Self-optimizing continuous reactions in supercritical carbon dioxide

Article abstract of DOI:10.1002/anie.201100412

Hands-free optimization: A combination of an automated flow reactor, online analysis, and a control algorithm leads to efficient optimization of reactions to a given product without the need for human intervention.

Full text of DOI:10.1002/anie.201100412

Communications
DOI: 10.1002/anie.201100412
Reaction Optimization
Self-Optimizing Continuous Reactions in Supercritical Carbon
Dioxide**
Andrew J. Parrott, Richard A. Bourne, Geoffrey R. Akien, Derek J. Irvine,* and
Martyn Poliakoff*
[
2]
Currently there is considerable interest in the automation of
continuous reactors, where the key aim is to create self-
optimizing processes. In effect, the reactor and its process
control instrumentation become an autonomous unit into
which the reactants are pumped, and from which products
emerge with optimized yield without requiring any interven-
tion from the operator. The development strategy utilizes
reactors, on-line analytical techniques, and control algorithms
that are all relatively well known. What is novel is the
integration of the three, so that they operate as a single
autonomous unit.
complete the feedback loop. In separate studies, the same
group used three different algorithms for the two-parameter
optimization of a Knoevenagel condensation reaction and the
[
3]
NMSIM algorithm for the four-parameter optimization of
the oxidation of benzyl alcohol to benzaldehyde.
All three of these studies have been carried out in
[4]
[
1,2,4]
microreactors,
and the application of this approach has
been restricted to the very small scale. Furthermore, in each
of these cases, the focus has also been limited to a single target
product. Only in the Heck reaction did the authors transfer
the optimized reaction parameters to a larger reactor.
However, their subsequent work to match conditions at two
different reactor scales was achieved manually.
In parallel to Krishnadasan et al., we have been devel-
oping a self-optimizing reactor for reactions in supercritical
CO (scCO ). We have previously reported the development
of an automated reactor for heterogeneous acid catalyzed
etherification reactions in scCO2 capable of continuously
changing key reaction parameters and monitoring the effect
on the reaction outcome by on-line gas–liquid chromatog-
raphy (GLC). We have since applied this technique to a
range of different reactions, such as hydrogenations,
condensations, and methylations.
However, this reactor, like many other automated reac-
uses a univarient approach where only a single
variable at a time is adjusted during an experiment. This
approach is inherently inefficient, because it does not account
for interactions between parameters; rather, a large amount
of superfluous data must be collected for every possible
parameter combination, to ensure that all the potential
reaction environments are covered. Ultimately, a large
[
1]
For example, Krishnadasan et al. have used an auto-
mated microreactor to synthesize CdSe quantum dots. The
flow rates of the pumps and the temperature of the reactor
were monitored and could be controlled by computer. The
same computer also received data from an on-line fluorim-
eter, the results from which were used to determine the
quality of the nanoparticles produced under those reaction
conditions. Thus it was possible to construct an automated
feedback loop by the sequential application of the stable noisy
optimization by branch and fit (SNOBFIT) algorithm to
generate the new conditions required to produce nanoparti-
cles continuously that emit with optimal intensity at certain
emission wavelengths.
[
1]
2
2
[
5]
[6,7]
aldol
[
8]
[9–11]
[2]
[12]
More recently, McMullen et al. used a similar automated
and integrated approach for the optimization of a Heck
reaction conducted in a microreactor. On-line high-perfor-
mance liquid chromatography (HPLC) was employed to
determine the yield of the product and the Nelder–Mead
tors,
[3]
Simplex (NMSIM) algorithm to generate new conditions to
[
13,14]
percentage of the data collected will be redundant.
[*] A. J. Parrott, Dr. R. A. Bourne, Dr. G. R. Akien, Dr. D. J. Irvine,
Herein we describe a highly efficient approach to self-
optimization of etherification reactions conducted in scCO2
that combines the use of an automated reactor with feedback
generated by a Simplex search algorithm. In particular, we
demonstrate that it is possible to 1) optimize on the yield of
the target product; 2) optimize for multiple products from
the same reaction mixture; 3) operate on a larger scale than
Prof. M. Poliakoff
School of Chemistry, The University of Nottingham
University Park, Nottingham, NG7 2RD (UK)
E-mail: derek.irvine@nottingham.ac.uk
martyn.poliakoff@nottingham.ac.uk
Homepage: http://www.nottingham.ac.uk/supercritical
Dr. D. J. Irvine
À1
Department of Chemical and Environmental Engineering
The University of Nottingham (UK)
previous literature reports (ca. 0.1–0.7 kgday ); and
) access a wider range of reaction conditions than in the
4
[
1,2,4]
[
**] We thank the EPSRC, grant no. EP/D501229/1 (the DICE project),
the EU SYNFLOW project, AstraZeneca, and Croda Europe Ltd. for
funding. We also thank David Litchfield, James Warren, Peter Fields,
Richard Wilson, and Mark Guyler for technical support. We are
grateful to Prof. Walter Leitner for suggesting the Simplex algorithm
for self-optimization.
studies discussed above.
Our reactor (Figure 1) indeed operates like the autono-
mous unit described above, namely reactant in and product
out. HPLC pumps pressurize the reactants and CO which
pass through the reactor and the product(s) flow out of the
back pressure regulator (BPR). The temperature (controlled
by proportional-integral-derivative (PID) heating control-
lers) is monitored by thermocouples, pressure (controlled by a
2
Supporting information for this article (including color figures) is
available on the WWW under http://dx.doi.org/10.1002/anie.
201100412.
3
788
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Angew. Chem. Int. Ed. 2011, 50, 3788 –3792

