Available via license: CC BY 4.0
Content may be subject to copyright.
FULL PAPER
Flow chemistry for process optimisation using design of experiments
Connor J. Taylor
1
&Alastair Baker
1
&Michael R. Chapman
1
&William R. Reynolds
1
&Katherine E. Jolley
1
&
Graeme Clemens
2
&Gill E. Smith
3
&A. John Blacker
1
&Thomas W. Chamberlain
1
&Steven D. R. Christie
4
&
Brian A. Taylor
2
&Richard A. Bourne
1
Received: 6 September 2020 / Accepted: 14 December 2020
#The Author(s) 2021
Abstract
Implementing statistical training into undergraduate or postgraduate chemistry courses can provide high-impact learning expe-
riences for students. However, the opportunity to reinforce this training with a combined laboratory practical can significantly
enhance learning outcomes by providing a practical bolstering of the concepts. This paper outlines a flow chemistry laboratory
practical for integrating design of experiments optimisation techniques into an organic chemistry laboratory session in which
students construct a simple flow reactor and perform a structured series of experiments followed by computational processing and
analysis of the results.
Keywords Flow chemistry .Design of experiments .Optimisation .Curriculum .Hands-on learning
Introduction
Flow chemistry is increasing in popularity as synthetic chem-
ists continue to discover the numerous advantages afforded to
them by swapping their round-bottom flasks and condensers
for pumps and tubes [1]. The rate of adoption of continuous
flow chemistry is continuing to grow, as more enabling tech-
nologies are developed and more research groups are building
their own reactor platforms [2–4]. Whilst there are many in-
stances of harnessing the capabilities of flow chemistry for
synthesis over traditional batch methods [5–7], there are also
groups that have used these setups to optimise chemical pro-
cesses. Flow optimisation by Design of Experiments (DoE) is
a very useful and efficient method, where examples have been
reported with varying experimental criteria, whether this be
yield, purity, E-factor etc [8–12]. DoE as a technique can be
used for a number of applications aside from chemical reac-
tions, and has been reported often in the pedagogical literature
[13–16]. The teaching ofthis technique provides students with
a statistical basis for their experimentation, with the aim of
solidifying transferrable skills that ultimately lead them to
becoming well-rounded scientists. However, this optimisation
resource is still under-utilised in a lab setting, with one-factor-
at-a-time (OFAT) optimisation approaches often substituting
as a method for chemical process optimisation and under-
standing [17–19]. The aim of this paper is to provide a flow-
chemistry-specific optimisation case study, where students
can learn DoE and statistics by performing a continuous flow
based practical experiment, as well as experiencing and over-
coming challenges that they would not usually encounter if
they were running a synthetic experiment in batch.
The OFAT approach is a common method of optimisation,
especially in academia, in which experiments guided by scien-
tific intuition are performed, by fixing all process factors except
for one [20]. These factors are experimental conditions (such as
temperature, reagent stoichiometry, reaction time etc.) which
when combined, make a multi-dimensional space where there
are a large number of possible combinations of these factors to
Supplementary Information The online version contains
supplementary material available at https://doi.org/10.1007/s41981-020-
00135-0.
*Richard A. Bourne
R.A.Bourne@leeds.ac.uk
1
Institute of Process Research and Development, School of Chemistry
and School of Chemical and Process Engineering, University of
Leeds, Leeds LS2 9JT, UK
2
Chemical Development, Pharmaceutical Technology &
Development, Operations, AstraZeneca, Macclesfield, UK
3
University of Southampton, University Road, Southampton SO17
1BJ, UK
4
Department of Chemistry, Loughborough University, Epinal Way,
Loughborough LE11 3TU, UK
Journal of Flow Chemistry
https://doi.org/10.1007/s41981-020-00135-0
make up one experiment - this is called the parameter space,
and is constrained by the lower and upper limits of each factor
(e.g. max and min temperature). After the best value for one
factor has been optimised, another set of experiments are exe-
cuted to then optimise another factor, until all factors are
optimised and the scientist believes that they have arrived at
the optimal reaction conditions [21,22]. However, this method
gives an incomplete picture of the chemical process, as it dis-
regards any synergistic effects between any factors in the multi-
dimensional and complex parameter space. This means that
interactions between the experimental factors are not consid-
ered. An example of this could be the difference between a
reaction at a high temperature with a short reaction time, com-
pared with a long reaction time - there may not be a linear
relationship between these factors and the desired output, mean-
ing that a change in temperature after the optimisation of the
reaction time could lead to a suboptimal result [17,23,24].
