- Email: [email protected]
Enhancing Fine Chemicals Process Chemistry
0263–8762/05/$30.00+0.00 # 2005 Institution of Chemical Engineers Trans IChemE, Part A, June 2005 Chemical Engineering Research and Design, 83(A6): 655–661
www.icheme.org/journals doi: 10.1205/cherd.04358
ENHANCING FINE CHEMICALS PROCESS CHEMISTRY A Practical Approach D. A. OBENNDIP and P. N. SHARRATT School of Chemical Engineering and Analytical Science, Faculty of Engineering and Physical Sciences, The University of Manchester, Manchester, UK
I
n the fine chemicals industry, development is driven by a constant strive for higher yields and selectivity of existing processes and faster development of new processes and products. Key constraints in process development in today’s low-tonnage chemicals sectors (fine and effect chemicals) remain the time and cost spent obtaining data to identify and verify new processes. This often limits the amount of knowledge of the process chemistry acquired from any process development activity. In the present work, the capability of a simple high throughput experimental device is exploited to develop, apply and evaluate some laboratory-based tools to provide valuable insights into the process chemistry, especially in the critical early stages of process development. This paper discusses the experimental and modelling approaches (based on a model heterogeneous solid-catalysed acylation reaction) adopted to investigate a set of conditions that should deliver good process performance. The use of the experimental design and data reconciliation concepts in an integrated way to deliver a better process design is also demonstrated. Keywords: process chemistry; competing reactions; experimental designs; data reconciliation; kinetic modelling.
INTRODUCTION
technology within such industry sectors has greatly relied on the batch stirred tank, which is flexible but in which reaction conditions are less than optimal (Sharratt, 1997). These factors have limited the time for proper investigation of the process chemistry at the laboratory scale, especially for multi-step chemical reactions. This often results in unexpected outcomes (e.g., low yields and selectivity of desired product). Of increasing concern is the interplay between various process variables towards obtaining the desired processing objective (BRITEST Limited, 2003). The determination of the best process remains a challenge. Relatively little effort is often made towards a deep understanding of the process chemistry in low-tonnage chemicals manufacturing operations. There is thus a gap with conventional chemical engineering methods, which assume known reaction kinetics and idealized reactor conditions, then develop a mathematical model which optimizes some property of the reaction system. Kittrell (1970) and Froment and Bischoff (1990) discuss this in detail, including the application of statistical approaches to model discrimination and parameter estimation for some industrially relevant reaction systems. Miller and Davies (2000) rule out the idea of using traditional sophisticated simulation techniques. They develop a process design decision support system which uses existing information to provide a preliminary process analysis of the chemistry. Sharratt and co-workers (Sharratt et al., 2001, 2003) also use the BRITESTTM methodology (BRITEST
Traditionally, process research and development (PRD) in the low-tonnage chemicals industry (fine and effect chemicals) has relied on laboratory experimentation to create new products and improve on the processes by which these products are made. Despite the relatively small fraction of expenditure which experimentation consumes, and in spite of its proven success, it is regarded as burdensome because of the considerable degree of business risk associated with the industrialization of laboratory processes. The object of a process development is to increase the knowledge of a process to the point where a plant can be costed and designed safely and reliably, and in which the potential of the reaction can be fully exploited (McConville, 2002). This involves the exploitation of all the process variables which are delivered in such a way as to give the best yield in terms of product and cost of the manufacturing plant, and in the low-tonnage sectors is known as process optimization. With increasing pressure on profits, the time available for PRD became squeezed under ever tighter deadlines for time-to-market to maximize patent coverage (Watson, 2003). The reactor Correspondence to: Mr D. A. Obenndip, School of Chemical Engineering and Analytical Science, Faculty of Engineering and Physical Sciences, The University of Manchester, PO Box 88, Manchester M60 1QD, UK. E-mail: [email protected]
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Limited, 2003) to introduce a novel process representation which describes the physicochemical experience of the processed materials, rather than the traditional flowsheet or recipe. They argue that equipment selection is based on the ability to deliver favourable processing conditions, instead of fitting the process to an available ‘unit operation’. Jeoung (2001) develops a short-cut method which allows a meaningful interpretation of the limited information available for elucidation of reaction pathways, evaluation of mixing effects and selection of favourable contacting patterns. The foregoing developments have potential for supporting innovative process strategies in the low-tonnage chemicals industry. On the other hand, work on the critical early stages of process development where the chemistry is being developed (and thus poorly understood) is just emerging from its infancy (BRITEST Limited, 2003). Thus the need to develop coherent approaches to the development of fine chemicals processes remains a major cause for concern (Atherton and Carpenter, 1999; Tirronen and Salmi, 2003). The objective of the work reported herein is to use the experimental design and data reconciliation approaches to establish a framework that provides valuable insights into fine chemicals process chemistry, against a background of limited process knowledge. The integrated approach is illustrated with the concept of competing chemical reactions, which pose a significant processing problem in fine chemicals manufacturing, especially in terms of yield and selectivity. REACTION SYSTEM In order to move beyond abstract concepts, a heterogeneous liquid phase solid-catalysed acylation was chosen. The products of such Friedel – Crafts substitution reactions are generally aromatic ketones (Heany, 1991), which are important intermediates in many chemical applications, such as fungicides, herbicides and insecticides (Valkenberg et al., 2001; Yadav and Asthana, 2002). These authors have established that under specified reaction conditions the overall reaction scheme may be represented as shown in Scheme 1, where the desired product (R) may react further with the acylating agent (B) to yield the competing by-product (S). Although this behaviour has been attributed to the planar nature of the aromatic substrate which exposes other ring positions for multiple acylation (Heany, 1991), the proposed reaction scheme remains to be confirmed by this study. EXPERIMENTAL
The selection and application of the relevant experimental strategies was subject to establishing an information-based framework for the reaction scheme and components. Table 1 shows a driving force analysis (DFA) that summarizes existing qualitative knowledge about the reaction scheme (Valkenberg et al., 2001; Yadav and Asthana, 2002). A summary of the initial information on selected phase properties of the reaction components is given in Table 2. Generation of such preliminary information is an excellent start point for interaction between chemists and chemical engineers involved in process development (Sharratt et al., 2001, 2003; Walsh, 2004). This method captures the knowledge that could be available in the very early stages of process development to allow for the design of appropriate and relevant laboratory experiments. Given the short development timescales, waiting until there is complete process knowledge before defining the most appropriate operating strategy is seldom an option. Table 1 suggests that the most important variables affecting the rate of the main reaction are the concentration of the
Table 1. DFA table for the acylation reaction scheme based on data from the literature. Driving force
Main reaction (1)
Competing reaction (2)
Reaction component concentrations A þ B þ Catalyst þ R o S o Gaseous product o Reaction conditions Temperature Heat of reaction Reaction time Agitation intensity
o þ ? þ o o
? ? Slow (10 h) o
? ? Very slow ?
