the dynamics and control of a reactive distillation column for the production of ethyl acetate. They considered two control configurations. In the first control configuration, the manipulated inputs were the distillate flow rate, the reflux flow rate, the condenser heat duty, and the reboiler heat duty. The bottom flow rate remained constant at its steady-state value. In the second control configuration, the manipulated inputs were the distillate flow rate, the reflux flow rate, the condenser heat duty, and the bottom flow rate. The reboiler heat duty remained constant at its steady-state value. They designed model based linear and nonlinear state feedback controllers, along with conventional SISO PI controllers and demonstrated the superior performance of the nonlinear controller over both the linear controller and the conventional PI controller.

Other authors have also studied the control of RD columns for ethyl acetate. Tang et al. (2003) proposed a complete process for the production of EtAc using RD. The process includes two columns (RD and stripper), one decanter, and two recycle streams. The process delivered a high-purity EtAc product with a stringent impurity specification. Tang et al. (2005) studied plant-wide control with four schemes based on this process. The simplest one was the single-point control scheme, which has a fast settling time following fluctuations of throughput and




Tang et al. are workable but have some drawbacks which was improved and investigated by Lee et al. (2007).

Lee et al. (2007) studied the plant-wide control of reactive distillation process for production of ethyl acetate. They used four control schemes to control this system. The first way is to move the operating condition to a more conservative point. At this more conservative operating point, a simple single-point control scheme can be used to obtain faster closed-loop settling time without oscillation. The closed-loop dynamic response of the single-point control scheme is very fast; however, there is steady-state deviation in the impurity content of the final product as compared to product specifications. For the dual-point control scheme, although the deviation problem in the product impurity is improved, the closed-loop transient response is rather oscillatory and needs quite a long time to settle.

Recently, Tsai et al. (2008) investigate the design of the side reactor configuration for the reactive distillation column for production of ethyl acetate via esterification where the reactive zone is placed at the lower section of the RD with no product removal from the column base. They used a systematic approach to design the control structure. The design steps suggested by the authors are as follows: (1) determine manipulated variables; (2) use NRG (nonsquare RGA) to determine temperature control trays; (3) use decentralized PI controller; (4) use RGA for variable pairing; (5) use relay feed back to find Ku and Pu; (6) use TL (Tyreus-Luyben) tuning to find controller settings. They took two approaches: one is fixing the reflux ratio (RR), and the other is the ratio of the reflux to feed (R/FEtOH). Therefore, they had two control structures: CS1 for keeping RR constant and CS2 for fixing reflux to feed ratio as shown in Figure 7.

feed composition. However, the drawback of this scheme is a large overshoot and the possibility of steady-state deviation in product purity. Although another scheme with RD dual point control that they recommended can overcome these problems, the process responses become more oscillatory. Thus, the design of a control system for this process is very problematic. The control schemes recommended by




Fig. 7 Control of the side reactor configuration (SRC) process: (a) CS1 (fixing RR), (b) CS2 (fixing R/F ratio) (Tsai et al. 2008)




3.5 Control of Methyl acetate RD System

Methyl acetate is an ester that is synthesized from acetic acid and methanol in the presence of strong acids such as sulfuric acid in an esterification reaction. Reactive distillation was first patented for methyl acetate by Victor H. Agreda and Lee R. Partin in 1984 (U. S. Pat. No. 4435595).

The production of methyl acetate (MeOAc) via reactive distillation system involves the reaction:

Methanol + Acetic Acid ⇌ Methyl Acetate + Water

Al-Arfaz (2002a) explore three control structures applied to both the methyl acetate and the ideal systems (Figure 8). All the structures are SISO structures with PI controllers (P only on levels). The controllers are tuned using the Tyreus–Luyben turning method (Tyreus & Luyben 1992). The relay-feedback method (Yu 1999) is used to obtain the ultimate gain and ultimate period. All valves are designed to be half open at steady state. In control structure 1 (Figure 8a), three compositions are measured and controlled and in control structure 5 (Figure 8b) only one composition is controlled (the column internal composition) and a temperature is controlled in the stripping section. This temperature controller maintains bottoms purity at or above its specified value by keeping light components from dropping out the bottom with the heavy product component (HOAc). But control structure 7 (Figure 8c) features the use of two temperatures that manipulate the two fresh feeds.

Other authors also studied the control of methyl acetate in reactive distillation column. Kumar and Kaistha studied the effect of internal heat integration by catalyst redistribution on the controllability of an ideal and a methyl acetate reactive distillation (RD) column (Kumar & Kaistha 2008c). They also evaluated the two-temperature control structure for a methyl acetate reactive distillation (RD) column on the basis of ratio control scheme (Kumar & Kaistha 2009a). Volker et al. (2007) studied the heterogeneously catalyzed esterification of methanol and acetic acid to methyl acetate in a semi batch process in a reactive distillation column.




(a) (b)

(c)

Fig. 8 (a) Control structure 1 (b) control structure 5 (c) control structure 7 (Al-Arfaj 2002a)




Kumar and Daoutidis (1999) studied the control of an ethylene glycol reactive distillation column and concluded that an advanced nonlinear inverse-based controller is needed. Fresh ethylene oxide and fresh water are fed into the column, and the product is removed from the base. There is no distillate product. The variables the authors chose to control are pressure, base level, and the purity of the bottoms product (the concentration of ethylene glycol). Monroy-Loperens (2000) also studied the control of ethylene glycol reactive distillation column. They regulate the ethylene glycol composition in the product by manipulating the reboiler boil-up ratio. They used a modeling error compensation approach to demonstrate that a PI configuration with anti-reset windup (ARW) is able to control the ethylene glycol reactive distillation column. Similarly, Al-Arfaz (2002b) in their study demonstrated that ethylene glycol reactive distillation columns can be controlled effectively by a simple PI control scheme. The structure achieves the stoichiometric balancing of the reactants and maintains the product purity within reasonable bounds. This structure should be generally applicable to other systems that are similar to the ethylene glycol system in stoichiometry, kinetics, VLE, and design. In the ethylene glycol reactive column, there is a large temperature change in the stripping section as the water is separated from the ethylene glycol. Therefore, we control the temperature on tray 3 numbering from the bottom by manipulating reboiler heat input. The ethylene glycol reactive distillation column is run essentially ‘‘neat’’ that is, there is a little excess of ethylene oxide to compensate for the second reaction. An effective control structure must be able to perfectly balance the two fresh feed streams. Ratioing the two feeds cannot work because of flow measurement inaccuracy. Some type of information feedback is required about the inventories of reactant components in the system.