As research laboratories are diversifying their equipment,
by incorporating flow and automation technologies, it is also
necessary for chemists to evolve at the same pace, by diversi-
fying their skillsets to fully harness the capabilities and ways
of working enabled by this new equipment. Synthetic chem-
ists are embracing facets of process chemistry, chemical engi-
neering, analytical chemistry, and programming, to name a
few. Concurrently, better understanding and adoption of reac-
tion optimisation methods should be implemented: OFAT op-
timisations need to be replaced by more robust and more ef-
ficient techniques [25–28].
In this paper, the use of a structured experimental
design integrated with a flow chemistry platform is re-
ported. It is shown how this can be used as a teaching
resource to introduce students to performing flow chem-
istry experiments and to better understand the type of
data required for the optimisation of chemical processes.
We herein report a demonstration of Design of
Experiments for teaching the next generation of chem-
istry in a practical lab setting, whereby a chemical pro-
cess with a number of possible products is optimised
for the highest yield of a particular product. The chem-
ical process chosen to be optimised was the S
N
Ar reac-
tion between 2,4-difluoronitrobenzene, 1, and pyrrol-
idine, 2, to form the desired ortho-substituted product,
3,andimpurities4–5,showninScheme1.
Learning objectives
&To set up a flow chemistry system to execute flow
experiments.
&To methodically plan flow experiments using DoE.
&To statistically analyse DoE results and generate empirical
models for an experimental data set.
&To use DoE models to optimise an S
N
Ar flow process.
Design of experiments
DoE is a statistical method of reaction optimisation that is
often practiced in industry [25], but is less commonly used
in academia, where a OFAT approach is much more common.
Although OFAT can give an idea as to how particular factors
influence the yield of a reaction, the parameters of interest are
explored less comprehensively and no indication of how these
factors affect each other and themselves at varying levels is
obtained. When using a DoE approach, however, the entire
parameter space is mapped in an efficient manner, which ex-
plores multidimensional space at the same time. This is be-
cause in each sequential experiment, multiple factors are
changed at the same time. A comparison of the parameter
space exploration by these two methods is shown in Fig. 1,
by observing three experimental factors for the optimisation of
the demonstration reaction: temperature (°C), residence time
(min) and pyrrolidine equivalents. It is typically the case that
OFAT experiments mostly explore individual planes of pa-
rameter space which makes it difficult to infer the overall
space behaviour, whereas experimental designs can interpo-
late factor interactions much more effectively. This is true
regardless of the number of experiments undertaken in either
approach. The face-centred central composite (CCF) design
shown in Fig. 1splits each experimental factor into different
levels. These levels: (-1, 0, + 1), are named by convention and
correspond to the degree of the experimental factor, where −1
is the lower bound of the parameter space, + 1 is the upper
bound and 0 is the midpoint. For example, if the experimental
bounds for residence time were between 1 and 5 minutes, the
levels would be: 1 minute (-1), 3 minutes (0) and 5 minutes (+
1). These levels are defined to ensure that all areas of param-
eter space can be explored, regardless of the range of the
factors.
These structured experiments allow statistical models to be
constructed from the experimental results, that accurately de-
scribe the changes in responses to experimental factor chang-
es. If a DoE analysis tool such as MODDE (from Umetrics) or
Design-Expert (from Stat-Ease) is used, the generation of
these models is performed easily and intuitively. Empirical
models, made up of experimental responses, can then be used
to predict further experimental results based on how the model
weights a particular input variable. These variables can simply
be the experimental factors, but can also be interaction terms
between the different factors, or squared interactions of the
same factor. These interaction and squared terms indicate
how experimental factors influence the reaction output, when
other factors are changed alongside them. In the case of our
S
N
Ar example, it may be insufficient to describe the experi-
mental data by simply incorporating model terms of ‘resi-
dence time’,‘temperature’and ‘pyrrolidine equivalents’.It
may be a significant factor in the modelling of the data to
include an interaction term between residence time and
J Flow Chem
temperature, meaning in real terms that there is a higher influ-
ence of residence time/temperature at higher residence times/
temperatures. Similarly, a squared temperature model term
could better describe larger effects of temperature changes
when the temperature is generally higher, meaning that tem-
perature has a non-linear effect.
These interaction considerations can give a better descrip-
tion of the experimental data, as the synergistic effects be-
tween the factors are also incorporated into the empirical mod-
el. In this case, an empirical model is a purely statistical rep-
resentation of the experiments and their outcomes, as opposed
to a physical model determined by the underlying chemistry.
This model can then allow response surfaces to be plotted and
optimum operating regions to be identified, by interpolating
the areas between the equidistant experimental points.
In this paper, we describe the use of a CCF design in the
MODDE software. After already determining that the three
factors of residence time, temperature and pyrrolidine equiv-
alents are significant, the CCF optimisation design identifies
all interaction and squared terms between these factors. The
generated model will then be used to portray the entire param-
eter space, and hence identify the optimum operating condi-
tions for the highest yield of the ortho-substituted product, 3.
