Merging information from batch and continuous flow ...

Zdravko Kravanja (Editor), Proceedings of the 26th European Symposium on Computer Aided Process Engineering – ESCAPE 26 June 12th -15th, 2016, Portorož, Slovenia © 2016 Elsevier B.V. All rights reserved.

Merging information from batch and continuous flow experiments for the identification of kinetic models of benzyl alcohol oxidation Federico Galvanin1, Noor Al-Rifai1, Enhong Cao1, Meenakshisundaram Sankar2, Graham Hutchings2, Asterios Gavriilidis1, Vivek Dua1,* 1

Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK. (*E-mail: [email protected]) 2 Department of Chemistry, Cardiff University, Cardiff CF10 3AT, UK

Abstract The catalytic oxidation of benzyl alcohol to benzaldehyde is a reaction of great industrial interest due to the importance of benzaldehyde as a reaction intermediate. However, only a few attempts have been made in literature for developing kinetic models for the quantitative characterisation of this transformation, for which kinetics can be studied in both batch and continuous flow reaction systems. The purpose of this paper is to merge the information obtained from a laboratory scale batch glass stirred reactor (GSR) with the information obtained from a continuous-flow micro-packed bed reactor (MPBR) for an accurate and quantitative description of the products distribution in this reaction systems. A two-stage procedure is applied for this purpose where experimental design techniques are used for evaluating the most promising regions of the experimental space for the identification of kinetic models. Keywords: kinetic modelling, microreactor systems, benzyl alcohol oxidation.

1. Introduction Benzyl alcohol oxidation is one of the most significant alcohol oxidation reactions in industry due to the demand for benzaldehyde as an intermediate in the production of fine chemicals, fragrances and flavouring additives (Burdock, 2005). The process is regarded as an oxidative dehydrogenation of benzyl alcohol to benzaldehyde, where toluene and water are the main observed by-products, together with lesser amount of benzoic acid, benzyl benzoate and dibenzyl ether (Ferri et al., 2006). Despite the industrial importance of the reaction, only a few attempts have been made to develop kinetic models in order to reveal the complex reaction mechanisms involved in benzyl alcohol oxidative dehydrogenation. A reliable kinetic model is crucial for a quantitative description of the species involved in the system for process optimisation (to obtain benzaldehyde in high yield by suppressing the formation of by-products) and for catalyst design (to fine-tune the catalyst formulation). Two competitive pathways (Meenakshisundaram et al., 2010) have been identified in the gas–liquid–solid multiphase reaction system as the main sources of benzaldehyde: i) a direct catalytic oxidative dehydrogenation of benzyl alcohol (PhCH2OH + ½ O2 → PhCHO + H2O), yielding benzaldehyde and water taking place in the presence of oxygen; ii) a disproportionation reaction (2PhCH2OH → PhCHO + PhCH3 + H2O), where 2 molecules of benzyl alcohol disproportionate to give equal amounts of benzaldehyde and toluene occurring even in the absence of oxygen and thus limiting the selectivity to

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benzaldehyde. The resulting kinetic model is able to represent the data obtained in a conventional glass stirred reactor (GSR) operated in a batch mode at low conversion, but reveals several limitations on the representation of selectivity at high conversions. Micro-packed bed reactors (MPBRs) have been recently proposed for the study of benzyl alcohol oxidation on AuPd catalyst supported on TiO 2 (Cao et al., 2011). These systems are ideal for the determination of intrinsic reaction kinetics, allowing the reactions to be performed isothermally. However, the region of operability in terms of hydrodynamics which can be exploited for the evaluation of kinetics in the MPBR (i.e. the kinetically controlled conditions) has to be determined by a careful design of continuous flow experiments in terms of gas and liquid flow rates. The goal of this paper is to develop an integrated strategy for the identification of kinetic models of benzyl alcohol oxidation over supported AuPd catalyst, merging the information obtained from batch experiments (performed in a GSR) with the one obtained from continuous-flow experiments (performed in a MPBR). A two-step procedure is proposed for this purpose where kinetic models, for which mathematical structure has been identified from batch experiments, are used for the validation of continuous flow experiments. In the procedure, design of experiments techniques are used for determining suitable hydrodynamic conditions to be used in the MBR.