thermore, this reaction was particularly useful for testing the
optimization procedure because it produces multiple prod-
ucts, including the desired product diethyl ether (1b), ethene
[
18]
(1c), and acetaldehyde (1d).
The SMSIM algorithm requires n + 1 sets of conditions to
optimize n parameters. Thus, as our experiment aimed to
optimize three parameters, the algorithm required four pre-
defined starting points. Table 1 lists the parameters and the
Table 1: Initial parameters (runs 1–4) and optimal conditions for the
three-parameter optimization of 1b yield from the dehydration of 1.
À1
Run
Yield of 1b [%]
CO flow [mLmin
]
T [8C]
P [bar]
2
1
2
3
4
1.8
1.6
0.0
2.8
75.4
0.88
1.10
0.99
0.99
0.88
200
200
228
209
314
150
150
150
158
189
optimal
Figure 1. An automated supercritical reactor equipped with software
for a controlled feedback loop. The left-hand side indicates the control
paths and the right-hand side the reactor monitoring. The reactor
comprises a CO HPLC pump and a liquid HPLC pump which feed
2
corresponding yield for these four starting points. The reactor
then carried out the optimization, eventually reaching the
optimal parameters and yield as shown in the last entry of
Table 1.
Figure 2 and Figure 3 show different representations of
the same optimization pathway, which improved the yield of
1b from 2% to 75%, with a selectivity of 86%. The algorithm
stops the optimization process at only 87% conversion of 1
because changing the parameters further to increase the
conversion (for example by increasing the temperature) also
reduces the selectivity to 1b and increases the formation of
by-products.
into a static mixer (M; length 157 mm, 9 mm internal diameter, sand-
packed). The material then passes into a tubular fixed bed reactor (R;
same dimensions as M, packed with catalyst). The static mixer and
reactor were heated by heating cartridges inserted into aluminum
heating blocks. The downstream composition of the reactor output
was determined by on-line GLC analysis using a high-pressure sample
loop (SL). Pressure control was achieved with a back-pressure regu-
lator (BPR) at the outlet of the system. The internal reactor temper-
ature was measured by a thermocouple inserted into the centre of the
reactor tube, and pressure by transducers in the BPR.
BPR) by pressure transducers and the product composition
by on-line GLC. All these data are used as inputs to the
control algorithm, which is based on the super modified
This result establishes that the automated reactor com-
bined with the SMSIM algorithm can successfully optimize a
simple multi-component system. Therefore, a more complex
[
15,16]
simplex (SMSIM) algorithm.
The algorithm then outputs
control signals to vary the temperature, pressure, and the flow
rate of the CO pump to maximize the yield of the desired
2
product.
In the experiments described herein, the flow rate of the
À1
organic pump was kept constant (0.2 mLmin ) so as to
define the flow rate of the reactants and thus the rate at which
product is delivered. The feedback control software will be
the subject of a future publication.
The first reaction to be optimized was the dehydration of
ethanol (1) over g-alumina (Scheme 1). This reaction was
[
17–20]
chosen because it is well-understood in the literature
and
also formed the basis of the proof-of-concept experiments in
[
5]
the development of our original automated reactor. Fur-
Figure 2. The optimization pathway for 1b from the acid-catalyzed
dehydration of 1 with three parameters: CO flow, temperature, and
2
pressure. The optimization search route was determined by the
SMSIM algorithm. The shading of each point indicates the percentage
composition of 1b in the product stream, which increased from 2% to
75% during the optimization process.
Scheme 1. Dehydration of ethanol over g-alumina.
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Figure 3. Composition of the output stream determined by GLC
analysis for a run during the optimization for 1b. * starting material 1,
desired product 1b, ~ by-products.
Figure 4. Three-parameter optimization pathway for 2b from the
reaction of 2 with dimethyl carbonate (DMC). Optimization search
route determined by the SMSIM algorithm. The shading of each point
represents the percentage composition of 2b in the product stream,
which increased from 20% to 30% during the optimization routine.
*
multiple component reaction optimization was attempted.
The reaction of an aliphatic alcohol with dimethyl carbonate
(