There are also other experimental designs one can consider
using depending on the outcomes that are desired, but these
are not covered in this paper [29].
Necessary equipment
In order to run the experiment as described, it is recommended
to have the following equipment and chemicals. A full list of
recommended vendors is located in the ESI.
&PTFE tubing, 1/16”internal diameter.
&Tubing fittings.
&A tubing cutter.
&Two syringe pumps, or equivalent.
&Three stirrer-hotplates to place water baths on.
&Three water baths, 500 mL.
&2,4-Difluoronitrobenzene (CAS: 446-35-5).
&Pyrrolidine (CAS: 123-75-1).
NO
2
F
F
+
H
N
12
NO
2
N
F
NO
2
F
N
3
Desired product
4
NO
2
N
N
5
Scheme 1 The S
N
Ar reaction of
interest, where the yield of the
ortho-substituted product, 3,isto
be optimised in a flow setup
Fig. 1 A comparison of the
parameter space exploration when
conducting a OFAT optimisation
alongside a structured DoE
design, where • represents an
experiment. Note also that a
OFAT optimisation does not
require a pre-determined number
of experiments, and may or may
not exceed the number of experi-
ments in an experimental design
JFlowChem
&Triethylamine (CAS: 121-44-8).
&Hydrochloric acid (CAS: 7647-01-0).
&Common laboratory solvents: ethanol, water, isopropyl
amine.
&Access to HPLC, or an equivalent quantitative analytical
technique.
&MODDE Pro, or equivalent DoE software.
Experimental setup
The experimental bounds for each of the factors are: residence
time (0.5 to 3.5 minutes), temperature (30 to 70 °C) and equiv-
alents of pyrrolidine (2 to 10). The concentrations of 2,4-
difluoronitrobenzene and triethylamine are kept constant.
The rationale behind these pre-determined experimental
bounds came from the kinetic understanding of the work re-
ported by Hone et al. on the same reaction [30]. The HPLC
peak areas are converted to relative concentration percent for
each of the species, each of which are reported as outputs for
that particular experiment. The run order of the experiments
was randomised, to prevent any extraneous (uncontrolled)
variables affecting the results, shown in Table 1.
When running the experiments, undergraduate students can
be placed into groups of 5 or 6. Recommended tasks within
the group can be split into: making up stock solutions, prepar-
ing the tubing, connecting the tubing, running/timing the ex-
periments, experimental sampling, running HPLC analysis
etc. However, in our case the HPLC calibration was conduct-
ed in advance by a trained instructor but this could be a task
for the students as part of the experimental procedure. It is
recommended also for students to read introductions to DoE
papers or seek advice from postgraduates or academic super-
visors prior to experimentation. Key introductory reading
could include references reported by Krawczyk et al. [16]
andAggerwaletal.[20], as well as the book written by
Antony [31] which are all useful resources.
The experimental flow setup is shown schematically in
Fig. 2, and pictorially in Fig. 3.Fourreservoirswereused,
one containing 2,4-difluoronitrobenzene (0.1 M) and
triethylamine (0.11 M) in ethanol, then three other reservoirs
containing triethylamine (0.11 M) and varied pyrrolidine con-
centration (0.1 M, 0.5 M and 1 M) in ethanol. Each experi-
ment setup contains the 2,4-difluoronitrobenzene solution in
one syringe, and one of the triethylamine/pyrrolidine in etha-
nol solutions in the second syringe, depending on the low/
medium/high equivalents of pyrrolidine that were investigated
in a particular run. Harvard syringe pumps are used in each
experiment to pump the solutions into a PTFE length of tubing
(1/16”internal diameter, 6.3 cm, equal to 1 mL volume),
submerged in one of three water baths at 30 °C, 50 °C or
70 °C. Three water baths were set up so that there are no
waiting times between experiments for the water baths to
achieve the desired temperature. This is important as the lab
time is the most crucial resource, and the experiments must be
executed in a specific order; running each block of
Table 1 The experimental conditions ran to perform the DoE study
ID Run order Residence time /min Temperature /°C Pyrrolidine eq. Pump 1 Flow /mL min
−1
Pump 2 Flow /mL min
−1
Pump2Conc/M
N1 3 0.5 30 2 0.096 1.920 0.1
N2 7 3.5 30 2 0.014 0.274 0.1
N3 12 2 30 6 0.038 0.463 0.5
N4 16 0.5 30 10 0.180 1.820 1
N5 2 3.5 30 10 0.026 0.260 1
N6 8 2 50 2 0.024 0.480 0.1
N7 13 0.5 50 6 0.150 1.850 0.5
N8 1 2 50 6 0.038 0.463 0.5
N9 11 2 50 6 0.038 0.463 0.5
N10 17 2 50 6 0.038 0.463 0.5
N11 4 3.5 50 6 0.021 0.264 0.5
N12 9 2 50 10 0.045 0.455 1
N13 5 0.5 70 2 0.096 1.920 0.1
N14 14 3.5 70 2 0.014 0.274 0.1
N15 6 2 70 6 0.038 0.463 0.5
N16 10 0.5 70 10 0.180 1.820 1
N17 15 3.5 70 10 0.026 0.260 1
The pump flow rates and the concentration in Pump 2 was changed for each experiment to vary residence time and pyrrolidine equivalents, and the
tubular reactor was placed in a different temperature water bath to vary temperature. The flow rates are calculated for a 1 mL reactor. Run order should be
generated randomly. See Fig. 2for further details.