The PI control scheme demonstrated by (Al-Arfaj 2002b) is shown in Figure 9. The column pressure is controlled by manipulating condenser heat duty, and the column base level is controlled by manipulating bottoms product flow. The temperature control loop has a first-order lag with a time constant of 0.5 min and a dead time of 4 min, which give very conservative estimates of performance. The temperature loop is tuned using the relay-feedback test (Yu 1999).and Tyreus-Luyben settings.

C2H4O + H2O → HOCH2CH2OH

3.6 Control of Ethylene glycol RD system

Ethylene glycol is produced from ethylene, via the intermediate ethylene oxide. Ethylene oxide reacts with water to produce ethylene glycol according to the chemical equation




Fig. 9 control structure of ethylene glycol shown by (Al-Arfaj 2002b)

There are many other multiple reaction systems which are used in reactive distillation like tertiary-amyl ethyl ether(TAEE) , production of cyclohexanol fromcyclohexane, diaryl oxalate, esterification of adipic acid and glutaric acid, dimethyl acetamide, esterification of acrylic acid with 1,4-butanediol,hydrolysis and alcoholysis of alkyl halides(3-pentanoic acid) etc. The major advantage of reactive distillation is reduction in formation of by-products as compared to the conventional process and the conversion beyond an equilibrium limitation can easily be achieved. But, there are some limitations like in some cases of multiple reaction system there is a formation of middle boiling point products. So, for removing such type of products we require further configuration in the design of reactive distillation column like addition of divided wall. Use of reactive distillation in divided wall column can make system more energy efficient. However, the integration of RD with other unit operations should be considered to improve its performance. Further study is required in future for processing and control of such type of systems. Summary of some reaction systems for control of reactive distillation column has been discussed in Table 1.




reboiler duty (V or Qr), and condenser duty (Qc). Three of these streams must be used to control the state of the column (pressure, reboiler sump level, and reflux drum level), leaving the other two streams to control the operation of the column. In the two-temperature control structure control degrees of freedom are allocated as follows:

1. Throughput set by vapor boilup 2. Pressure controlled by condenser duty 3. Base level controlled by bottoms flowrate 4. Reflux-drum level controlled by distillate flowrate 5. Reflux ratio maintained by measuring distillate flowrate and adjusting reflux flowrate 6. Temperature on some tray in the column controlled by one of the fresh feeds 7. Another temperature in the column controlled by the other fresh feed

Sneesby et al. (1999) had studied the two point control of reactive distillation column. They showed that a simple two-product distillation column has five degrees of freedom. Similarly Wang et al. (2008) studied the design and control strategies of a reactive distillation process with partially thermal coupling for the production of methanol and n-butyl acetate by transesterification reaction of methyl acetate and n-butanol. They showed that there are four design degrees of freedom: reflux ratio and boilup flow of the reactive distillation column, boilup flow of the side stripper column, and liquid split ratio with only two product specifications for the reactive distillation process with thermal coupling.

4. Control Degrees of Freedom

The number of manipulated variables is called the control degrees of freedom, which is equal to the number of control valves. The degrees of freedom for a reactive distillation column are essentially the same as those for a conventional binary distillation column: distillate rate (D), bottoms rate (B), reflux rate (L or R),




Table 1. Summary of control of reactive distillation column

S. No. Reaction Controllers used Parameters controlled

Remarks on motive and achievements References

1. Ethanol + Acetic acid ↔Ethyl acetate + Water

Reduced order nonlinear model based control

Product purity, Temperature

Minimize the computational complexity by using a reduced order fundamental model

Balasubramhanya and Doyle III (2000)

Model-based linear and nonlinear state feedback controllers along with conventional SISO PI controllers

Reactant conversion , product purity

Performance of the nonlinear controller is superior over both the linear controller and the conventional PI controller

Vora et al. (2001)

Nonlinear model predictive control

Overhead and bottoms composition

NLMPC was found to provide significantly better control performance than PI controller

Kawathekar and Riggs (2007)

PID controller Product purity Present alternative ways to improve control of an EtAc reactive distillation process. 99.78wt% purity

Lee et al. (2007)

Decentralized PI controller with Tyreus-Luyben tuning method

Pressure, temperature, product purity

Study of the design of side reactor configuration for getting maintenance advantage as compared to conventional reactive distillation. 99% purity

Tsai et al. (2008)


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PID controller EtAc purity 99.78 mol% EtAc purity Chien et al. (2008)

Adaptive control Product purity A novel design technique is provided for the GMC–ASE control strategy for the batch reactive rectifier. 93.44% purity

Jana and Adari (2009)

2. MEOH + IBUT ↔ MTBE

PI controller Feed composition

Control over feed stoichiometry to avoid an excess of methanol

Barlett (1999)

PI controller with relay feedback test

Product purity and reactant conversion

99% purity Wang et al. (2003)

PI controller (P for level control) with relay feedback test

Top and bottom Product purity

MTBE Decomposition 94.36% conversion (MTBE), 94% purity (top product, IBUT), 94% purity (bottom product, MEOH)

Huang et al. (2007)

3. Ethanol + tert butyl alcohol ↔ ETBE + water

Inferential controller Product purity and conversion

Conversion 97.7%, purity 96.6% Sneesby et al. (1997)

Linear (PI) controller Product purity and conversion

Conversion 96.6%, purity 96.9% Sneesby et al. (2000)

Table 1, cont.




