Integration of enabling methods for the automated flow preparation of piperazine-2-carboxamide

Article abstract of DOI:10.3762/bjoc.10.56

Here we describe the use of a new open-source software package and a Raspberry Pi computer for the simultaneous control of multiple flow chemistry devices and its application to a machine-assisted, multi-step flow preparation of pyrazine-2-carboxamide - a component of Rifater, used in the treatment of tuberculosis - and its reduced derivative piperazine-2- carboxamide.

Full text of DOI:10.3762/bjoc.10.56

Integration of enabling methods for the automated
flow preparation of piperazine-2-carboxamide
Richard J. Ingham1, Claudio Battilocchio1, Joel M. Hawkins2
and Steven V. Ley*1
Full Research Paper
Open Access
Address:
Beilstein J. Org. Chem. 2014, 10, 641–652.
1Innovative Technology Centre, Department of Chemistry, University
of Cambridge, Lensfield Road, CB2 1EW, Cambridge, UK and 2Pfizer
Worldwide Research and Development, Eastern Point Road, Groton,
CT 06340, USA
doi:10.3762/bjoc.10.56
Received: 24 January 2014
Accepted: 07 March 2014
Published: 12 March 2014
Email:
Steven V. Ley* - svl1000@cam.ac.uk
This article is part of the Thematic Series "Automated synthesis".
Guest Editor: P. H. Seeberger
* Corresponding author
Keywords:
© 2014 Ingham et al; licensee Beilstein-Institut.
License and terms: see end of document.
automation; flow chemistry; hydration; hydrogenation; sustainable
processing
Abstract
Here we describe the use of a new open-source software package and a Raspberry Pi® computer for the simultaneous control of
multiple flow chemistry devices and its application to a machine-assisted, multi-step flow preparation of pyrazine-2-carboxamide –
a component of Rifater®, used in the treatment of tuberculosis – and its reduced derivative piperazine-2-carboxamide.
Introduction
Enabling synthesis technologies such as flow chemistry are in a format that can be readily extended by the user to imple-
becoming commonplace in modern laboratories (for recent ment control of additional hardware.
reviews of flow chemistry in synthesis see [1] and [2]). As more
groups start to use this technology, there is an increasing Whilst the control of multiple devices and instruments is well-
demand to expand the capabilities of laboratory apparatus, in developed and standard practice for multi-step continuous
particular for the seamless integration of different types of processing on a large scale, these control systems tend to be
apparatus from different manufacturers so that they can be used custom built at a high cost and hence they are not usually appro-
simultaneously and synergistically. Although manufacturers priate for the research environment. However, even on a labora-
generally provide appropriate control software for use with their tory scale the ability to connect and share data between different
particular device ecosystem, these frequently have a limited devices is of critical importance for performing complex
scope and do not always integrate well with other equipment. processes: recently reported examples include matching down-
For commercial reasons, the control software is rarely provided stream flow rates to the concentration profile of a dispersed
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reaction slug [3], the linking of synthesis to purification appar-
atus [4] and the development of new work-up technologies [5].
Most manufacturers are willing to share control commands for
their products, making these applications possible, but extensive
work is often required to coordinate a new process into a
smooth and simple operation. This may take the form of a
significant programming effort to automate the apparatus, or
involve the complex manual timing of events to ensure that the
desired operational sequence takes place.
Ideally, we need the ability to create a control algorithm for any
new process with minimal up-front effort. Furthermore, control
software should be sufficiently flexible such that different items
of apparatus can be swapped in and out of the integrated system
without having to make significant changes to the automation
protocol. Following a review of typical software packages and
technologies, we chose to implement a framework for running
scripted control algorithms that would allow interfaces for new
instruments to be prototyped easily. We hoped that by defining
a specification for each class of device (i.e., sensors, pumps,
heaters, etc.), these unified interfaces would provide the desired
flexibility between devices. In this way, more time can be spent
on the engineering and chemistry challenges inherent in the
Figure 1: Raspberry Pi® (RPi) computer operating in the laboratory,
shown here without its protective case. Underneath is a USB hub
(D-Link DUB-H7) which supplies power to the RPi via a short USB
cable. The RPi is controlled remotely through the Ethernet connection
(red cable) so no monitor, keyboard or mouse is required which
reduces the space taken up inside the fume hood. In this case the RPi
also communicates with the instruments via the Ethernet connection
(the Vapourtec unit pictured in the background is connected using a
Brainboxes ES-257 Ethernet-serial converter). Alternatively, a USB-
serial connector (such as Lindy P/N 42689) can be plugged directly
into the RPi or through the USB hub for serial communication.