J Flow Chem
temperature experiments (for example, all 30 °C experiments
at once) could introduce extraneous variables, and must be
avoided.
Each experiment was allowed to reach steady-state by
equilibrating for 2 reactor volumes, meaning that for each
flow experiment, a wait time of two residence times is neces-
sary before collection of material for analysis. For example, if
the residence time for the reaction is 0.5 minutes, then 1 min-
ute of reaction mixture is purged to waste before steady-state
is established. For each experiment, the desired temperature is
reached by placing the tubular reactor in a separate water bath
at the corresponding temperature. Samples can then be taken
from the end of the reactor, by immediately quenching a few
droplets of material into a vial containing a drop of hydrochlo-
ric acid at the outlet of the flow system. This can then be
diluted with methanol before transferring to analysis. These
samples can then be sampled by HPLC, or other analytical
techniques such as GC, requiring that quantitative yields of
each of the species can be obtained - this is shown in Fig. 2.
HPLC analysis was performed using an Ascentis Express C18
column (5 cm x 4.6 mm x 2.7 µm), using an isocratic flow
gradient (51% water/49% acetonitrile, each reservoir contain-
ing 0.1% TFA)at 1.5 mL min
−1
flow rate for 2 minutes HPLC
run time. It is beneficial to have short analytical methods to
allow fast analysis and turnaround between different sets of
experimental conditions.
Hazards
Safety goggles and lab coats should be worn throughout the
course of the experiment. All handling of organic solvents and
preparation of solutions should be conducted inside the fume
hoods. Special care should be taken when handling concen-
trated hydrochloric acid to quench the reaction in the HPLC
vial. If any reagent is spilled on the body, wash the area with
copious amounts of water for at least 15 minutes. Consult the
MSDS for the specific guidance on handling each of the
chemicals. After experimentation, any tubing can be washed
by pumping isopropyl alcohol through the reactor for 10 re-
actor volumes in order to keep the tubing, or it can be
discarded.
Fig. 2 A schematic of the
experimental flow setup used for
the S
N
Ar reaction. The
pyrrolidine concentration is
changed for varying equivalent
experiments, and the reactor is
movedbyhandintodifferent
water baths corresponding to the
temperature that the experiment
requires
Fig. 3 The flow setup used for the
S
N
Ar experimentation, where the
tubular reactor is submerged in
one of three different temperature
water baths
JFlowChem
Analysis, results and discussion
The full CCF DoE (shown in Fig. 1) was run using the experi-
mental setup described in Figs. 2and 3, which consisted of
running the experiments shown in Table 2. Three centre-point
experiments were also run throughout the course of data acqui-
sition, to monitor the reproducibility of the experiments as time
passed. These repeated experiments, or replicates, ensure that
any extraneous variables are identified (uncontrolled variables
that are being changed unknowingly, e.g. stock solution contam-
ination or degradation). The outputsareshownasmolarpercent-
ages, where the starting material 2,4-difluoronitrobenzene (1),
the desired product (3), the para-substituted impurity (4), and
the di-substituted impurity (5). We assumed that each of the
materials have equivalent HPLC response and did not run prior
calibrations with standards, although this could be done with
additional time. Molar percentages were calculated using inter-
nal normalisation for each of the species, where the area of the
HPLC peak for the species of interest was divided by the total
summed HPLC area for each peak, multiplied by 100. This is
shownintheequationbelow,where(x)istheHPLCareaforthe
species of interest, and (1)/(3)/(4)/(5) are the HPLC areas of the
species in this study:
Molar percentage ¼xðÞ
1ðÞþ 3ðÞþ 4ðÞþ5ðÞ½
100
Using this dataset, MODDE can fit a model automatically
using the ‘Analysis wizard’tool. Full instructions can be
found in the ESI. MODDE then fits a saturated model for each
of the responses given. A saturated model is where all model
terms, including all interactions and squared terms, are includ-
ed in the model. When a saturated model is initially generated,
the R
2
value is the largest value it can be. R
2
is a percentage
measure of how well a given model fits the data, which is
usually represented as a number between 0 and 1. When a
model uses all of the possible terms available to it, the varia-
tion in the experimental response is best described. This
means that as R
2
tends to 1, more of the variation is explained
by terms in the model, as closer to 100% of the experimental
variation can be attributed to specific terms. However, satu-
rated models typically contain non-significant model terms
that lead to a low Q
2
value. The Q
2
value is the percentage
of the variation of the response predicted by the model by
using cross validation, represented as a number between 0
and 1; simply put, Q
2
tells you how well the model can predict
new data. For a useful model, it is necessary to have a high R
2
that explains the dataset well, as well as high Q
2
that can
interpolate new data points accurately. To achieve this, the
model for each response must be edited as to remove any
nonsignificant terms. Figure 4shows the coefficients plots
for a particular response, in this case the response for the
amount of the desired product, (3), which graphically indi-
cates each model term (x axis) and their respective signifi-
cance (y axis). Each of the model terms are ‘scaled and cen-
tered’, meaning factors with different units can be compared
to determine the influence of model terms over the range of
the factors studied.