Positive effect; ono effect; ?missing information.
Analytical grade reagents were obtained from Acros Organics and Aldrich Fine Chemicals, UK. The chemicals were checked by gas chromatography (GC) and used without further purification. The catalyst was dried at 1208C in k
(1)
k
(2)
2 R þ B !S
Organization and Planning
þ
Raw Materials
1 A þ B !R
an oven for 3 h each time before it was to be used in a reaction. The drying process was to remove any physisorbed and chemisorbed water. The presence of water has the effect of filling the catalyst’s pores and preventing access to the acid sites for molecules of the acylating agent. The adsorbed water is also protonated by the acidic protons, rather than the reagent molecules being protonated. Water-free conditions were also necessary to avoid a vigorous undesired reaction of any water present with the acylating agent.
Scheme 1. Main parts of the proposed mechanism of the acylation reaction.
Table 2. Set of phase properties. Reaction component A B Catalyst R Gaseous product
Melting point (8C)
Boiling point (8C)
Polarity
245 2112 n/a — n/a
166 51 — 235 —
Low–moderate Moderate–high Very low Low High
Trans IChemE, Part A, Chemical Engineering Research and Design, 2005, 83(A6): 655–661
ENHANCING FINE CHEMICALS PROCESS CHEMISTRY
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Table 3. Range data for the studied factors. Experimental factor Reaction temperature (8C)
Low level (21) 30
High level (þ1) 50
B/A feed molar ratio
0.5
4.0
Catalyst loading (g mL21)
0.01
0.05
acylating agent, the catalyst loading and the reaction temperature. It is important to note that the selection of factors (see Table 3) for subsequent experimental design was based on the DFA. In addition, the very low melting points of the reactants (Table 2) limit their use in the solid form, since this would require very low operating temperatures which render the reaction kinetically unfeasible (Heany, 1991). Possibilities for viable operating strategies that can be deduced from these limited data will be discussed later in this paper. Reaction pathway assessment This strategy involved two approaches: a reagent-limited approach in which the final reaction products were followed as a function of the feed molar ratios, and a dynamic approach in which the amounts of reaction components were followed with time. The time-dependent data were generated from each run in the factorial experiments described in the following section. Factorial design Following from Table 1, the design evaluated the effect of three factors, including the reaction temperature, initial feed molar ratio and catalyst loading. A 2321 fractional factorial design (Montgomery, 2001) was employed to develop an understanding of the interaction between the factors studied. Table 3 gives details of the assigned values for each factor. The experimental design (DoE) and data analysis were conducted using MINITABw software (MINITAB Inc., 1996). Although Table 4 sets out the design in standard order for clarity, the experiments were carried out in random order. Reproducibility of the generated data can be evidenced by the close agreement between the output values for standard order two and its
Selection criteria (as evidenced by Tables 1 and 2) To obtain short reaction times above room temperature under non-boiling conditions (508C), as temperature effect is unknown. Reagent-limited data (e.g., Figure 3) revealed near minimum and maximum R yields at B/A ratios of 0.5 and 4, respectively. Need for using a molar excess of 5 molar equivalents for such solid-catalysed acylations (Olah, 1963; Heany, 1991), as the main and side reactions have apparently different dependencies on B/A. Adsorption of reaction components on catalyst particles at high catalyst loadings 0.05 g mL21 (Obenndip, 2003). Unknown effect on the side reaction.
duplicate run (i.e., 2 Dup). In order to conduct a mass balance necessary to establish the reaction kinetics, timedependent yields for all the reaction components were obtained (although not shown in Table 4).
Apparatus Experiments were conducted in batch reactor (glass) tubes (approximately 50 mL capacity) on a Radleys Carousel Parallel Synthesiser fitted on a standard hotplate stirrer (Figure 1) and mounted in a fume cupboard. Each glass reactor was equipped with a magnetic stirrer. The temperature in the reaction vessels was automatically controlled and displayed. Refluxing within individual reaction tubes was provided by a water-cooled aluminium head at the upper part of the equipment.
Procedure In a typical experiment, the reagents were added into 11 reaction tubes and the temperature and agitator speed adjusted. Different masses of the catalyst were added to each reaction tube once the desired temperature was attained, according to the Elbs procedure (Olah, 1963). The catalyst loading was based on the total liquid volume of the reaction mixture. While the reaction in each tube was allowed to go to completion (12 h) in the reagentlimited runs, the amounts of reagents and reaction conditions were varied according to the 2321 design (see Table 4) in the factorial runs. The reaction tubes (factorial runs) were withdrawn at hourly intervals over an 11 h period, and the reaction components analysed by GC (Hewlett Packard 5890 II; 10 m FFAP, 0.53 mm diameter, 1.5 mm film thickness; N2 gas as carrier).
Table 4. 2321 fractional factorial design and results. Experimental conditions Standard order 1 2 3 4 2 Dup
Reaction temperature (8C)
Feed molar ratio (i.e., Bo/Ao)
Catalyst loading (g/mL21)
Rmax (mole %)a
Time to Rmax (h)
2 þ 2 þ þ
2 2 þ þ 2
þ 2 2 þ 2
3.71 8.03 8.79 42.8 8.23
8 8 10 8 8
a
Relative to the initial molar amount of substrate A.
Trans IChemE, Part A, Chemical Engineering Research and Design, 2005, 83(A6): 655–661
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Figure 1. The Radleys 12-slot Carousel Reaction StationTM. Figure 2. A comparison of measured (mea) and reconciled (rec) concentrations from a typical reagent-limited experiment.