2. Modelling benzyl-alcohol oxidation system: proposed strategy A sketch of the proposed procedure for the identification of kinetic models from batch and continuous flow experiments is given in Figure 1. Step 1: Structural identification of kinetic models from batch reactor data

Definition of suitable hydrodynamic regions

Batch system (GSR)

Design of experiments

G/L Microreactor system (MPBR)

data

Model discrimination

Step 2: Estimation of kinetic parameters from continuous flow microreactor data

Kinetic model selection (Mi, θ0)

data MPBR model

Design of experiments

G Model 4 Model 3 Model 2

Candidate kinetic models

Estimation of kinetic parameters

Model 1 Model 0

No Estimation Ok?

Yes

END

Figure 1 Merging batch and continuous flow experimental information: proposed procedure.

In the first step, potential kinetic mechanisms are formulated based on chemical knowledge of the system and batch experiments are carried out in a GSR for the structural identification of chemically consistent competitive kinetic models. Each candidate model is described by a model structure Mi and a Nθ-dimensional set of kinetic parameters. A model discrimination procedure based on a-posteriori statistics obtained after parameter estimation (Box et al., 1978) is carried out for the selection of

Merging information from batch and continuous flow experiments

3

the “best” model. In the second step, the selected kinetic model from step 1 is used in the development of the MPBR model where design of experiments techniques can be carried out for the definition of suitable hydrodynamic regions. Experiments are executed in the MPBR and the kinetic models are then experimentally validated.

3. Model equations for reaction systems GSR and MPBR models were implemented in gPROMS. The software was also used for parameter estimation and for the design of experiments. The sets of model equations used for describing the two systems are reported in the following. 3.1. Batch glass stirred reactor (GSR) Benzyl alcohol oxidation batch experiments were carried out in a 50 mL GSR agitated using a magnetic bar at 1000 rpm stirring speed. In a typical reaction, the required amounts of catalyst (20 mg, 1% AuPd/TiO2 prepared via sol-immobilization method) and substrate (1g pure benzyl alcohol) were charged into the reactor at room temperature which was then purged with the required gas (pure O2). Concentration measurements were obtained from GC analysis with the aid of an external standard and calibration. More details on the experimental method can be found in (Cao et al., 2011). The batch reactor was modelled through a system of differential and algebraic equations (DAEs) in the form: N reaz

dC j dt

  r ij



i ij

i 1

(1)

ms

where Cj is the j-th component concentration [mol/kg] (benzyl alcohol, benzaldehyde, water, toluene), rij is the i-th reaction rate [mol/s] with respect to the j-th component, ms is the substrate mass [kg], νij is the stoichiometric coefficient of the j-th species in the ith reaction, Nreaz is the number of reactions involved in the kinetic model and αi is a factor introduced to account for the amount of catalyst used in the reaction system. This factor is evaluated from αi = mcat/mcat,0 where mcat is the catalyst mass [g] and mcat,0 is a reference catalyst mass (mcat,0 = 0.020 g), which is the amount of catalyst used in the reference experiments. Oxygen is the only species which is present in the gas phase and it is assumed to be present in the liquid phase at its equilibrium concentration:

CO2  PO2 CBzOH / K H

.

(2)

In Eq. (2) PO2 is the oxygen pressure [bar] and KH is the Henry constant (KH = 892 bar). The reaction rate constants ki in each reaction rate expression were evaluated using a modified Arrhenius equation in the form:

 θ   Ei  ki  exp lnAi   a   exp θ1,i  2,i  RT  T   

i  1...N reaz

.

(3)

This form was used with the purpose of minimising the impact of parameter correlation during the estimation of kinetic parameters Ea,i (activation energies) and Ai (pre Ea ,i / R for each candidate  ln Ai  and θ 2GSR exponential factors) by estimating θ1GSR ,i ,i kinetic model (Buzzi-Ferraris et al., 2008). The full set of model parameters to be  2GSR estimated is θGSR  1GSR (i = 1…Nreaz). ,i ,i