Table 2 The experimental dataset generated from the running of the DoE for the S
N
Ar reaction
Run Run order Residence time /min Temperature /°C Pyrrolidine eq. (1) /% (3) /% (4) /% (5) /%
N1 3 0.5 30 2 79.7 20.3 0.0 0.0
N2 7 3.5 30 2 36.3 60.0 0.0 3.6
N3 12 2 30 6 29.6 66.4 0.0 4.1
N4 16 0.5 30 10 52.7 44.6 0.0 2.7
N5 2 3.5 30 10 10.9 83.9 0.0 5.2
N6 8 2 50 2 34.0 62.0 0.0 4.0
N7 13 0.5 50 6 41.2 55.3 0.0 3.5
N8 1 2 50 6 13.8 80.9 0.0 5.3
N9 11 2 50 6 14.9 79.9 0.0 5.2
N10 17 2 50 6 14.9 79.9 0.0 5.2
N11 4 3.5 50 6 6.9 87.3 0.0 5.8
N12 9 2 50 10 9.1 84.9 0.4 5.6
N13 5 0.5 70 2 49.1 47.8 0.0 3.1
N14 14 3.5 70 2 11.8 82.2 0.4 5.6
N15 6 2 70 6 4.7 88.1 1.2 6.0
N16 10 0.5 70 10 15.8 78.8 0.0 5.4
N17 15 3.5 70 10 0.5 91.0 2.5 6.0
The replicates in this data set are experiments: N8, N9, N10
J Flow Chem
Each model term has a respective uncertainty (repre-
sented in the plot as an error bar), and if that uncertainty
overlaps with y = 0, then that model term can be deemed to
be statistically nonsignificant, Fig. 4a illustrates this point.
This is because there is a probability that the relative effect
of the model term could be zero. The saturated model for
the response of (3) is shown in Fig. 4b, where there are
several significant model terms and two non-significant
terms: Temp
2
and Temp*Eq.. The R
2
and Q
2
measures
are shown alongside the coefficients plot as a green bar
and a blue bar respectively. Upon removal of the two
non-significant terms, shown in Fig. 4c,theQ
2
value rises
from 0.764 to 0.894, meaning that the predictability of the
model is increased for an insignificant decrease in R
2
.
This process is then repeated for the other responses of
compounds (4)and(5), shown as Fig. 5a/b and Fig. 6respec-
tively. The response for (4), the saturated model (Fig. 5a)ap-
pears to describe the data well as the R
2
is high, however, there
are many non-significant terms. Because of this, the Q
2
is
negative, meaning there is no acceptable degree of predictabil-
ity to be obtained from the model. As these non-significant
terms are removed, even more terms become non-significant,
until the only significant term that remains is temperature
(Fig. 5b), but the R
2
and Q
2
values are still very low. This is
because the response for (4) remained largely unchanged
throughout our experimentation, meaning it is difficult to
model well, as there were no factors that could be shown to
have a strong effect on the outcome of this response.