ANALYSIS AND RESULTS Data Reconciliation Considerable variability observed in the GC concentration measurements indicated that the measured reaction outcome was not uniquely determined by the experimental procedure, but was also subject to change, i.e., it is a random variable. This stochastic characteristic is either rooted in the nature of the process, or it is a consequence of inevitable uncertainty in the analytical equipment (Mah, 1990). This measurement problem was associated with the staggered sample collection, the drift and error in the GC detector performance and the uncertainty in the response factors for the compounds. It was thus necessary to organize the calculations and observations in such a way so as to ensure as small an error as possible in the results, and at the same time minimizing the computational effort. Hence, a data reconciliation technique based on the mass balance equations [equations (3) and (4)] for the studied reaction system was developed to adjust the component concentrations so as to provide best estimates of the true concentrations. Ao ¼ Ai þ Ri þ Si
(3)
Bo ¼ Bi þ Ri þ 2Si
(4)
This was achieved by using statistical techniques to minimise the sum of squares error, a2j [see equation (5), where o and i represent initial and present conditions, respectively] for a given data set (i.e., j ¼ 1 to 11), subject to the constraint that the sensitivity adjustment factor, bj 0. For each data set, it is necessary to compute bj that minimizes a2j . This is done by adjusting the overall response to component i, foi , which is assumed to be the product of the injection volume for sample j (which may be non-reproducible) and the GC response factor for component i, fi [equation (6)]. min (a2j ) ¼ bj
½Ao ½Ai ½Ri ½Si 2 ½Ao j ½Bo ½Bi ½Ri 2½Si 2 þ ½Bo j
foi ¼ bj fi
The computations were executed with Microsoftw Excel’s equation and optimization tool, Solver, run at its default settings (i.e., 100 s maximum run time, 100 iterations, 0.000001 precision, 5% tolerance and 0.0001 convergence). This tool, which uses the generalized reduced gradient (GRG2) non-linear optimization code (Winston, 1995), works with a group of cells that are related to the formula in a target cell. The reconciled concentrations for each component, Cij,rec are obtained from equation (7). The measured and reconciled component concentrations from a typical experiment are shown in Figure 2, which reveals that the data reconciliation strategy was valuable in minimising the level of noise in the experimental data. Cij,rec ¼ foi (GC counts)i
(7)
Factorial Design Diagnostics The design output data (Table 4) were analysed for the most important factors affecting the yield of R by examining a normal probability plot (Figure 3) of the estimates of the effects. Negligible effects are normally distributed, with mean zero and variance s2 and will tend to fall along a straight line on this plot, whereas significant effects will have non-zero means and will not lie along the straight line (Daniel, 1959). Figure 3 shows all three main effects to be significant. This therefore provides qualitative evidence to support the validity of the DFA set out in Table 1. An analysis of variance (ANOVA) for the yield of R showed that the feed molar ratio and catalyst loading
(5) (6)
Figure 3. Normal probability plot (95% confidence interval) of the effects (C: catalyst loading; F: B/A feed molar ratio; T: reaction temperature).
Trans IChemE, Part A, Chemical Engineering Research and Design, 2005, 83(A6): 655–661
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Figure 4. MINITABw diagnostic plots for (a) single factor, and (b) interaction for the maximum relative yield % of R.
were respectively the most and least significant effects. Figure 4 shows the single-factor and interaction plots for the design response. Taking into account the alias structure of the design and neglecting third and higher order interactions, a main effects only model was fitted to the data. The adequacy of this model was tested by comparing the experimental data with the model predictions (Figure 5). Supplementary residual analysis showed that the model could predict relative maximum yields of R with an average accuracy to within 17.5%. Although this does not indicate a tight fit, the empirical model strongly evidences the dependence of the main reaction outcome on the selected factors (see Table 3). The prediction ability of this statistical model is restricted usually both by the number of, and the limits of changes of the parameters during DoE performance.
appears to be valid. This was done by re-interpreting the reaction information in Table 1 as being structurally equivalent to a set of differential equations describing the reaction outcome, together with variables equivalent to the surface reaction rate constants, kSR1 and kSR2 , which were computed from the slopes of the linear fits typified by Figure 7. Assuming simple mass action kinetics, the simulation methodology used MATLABw (v. 5.3) for numerical solution of the set of ODEs representing reaction rates [equations (8) – (11)]. Function ode23, which uses a selfadjusting step length algorithm (Shampine et al., 2003),
KINETIC MODELLING A kinetic model for related heterogeneous solid-catalysed acylations was proposed by Yadav and co-workers (Yadav and Doshi, 2000; Yadav and Asthana, 2002), in which the reaction between the adsorbed acylating agent and liquid phase substrate was assumed to be rate-determining. Their rate equation is derived from a single site idealized Eley –Rideal mechanism (Figure 6). Kinetics were fitted to the Eley – Rideal model using the data obtained (see Experimental section). Figure 7 compares the fitted results from this work with those reported by Yadav and Asthana (2002). The fits appear to be good, with a mean spread of 4.2%. Close agreement between corresponding values of the product of the overall surface reaction rate constant, kSR and the equilibrium constant, Ka further confirmed the accuracy of the kinetic model (insert in Figure 7) for this study (Obenndip, 2003).
Figure 5. A comparison of the measured data to the regression model predictions based on the 2321 fractional factorial design (see Table 4).
Numerical Simulation Based on the Eley – Rideal mechanism, a numerical model was developed to predict the range of reaction conditions for which the competing mechanism (see Scheme 1)
Figure 6. Single-site Eley–Rideal model: (a) adsorption of B onto catalytic surface (physisorption, immediately followed by chemisorption); (b) interaction between chemisorbed B (i.e., B ) with free substrate molecule (A); (c) formation of products (R, S), with subsequent desorption.
Trans IChemE, Part A, Chemical Engineering Research and Design, 2005, 83(A6): 655–661
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OBENNDIP and SHARRATT
Figure 9. Model simulated selectivity versus conversion profiles. Figure 7. Kinetic model fitting [insert depicts Yadav and Asthana’s (2002) model equation].
was employed as a numerical method to simulate the reaction system for an integration period of 12 h. d½A ¼ kSR1 ½A½B dt d½B ¼ kSR1 ½A½B kSR2 ½B½R rB ¼ dt d½R ¼ kSR1 ½A½B kSR2 ½B½R rR ¼ dt d½S ¼ kSR2 ½B½R rS ¼ dt
rA ¼
(8) (9)
Figure 10. Block diagram representing a possible operating strategy for continuous manufacture of R.