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PI controllers (P only on levels) tuned using the Tyreus- Luyben tuning method

Product composition

Double-feed system requires internal composition control to balance the stoichiometry, along with temperature control to maintain product purity. 85% product purity

Al-Arfaz and Lyuben (2002c)

PI controller and linear model predictive controller

Product purity and reactant conversion

The model predictive controller was able to handle the process interactions well and was found to be very efficient for disturbance rejection and set-point tracking.

Khaledi and Young (2005)

Pattern-based predictive control incorporated with conventional PI control.

Product purity 98% conversion, 90% purity Tian et al.(2003)

Decentralized PI controller and constrained MPC

Isobutene conversion and ETBE purity

The control performance was discussed to handle the nonlinearity and reduce the unwanted variability. 97.87 % Isobutylene conversion , 93.96 % product purity

Athimathi et al. (2006)

Adaptive PI control strategies eg. Nonlinear PI (NPI) and model gain scheduling (MGS)

One point control (product purity)

Recommended CMPC controller for the control of a reactive distillation process as it is effective for both set point and load disturbance rejection.

Bisowarno et al. (2004)

Table 1, cont.




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4. Methanol + acetic acid → Methyl acetate + Water

Proportional-integral (PI) controllers (P only on levels) tuned using the Tyreus-Luyben tuning method.

Tray temperature, product purity

99.2% purity Al-Arfaz and Lyuben (2002a)

Nonlinear predictive control using a neural network model

Product purity Better results can be obtained by neural network based nonlinear controller.

Engell et al. (2003)

Ratio control scheme, temperature controller

Product purity Tighter product purity control is achieved for a through-put change when the two feeds are fed in ratio with the reboiler duty, the through-put manipulator.

Kumar and Kaistha (2009a)

5. Ethylene oxide + Water → Ethylene glycol

Nonlinear inversion based controller

Product purity They analyzed a non minimum phase behavior and addressed in the design of a nonlinear inversion based controller that performs well with stability in the high purity region.

Kumar and Daoutidis (1999)

PI controller Product purity They proposed a new concept for robust stabilization which is based on an analysis of the underlying I/O bifurcation diagram and on modeling error compensation techniques.

Monroy-Loperena (2000)

PI controller Product purity A simple single temperature PI structure provides effective control, 94.8% purity

Al-Arfaz and Lyuben (2002b)

Table 1, cont.




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5. Types of Controllers

5.1 PID Controller

The PID controller, which consists of proportional, integrative and derivative elements, is widely used in feedback control of industrial processes. The RD control literature can be broadly classified into traditional decentralized PI control, e.g., (Al-Arfaj 2000; Kaymak & Luyben 2005; Hung 2006; Lin 2006.; Singh et al. 2007) and advanced model based control, e.g., (Kumar & Daoutidis 1999; Tian et al. 2003; Bisowarno 2003; Gruner 2003; Khaledi & Young 2005).

In one of the earliest control papers, Roat et al. (1986) studied a two-product reactive distillation column with the reaction

CABD

where the control objective was to control the purity of the distillate product (mostly A) and conversion. They proposed a control structure that used two conventional proportional-integral (PI) temperature controllers to maintain two tray temperatures in the column by adjusting the two fresh feed streams.

Al-Arfaz (2002d) have considered different designs for a single-feed pentene metathesis reactive distillation with various single input-single output (SISO) control structures involving PI controllers. Simulation results have shown that a control structure that uses two temperatures to maintain the desired purities of top and bottom products was found to be more effective. They have also shown that an ethylene glycol reactive distillation column can be effectively controlled by a simple PI control scheme.

Sneesby et al. (1999) proposed a two-point control scheme for an ethyl tert-butyl ether (ETBE) reactive distillation column in which both bottoms product purity and conversion are controlled. Conventional PI controllers are used to control a temperature in the stripping section by manipulating the reboiler heat input and to control conversion by manipulating the reflux flow rate. Conversion is calculated inferentially from several temperature and flow measurements.

Monrey-Loperens (2000) develop a nonlinear PID-type top product composition controller for the ethyl acetate process operated in batch mode, using the reflux ratio as the manipulated input. They show that their scheme generates the same reflux ratio profile as the optimization-based approach shown by Mujtaba and Macchietto (1997) while being robust to model errors. Another author Vora and Daoutidis (2001) have studied the application of model-based linear and nonlinear state feedback controllers and single input single output (SISO) controllers with PI configuration for ethyl acetate reactive distillation. The simulation results of these controllers have shown the better performance of the nonlinear model-based controller.




The proportional-integral-derivative (PID) controller has gained widespread use in many process control applications due to its simplicity in structure, robustness in operation, and easy comprehension of its principle. Numerous tuning methods have already been proposed to design PID controller, like Cohen-Coon and Zieglar-Nichols tuning rules, model-based design, relay feedback test, and dominant pole design. However, most of these design methods for PID controllers are based on a linear process model obtained experimentally around the nominal operating condition. Therefore, the performance of the conventional PID controller might degrade or even become unstable for nonlinear processes with a range of operating conditions. To improve the control performance several schemes, like neural network based model, of incorporating nonlinear control techniques in the design of PID controllers had been developed (Nuella et al. 2009).

PID algorithm was not capable of obtaining the full benefits of the optimization. A high percentage of the time the optimization used its degrees of freedom to solve for an equal number of physical constraints. The PID algorithm was not effective in controlling the highly interactive system concurrently at a large number of constraints.

