Batch Reactive Distillation Bread

9.1. Introduction

Reactive Distillation processes combine the benefits of traditional unit operations with a substantial progress in reducing capital and operating costs and environmental impact (BP Review, 1997; Taylor and Krishna, 2000).

Traditionally, as in many chemical industries, reaction and separation take place separately (Figure 9.1) in a batch reactor followed by a batch distillation column (Charalambides et al., 1994). Therefore, the distillation of desired species cannot influence the conversion of the reactants in the reactor.

However, conventional batch distillation with chemical reaction (reaction and separation taking place in the same vessel and hence referred to as Batch REActive Distillation- BREAD) is particularly suitable when one of the reaction products has a lower boiling point than other products and reactants. The higher volatility of this product results in a decrease in its concentration in the liquid phase, therefore increasing the liquid temperature and hence reaction rate, in the case of irreversible reaction. With reversible reactions, elimination of products by distillation favours the forward reaction. In both cases higher conversion of the reactants is expected than by reaction alone. Therefore, in both cases, higher amount of distillate (proportional to the increase in conversion of the reactant) with desired purity is expected than by distillation alone (as in traditional approach) (Mujtaba and Macchietto, 1997).

An extensive literature survey shows that very little attention has been given to modelling and simulation of batch reactive distillation, let alone optimisation of such process. The published literature deals with the mathematical modelling and numerical integration of the resulting dynamic equations systems, with few presenting computer simulation vs experimental results. Only few authors have discussed the design, control and optimal operational aspects of batch reactive distillation processes.

Reactive Distillation Column


Material Transfer

Figure 9.1. Traditional Batch Reaction-Distillation System

9.2. Review

9.2.1. Experimental Studies

Corrigan and Ferris (1969) studied the methanol esterification with acetic acid in an experimental batch distillation column, with emphasis on the design and construction of the equipment. It was noted that until 1963, analytical procedures such as gas phase chromatographic method for the system of methyl alcohol, methyl acetate, acetic acid and water had not been developed. Among the other objectives of Corrigan and Ferris (1969) were to develop an analytical method for such system and to obtain experimental data that could be used to verify the method. The basic pieces of equipment that were designed were a ten gallon jacketed glass lined reactor, a ten gallon glass-lined receiving or feed tank, three sections of Oldershaw sieve tray column, reflux splitter, feed tray, condenser, and a flexopulse timer.

Using appropriate reflux, methyl acetate (the lowest boiling component) at a purity of 92.5% (mol) was possible to produce with a very high recovery rate.

Lehtonen et al. (1998) considered polyesterification of maleic acid with propylene glycol in an experimental batch reactive distillation system. There were two side reactions in addition to the main esterification reaction. The equipment consists of a 4000 ml batch reactor with a one theoretical plate distillation column and a condenser. The reactions took place in the liquid phase of the reactor. By removing the water by distillation, the reaction equilibrium was shifted to the production of more esters. The reaction temperatures were 150-190┬░ C and the catalyst concentrations were varied between 0.01 and 0.1 mol%. The kinetic and mass transfer parameters were estimated via the experiments. These were then used to develop a full-scale dynamic process model for the system.

9.2.2. Modelling and Simulation

Egly et al. (1979), Cuille and Reklaitis (1986), Mujtaba (1989), Reuter et al. (1989), Albet et al. (1991), Basualdo and Ruiz (1995) and Wajge and Reklaitis (1999) considered the development of mathematical models to simulate BREAD processes. In most cases, the model was posed as a system of Differential and Algebraic Equations (DAEs) and a stiff solution method was employed for integration.

9.2.3. Design, Control and Optimisation

Egly et al. (1979) considered the minimum time optimisation problem using a detailed dynamic process model (Type V) but no details were given regarding the input and kinetic data of the problem.

Wilson (1987) discussed the optimal design of batch distillation processes using a simplified column model for BREAD and using repetitive simulation strategy. For a commercially used complex parallel reaction scheme and using a simple economic model the author showed the benefit of integrating reaction and distillation. A number of plots of process efficiency (in terms of product cost contribution per unit product) for a range of alternative process and design variable choices were generated and an optimal design and operation policy of reactive batch distillation were suggested.