synthesis process, rather than the logistics of control system the generation of fragment-based libraries for medicinal chem-
interfaces.
istry research programmes. “3D Fragments” have become very
attractive recently due to their potential to expand the available
Our group has experience using the Python programming chemical space. The presence of nitrogen in a small 3D struc-
language [6] to control laboratory devices [5]. This language ture can be important for its biological activity [14]; this is
claims to be ideal for rapid development and the use of free particularly important when developing unnatural amino acid
software fosters collaboration [7], enabling technology to be derivatives [15,16]. Such compounds represent an important
transferred without the large initial set-up costs typically contribution to a fragment database for medicinal chemistry.
involved with commercial control packages. Established tech- For example, piperazine-2-carboxamide (1, Figure 2a) is an
nologies such as chemical intelligence [8], statistical analysis amino acid derivative with interesting biological properties
[9] and computer vision [10,11] are available as third-party [17]. At the time of writing, racemic 1 was identified as a
libraries for easy integration. The simple text-based control notably expensive building block [18] and thus a good target for
scripts can be copied and pasted for simple re-use, and are this transformation. We have explored the possibility of devel-
compatible with version control systems [12]. Finally, these oping a machine-automated synthesis of this compound which
control programs tend to require low computational resources might later be extended to analogous structures.
and will run on cheap, low-power computers such as the Rasp-
berry Pi® (Figure 1) [13].
For the facile machine-assisted synthesis of 1 we devised and
optimised the fully continuous sequential hydration of nitrile 3
In the work reported here, we describe the application of auto- to amide 2 and hydrogenation of pyrazine 2 to piperazine 1
mation to performing routine research tasks such as the optim- (Figure 2b). Both of these steps involve flowing through hetero-
isation of experimental parameters for a particular transforma- geneous catalysts, a metal oxide for the hydration of the nitrile
tion and describe how the application of remote monitoring can and a supported precious metal for the hydrogenation of the
improve safety and efficiency within the research environment. heteroaromatic ring. Furthermore, both steps involve the addi-
tion of a volatile small molecule to the substrate with no
In order to test and demonstrate the development of simple byproducts: the addition of water for the nitrile hydration, and
inexpensive hardware and software solutions for the facile the addition of hydrogen to the heteroarene for the reduction.
integration of laboratory hardware, we chose the goal of the Thus, this sequence is ideal for a fully continuous multi-step
efficient synthesis of small molecules which are essential for process.
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Figure 2: Two step approach to piperazine-2-carboxamide via hydrolysis followed by reduction. (a) Retrosynthesis and (b) catalytic transformations.
Results and Discussion
Nitrile hydration
Primary amides can be prepared via a number of different
approaches but the most environmentally friendly procedure is
the hydration of nitriles [19]. Although this is generally
considered to be a simple transformation, there are some
inherent problems with the standard techniques of hydrolysis
[20]. Further to our previous work which demonstrated
the hydration of broad classes of nitriles by passing
aqueous–organic solutions through a packed bed of manganese
dioxide [21], we have found that heteroaromatic nitriles
possessing a β-heteroatom can also be hydrolysed using
hydrous zirconia [22,23] in a similar fashion (Figure 3). The
directed activity of zirconia is very similar to that of ceria, a
known nitrile hydration catalyst for batch reactions [24]. While
Figure 3: Heterogeneous hydration of pyrazine-2-carbonitrile with
hydrous zirconia.
zirconia has a lower turnover number than ceria, zirconia is less
expensive than ceria and it has better physical properties for
packed beds than ceria. Its use in packed beds facilitates recyc-
ling of the catalyst, providing a further cost advantage.
3 hours and then the catalytic properties of the system disap-
peared (Figure 4), suggesting that the surface of the catalyst had
Interestingly, we have now found that no activation of the been completely dehydrated by the nitrile. As soon as water was
zirconia is needed (this is the same as for nitrile hydrations with added to the reagent solution, quantitative hydration of the
manganese dioxide, but unlike the use of zirconia as a hetero- nitrile was again achieved for an extended period of time
geneous catalyst for Meerwein–Ponndorf–Verley reductions without any further drop in the catalytic activity.