Unit 1 – conventional Unit 2 – Model Predictive Control Structure Control Structure

FC LC PC TC FC LC PC TC

Unit-1 Local Optimizer Unit-2 Local Optimizer

Plant-wide optimization

High/Low Select Logic

PID L/L PID

SUM

Unit 1 DCS-PID controls

SUM

Model Predictive Control (MPC)

Unit 2 DCS-PID controls

Global Economics Optimization (Every day)

Local EconomicsOptimization (Every hour)

Dynamic constraint control (Every minute)

Basic dynamic control (Every second)

Fig. 10 Hierarchy of control system functions in a typical processing plant. Conventional structure is shown at the left; MPC structure is shown at the right.

(Qin & Badgwell 2003)




In modern processing plants the MPC controller is part of a multi-level hierarchy of control functions (Qin & Badgwell 2003). This is illustrated in Figure 10, which shows a conventional control structure on the left for Unit 1 and a MPC structure on the right for Unit 2. At the top of the structure a plant-wide optimizer determines optimal steady-state settings for each unit in the plant. These may be sent to local optimizers at each unit which run more frequently or consider a more detailed unit model than is possible at the plant-wide level. The unit optimizer computes an optimal economic steady state and passes this to the dynamic constraint control system for implementation. The dynamic constraint control must move the plant from one constrained steady state to another while minimizing constraint violations along the way. In the conventional structure this is accomplished by using a combination of PID algorithms, lead-lag (L/L) blocks and high/low select logic. It is often difficult to translate the control requirements at this level into an appropriate conventional control structure. In the MPC methodology this combination of blocks is replaced by a single MPC controller.

5.2 Model Predictive Control

The RD control literature can be broadly classified into traditional decentralized PI control and advanced model based control (Kumar & Kaistha 2008d). Further studies on control of reactive distillation have shown the inadequacy of conventional controllers and highlight the need for more advanced controllers.

During the past decade there has been an increase in the use of linear model predictive control (MPC) techniques.

Model Predictive Control (MPC) is an optimal-control based method to select control inputs by minimizing the predicted error from set point for the future. The objective function is defined in terms of both present and predicted system variables and is evaluated using an explicit model to predict future process outputs. MPC is normally applied to multivariable process control, where its real benefits can be realized. (Kawathekar 2004)

The most frequently cited MPC techniques are dynamic matrix control (DMC) and model algorithmic control (MAC) (Silva & Kwong 1999), which are used successfully in a larger number of industrial processes because they explicitly handle constraints (Prett 1979; Moro 1995; Odloak 1996).

Balasubramhanya and Doyle III (2000) used a model predictive control to study the control of batch reactive distillation column. A schematic of the controller design used by Balasubramhanya and Doyle III (2000) is presented in Figure 11. The solid lines trace the control loop associated with the amount of distillate collected while the dashed lines trace the temperature feedback to the controller. The dotted line represents the MPC controller that minimizes the difference between the reference trajectory and the output prediction within the constraints imposed.




Fig. 11 Schematic diagram of MPC algorithm with temperature feed-back. (Balasubramhanya & Doyle III 2000)

5.2.1 Dynamic Matrix Control (DMC)

Industrially popular model predictive control algorithms such as Dynamic Matrix Control (DMC) use a linear convolution model of the process for control algorithm. Cutler et al. (1983) developed the most popular form of MPC, which is called Dynamic Matrix Control (DMC) to solve the constrained multivariable control problem. Dynamic Matrix Control explicitly uses a lower triangular matrix called ‘dynamic matrix’ containing the step response coefficients corresponding to the deterministic input(s) to the process. The general topic of model identification is covered extensively by Box et al. (1994). Many researchers have reported applications of DMC on distillation column control. McDonald and McAvoy (1987) applied DMC to simulation of a benzene-toluene column and an isobutene-n-butane column. A DMC algorithm for startup and continuous operation of a reactive distillation column has been proposed by Baldon et al. (1997) .There are many applications of DMC reported for conventional distillation

Optimization

Reference Model

Condenser Model

Filter

Z-1

Condenser

Condenser Model

Distillate Collected

P-Horizon

P-Horizonu(k+1)

r (k)

-

d (k)

-

FilterReactive Distillation Column

Sensitive tray temperature




column, very few MPC applications are reported for reactive distillation control. Ruiz et al. (1995) have reported application of DMC for control of reactive distillation for ethyl acetate synthesis. Recently, Dwivedi and Kaistha ( 2008) evaluated dual ended temperature inferential and product composition compensated temperature inferential control of a double feed ideal reactive distillation column using constrained dynamic matrix control (CDMC) and traditional decentralized control.

For constructing the dynamic matrix, a step response model for the process is first obtained from the open-loop data. The step response coefficients are then arranged in a specific lower triangular form in the dynamic matrix. Key features of the DMC control algorithm according to Qin and Badgwell (2003) include:

linear step response model for the plant; Quadratic performance objective over a finite prediction horizon; Future plant output behavior specified by trying to follow the set-point

as closely as possible; Optimal inputs computed as the solution to a least squares problem.

5.2.2 Quadratic Dynamic Matrix Control (QDMC)

In the QDMC optimization problem, the output is predicted with a step response model, which is obtained at the most probable operating conditions. Cutler et al. (1983) first described the QDMC algorithm in a 1983 AIChE conference paper. Garcia and Morshedi (1986) published a more comprehensive description several years later. Although the QDMC algorithm is a somewhat advanced control algorithm, the quadratic programming (QP) itself is one of the simplest possible optimization problems that one could pose. The Hessian of the QP is positive definite for linear plants and so the resulting optimization problem is convex. This means that a solution can be found readily using standard commercial optimization codes. The QDMC algorithm can be regarded as representing a second generation of MPC technology, comprised of algorithms which provide a systematic way to implement input and output constraints. This was accomplished by posing the MPC problem as a QP, with the solution provided by standard QP codes (Reddy & Saha 2006).