Fig. 4 Thesignificanceofmodel
terms on the response for the
desired product, (3). aThe
difference between significant
and non-significant model terms.
bThe saturated model, R
2
=
0.990, Q
2
=0.764.cThe
optimised model, R
2
=0.986,
Q
2
= 0.894. Time = residence
time, Temp= temperature, Eq. =
pyrrolidine equivalents
JFlowChem
Conversely, the model for the response of (5) was found to be
excellent without any need for further optimisation - the R
2
and Q
2
measures were both high, and the saturated model
contained no non-significant model terms. This means that
by using the same experimental data set, a secondary response
can also be modelled and optimised for. This means that re-
sponse surfaces for (5) can also be predicted without any fur-
ther experimentation. Interestingly, it is not possible to do the
same for the response for (4) due to the low formation of the
product, as there are no changes in the experimental condi-
tions that lead to a significant amount of this product being
generated. This manifests itself in the uncertainty of the model
terms, as most of the error bars for these model terms intersect
y = 0 and are therefore their relative effects are non-
significant.
As the models were further optimised to have the highest
R
2
and Q
2
possible, the optimum operating conditions for the
production of the desired ortho-substituted (3), could then be
identified. By selecting the ‘4D Contour’option in MODDE,
the response for (3) can be interpolated across the entire land-
scape of the parameter space, providing a total insight into the
chemistry that could not be achieved by other means such as
OFAT. This contour plot is shown in Fig. 7, which indicates
clearly the yield of (3) that would be achieved with varying
experimental factors. Figure 8shows a similar plot on how the
yield of the di-substituted impurity, (5), also changes with
these differing inputs. It is important to note that to sensibly
use contour plots, DoE model performance metrics such as R
2
and Q
2
must be good. This is also a significant point for the
student learning and can be adapted into leading questions
Fig. 5 Thesignificanceofmodel
terms on the response for (4).
aThe saturated model, R
2
=
0.864, Q
2
= -0.200. bThe
optimised model, R
2
=0.246,Q
2
= -0.047
J Flow Chem
such as: ‘Using the 4D Contour Plot, predict the yield of the
major product at x,yand zexperimental conditions?’.
The optimum operating region for the highest yield of the
(3) have been identified using this DoE approach, whilst giv-
ing a full picture of the parameter space. The results show that
high temperature, high residence times and high pyrrolidine
equivalents lead to the highest yield of the desired product (3),
as well as the highest yield of the di-substituted impurity (5).
There are still other aspects of DoE that can be explored, such
as model validity and reproducibility, predicted kinetic plots
and ‘Sweet Spot’visualisations and ‘Optimizer’usage in
MODDE. These tools can use the same data set to give further
process understanding, and the empirical model can be
exported to further explore responses such as E-factor,
space-time yield etc. The same data set can also be used to
build further models on multiple responses, each of which can
be refined to give further understanding and predictability.
This could be warranted if there were additional experimental
needs, such as productivity of material. This can highlight
areas where the highest yields are present in the shortest res-
idence time, by compromising higher yields for quicker prod-
uct generation.
Upon completion of the experimental work, students were
asked to prepare a report on their findings - this can be in a word
document or a research article format. Conveying their ability
to report on statistical models and find optimum reaction con-
ditions for the production of (3)servesasthemainassessment
criteria for this work, where > 90% of students were successful.
Correct assignments of the optimum parameter regions indi-
cates that they have performed the experiments correctly and
should be considered when grading the report. Further ques-
tions can also be postulated to the students, such as ‘what are
Fig. 6 Thesignificanceofmodel
terms on the response for (5),
showing the saturated model with
no non-significant terms, R
2
=
0.997, Q
2
=0.944
Fig. 7 The contour plot for the
response of (3), showing how the
yield of the ortho-substituted
product changes with varying ex-
perimental conditions
JFlowChem
the advantages of running this reaction in flow?’and ‘why
perform a DoE?’. These questions can enhance the student
learning experience as they are asked to reflect upon their work
directly. Sample questions and full answers with suggested
grading criteria are provided in the ESI.
Student feedback
This example has formed part of the EPSRC Dial-A-Molecule
Summer School in 2018 and 2019 targeted at 1st year PhD
students. The summer school was a lively and interactive event
and in addition to the experiment/analysis outlined also included
a series of lectures from academic and industrial experts.
Furthermore, practical sessions on 3D printing and an evening
session outlining Design of Experiments by designing and mak-
ing paper helicopters and optimising the helicopter geometry
were also conducted, simply to solidify the concepts of DoE
and their applications to various real-life scenarios. These exer-
cises create an equal baseline of background knowledge that
drives the use of the DoE methodology, which forces hypotheses
to be made from understandings of factor selection and level
setting, rather than undisclosed assumptions based on prior ex-
periences. The content was very well received, with 88% on the
feedback rating the course as “Good”or “Very Good”,andthe
students particularly valued the combination of practical and
theoretical examples detailed in this publication.