(10) (11)
Results The generated concentration profiles (Figure 8) show a good match between predicted and experimental data. In addition to asserting that the acylation is a slow process, it can be seen from Figure 8 that measured concentrations are slightly less than the corresponding model predictions. As the influence of mass transfer may be unimportant in explaining this observation (Yadav and Doshi, 2000; Yadav and Asthana, 2002), the assumption of catalytic retention of the reaction components at high catalyst loadings was validated by a simple experimental procedure involving a comparison of the component concentrations in post-reaction mixtures containing different catalyst loadings (Obenndip, 2003). Further insights into the
analysis indicated that the reaction products were not adsorbed differentially. An analysis of the entire simulated data showed that formation of undesired S occurred at a minimum kSR1 =kSR2 ratio of 5, which can be met experimentally under conditions of high temperatures and excess molar amount of the acylating agent, B, as was observed in some of the reagent-limited experiments (although not reported in this paper). Formation of S can, in principle, be minimized by carrying out the acylation in a reactor that allows R to be removed as soon as it is formed (e.g., a membrane reactor). Although substrate conversions of up to 70% could be obtained under batch operation, operating at low conversions could also bring substantial yield benefits. This can be readily seen from Figure 9, which shows that a 60% reduction in substrate conversion increases the selectivity of the desired product by about 12%. This could be effected in practice if excess amounts of the acylating agent are avoided, with further separation of R and recycling of A (Figure 10).
CONCLUSION
Figure 8. Comparison of experimental data (symbols) with model predictions (lines) for factorial run 4 (see Table 4).
A combination of high throughput experimentation and simple process analysis tools has been shown to be a valuable approach to providing insights into low-tonnage process chemistry development. A rigorous experimental methodology utilising relatively few experiments with multiple data generation from a single run is clearly efficient. In addition to the reconciliation technique used for data smoothing, an efficient implementation of this methodology could be advantageous in shortening development time during early process development. The well designed experiments provide qualitative observations to support
Trans IChemE, Part A, Chemical Engineering Research and Design, 2005, 83(A6): 655–661
ENHANCING FINE CHEMICALS PROCESS CHEMISTRY the validity of the DFA, which has great potential for generating process innovation. The identification of a potential strategy for early process assessment remains a crucial option in any development activity. Figure 8, for instance, shows time-dependent factorial experimental data depicting ‘bad’ process outcome (i.e., incomplete conversion and low yields). This indicates that step change conditions are needed if factorial experimentation alone is considered. This contrasts sharply with the kinetic modelling approach which allows a more confident extrapolation to such step change conditions, even though the model is crude. Although the adopted strategy would depend on the current process development objectives, a consideration of both approaches is highly recommended if an information-rich framework is to be established. The integrated approach reported here establishes that the optimization of reaction outcome may be effected by applying three crucial steps: understanding the driving forces; reducing undesired paths; and, identifying sound processing methodologies and equipment. It is hoped that a successful application of these steps would require an interdisciplinary approach involving a close collaboration between chemists and chemical engineers involved in process development. REFERENCES Atherton, J.H. and Carpenter, K.J., 1999, Process Development: Physicochemical Concepts (Oxford University Press, New York, USA). BRITEST Limited, 2003, http://www.britest.co.uk/. Daniel, C., 1959, Use of half-normal plots in interpreting factorial two level experiments, Technometrics, 1: 311–342. Froment, G.F. and Bischoff, K.B., 1990, Chemical Reactor Analysis and Design, 2nd edition (John Wiley & Sons, Inc., New York, USA). Heany, H., 1991, The Bimolecular Aromatic Friedel-Crafts Reaction, in Trost, B.M. (ed.), Comprehensive Organic Synthesis: Selectivity, Strategy and Efficiency in Modern Organic Chemistry, pp. 733 –750 (Pergamon Press, Oxford, UK). Jeoung, S.-G., 2001, A short-cut method for reaction analysis and waste minimisation in complex chemical reactions, PhD thesis, Department of Chemical Engineering, UMIST, Manchester. Kittrell, J.R., 1970, Mathematical modelling of chemical reactions, Advances in Chemical Engineering, 8: 98– 183. Mah, R.S., 1990, Chemical Process Structures and Information Flows (Butterworths, Boston, USA). McConville, F.X., 2002, The Pilot Plant Real Book: A Unique Handbook for the Chemical Process Industry (FXM Engineering and Design, Worcester, UK).
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Miller, D.C. and Davies, J.F., 2000, Process design decision support system for developing process chemistry, Industrial & Engineering Chemistry Research, 39: 2954–2969. MINITAB Inc., 1996, MINITAB for Windows, Release 11 (http:// www.minitab.com/). Montgomery, D.C., 2001, Design and Analysis of Experiments, 5th edition (John Wiley & Sons, Inc., New York, USA). Obenndip, D.A., 2003, Process chemistry enhancement for fine chemicals applications, MPhil thesis, Department of Chemical Engineering, UMIST, Manchester. Olah, G. (ed.), 1963, Friedel-Crafts and Related Reactions (John Wiley & Sons, Ltd., London, UK). Shampine, L.F., Gladwell, I. and Thompson, S., 2003, Solving ODEs with MATLAB (Cambridge University Press, Cambridge, UK). Sharratt, P.N. (ed.), 1997, Handbook of Batch Process Design (Blackie Academic & Professional, London, UK). Sharratt, P.N., Wall, K. and Borland, J.N., 2001, Crossing the border— chemistry and chemical engineering in process design, Proceedings of the 6th International Congress of Chemical Engineers, 23–27 September, Melbourne, Australia. Sharratt, P.N., Wall, K. and Borland, J.N., 2003, Generating innovative process designs using limited data, Journal of Chemical Technology and Biotechnology, 78: 156–160. Tirronen, E. and Salmi, T., 2003, Process development in the fine chemical industry, Chemical Engineering Journal, 91: 103 –114. Valkenberg, M.H., de Castro, C. and Holderich, W.F., 2001, Friedel-Crafts acylation of aromatics catalysed by supported ionic liquids, Applied Catalysis, 215: 185– 190. Walsh, J., 2004, An integrated approach to chemical engineering and chemistry in process design, Proceedings of the Switching from Batch to Continuous Processing Conference, 22–23 November, London. Watson, W., 2003, Process chemistry, Chemistry in Britain, 39(5): 49. Winston, W.L., 1995, Introduction to Mathematical Programming: Applications and Algorithms, 2nd edition (Duxbury Press, Belmont, UK). Yadav, G.D. and Doshi, N.S., 2000, Alkylation of hydroquinone with methyl-tert-butyl-ether and tert-butanol, Catalysis Today, 60: 262–273. Yadav, G.D. and Asthana, N.S., 2002, Kinetics and mechanism of selective monoacylation of mesitylene, Industrial & Engineering Chemistry Research, 41: 5565– 5575.