[22] and Oppenauer oxidations [23] where zirconia activation is
required). In fact, it seems that the extent of hydration of In order to determine the optimal parameters for this reaction, a
pyrazine-2-carbonitrile is proportional to the initial water number of experiments were carried out. The reactor was
content of the zirconium catalyst. To confirm this hypothesis, configured as shown in Figure 3: using a Vapourtec R2+/R4
we ran a control experiment in which no additional water was reactor unit, solutions of nitrile 3 were passed through heated
added to the organic solvent (absolute ethanol). A solution of 3 column reactor R1 (Omnifit® glass column, 100 mm × 6.6 mm;
was continuously fed into a reactor column containing untreated a flow rate of 0.1 mL min−1 produced a residence time of
zirconium hydroxide [25]. The reaction profile was followed 20 minutes) packed with 2.5 g of zirconium hydroxide. (Due to
with an in-line infrared (IR) spectrometer in order to determine the practicalities involved with performing reactions using
the conversion. Notably, the hydrolysis process was constant for different solvents, these tests were run under manual control).
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Figure 4: FlowIR™ profile for the reactor output after hydration of pyrazine-2-carbonitrile using hydrous zirconia. Both peaks correspond to the prod-
uct. A solution of the nitrile in ethanol (0.06 M) was passed at 0.1 mL min−1 through the column, heated to 100 °C. The response rises after approxim-
ately one column volume, with a small degree of dispersion. It then starts to decrease after the water is used up and the ability of the column to
hydrate is exhausted. The fall-off takes place over about one column volume, indicating a low retention of material by the heterogeneous bed.
Quantitative transformation to the primary amide 2 was general-
Table 1: Optimisation for the hydration of the pyrazine-2-carbonitrile.
ly achieved within a 20 minute residence time at 100 °C
(Table 1). In general, we observed good solvent compatibility;
however, when using a water-immiscible solvent such as
toluene or ethyl acetate the zirconium hydroxide functioned as a
reagent rather than a catalyst. In this case, the reactivity of the
metal oxide structure could be regenerated by feeding the
reactor with an aqueous solution. As mentioned earlier, it seems
to be the water present in the lattice, or fed into the column
reactor, which is responsible for the hydration activity of the
metal oxide. Consequently, the use of water-miscible solvents is
preferred in order to have a continuous process instead of a plug
flow protocol using water-immiscible solvents.
Reactions were performed with dry solvents and a fresh batch of cata-
lyst for each. The reactions were carried out on a 1 mmol plug of
pyrazinecarbonitrile such that the zirconia was present in excess.
Solventa
Temperature Residence timeb
Yieldc
[°C]
[min]
isopropanol
isopropanol
ethanol
100
100
100
100
100
100
100
100
10
20
10
20
20
20
20
20
20
20
49%
98%
51%
100%
100%
45%
92%
88%
95%
94%
ethanol
methanol
ethyl acetate
toluene
water
tetrahydrofuran 100
dioxane 100
Under the optimised conditions, a solution of nitrile 3 in
ethanol/H2O (0.6 M, 8:1 v/v) was passed through the column
reactor R1 heated at 100 °C, with a residence time of
20 minutes, to obtain a quantitative yield of the primary amide 2
after concentration of the reactor output.
aNo water was added to the organic solution; bresidence time within
the column reactor; cthe yield refers to that of the isolated product.
To assist with the processing of a large amount of material, we number of parameters are read from devices or sensors, and the
applied an automated control and monitoring system being effluent is only collected when all of the parameters are stable,
developed within our group [26], which can carry out a ensuring a high degree of purity in the collected material.
programmed sequence of operations written using the Python™
language. There is also a remote interface for observing the Larger zirconia columns were also used to improve the
status of an ongoing reaction in real-time. In common with the throughput. Employing 5 g of zirconium hydroxide within a
industrial use of process analytical technologies (PAT), a 100 mm × 10 mm diameter Omnifit® column (R2, Figure 5a)
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Figure 5: (a) Fluidic setup for the zirconia catalysed hydration of aromatic nitrile. (b) Raspberry Pi® microcomputer, FlowIR spectrometer and
Vapourtec R2+/R4 reactor unit as used for this procedure.
gave a two-fold increase in throughput. Under these conditions,
we could use a flow rate of 0.2 mL min−1 and still generate a
quantitative hydration of the nitrile, generating an output
equating to 0.45 g h−1 of amide product.
Using a Mettler–Toledo FlowIR™ fitted with a detector with a
silicon window (required for visualising the nitrile region of the
spectrum, which is blocked by the standard diamond window)
the change from nitrile to amide can be observed, providing
real-time feedback on the state of the reaction.