Key features of the QDMC algorithm according to Qin and Badgwell (2003)include:

Linear step response model for the plant; Quadratic performance objective over a finite prediction horizon; Future plant output behavior specified by trying to follow the setpoint as

closely as possible subject to a move suppression term;




Optimal inputs computed as the solution to a quadratic program.

Balasubramhanya and Doyle III (2000) used the approach of Nonlinear Quadratic Dynamic Matrix Control with State Estimation (NLQDMC/SE) to study the nonlinear behavior of batch reactive distillation column. They used a reduced wave model to predict outputs into the future. The control objective is to obtain a pure sample of the product over the entire batch as possible. It is difficult to obtain the composition of the product at every time step for feedback control. However, since the composition in the column is intimately linked to the temperature, the composition of the product can be inferentially controlled by controlling the temperature in the column. The temperature on tray 2 was used as the controlled variable. The objective function for the minimization problem to be solved at each instance of time is given by:

))()((min 2iytr disdis

pk

kiu

Subject to the constraints:

u(i)≥0.0 i=k,…,k+mu(i)≤umax i=k,…,k+mTtray,2(i) ≤Tmax i=k,…,k+p ∆u(i)≤∆umax i=k,…,k+m

pkki

TiLiviyiy sdisdis

,.......,

))1()1(()1()(

rdis(i) is the reference trajectory for the amount of distillate to be produced at time instant k+i, Ts, is the sample time (0.3 h), ydis(i). is the predicted amount of distillate produced, umax is the maximum input, ∆umax is the maximum input change at every time period, and Tmax is the maximum temperature allowed on tray 2. u is a vector of magnitude 2m x 1 corresponding to the two inputs (vapor flow and liquid flow) and the m moves that the controller calculates at every time step. The temperature constraint at Tmax places a lower bound on the purity of the distillate. Ttray,2 (i) is the output of the reduced model used to predict the temperature i steps into the future.

5.2.3 Linear Model Predictive Control (LMPC)

Linear Model Predictive Control employs linear or linearized models to obtain the predictive response of the controlled process. Linear MPC has proved useful for




controlling processes that exhibit even some degree of nonlinear behavior (Venkateswarlu & Reddy 2008).

Olanrewaju and Al-Arfaz (2005) shows the linearized state space process model for a generic two-reactant-two-product reactive distillation system. Many model-based controllers use linear models because the linear models are easier to analyze and require less computational recourses than nonlinear models. Besides, most of the nonlinear systems often have the same general phase-plane behavior as the model linearized about the steady state condition when the system is close to that particular condition. Therefore, it is important to derive a suitable linearized dynamic model that when used in the model-based control applications could yield an effective and robust control system.

5.2.4 Nonlinear Model Predictive Control (NMPC)

MPC employs linear models such as step or impulse response models in the control algorithms. For highly nonlinear processes when operating conditions vary widely in the presence of disturbances or when large transitions in the state variables are desired, conventional PID controllers or linear model based controllers (LMPC) are often less than satisfactory. Nonlinear Model Predictive Control (NMPC) can be defined as a MPC algorithm, which employs nonlinear models of the process

Reactive distillation processes exhibits highly nonlinear behavior, hence the use of NMPC for control of reactive distillation process is expected to provide improved performance compared to linear control strategies. Al-Arfaj and Luyben (2002), Sneesby et al. (1997), Kumar and Daoutidis (1999), discussed the decentralized PI control structures for reactive distillation column. Sneesby et al. (1998), Al-Arfaj and Luyben (2002) discussed the possibility of multiple steady states in many reactive distillation systems. The presence of multiplicities and the highly nonlinear nature of reactive distillation may impose limitations on use of linear controllers.

Other authors have also studied the Nonlinear Model Predictive control of RD columns. Kumar and Daoutidis (1999) have discussed the superior performance of nonlinear controller compared to linear controller for reactive distillation systems. Silva and Kwong (1999) describe a new algorithm for model predictive control using the simultaneous solution and optimization strategy. They use equidistant collocation because it is a simpler alternative than orthogonal collocation on finite elements for discretization of the model differential equations. Balasubramhanya and Doyle III (2000) applied nonlinear model-based control to a batch reactive distillation column producing ethyl acetate. Lim (2001) also applied nonlinear wave model based control scheme to the same reactive batch distillation column and showed that tight distillate composition control is possible.




Nonlinear model-based control algorithms can be applied to processes described by a wide variety of model equations, such as nonlinear ordinary differential/algebraic equations, partial differential equations and integral-differential and delay-differential equations. Reactive distillation columns are generally being modeled by a set of highly nonlinear first order differential equations (Baur et al. 2001; Kumar & Daoutidis 1999; Roat et al. 1986; Taylor & Krishna 2000).

The solution procedure for NMPC involves setting up the control problem as a nonlinear programming (NLP) problem and solving it over some prediction horizon. There are two ways of implementing model predictive control (Kawathekar 2004). The first method employs separate algorithms to solve model equations and to carry out optimization. This method is called sequential solution and optimization approach. An alternative to solve the NLP problem is to use a simultaneous solution and optimization strategy.

Recently Kathel and Jana (2010) proposed a nonlinear model-based control algorithm for a simulated batch reactive distillation column for the production of butyl acetate. The control scheme consists of the generic model controller (GMC) and a state predictor.

5.3 Other Advanced Control Techniques

In view of the high process nonlinearity, the application of advanced control techniques such as advanced adaptive control, pattern based predictive control, inferential control, stochastic optimization algorithms and model predictive control have been propounded in the RD control literature. 5.3.1 Adaptive Control

Adaptive control is known as a control strategy whose parameters change continuously. Basically, the parameters of the process model are tuned using on line identification of the process and then a control action is derived and implemented accordingly. Therefore, adaptive control is suitably applied when the process exhibits nonlinear behavior such as directionality of the process gain or the structure of the process is unknown.