ACKNOWLEDGEMENT The authors gratefully acknowledge an especially helpful conversation with Dr Arthur Garforth, and thank him for providing them with the experimental equipment. Financial support through a UK Commonwealth and UMIST Graduate Research School scholarships is also acknowledged. This paper was presented at the 7th World Congress of Chemical Engineering held in Glasgow, UK, 10–14 July 2005. The manuscript was received 15 December 2004 and accepted for publication after revision 4 April 2005.
Trans IChemE, Part A, Chemical Engineering Research and Design, 2005, 83(A6): 655–661
www.icheme.org/journals doi: 10.1205/cherd.04358
ENHANCING FINE CHEMICALS PROCESS CHEMISTRY A Practical Approach D. A. OBENNDIP and P. N. SHARRATT School of Chemical Engineering and Analytical Science, Faculty of Engineering and Physical Sciences, The University of Manchester, Manchester, UK
I
n the fine chemicals industry, development is driven by a constant strive for higher yields and selectivity of existing processes and faster development of new processes and products. Key constraints in process development in today’s low-tonnage chemicals sectors (fine and effect chemicals) remain the time and cost spent obtaining data to identify and verify new processes. This often limits the amount of knowledge of the process chemistry acquired from any process development activity. In the present work, the capability of a simple high throughput experimental device is exploited to develop, apply and evaluate some laboratory-based tools to provide valuable insights into the process chemistry, especially in the critical early stages of process development. This paper discusses the experimental and modelling approaches (based on a model heterogeneous solid-catalysed acylation reaction) adopted to investigate a set of conditions that should deliver good process performance. The use of the experimental design and data reconciliation concepts in an integrated way to deliver a better process design is also demonstrated. Keywords: process chemistry; competing reactions; experimental designs; data reconciliation; kinetic modelling.
INTRODUCTION
technology within such industry sectors has greatly relied on the batch stirred tank, which is flexible but in which reaction conditions are less than optimal (Sharratt, 1997). These factors have limited the time for proper investigation of the process chemistry at the laboratory scale, especially for multi-step chemical reactions. This often results in unexpected outcomes (e.g., low yields and selectivity of desired product). Of increasing concern is the interplay between various process variables towards obtaining the desired processing objective (BRITEST Limited, 2003). The determination of the best process remains a challenge. Relatively little effort is often made towards a deep understanding of the process chemistry in low-tonnage chemicals manufacturing operations. There is thus a gap with conventional chemical engineering methods, which assume known reaction kinetics and idealized reactor conditions, then develop a mathematical model which optimizes some property of the reaction system. Kittrell (1970) and Froment and Bischoff (1990) discuss this in detail, including the application of statistical approaches to model discrimination and parameter estimation for some industrially relevant reaction systems. Miller and Davies (2000) rule out the idea of using traditional sophisticated simulation techniques. They develop a process design decision support system which uses existing information to provide a preliminary process analysis of the chemistry. Sharratt and co-workers (Sharratt et al., 2001, 2003) also use the BRITESTTM methodology (BRITEST
Traditionally, process research and development (PRD) in the low-tonnage chemicals industry (fine and effect chemicals) has relied on laboratory experimentation to create new products and improve on the processes by which these products are made. Despite the relatively small fraction of expenditure which experimentation consumes, and in spite of its proven success, it is regarded as burdensome because of the considerable degree of business risk associated with the industrialization of laboratory processes. The object of a process development is to increase the knowledge of a process to the point where a plant can be costed and designed safely and reliably, and in which the potential of the reaction can be fully exploited (McConville, 2002). This involves the exploitation of all the process variables which are delivered in such a way as to give the best yield in terms of product and cost of the manufacturing plant, and in the low-tonnage sectors is known as process optimization. With increasing pressure on profits, the time available for PRD became squeezed under ever tighter deadlines for time-to-market to maximize patent coverage (Watson, 2003). The reactor Correspondence to: Mr D. A. Obenndip, School of Chemical Engineering and Analytical Science, Faculty of Engineering and Physical Sciences, The University of Manchester, PO Box 88, Manchester M60 1QD, UK. E-mail: [email protected]
655
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OBENNDIP and SHARRATT
Limited, 2003) to introduce a novel process representation which describes the physicochemical experience of the processed materials, rather than the traditional flowsheet or recipe. They argue that equipment selection is based on the ability to deliver favourable processing conditions, instead of fitting the process to an available ‘unit operation’. Jeoung (2001) develops a short-cut method which allows a meaningful interpretation of the limited information available for elucidation of reaction pathways, evaluation of mixing effects and selection of favourable contacting patterns. The foregoing developments have potential for supporting innovative process strategies in the low-tonnage chemicals industry. On the other hand, work on the critical early stages of process development where the chemistry is being developed (and thus poorly understood) is just emerging from its infancy (BRITEST Limited, 2003). Thus the need to develop coherent approaches to the development of fine chemicals processes remains a major cause for concern (Atherton and Carpenter, 1999; Tirronen and Salmi, 2003). The objective of the work reported herein is to use the experimental design and data reconciliation approaches to establish a framework that provides valuable insights into fine chemicals process chemistry, against a background of limited process knowledge. The integrated approach is illustrated with the concept of competing chemical reactions, which pose a significant processing problem in fine chemicals manufacturing, especially in terms of yield and selectivity. REACTION SYSTEM In order to move beyond abstract concepts, a heterogeneous liquid phase solid-catalysed acylation was chosen. The products of such Friedel – Crafts substitution reactions are generally aromatic ketones (Heany, 1991), which are important intermediates in many chemical applications, such as fungicides, herbicides and insecticides (Valkenberg et al., 2001; Yadav and Asthana, 2002). These authors have established that under specified reaction conditions the overall reaction scheme may be represented as shown in Scheme 1, where the desired product (R) may react further with the acylating agent (B) to yield the competing by-product (S). Although this behaviour has been attributed to the planar nature of the aromatic substrate which exposes other ring positions for multiple acylation (Heany, 1991), the proposed reaction scheme remains to be confirmed by this study. EXPERIMENTAL
The selection and application of the relevant experimental strategies was subject to establishing an information-based framework for the reaction scheme and components. Table 1 shows a driving force analysis (DFA) that summarizes existing qualitative knowledge about the reaction scheme (Valkenberg et al., 2001; Yadav and Asthana, 2002). A summary of the initial information on selected phase properties of the reaction components is given in Table 2. Generation of such preliminary information is an excellent start point for interaction between chemists and chemical engineers involved in process development (Sharratt et al., 2001, 2003; Walsh, 2004). This method captures the knowledge that could be available in the very early stages of process development to allow for the design of appropriate and relevant laboratory experiments. Given the short development timescales, waiting until there is complete process knowledge before defining the most appropriate operating strategy is seldom an option. Table 1 suggests that the most important variables affecting the rate of the main reaction are the concentration of the
Table 1. DFA table for the acylation reaction scheme based on data from the literature. Driving force
Main reaction (1)
Competing reaction (2)
Reaction component concentrations A þ B þ Catalyst þ R o S o Gaseous product o Reaction conditions Temperature Heat of reaction Reaction time Agitation intensity
o þ ? þ o o
? ? Slow (10 h) o
? ? Very slow ?