The monitoring software was connected to the Vapourtec
R2+/R4 reactor via RS-232, and to the FlowIR™ spectrometer
via the Auto-Export feature of the Mettler–Toledo iC IR control
software running on a separate computer. The desired steady-
state parameters were defined, and an output valve (V1) was
controlled based on their states. Importantly, different responses
were defined for each parameter: the IR absorbance represents
the current reactor output and thus can command immediate
responses from the valve. On the other hand, fluctuations in
temperature or pressure could compromise an entire column
volume, so a delay was added between the time that these para-
meters stabilised and the time that collection resumed
(Figure 6). An alerting system was also included that could
Figure 6: Flowchart describing the control sequence for operating and
notify the operator if the reactor lost pressure, indicating an air
bubble or a leak, or if it over-pressured – situations that
currently require manual intervention to rectify.
monitoring the hydration reaction. The black line indicates the execu-
tion sequence from start to end. Branch points indicate parallel execu-
tion. (See Supporting Information File 2 for the control program).
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All of the reaction parameters could be observed on remote the output. After approximately 1.5 h, a loss in pressure is
computers, or wirelessly on a tablet computer when moving detected corresponding to an air bubble in the input stream.
around the lab. The monitoring software generates an interface Valve V1 is switched to waste, and an SMS notification is sent
for each running experiment, which can be accessed through a to the operator, who re-primes the pump. After the pressure has
web browser. The ability to access real-time experimental returned to normal there is a delay calculated to be the dead
information from anywhere – as opposed to only on computers volume of the column before the output is collected again.
situated next to the apparatus – is very important, because it
gives the chemist freedom to perform other tasks at the same Pleasingly, experiments on scale (50 mmol) gave very positive
time. This is particularly beneficial when data from multiple results. The catalystic activity of the system remained constant
reactors and devices is combined into a single interface.
and the product 2 was recovered quantitatively and, more
importantly, characterised by high purity as determined by
The sensors were interrogated approximately once a second; a elemental analysis (>98%).
Raspberry Pi® microcomputer (as shown in Figure 5b) has
more than sufficient processing power to perform the required Pyrazine ring reduction
data collection, interpretation and control. We anticipate that An initial investigation showed that this aromatic carboxamide
much more complex systems than this one could be controlled could be efficiently reduced using an H-Cube® reactor [27,28].
using this miniature computer system.
There has been recent interest in the use of automation to
optimise reaction conditions [29-31], and thus we hoped to use
An example of the error handling behaviour is shown in a linear programming method [32,33] in a similar way to iterat-
Figure 7. After approximately 40 minutes the product begins to ively improve the hydrogenation settings. This turned out to be
elute and after it passes a threshold in the absorbance as impractical for two reasons: the flow rate, dead volume, and
detected by the FlowIR™ unit, valve V1 is switched to collect stabilisation time required for the H-Cube to reach steady state
Figure 7: Profile for a 3 hour reaction simulating a long run. The absorbance shown is that at 1685 cm−1, which is indicative of the amide bond in 2.
The nitrile stretching absorbance at 2245 cm−1 was not observed. The thick bar represents the time over which valve V1 is set to collect the output as
opposed to directing it to waste. The pressure drop at 1.5 h, caused by a bubble in the inlet stream, triggered the system to send the effluent to waste
until a predetermined wait time – corresponding to one column volume – elapsed after the pump had been manually re-primed.
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meant that each iteration would take 30–60 minutes; and the
discrete nature of the available parameters (for example, the
column temperature can only be set in units of 10 °C) mean that
simple linear optimisation methods are not suitable [34]. There-
fore, we decided to use a Design of Experiments [35-38]
method to determine which of the available parameters were
important for the conversion and selectivity of this reaction.
This process requires a lot of repetitive work to be done, and
thus can greatly benefit from reaction automation.
A two-level factorial design with three parameters (temperature:
40 °C and 100 °C; H2 pressure: 20 bar and full hydrogen mode;
and flow rate: 0.1 mL min−1 and 0.2 mL min−1) suggested
16 experiments – two repeats each of eight sets of conditions. A
single catalyst was used, a 10% Pd/C cartridge supplied by
ThalesNano. With a large amount of material from the previous
step in hand, we decided to use an automated system to perform
these experiments in order to reduce the amount of operator’s
time that is required.