Conclusions
It has been shown that by using a simple continuous flow setup,
consisting of syringe pumps, water baths and a method of quan-
titative analysis, alongside a methodical experimental technique
such as DoE, that multistep chemical processes can be optimised
for a desired output. The effect of varying reaction conditions on
the outcome of a chemical reaction is explored and therefore
allows better understanding of the reaction system than a OFAT
approach. This particular experiment is run annually as part of the
undergraduate chemistry course at the University of Leeds, but
can be used as an exercise in teaching flow chemistry and opti-
misation to researchers at any level. Third year undergraduates
that select this optional project learn the theory of DoE as part of
the pre-laboratory preparation - these theory PowerPoint slides are
provided in the ESI. Depending on the experience of the students,
the experiment can be altered to constrain what each participant
will conduct experimentally and what is provided for them. The
experimental setup requires low cost equipment alongside com-
mon laboratory analytical equipment, and the experimentation
itself is suitable for undergraduates and upwards; all experimental
results can be obtained in a 2–3 hour lab session.
This experiment demonstrates that the outlined statistical
modelling methodologies provide a greater insight into process
optimisation than can be achieved by a OFAT approach and
represent some of the most efficient and effective data analysis
techniques to explain the chemistry and identify regions of
interest. As many exercises in undergraduate courses are based
around synthetic batch experiments, this continuous flow ex-
periment can be incorporated into the course as a different ap-
proach to carrying out a synthetic reaction and obtaining reac-
tion data, and simultaneously provide an opportunity to learn
about statistics and optimisation techniques. This also enable
the students to work as part of a group to design and perform the
experiment, working towards a common goal, broadening their
skills and encouraging new ways of thinking.
It is the hope of the authors that as the skillset required of a
chemist is diversifying and expanding in sync with the in-
creasing capabilities and technologies of a ‘typical’laboratory
- so will the teaching of both chemistry and optimisation
Fig. 8 The contour plot for the
response of (5), showing how the
yield of the di-substituted product
changes with varying experimen-
tal conditions
J Flow Chem
techniques for process development. We are making strides
globally in a positive and constructive way towards laborato-
ries which contain scientists with a wide variety of skillsets,
and this paper aims to serve as a guide to teaching a number of
these key skills, i.e. continuous flow synthesis, statistical data
analysis, experimental design and reaction optimisation. The
evolution of curricula, the paradigm shift of academic labs and
overall increased awareness of other methodologies means it
is now a very exciting time to be in a chemistry setting: where
being a chemist is more than just being a chemist.
Acknowledgements The authors thank the School of Chemical and
Process Engineering and the School of Chemistry at the University of
Leeds for their support, and EPSRC and AstraZeneca for their funding
and support. We would like to thank the following people for helping to
make the 2019 Dial-A-Molecule event such a success: Anna Slater
(University of Liverpool), Dan Tray (GlaxoSmithKline), Adam Price
(University of Loughborough), Laurence Coles (Added Scientific),
Matt Penny (UCL) and John Blacker (University of Leeds).
Compliance with ethical standards
Conflict of interest On behalf of all authors, the corresponding author
states that there is no conflict of interest.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate if
changes were made. The images or other third party material in this article
are included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in the
article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
References
1. Plutschack MB et al (2017) The hitchhiker’s guide to flow chem-
istry. Chem Rev 117(18):11796–11893
2. O’Brien M et al (2017) Harnessing open-source technology for
low-cost automation in synthesis: Flow chemical deprotection of
silyl ethers using a homemade autosampling system. Tetrahedron
Lett 58(25):2409–2413
3. Christensen M et al (2019) Development of an automated kinetic
profiling system with online HPLC for reaction optimization. React
Chem Eng 4(9):1555–1558
4. Noël T (2019) Flow into the chemistry curriculum.Chemistry World.
https://www.chemistryworld.com/opinion/flow-into-the-chemistry-
curriculum/4010382.article
5. McQuade DT, Seeberger PH (2013) Applying flow chemistry: methods,
materials, and multistep synthesis. J Org Chem 78(13):6384–6389
6. Wegner J, Ceylan S, Kirschning A (2012) Flow chemistry–akey
enabling technology for (multistep) organic synthesis. Adv Synth
Catal 354(1):17–57
7. Baumann M, Baxendale IR (2015) The synthesis of active pharma-
ceutical ingredients (APIs) using continuous flow chemistry.
Beilstein J Org Chem 11(1):1194–1219
8. Holmes N et al (2016) Online quantitative mass spectrometry for
the rapid adaptive optimisation of automated flow reactors. React
Chem Eng 1(1):96–100
9. Ingham RJ et al (2014) Integration of enabling methods for the
automated flow preparation of piperazine-2-carboxamide.