Sorensen and Skogestad (1994) developed control strategies for BREAD processes by repetitive simulation strategy using a simple model in SPEEDUP package. Wilson and Martinez (1997) developed EKF (Extended Kalman Filter) based composition estimator to control BREAD processes. The estimator was found to be quite robust and was able to estimate composition within acceptable accuracy, even in the face of process/model mismatches. Balasubramhanya and Doyle III

(2000) studied nonlinear model based control of a BREAD process using an example of ethanol esterification. The main focus of the work was to develop a reduced order nonlinear process model that could be used efficiently within a model predictive control strategy.

Mujtaba and Macchietto (1992, 1994, 1997) and Wajge and Reklaitis (1999) developed optimisation strategies for BREAD processes. Walsh et al. (1995) included in their work, the effect of uncertain model parameters on the design of operating procedures of BREAD processes.

In this chapter some of the published work in BREAD process optimisation will be presented in detail. Note that although formation of azeotropes is quite common in reactive distillation, such situation is not considered in this book. Readers are directed to Van Dongen and Doherty (1985), Bernot et al. (1991) and Diwekar (1991) regarding this.

9.3. Selecting the Right Column for BREAD

Barbosa and Doherty (1988) listed a number of chemical reaction schemes which were previously used mainly in continuous distillation. The reaction products in the different reaction schemes considered do not always have lower boiling point than the rea├ętants. The use of conventional batch distillation for those reactions would result in removal of reactants as the distillation proceeds thus lowering conversion and yield of product. Therefore, it is very important to select the right batch distillation column for each type of chemical reaction.

For example, if all the reaction products are valuable and have lower boiling temperature than the reactants, then conventional batch distillation would be most suitable. As the reaction proceeds the products will be separated in different maincuts in sequential order. Conversion and yield can be greatly improved in such cases. If only some of the reaction products have low boiling temperature, then a conventional batch column will only remove those products as distillation proceeds. To separate the rest of the products by conventional distillation would require the removal of unreacted reactants from the column first.

Conventional batch distillation is not suitable when all reaction products have higher boiling temperatures than those of the reactants. Inverted batch distillation is suitable for such situation. If all the reaction products are valuable, as the reaction proceeds, the products will be separated from the bottom of the column in different main bottom cuts in sequential order.

For cases where some of the reaction products have higher and some lower boiling points than those of the reactants, then neither the conventional nor the inverted batch distillation are suitable. For such reaction schemes, the MVC column will be the most suitable one because the light and heavy products can now be withdrawn simultaneously from the column, thus pushing the reaction further to the product side.

However, there are cases where none of these batch distillation columns can be used economically to improve conversion or yield. Also, complications typically arise if there are any azeotropes present in the mixture.

Mujtaba and Macchietto (1992) summarises a list (Table 9.1) of reaction schemes with boiling points of the species involved. For each reaction scheme (with reactants shown on the left and products on the right), the right type of batch distillation column is indicated. The recommendation does not hold when the desired products are those shown on the left hand side of each reaction scheme. For example, acetic anhydride is produced by dehydration of acetic acid (Acetic Acid <=> Acetic Anhydride + Water ). In such cases, the use of CBD will be favourable to remove the water or the use of IBD will be favourable to remove the anhydride. In both configurations the equilibrium will shift to the right and the productivity of anhydride will improve. Wajge and Reklaitis (1999) considered such reaction scheme in a CBD column. Further details are in section 9.9.

9.4. Process Modelling and Simulation

Chapter 4 presents models of different complexity for BREAD processes. For other types of models and underlying assumptions, the readers are directed to Cuille and Reklaitis (1986), Albet et al. (1991), Basualdo and Ruiz (1995), Wajge and Reklaitis (1999), Leversund et al. (1993). An example of BREAD process simulation from Greaves (2003) using the model of Mujtaba and Macchietto (1997) is presented in Chapter 4.

Table 9.1. Most Suitable Batch Column Configuration for Several Chemical _ Reaction Schemes

A B C Recommended

(Boiling Point, K) (Boiling Point, K) (Boiling Point, K) Column

Ethylene Oxide Water Ethylene Glycol Inverted

Benzene Xylene Toluene N.S. *

Acetic Anhydride Water Acetic Acid N.S.

A + B o C + D

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