Bisowarno et al. (2004) developed the two adaptive PI control strategies, e.g. non-linear PI (NPI) and model gain-scheduling (MGS) and implemented these on an ETBE reactive distillation column. Both adaptive control strategies are based on a PI controller integrated with a tuning method. For the NPI, they developed the controller gain, which is allowed to vary to accommodate the directionality of the process gain. For the MGS, they derived several local models, which cover relevant operating conditions and cope with nonlinear characteristics (e.g. directionality of




process gain). The performance of the NPI and the MGS will be evaluated and compared to that of a standard PI controller in both set-point tracking and disturbance rejection.

Jana and Adari (2009) discussed the advanced adaptive control i.e. generic model controller (GMC) and an adaptive state estimator (ASE) for nonlinear process, of a batch reactive distillation (BRD) column that produces ethyl acetate by the esterification of ethanol with acetic acid. The adaptive control algorithm consists of the nonlinear generic model controller and an adaptive state estimator. The closed-loop system having different controller elements and the process is shown in Figure 12.

Fig. 12 Block diagram for the nonlinear adaptive control algorithm.(Jana and Adari, 2009)

They conclude that GMC–ASE control law provides tight composition control throughout the batch operation. Better set point tracking and disturbance rejection performance is achieved by the nonlinear adaptive controller compared to the gain-scheduled proportional integral (GSPI) scheme. The simple design, easy tuning and good performance make the adaptive controller attractive for online use.

Bisowarno et al. ( 2003) investigated model gain scheduling for inferential one point control of an ethyl tert-butyl ether reactive distillation column. They studied the directionality of the process gain, which cannot be well controlled by using standard PI control with fixed parameters, and found that the problem can be overcome for a wide range of operating conditions by using a gain scheduled PI controller. However, model gain scheduling demands preprogramming or online identification of the process gain.

Nonlinear ASE

Nonlinear GMC

Process Output MapySP

x y

e

u

GMC-ASE

+

-




5.3.2 Pattern Based Predictive Control

Due to the complexity of the RD process dynamics, conventional control technologies, e.g. PI control, cannot provide satisfactory control performance, while the application of modern control technology requires good process models. Pattern-based predictive control (PPC) is such a method that does not rely on exact process models while providing improved control performance for complex processes over conventional, e.g. PI, control algorithms. Tian et al. (2003) develops a pattern-based predictive control (PPC) scheme incorporating with conventional proportional-integral (PI) controller for the non-linear and complex RD process. The PPC system for the RD process is shown in Figure 13. It consists of two main parts: a non-linear transformation u=f(v) and a pattern-based predictor (PP). The former is used for input-output linearization of the process gain, while the latter is employed to anticipate process output some (e.g. d) steps ahead. The PP utilizes process feature patterns qualitatively and quantitatively, which are extracted from the controlled and manipulated variables, and is incorporated with a conventional controller Gc (e.g. PI) in the PPC system. For the RD system, y and u in Figure 13 correspond to T7 and Qr, respectively. Ideally, The PP acts as a time lead component as it provides d steps ahead prediction of the controlled variable. It will effectively compensate for the time delay in the RD process and thus allows more aggressive controller settings compared with the control systems without PP. Therefore, the PPC will provide improved performance in both set-point tracking and disturbance rejection, as will be shown later for the RD process.

Fig. 13 PPC system structure (Tian et al. 2003).




problems. However, the optimization problem often becomes non-convex in the presence of nonlinear characteristics/constraints and is usually more complex than convex optimization. The goal of optimization is to find the values of the variables in the process that yield the best value of the performance criterion. Sequential quadratic programming (SQP) is a widely used classical optimization algorithm to solve nonlinear optimization problems. However, for the solution of large problems, it has been reported that gradient-based methods like SQP require more computational efforts. Moreover, classical optimization methods are more sensitive to the initialization of the algorithm and usually lead to unacceptable solutions due to convergence to local optima. These methods are not expected to provide the global optimum for a multimodal function. Consequently, new optimization techniques are being proposed to achieve efficient control performance. Stochastic search and optimization algorithms such as genetic algorithm (GA) and simulated annealing (SA) derived from the principles of natural phenomena are useful to find the global optimum of complex engineering problems. These algorithms are attractive because of their flexibility, ease of operation, and global perspective. (Venkateswarlu & Reddy 2008).

Venkateswarlu and Reddy (2008) studied the development of nonlinear model predictive control strategies for reactive distillation based on stochastic optimization techniques. Stochastic optimization algorithms such as genetic algorithms (GAs) and simulated annealing (SA) are combined with the polynomial-type nonlinear empirical process model identified from input-output data of the process to develop nonlinear model predictive control (NMPC) strategies, namely, GANMPC and SANMPC (Figure 14). The growing nonlinear control applications will certainly continue due to the nonlinear nature of RD systems for higher product competitiveness, tighter safety and environmental regulations.

5.3.3 Stochastic Optimization Algorithms

One more important aspect of highly nonlinear systems is the optimization algorithm. Efficient optimization algorithms exist for convex optimization

They conclude that both the GANMPC and SANMPC were found to exhibit almost equal performance, SANMPC involves a smaller number of tuning parameters and requires less execution time. Hence, SANMPC is better suited for the control of reactive distillation.




Fig. 14 Structure of stochastic optimization based NMPC (Venkateswarlu & Reddy 2008)

Control of batch reactive distillation column is a very complex operation. Recently, Bahar and Ozgen (2010) developed an inferential control methodology that uses ANN estimator system to infer the product compositions from temperature measurements which is further used to control the optimal reflux profile.