Positive effect; ono effect; ?missing information.
Analytical grade reagents were obtained from Acros Organics and Aldrich Fine Chemicals, UK. The chemicals were checked by gas chromatography (GC) and used without further purification. The catalyst was dried at 1208C in k
(1)
k
(2)
2 R þ B !S
Organization and Planning
þ
Raw Materials
1 A þ B !R
an oven for 3 h each time before it was to be used in a reaction. The drying process was to remove any physisorbed and chemisorbed water. The presence of water has the effect of filling the catalyst’s pores and preventing access to the acid sites for molecules of the acylating agent. The adsorbed water is also protonated by the acidic protons, rather than the reagent molecules being protonated. Water-free conditions were also necessary to avoid a vigorous undesired reaction of any water present with the acylating agent.
Scheme 1. Main parts of the proposed mechanism of the acylation reaction.
Table 2. Set of phase properties. Reaction component A B Catalyst R Gaseous product
Melting point (8C)
Boiling point (8C)
Polarity
245 2112 n/a — n/a
166 51 — 235 —
Low–moderate Moderate–high Very low Low High
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Table 3. Range data for the studied factors. Experimental factor Reaction temperature (8C)
Low level (21) 30
High level (þ1) 50
B/A feed molar ratio
0.5
4.0
Catalyst loading (g mL21)
0.01
0.05
acylating agent, the catalyst loading and the reaction temperature. It is important to note that the selection of factors (see Table 3) for subsequent experimental design was based on the DFA. In addition, the very low melting points of the reactants (Table 2) limit their use in the solid form, since this would require very low operating temperatures which render the reaction kinetically unfeasible (Heany, 1991). Possibilities for viable operating strategies that can be deduced from these limited data will be discussed later in this paper. Reaction pathway assessment This strategy involved two approaches: a reagent-limited approach in which the final reaction products were followed as a function of the feed molar ratios, and a dynamic approach in which the amounts of reaction components were followed with time. The time-dependent data were generated from each run in the factorial experiments described in the following section. Factorial design Following from Table 1, the design evaluated the effect of three factors, including the reaction temperature, initial feed molar ratio and catalyst loading. A 2321 fractional factorial design (Montgomery, 2001) was employed to develop an understanding of the interaction between the factors studied. Table 3 gives details of the assigned values for each factor. The experimental design (DoE) and data analysis were conducted using MINITABw software (MINITAB Inc., 1996). Although Table 4 sets out the design in standard order for clarity, the experiments were carried out in random order. Reproducibility of the generated data can be evidenced by the close agreement between the output values for standard order two and its
Selection criteria (as evidenced by Tables 1 and 2) To obtain short reaction times above room temperature under non-boiling conditions (508C), as temperature effect is unknown. Reagent-limited data (e.g., Figure 3) revealed near minimum and maximum R yields at B/A ratios of 0.5 and 4, respectively. Need for using a molar excess of 5 molar equivalents for such solid-catalysed acylations (Olah, 1963; Heany, 1991), as the main and side reactions have apparently different dependencies on B/A. Adsorption of reaction components on catalyst particles at high catalyst loadings 0.05 g mL21 (Obenndip, 2003). Unknown effect on the side reaction.
duplicate run (i.e., 2 Dup). In order to conduct a mass balance necessary to establish the reaction kinetics, timedependent yields for all the reaction components were obtained (although not shown in Table 4).
Apparatus Experiments were conducted in batch reactor (glass) tubes (approximately 50 mL capacity) on a Radleys Carousel Parallel Synthesiser fitted on a standard hotplate stirrer (Figure 1) and mounted in a fume cupboard. Each glass reactor was equipped with a magnetic stirrer. The temperature in the reaction vessels was automatically controlled and displayed. Refluxing within individual reaction tubes was provided by a water-cooled aluminium head at the upper part of the equipment.
Procedure In a typical experiment, the reagents were added into 11 reaction tubes and the temperature and agitator speed adjusted. Different masses of the catalyst were added to each reaction tube once the desired temperature was attained, according to the Elbs procedure (Olah, 1963). The catalyst loading was based on the total liquid volume of the reaction mixture. While the reaction in each tube was allowed to go to completion (12 h) in the reagentlimited runs, the amounts of reagents and reaction conditions were varied according to the 2321 design (see Table 4) in the factorial runs. The reaction tubes (factorial runs) were withdrawn at hourly intervals over an 11 h period, and the reaction components analysed by GC (Hewlett Packard 5890 II; 10 m FFAP, 0.53 mm diameter, 1.5 mm film thickness; N2 gas as carrier).
Table 4. 2321 fractional factorial design and results. Experimental conditions Standard order 1 2 3 4 2 Dup
Reaction temperature (8C)
Feed molar ratio (i.e., Bo/Ao)
Catalyst loading (g/mL21)
Rmax (mole %)a
Time to Rmax (h)
2 þ 2 þ þ
2 2 þ þ 2
þ 2 2 þ 2
3.71 8.03 8.79 42.8 8.23
8 8 10 8 8
a
Relative to the initial molar amount of substrate A.
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Figure 1. The Radleys 12-slot Carousel Reaction StationTM. Figure 2. A comparison of measured (mea) and reconciled (rec) concentrations from a typical reagent-limited experiment.