DMC) which was chosen for study because: 1) DMC can
replace toxic methylating reagents, such as methyl hal-
improvement compared to the starting conditions, which has
been attributed to the fact that 2b is an intermediate product.
However, it can be seen that the algorithm carried out a broad
exploration of reaction space such that this optimization
pathway was certainly more efficient and more cost-effective
than would be achieved by manual operation.
The optimization of the reaction to maximize the yield of
1-methoxypentane (2c) was then attempted (Table 3,
Figure 5). It is clear that this optimization was more successful
[
21–27]
ides,
and 2) we have recently reported the continuous
synthesis of methyl ethers from aliphatic alcohols using DMC
[10,11]
with scCO and g-alumina as a catalyst.
However, whilst
2
we reported that a high yield of methyl ethers could be
achieved, we found that the reaction could also give rise to
several different products.
Initially, our aim was to optimize for the carboxymethy-
lation reaction of DMC with 1-pentanol (2) to produce methyl
pentyl carbonate (2b) using a 1:1 molar mixture of 2 and
DMC (Scheme 2). The starting conditions used were those
Table 3: Initial parameters (runs 1–4) and optimal conditions for the
three-parameter optimization of 2c yield from the reaction of 2 with
DMC.
À1
Run
Yield of 1b [%]
CO flow [mLmin
]
T [8C]
P [bar]
2
1
2
3
4
47.3
12.7
49.6
49.6
69.7
0.80
1.01
0.91
0.91
0.91
200
200
222
207
267
100
100
100
116
116
Scheme 2. Reaction of dimethyl carbonate (DMC) with 2 to give 2b
[
10,11]
and 2c.
optimal
that had been found to give significant yields of 2b
[
10]
previously.
Table 2 lists the starting conditions and the
than the 2b case and the maximum yield achieved was 70%.
The algorithm also explored a significantly different volume
of parameter space in this latter case than it did when the
target was 2b.
optimized yield of 2b, while Figure 4 shows the optimization
pathway. The optimization produced only a moderate
The optimizations in Figure 4 and 5 each took about 35 h
to complete, which is far less than the time required to cover
the same volume of parameter space with our previous
automated reactor. For example, if the reaction parameter
Table 2: Initial parameters (runs 1–4) and optimal conditions for the
three-parameter optimization of 2b yield from the reaction of 2 with
DMC.
À1
Run
Yield of 2b [%]
CO flow [mLmin
]
T [8C]
P [bar]
2
space was split into 10 values of CO flow rate and 10 values
2
1
2
3
4
20.2
18.9
19.2
22.7
36.9
0.80
1.01
0.91
0.91
0.78
150
150
177
159
165
90
90
90
108
146
of reactor pressure, and a standard temperature ramp (1008C
to 3008C, 20 h duration) was conducted to cover all combi-
nations, it would therefore require 100 temperature ramps,
taking more than 83 days and providing an accuracy of about
optimal
À1
Æ 0.1 mLmin and Æ 6 bar. However the rate of optimization
3
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15 min before the GLC analysis commenced. Once GLC analysis was
complete the algorithm would calculate the values for the next
condition and send control signals to the equipment. The coordinates
for the initial four conditions were calculated to produce a regular
tetrahedron with the distance between each experiment being 25% of
the total range between the first experiment and the maximum
boundary condition for that parameter. All command and monitoring
signals were communicated via RS-232 connections and all the
software was written in Matlab, apart from the commercial software
for controlling the Shimadzu GLC (GCSolution).
Optimization for 1b: Ranges allowed CO flow rate 0.35–
2
À1
1
7
.77 mLmin , reactor temperature 100–3308C and reactor pressure
0–190 bar, GLC analysis time was 5 min.
Optimization for 2b and 2c: Ranges allowed CO flow rate 0.15–
2
À1
1
.62 mLmin , reactor temperature 100–2758C (3008C for 2c) and
reactor pressure 70–180 bar, GLC analysis time was 17.5 min.
Received: January 17, 2011
Published online: March 25, 2011
Figure 5. Three-parameter optimization trajectory for 2c from the
reaction of 2 with DMC. The optimization search routine was
determined by the same algorithm as in Figure 4. The shade of each
point represents the percentage composition of 2b in the product
stream, which increased from 48% to 70% during the optimization
process.
Keywords: alkylation · heterogeneous catalysis ·
reaction optimization · supercritical fluids ·
sustainable chemistry
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