6. Other Reactive Separation Processes

Process intensification and process integration represent ways of economical efficiency, as well as environmental friendly operating conditions, by integrating different phenomena or operations, as for example: reactive separations, dividing-wall columns, heat integrated reactors or columns (Kiss et al. 2009). Several successful examples of integrated processes can be found among reactive-separations that combine reaction and separation steps in a single unit, such as reactive distillation, reactive absorption and reactive extraction. As we discussed above reactive distillation has significant economic advantages over conventional reactor-separator-recycle systems, particularly for reversible reactions in which conversion is limited by chemical equilibrium constraints and/or separation is restricted by VLE limitations. However, due to the integration of reaction and separation into a single vessel, reactive distillation is limited to systems in which the reaction and separation conditions are similar – mainly in terms of pressure and temperature.




6.1 Reactive Dividing Wall Distillation Column

Beside reactive separations, there is also the option to integrate two different separation units together. To make distillation column as energy efficient system, fully thermally coupled distillation column (or petlyuk column) or dividing wall column (DWC, that integrates the two columns of a Petlyuk system into one column shell) is used (Figure 15). These systems can reduce energy consumption by 30-50% over conventional distillation sequences for the separation of some mixtures. Feed, typically containing three or more components, is introduced into one side of the column facing the wall. Deflected by the wall, the lightest component A flows upward and exits the column as top distillate while the heaviest component C drops down and is withdrawn from the bottom of the column. The intermediate boiling component B is initially entrained up and down with both streams, but the fluid that goes upward subsequently separates and falls down on the opposite side of the wall. Similarly, the amount of B that goes toward the bottom separates and flows up to the back side of the wall, where the entire product B is recovered by a side draw stream. Note however that using DWC requires a match between the operating conditions of the two standalone columns in a conventional direct or indirect sequence (Van Diggelen et al. 2010).

Fig. 15 Dividing wall column (DWC) (Van Diggelen et al. 2010)

Reactive distillation column and dividing wall column both are good examples of process intensification. If reactive distillation and DWC are further integrated, a reactive divided wall distillation column (RDWDC) will be generated. RDWDC has a highly integrated configuration that consists of one condenser, one reboiler, reactive zones, a pre-fractionator and the main column together in a single-shell distillation setup. The following reactive systems can be considered




RDWDC seems to be a very attractive and challenging process and some theoretical studies have been focused on modeling and control of a RDWDC (Bumbac et al. 2009). Integration of reaction, separation, prefractionator in a single shell makes RDWDC system more complex.

Sander et al. (2006) discussed well this novel concept using the hydrolysis of methyl acetate as a test system. With this reactive divided wall column, a process alternative for the hydrolysis of methyl acetate is available (Figure 16), which leads to a further reduction of devices compared to the reactive distillation process and minimizes the back reactions of methanol and acetic acid to methyl acetate. The use of the reactive divided wall column is not limited to the hydrolysis of methyl acetate. It should be suitable for all processes in which the product of a reactive distillation is the intermediate boiling component. Kiss et al. (2007; 2009) discussed overcoming equilibrium limitations in RDWDC. They showed that equilibrium limitations can be overcome and high purity components can be obtained by integrating reaction and separation into a reactive dividing-wall column. Bumbac et al. (2007; 2009) presented the process of reactive distillation with divided wall column for the synthesis of ETBE and TAEE. Simulation of flowsheet configuration was performed with ASPEN PLUS. Barroso-munoz et al. (2009) present the thermodynamic analysis and hydrodynamic behavior of a reactive dividing wall distillation column. They revealed that RDWDC are higher energy efficient than classical configuration of a reactor plus a distillation column.

Fig. 16 Principle of combining the reactive distillation column with the following separation column to form the reactive divided wall column (RDWC) (sander et al.

2006)

suitable for this type of integration: reactive systems with more than two products (e.g. with consecutive and side reactions), system containing both reactive and non-reactive components, reactive systems with an excess of a reagent. The




Cho et al. (2008) proposed a purification of lactic acid using reactive divided wall distillation column. The proposed process increases the recovery of lactic acid and decrease the number of equipments for the recovery, reducing capital cost significantly. Hernandez et al. (2009) studied the dynamics of a reactive divided wall distillation column for the production of ethyl acetate. They implemented and tested the two control loops of temperature for different scenarios. The results showed that the system can achieve changes in the two set points and also eliminate disturbances in the composition of the feed stream. The dynamic simulation results indicate that it is possible to control either the composition of the top and bottoms products or two temperatures by manipulating the reflux rate and the heat duty supplied to the reboiler, respectively. As reactive dividing wall distillation column is very complex system so, further study will be required in future in this field for controlling such type of systems.

6.2 Semi-Continuous Separation Processes with Chemical Reaction

Semicontinuous processes have been introduced in recent years as a process intensification technique that allows multiple separations to be performed in one separation column ((Phimister & Seider 2000a; 2000b; 2000c; 2001; Adams II & Seider 2006a; 2008; 2009a) Several semicontinuous processes have been explored for a variety of design scenarios, Semicontinuous distillation with chemical reactions in middle vessels (MV) where streams are continuously exchanged between the middle vessels and columns is advantageous over conventional reactive distillation column for several reasons (Adams II & Seider 2006a): First, because the two reaction products have intermediate boiling points and the reaction is highly reversible, it is difficult to recover the product in a sidedraw. Second, because one of the reagents leaves in the distillate (acetaldehyde) and one leave in the bottoms product (propylene glycol), the reaction is shifted to the left. To counter this effect, a large excess of one reagent can be used, with high recirculation costs. Third, in reactive distillation, the temperatures in the column are thermodynamically related to the pressure, which cannot exceed 2.1 bar for the case study. Hence, temperature cannot be adjusted to control the rate and extent of the reaction The semicontinuous reactive distillation (SRD) system has been shown to be the economically optimal design strategy for intermediate production rates when comparing it to equivalent batch and continuous processes (Adams II & Seider 2009a).