ANALYSIS AND RESULTS Data Reconciliation Considerable variability observed in the GC concentration measurements indicated that the measured reaction outcome was not uniquely determined by the experimental procedure, but was also subject to change, i.e., it is a random variable. This stochastic characteristic is either rooted in the nature of the process, or it is a consequence of inevitable uncertainty in the analytical equipment (Mah, 1990). This measurement problem was associated with the staggered sample collection, the drift and error in the GC detector performance and the uncertainty in the response factors for the compounds. It was thus necessary to organize the calculations and observations in such a way so as to ensure as small an error as possible in the results, and at the same time minimizing the computational effort. Hence, a data reconciliation technique based on the mass balance equations [equations (3) and (4)] for the studied reaction system was developed to adjust the component concentrations so as to provide best estimates of the true concentrations. Ao ¼ Ai þ Ri þ Si
(3)
Bo ¼ Bi þ Ri þ 2Si
(4)
This was achieved by using statistical techniques to minimise the sum of squares error, a2j [see equation (5), where o and i represent initial and present conditions, respectively] for a given data set (i.e., j ¼ 1 to 11), subject to the constraint that the sensitivity adjustment factor, bj 0. For each data set, it is necessary to compute bj that minimizes a2j . This is done by adjusting the overall response to component i, foi , which is assumed to be the product of the injection volume for sample j (which may be non-reproducible) and the GC response factor for component i, fi [equation (6)]. min (a2j ) ¼ bj
½Ao ½Ai ½Ri ½Si 2 ½Ao j ½Bo ½Bi ½Ri 2½Si 2 þ ½Bo j
foi ¼ bj fi
The computations were executed with Microsoftw Excel’s equation and optimization tool, Solver, run at its default settings (i.e., 100 s maximum run time, 100 iterations, 0.000001 precision, 5% tolerance and 0.0001 convergence). This tool, which uses the generalized reduced gradient (GRG2) non-linear optimization code (Winston, 1995), works with a group of cells that are related to the formula in a target cell. The reconciled concentrations for each component, Cij,rec are obtained from equation (7). The measured and reconciled component concentrations from a typical experiment are shown in Figure 2, which reveals that the data reconciliation strategy was valuable in minimising the level of noise in the experimental data. Cij,rec ¼ foi (GC counts)i
(7)
Factorial Design Diagnostics The design output data (Table 4) were analysed for the most important factors affecting the yield of R by examining a normal probability plot (Figure 3) of the estimates of the effects. Negligible effects are normally distributed, with mean zero and variance s2 and will tend to fall along a straight line on this plot, whereas significant effects will have non-zero means and will not lie along the straight line (Daniel, 1959). Figure 3 shows all three main effects to be significant. This therefore provides qualitative evidence to support the validity of the DFA set out in Table 1. An analysis of variance (ANOVA) for the yield of R showed that the feed molar ratio and catalyst loading
(5) (6)
Figure 3. Normal probability plot (95% confidence interval) of the effects (C: catalyst loading; F: B/A feed molar ratio; T: reaction temperature).
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Figure 4. MINITABw diagnostic plots for (a) single factor, and (b) interaction for the maximum relative yield % of R.
were respectively the most and least significant effects. Figure 4 shows the single-factor and interaction plots for the design response. Taking into account the alias structure of the design and neglecting third and higher order interactions, a main effects only model was fitted to the data. The adequacy of this model was tested by comparing the experimental data with the model predictions (Figure 5). Supplementary residual analysis showed that the model could predict relative maximum yields of R with an average accuracy to within 17.5%. Although this does not indicate a tight fit, the empirical model strongly evidences the dependence of the main reaction outcome on the selected factors (see Table 3). The prediction ability of this statistical model is restricted usually both by the number of, and the limits of changes of the parameters during DoE performance.
appears to be valid. This was done by re-interpreting the reaction information in Table 1 as being structurally equivalent to a set of differential equations describing the reaction outcome, together with variables equivalent to the surface reaction rate constants, kSR1 and kSR2 , which were computed from the slopes of the linear fits typified by Figure 7. Assuming simple mass action kinetics, the simulation methodology used MATLABw (v. 5.3) for numerical solution of the set of ODEs representing reaction rates [equations (8) – (11)]. Function ode23, which uses a selfadjusting step length algorithm (Shampine et al., 2003),
KINETIC MODELLING A kinetic model for related heterogeneous solid-catalysed acylations was proposed by Yadav and co-workers (Yadav and Doshi, 2000; Yadav and Asthana, 2002), in which the reaction between the adsorbed acylating agent and liquid phase substrate was assumed to be rate-determining. Their rate equation is derived from a single site idealized Eley –Rideal mechanism (Figure 6). Kinetics were fitted to the Eley – Rideal model using the data obtained (see Experimental section). Figure 7 compares the fitted results from this work with those reported by Yadav and Asthana (2002). The fits appear to be good, with a mean spread of 4.2%. Close agreement between corresponding values of the product of the overall surface reaction rate constant, kSR and the equilibrium constant, Ka further confirmed the accuracy of the kinetic model (insert in Figure 7) for this study (Obenndip, 2003).
Figure 5. A comparison of the measured data to the regression model predictions based on the 2321 fractional factorial design (see Table 4).
Numerical Simulation Based on the Eley – Rideal mechanism, a numerical model was developed to predict the range of reaction conditions for which the competing mechanism (see Scheme 1)
Figure 6. Single-site Eley–Rideal model: (a) adsorption of B onto catalytic surface (physisorption, immediately followed by chemisorption); (b) interaction between chemisorbed B (i.e., B ) with free substrate molecule (A); (c) formation of products (R, S), with subsequent desorption.
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Figure 9. Model simulated selectivity versus conversion profiles. Figure 7. Kinetic model fitting [insert depicts Yadav and Asthana’s (2002) model equation].
was employed as a numerical method to simulate the reaction system for an integration period of 12 h. d½A ¼ kSR1 ½A½B dt d½B ¼ kSR1 ½A½B kSR2 ½B½R rB ¼ dt d½R ¼ kSR1 ½A½B kSR2 ½B½R rR ¼ dt d½S ¼ kSR2 ½B½R rS ¼ dt
rA ¼
(8) (9)
Figure 10. Block diagram representing a possible operating strategy for continuous manufacture of R.
(10) (11)
Results The generated concentration profiles (Figure 8) show a good match between predicted and experimental data. In addition to asserting that the acylation is a slow process, it can be seen from Figure 8 that measured concentrations are slightly less than the corresponding model predictions. As the influence of mass transfer may be unimportant in explaining this observation (Yadav and Doshi, 2000; Yadav and Asthana, 2002), the assumption of catalytic retention of the reaction components at high catalyst loadings was validated by a simple experimental procedure involving a comparison of the component concentrations in post-reaction mixtures containing different catalyst loadings (Obenndip, 2003). Further insights into the
analysis indicated that the reaction products were not adsorbed differentially. An analysis of the entire simulated data showed that formation of undesired S occurred at a minimum kSR1 =kSR2 ratio of 5, which can be met experimentally under conditions of high temperatures and excess molar amount of the acylating agent, B, as was observed in some of the reagent-limited experiments (although not reported in this paper). Formation of S can, in principle, be minimized by carrying out the acylation in a reactor that allows R to be removed as soon as it is formed (e.g., a membrane reactor). Although substrate conversions of up to 70% could be obtained under batch operation, operating at low conversions could also bring substantial yield benefits. This can be readily seen from Figure 9, which shows that a 60% reduction in substrate conversion increases the selectivity of the desired product by about 12%. This could be effected in practice if excess amounts of the acylating agent are avoided, with further separation of R and recycling of A (Figure 10).