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3.2. Micro-packed bed reactor (MPBR) Continuous flow experiments were performed in a silicon-glass MPBR fabricated by photolithography and deep reactive ion etching (Cao et al., 2011). The serpentine microchannel dimensions were 600µm (W) × 300µm (H) × 190mm (L). The catalyst used was a 0.05 wt% Au-0.95 wt% Pd supported on TiO2, prepared via modified impregnation method. Pure liquid benzyl alcohol was delivered into the reactor by a syringe pump and oxygen delivered and regulated by a mass flow controller. The liquid product was collected for GC analysis. The investigated reaction temperature was kept constant using a heating and temperature control unit, while the pressure in the reactor was regulated by a backpressure regulator. In the MPBR the gas flows through the catalytic bed with velocity vg [m/s], while the liquid flows with velocity vl [m/s] under a flow regime determined by the chosen gas to liquid volumetric flow ratio (as G/L). For a preliminary investigation of the system, the MPBR can be considered as a simple plug-flow reactor, under the following assumptions: i) absence of axial/radial dispersion; ii) mass transfer to/from the solid can be neglected. The MPBR lumped model becomes: v g z Cig t , z  dCig t , z   kigl aCi*l  Cil   dt z l t , z   N reaz   r t, z  . dCil t , z   C  kigl aCi*l  Cil   v l z  i j i, j j dt z j 1



(4) (5)

In Eq. (4-5) Cig  COg2 is the oxygen concentration in the gas phase [mol/kg], Cil is concentration of the i-th reactant/product in the liquid phase [mol/kg], rj is the reaction rate for the j-th reaction [mol/(kg s)], kig l is the gas-liquid mass transfer coefficient [m s1 ] and a is the interfacial area [m-1]. In this work the product θGL = kgla [s-1] was estimated from MPBR concentration data adopting a two-step identification procedure: i) fixing the values of kinetic parameters and estimating  GL from MPBR data; ii) fixing  GL to the value estimated in step i and re-estimating the kinetic parameters by using MPBR data. The iterative procedure i-ii continues until the best data fitting has been achieved. The values of kinetic parameters obtained from the batch system (θGSR) are used for the initialisation of the parameter estimation procedure.

4. Model identification results 4.1. Structural identification of kinetic models from batch reactor data Candidate kinetic models based on Langmuir-Hinshelwood kinetics have been developed based on the following assumptions: 

Benzaldehyde (PhCHO) can be produced from DP (disproportionation) reaction 2PhCH2OH → PhCHO + PhCH3 + H2O and ODH (oxidative dehydrogenation) reaction PhCH2OH + ½ O2 → PhCHO + H2O.



Toluene (PhCH3) can be produced from DP reaction 2PhCH2OH → PhCHO + PhCH3+ H2O according to a bimolecular mechanism or via hydrogenolysis (HL).

The following kinetic expressions for the reaction rates ri have been determined:

Merging information from batch and continuous flow experiments

5

PhCH OH O  H O PhCH OH   k PhCHOPhCH H O PhCH OH H O  k H O O  . O  1/ 4

rODH  k ODH11

2

2

(6)

1/ 2

2

rDP  k DP1

2

DP2

2

3

(7)

2

1/ 2

rHL  k HL21

2

2

1/ 4

1/ 2

HL22

1/ 4

2

(8)

2

2

The model described by Eqs. 6-8 (Model 1) represents well the concentration data at standard operating conditions (T = 80-120°C, P = 1bar, ms = 1 g), and provides a better fitting performance than alternative kinetic models where only DP and ODH reactions are considered (Model 2) or where DP is totally neglected (Model 3). Model discrimination results in terms of chi-square statistics for the three models are: i) Model 1: χ2 = 21.3; ii) Model 2: χ2 = 127.3; iii) Model 3: χ2 = 183.2. Being the reference χ2 value equal to χ2ref =26.3 a good fitting of experimental data can be obtained only from Model 1 (only for this model χ2 is less than the reference χ2 and the test is passed). 4.2. Estimation of kinetic parameters from MPBR data The first step in the identification of kinetics from MPBR data is the characterisation of hydrodynamic regions. For this purpose, continuous flow experiments have been designed in terms of gas and liquid flow rates adopting a response surface methodology (RSM) (Box and Draper, 1987). A quadratic model with interaction has been determined for describing conversion and selectivity to benzaldehyde at different flow regimes in the MPBR. Results in terms of conversion are shown in Figure 2a. A sudden fall on conversion has been observed when moderate to high gas flow rates are used in conjunction with low liquid flow rates. The best hydrodynamic conditions for the identification of kinetics are located in the continuous (fully wetted) gas-phase region, where a stable benzyl alcohol conversion can obtained and selectivity to benzaldehyde is around 85%. 100 AR1