Adams and Seider published a series of articles on semicontinuous separation with chemical reaction in a middle vessel (Adams II & Seider 2005; 2006a; 2006b; 2008; 2009b; 2009a). A middle vessel is tightly integrated with the separation column, where the middle vessel receives a stream from the column (the distillate, bottoms, or sidedraw), while the column simultaneously receives a feed




stream from the middle vessel. Depending on the process objective, chemical reaction(s) can be integrated into the separation process, either in an external vessel, in the separation column, or in a CSTR used as a middle vessel. The process operates in a forced cycle (or campaign), that is, a predefined sequence of operating modes, which may include charge steps, product collection steps, and transitional modes.

Advantages of using semicontinuous reactive distillation are:

a) Semicontinuous processes require fewer separation units than their continuous analogs, the capital cost of semicontinuous processes are normally lower than those of continuous processes.

b) Energy requirement are normally lower because Semicontinuous processes do not involve column startup or shutdown modes often required in batch processes.

c) Provide a cost effective means of separating chemicals at intermediate production rates.

d) Temperature is easily controlled in semicontinuous reactive distillation as reaction is carried out in a separate middle vessel.

In their recent article, Adams and Seider (2009a) developed three configurations for semi-continuous processes as shown in Figure 17. In Scheme 1, a single column interacts with two middle vessels in an alternating sequence. This configuration is most appropriate when the column functionality differs significantly in each phase of operation, or when undesirable side reactions are to be reduced. They have shown this type of configuration in the semicontinuous process by recovering 2,4-dimethyl-1,3-dioxolane(2,4 DMD) by the reversible reaction of acetaldehyde and propylene glycol. In Scheme 2, only one middle vessel is needed, but the column functionality differs significantly in each phase of operation. Here, chemical reaction in the middle vessel is usually not effective, but may be appropriate inside the column. Scheme 3 is chosen when the two separation steps can be performed simultaneously inside the column, and consequently, only one middle vessel and one operating phase are needed. This is also the most appropriate configuration for reaction systems in which the middle-volatility species is the reagent. They discussed this type of configuration in the semicontinuous process to produce and recovered the ethyl lactate by reversible reaction of ethanol and lactic acid.




(a)

(b) (c)

Fig. 17 Design configurations for semi-continuous ternary separation systems as shown by Adams and Seider (2009a): (a) a semi-continuous process with two MVs operating in two different phases of operation in a cycle. (b) A semi-continuous process with one MV operating in two phases. (c) A semi-continuous process with

one MV operating in one phase of operation

7. Conclusions

The current status of control of reactive distillation column has been discussed. Control of reactive distillation is a challenging problem due to process nonlinearity, complex interactions between vapor-liquid equilibrium and chemical kinetics. The first step in designing a decentralized control system is to choose the set of controlled and corresponding manipulated variables, i.e. the control structure. The appropriate control structure depends on the flow sheet and on the type of reactions occurring in the column. In this paper, control structures for different chemical




systems, i.e., MTBE, ETBE, TAME, ethyl acetate, methyl acetate, etc. have been discussed. One of the key challenges in the successful commercialization of RD technology is designing a robust control system to effectively reject the load disturbances such as through-put and feed composition changes

There are several types of controllers which have been studied in the reactive distillation literature, ranging from simple proportional-integral (PI) controllers to advanced model predictive controllers (MPC), such as dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC), nonlinear model predictive control (NMPC), etc. The proportional-integral-derivative (PID) controller has gained widespread use in many process control applications due to its simplicity in structure, robustness in operation, and easy comprehension of its principle but PID algorithm was not effective in controlling the highly interactive system concurrently at a large number of constraints so the application of advanced control techniques such as advanced adaptive control, pattern based predictive control, stochastic optimization algorithms and model predictive control have been propounded in the RD control literature. The presence of multiple steady states and the highly nonlinear nature of reactive distillation may impose limitations on use of linear controllers. Hence the use of nonlinear model predictive control (NMPC) for control of reactive distillation process is expected to provide improved performance.

Some other reactive separation processes are also included here like reactive divided wall distillation column and semi-continuous distillation with chemical reaction in a middle vessel. Reactive divided wall distillation column (RDWDC) is a novel concept to reduce capital cost and make system more energy efficient. These systems are useful for such type of reactions where intermediate boiling product is formed. RDWDC has a highly integrated configuration that consists of one condenser, one reboiler, reactive zones, a pre-fractionator and the main column together in a single-shell distillation setup. So, further study is required in this field by applying different controllers like model predictive control to reduce the complexity of the system. Similarly, semi-continuous distillation with chemical reaction in a middle vessel is also a novel concept and advantageous in some aspects but it is not capable to remove azeotropes formed during the process as they separate out using some other distillation column (probably azeotropic distillation) and they also require a separate reaction vessel and distillation tower to carry out the process which increases the capital cost; in those cases reactive distillation is more advantageous. These systems are also not energy efficient as compared to reactive distillation column and reactive dividing wall distillation column.

Some chemical systems contain electrolytes and solid salts which clogs the sieve holes by depositing on the tray. The modeling of the vapor-liquid equilibrium of the system becomes complicated by the presence of electrolytes and salting out




effect. As Bezzo et al. (1999) mention the effect of electrolyte and salting out on thermodynamic modeling for the production of propylene oxide from propylene chlorohydrin and calcium hydroxide by using reactive distillation column. Such type of problem affects the controller system which is basically based on the model of the system such as model based controllers (MPC). Further research is required to solve such type of problems and more research study is required in the field of control of reactive distillation column to make the system simpler, economical and more energy efficient.

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control of reactive distillation column: a review

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