CONCLUSION
Figure 8. Comparison of experimental data (symbols) with model predictions (lines) for factorial run 4 (see Table 4).
A combination of high throughput experimentation and simple process analysis tools has been shown to be a valuable approach to providing insights into low-tonnage process chemistry development. A rigorous experimental methodology utilising relatively few experiments with multiple data generation from a single run is clearly efficient. In addition to the reconciliation technique used for data smoothing, an efficient implementation of this methodology could be advantageous in shortening development time during early process development. The well designed experiments provide qualitative observations to support
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ENHANCING FINE CHEMICALS PROCESS CHEMISTRY the validity of the DFA, which has great potential for generating process innovation. The identification of a potential strategy for early process assessment remains a crucial option in any development activity. Figure 8, for instance, shows time-dependent factorial experimental data depicting ‘bad’ process outcome (i.e., incomplete conversion and low yields). This indicates that step change conditions are needed if factorial experimentation alone is considered. This contrasts sharply with the kinetic modelling approach which allows a more confident extrapolation to such step change conditions, even though the model is crude. Although the adopted strategy would depend on the current process development objectives, a consideration of both approaches is highly recommended if an information-rich framework is to be established. The integrated approach reported here establishes that the optimization of reaction outcome may be effected by applying three crucial steps: understanding the driving forces; reducing undesired paths; and, identifying sound processing methodologies and equipment. It is hoped that a successful application of these steps would require an interdisciplinary approach involving a close collaboration between chemists and chemical engineers involved in process development. REFERENCES Atherton, J.H. and Carpenter, K.J., 1999, Process Development: Physicochemical Concepts (Oxford University Press, New York, USA). BRITEST Limited, 2003, http://www.britest.co.uk/. Daniel, C., 1959, Use of half-normal plots in interpreting factorial two level experiments, Technometrics, 1: 311–342. Froment, G.F. and Bischoff, K.B., 1990, Chemical Reactor Analysis and Design, 2nd edition (John Wiley & Sons, Inc., New York, USA). Heany, H., 1991, The Bimolecular Aromatic Friedel-Crafts Reaction, in Trost, B.M. (ed.), Comprehensive Organic Synthesis: Selectivity, Strategy and Efficiency in Modern Organic Chemistry, pp. 733 –750 (Pergamon Press, Oxford, UK). Jeoung, S.-G., 2001, A short-cut method for reaction analysis and waste minimisation in complex chemical reactions, PhD thesis, Department of Chemical Engineering, UMIST, Manchester. Kittrell, J.R., 1970, Mathematical modelling of chemical reactions, Advances in Chemical Engineering, 8: 98– 183. Mah, R.S., 1990, Chemical Process Structures and Information Flows (Butterworths, Boston, USA). McConville, F.X., 2002, The Pilot Plant Real Book: A Unique Handbook for the Chemical Process Industry (FXM Engineering and Design, Worcester, UK).
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Miller, D.C. and Davies, J.F., 2000, Process design decision support system for developing process chemistry, Industrial & Engineering Chemistry Research, 39: 2954–2969. MINITAB Inc., 1996, MINITAB for Windows, Release 11 (http:// www.minitab.com/). Montgomery, D.C., 2001, Design and Analysis of Experiments, 5th edition (John Wiley & Sons, Inc., New York, USA). Obenndip, D.A., 2003, Process chemistry enhancement for fine chemicals applications, MPhil thesis, Department of Chemical Engineering, UMIST, Manchester. Olah, G. (ed.), 1963, Friedel-Crafts and Related Reactions (John Wiley & Sons, Ltd., London, UK). Shampine, L.F., Gladwell, I. and Thompson, S., 2003, Solving ODEs with MATLAB (Cambridge University Press, Cambridge, UK). Sharratt, P.N. (ed.), 1997, Handbook of Batch Process Design (Blackie Academic & Professional, London, UK). Sharratt, P.N., Wall, K. and Borland, J.N., 2001, Crossing the border— chemistry and chemical engineering in process design, Proceedings of the 6th International Congress of Chemical Engineers, 23–27 September, Melbourne, Australia. Sharratt, P.N., Wall, K. and Borland, J.N., 2003, Generating innovative process designs using limited data, Journal of Chemical Technology and Biotechnology, 78: 156–160. Tirronen, E. and Salmi, T., 2003, Process development in the fine chemical industry, Chemical Engineering Journal, 91: 103 –114. Valkenberg, M.H., de Castro, C. and Holderich, W.F., 2001, Friedel-Crafts acylation of aromatics catalysed by supported ionic liquids, Applied Catalysis, 215: 185– 190. Walsh, J., 2004, An integrated approach to chemical engineering and chemistry in process design, Proceedings of the Switching from Batch to Continuous Processing Conference, 22–23 November, London. Watson, W., 2003, Process chemistry, Chemistry in Britain, 39(5): 49. Winston, W.L., 1995, Introduction to Mathematical Programming: Applications and Algorithms, 2nd edition (Duxbury Press, Belmont, UK). Yadav, G.D. and Doshi, N.S., 2000, Alkylation of hydroquinone with methyl-tert-butyl-ether and tert-butanol, Catalysis Today, 60: 262–273. Yadav, G.D. and Asthana, N.S., 2002, Kinetics and mechanism of selective monoacylation of mesitylene, Industrial & Engineering Chemistry Research, 41: 5565– 5575.
ACKNOWLEDGEMENT The authors gratefully acknowledge an especially helpful conversation with Dr Arthur Garforth, and thank him for providing them with the experimental equipment. Financial support through a UK Commonwealth and UMIST Graduate Research School scholarships is also acknowledged. This paper was presented at the 7th World Congress of Chemical Engineering held in Glasgow, UK, 10–14 July 2005. The manuscript was received 15 December 2004 and accepted for publication after revision 4 April 2005.
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