Beilstein J Org Chem 10(1):641–652
10. Mostarda S et al (2014) Glucuronidation of bile acids under flow
conditions: design of experiments and Koenigs–Knorr reaction op-
timization. Org Biomol Chem 12(47):9592–9600
11. Cyr P et al (2013) Flow heck reactions using extremely low loadings of
phosphine-free palladium acetate. Org Lett 15(17):4342–4345
12. Nieuwland PJ et al (2011) Fast scale-up using microreactors: pyrrole
synthesis from micro to production scale. Org Process Res Dev 15(4):
783–787
13. Penteado JC, Masini JC (2011) Exploring liquid sequential injection
chromatography to teach fundamentals of separation methods: A very
fast analytical chemistry experiment. J Chem Educ 88(2):235–238
14. Hope WW, Johnson C, Johnson LP (2004) Tetraglyme trap for the
determination of volatile organic compounds in urban air. Projects
for undergraduate analytical chemistry. J Chem Educ 81(8):1182
15. McAllister GD, Parsons AF (2019) Going green in process chem-
istry: optimizing an asymmetric oxidation reaction to synthesize the
antiulcer drug esomeprazole. J Chem Educ 96(11):2617–2621
16. Krawczyk T, Słupska R, Baj S (2015) Applications of chemilumines-
cence in the teaching of experimental design. J Chem Educ 92(2):317–
321
17. Lendrem DW et al (2015) Lost in space: design of experiments and
scientific exploration in a Hogarth universe. Drug Discov Today
20(11):1365–1371
18. Lendrem D, Owen MR, Godbert S (2001) DoE (design of experi-
ments) in development chemistry: potential obstacles. Org Process
Res Dev 5(3):324–327
19. Czitrom V (1999) One-factor-at-a-time versus designed experi-
ments. Am Stat 53(2):126–131
20. Aggarwal VK, Staubitz AC, Owen MR (2006) Optimization of the
mizoroki-heck reaction using design of experiment (DoE). Org
Process Res Dev 10(1):64–69
21. Lovallo D, Kahneman D (2003) Delusions of success. Harv Bus
Rev 81(7):56–63
22. Mynatt CR, Doherty ME, Tweney RD (1977) Confirmation bias in
a simulated research environment: An experimental study of scien-
tific inference. Q J Exp Psychol 29(1):85–95
23. Owen MR et al (2001) Efficiency by design: optimisation in pro-
cess research. Org Process Res Dev 5(3):308–323
24. Wahid Z, Nadir N (2013) Improvement of one factor at a time
through design of experiments. World Appl Sci J 21:56–61
25. Weissman SA, Anderson NG (2015) Design of experiments (DoE)
and process optimization. A review of recent publications. Org
Process Res Dev 19(11):1605–1633
26. Elliott P et al (2013) Quality by design for biopharmaceuticals: a his-
torical review and guide for implementation. Pharm Bioprocess 1(1):
105–122
27. Tye H (2004) Application of statistical ‘design of experiments’
methods in drug discovery. Drug Discov Today 9(11):485–491
28. Murray PM et al (2016) The application of design of experiments
(DoE) reaction optimisation and solvent selection in the development
of new synthetic chemistry. Org Biomol Chem 14(8):2373–2384
29. Engineering Statistics Handbook: 5.3.3.6.3. Comparisons of re-
sponse surface designs. https://www.itl.nist.gov/div898/handbook/
pri/section3/pri3363.htm. Accessed online: 20/03/2020.
30. Hone CA et al (2017) Rapid multistep kinetic model generation
from transient flow data. React Chem Eng 2(2):103–108
31. Antony J (2014) Design of experiments for engineers and scientists.
Elsevier, Amsterdam
JFlowChem
Connor J. Taylor is currently a
postdoctoral research associate
at Astex Pharmaceuticals in
collaboration with the
University of Cambridge. He
completed an M.Chem degree
at the University of Leeds
(2017) and his PhD under the
supervision of Richard Bourne
and Thomas Chamberlain
(2021). During his PhD he
founded the process optimisa-
tion company, Compunetics,
in partnership with the
University of Leeds and main-
tains a strong interest in optimisation, kinetic analysis and reaction
modelling.
Richard A. Bourne is currently an
Associate Professor at the
University of Leeds. He complet-
ed a M.Chem degree at the
University of Nottingham (2004)
and his PhD under the supervision
of Prof. Martyn Poliakoff, CBE,
FRS. He is now a Royal
Academy of Engineering Senior
Research Fellow working on the
development of new sustainable
processes with focus on continu-
ous flow routes to pharmaceutical
and fine chemical products. His
group is based within the
Institute of Process Research and Development (IPRD) at the
University of Leeds, a joint institute between Chemical Engineering
and Chemistry.
J Flow Chem