90

AR3AR4 AR2 AR4 AR3 AR2 AR1 AR6AR7

Predicted [%]

80 70 AR5

60

AR6 AR7

50 40

AR5

30

AR7 AR6 AR5 AR1 AR2 AR3 AR4

20

Conversion BzAld Selectivity Toluene Selectivity

10 0 0

20

40

60

80

100

Experimental [%]

(a)

(b)

Figure 2. (a) Application of RSM for the description of hydrodynamic regimes in the MPBR (the operating condition used for kinetic modelling is indicated by the black ball). (b) Parity plot for conversion and selectivity to benzaldehyde and toluene in the investigated experimental space.

A number of experiments have been performed in the gas continuous region by fixing the flow rates to L = 0.75 μL/min and G = 2 NmL/min. At these conditions, the following experimental design variables have been investigated: pressure P (range 1-3 bar); temperature T (range 80-120 °C); amount of catalyst mcat (range 0.0005-0.0011 g). Results from kinetic model identification are given in Figure 2b. Notwithstanding the

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simplicity of the MPBR model (and the related modelling assumptions), a relative good agreement is observed in the description of selectivity towards benzaldehyde and toluene (always within the ±10% bands in terms of absolute deviation), whilst the description of conversion is more critical, particularly for experiments with very low amount of catalyst (AR1, AR6-7). Parameter estimation results are given in Table 1. By exploiting the full set of experiments θGL = 10.2 s-1, while activation energies Ea,i where kept at the values estimated from batch data. All the kinetic parameters were estimated in a statistically precise way except ADP2, related to the inverse DP reaction. Table 1 Parameter estimation results from MPBR data: estimated values and a-posteriori statistics including 95% confidence interval and t-values. Parameters failing t-test are indicated in boldface. Parameter ODH11

Log A DP11

Log A DP12

Log A HL21

Log A HL22

Log A

Estimated value

95% c.i.

95% t-value

60.06

0.27

305.42

17.01

0.16

140.31

212.51

436.21

0.48

39.63

0.09

551.83

2.37

0.78

3.03

Reference t-value (95%):

1.71

5. Conclusions An approach for identification of kinetic models of benzyl alcohol oxidation, merging the information obtained from batch and continuous flow experiments has been presented in this paper. Notwithstanding the relative simplicity of the proposed kinetic model and the uncertainty in the determination of the kinetically controlled hydrodynamic regions in the MPBR, some very promising results in terms of descriptive capability of the reaction system have been obtained. Parameter estimation results show a difficulty on the estimation of kinetic parameters related to disproportionation reaction which will require further experimental investigation.

References G. A. Burdock, 2005, Fenaroli's Handbook of Flavor Ingredients, Vol. II, CRC Press, London D. Ferri, C. Mondelli, F. Krumeich, A. Baiker, 2006, Discrimination of active palladium sites in catalytic liquid-phase oxidation of benzyl alcohol, J. Phys. Chem. B, 110, 22982-22986 S. Meenakshisundaram, E. Nowicka, P.J. Miedziak, G.L. Brett, R.L. Jenkins, N. Dimitratos, S.H. Taylor, D.W. Knight, D. Bethell, G.J. Hutchings, 2010, Oxidation of alcohols using supported gold and gold-palladium nanoparticles, Faraday Discuss., 145, 341-356 E. Cao, M. Sankar, S. Firth, K.F. Lam, D. Bethell, D.K. Knight, G.J. Hutchings, P.F. McMillan, A. Gavriilidis, 2011, Reaction and Raman spectroscopic studies of alcohol oxidation on goldpalladium catalysts in microstructured reactors, Chem. Eng. J.,167, 734-753 G. E. P. Box, W. G. Hunter, J. S. Hunter, 1978, Statistics for experimenters. An introduction to design, data analysis and model building. John Wiley & Sons, New York. G. Buzzi-Ferraris, F. Manenti, 2008, Kinetic model analysis, Chem. Eng. Sc., 64(5), 1061-1074 G. E. P. Box, N.R. Draper, 1987. Empirical model building and Response Surfaces. John Wiley & Sons Inc., New York