Figure 9: Representation of the control sequence for running experi-
ments under a set of conditions. (See Supporting Information File 3 for
control program).
undesired compounds, based on the integration of peaks visible
in the 1H NMR spectra (a triplet at 3.2 ppm and a doublet at
Combining the control of a Knauer HPLC pump, the H-Cube® 3.8 ppm, the first corresponding to 4 and the other to a second
reactor and a multi-position valve (V2) (Figure 8, Figure 9) we unidentified compound) relative to the integration of 1 and 2
could perform up to nine reactions in a row. A sample was (Figure 10). As expected, the hydrogen pressure had the most
taken at steady state for each set of conditions: we noticed two significant effect on the conversion. The temperature and the
major reduction products, fully-reduced pyrazine-2-carbox- flow rate had a lower effect on the conversion, although the
amide (1) and 1,4,5,6-tetrahydropyrazine-2-carboxamide (4). combination of higher temperatures and lower flow rates gave a
Unfortunately, the carbonyl stretching frequencies of the higher purity output as intermediates such as 4 are fully
different amides proved to be remarkably similar and the sec- reduced. For maximum efficiency further hydrogenation pro-
ondary frequencies had very low intensities, so the FlowIR™ cedures were carried out at the lower flow rate 0.1 mL min−1
was not suitable for analysis of this reaction mixture and these and higher temperature 100 °C.
were subsequently analysed by NMR to quantify the results.
In situ generation of the intermediate
For each sample, four parameters were calculated: the degree of Performing an optimisation experiment in continuous flow –
conversion, based on the residual 2 observed; the amount of the such as described in the previous section – has a notable disad-
desired product 1 formed; and an estimated amount of vantage. After making a change to the reaction conditions there
Figure 8: Reactor setup for optimisation reactions. A multi-position valve (V2) was used for collecting samples.
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amount of intermediate available. The Raspberry Pi® computer
was found to struggle when working with a USB webcam so the
control protocol was instead run on a standard desktop
computer (A dedicated camera module for the Raspberry Pi®
has recently been released which should solve this problem
as this module requires minimal processor time to capture
images).
Figure 10: Reduction products of piperazine-2-carboxamide.
is some delay before the system settles to a steady state, at
which point a new measurement can be taken. This is particu-
larly relevant for reactions that take some time, as large quan-
tities of material may be required to perform a number of
different trials. In the previous case, we had a significant
amount of the intermediate so this was not considered to be a
problem, but we would like to avoid stockpiling of intermedi-
ates in this way. One major advantage of continuous processing
is that material can be used directly from one step to another
so that collecting large quantities of intermediates can be
avoided.
Consequently, we started to investigate whether the two steps
could be combined so that there is always enough feedstock to
run the second step. Normally, this would dictate that the flow
rate of the second step is always the same as that of the first step
(or greater, using an additional pump). However, in some
processes, such as the one described in the previous section, we
would like to be able to vary the flow rate of a step to adjust the
residence time. We envisaged the use of a reservoir to keep a
small amount of intermediate ready for the second step. This
required some means to measure the volume present in the
reservoir so that the control software can decide whether there
is enough material ready to perform an experiment.
Figure 11: (a) In-line reservoir schematic. The liquid level is meas-
ured by observation of a plastic float. (b) Image processing to measure
the liquid level. The computer receives an image of the reservoir and
processes it as follows: (1) Separate the image into red, green and
blue components (“channels”) and then identify the green pixels by
subtracting the red channel from the green. (2) Convert the resulting
greyscale image to black and white based on a certain threshold of
lightness. (3) Find the centre of the largest white region (shown high-
lighted with a box) and report its height from the bottom of the image.
By combining the control sequences for the two synthesis steps
with the volume-measuring logic, the second step could be
started when enough material of the intermediate had been
collected to start pumping out. Furthermore, if the meniscus
were to rise high enough to pose a risk of the reservoir over-
flowing, this can trigger an alert or cause valve V1 to cease
collecting the intermediate. If the liquid level were to fall too
low, then the next iteration of the hydrogenation condition
testing loop could pause until there was enough material to
continue (see Supporting Information File 4 for the control
sequence, and Supporting Information File 5 for the sequence
diagram).
In a recent review [11], we described the application of cameras
and computer vision to synthesis procedures. Building on work
in which a camera and a float were employed to measure the
position of a biphasic (aqueous/organic) mixture within a
settling column [5], we manufactured a float containing an air
bubble [39] which would float on less dense solvents than the
solid polyethylene version used previously. Using a pear-shaped
flask as a reservoir, so that the inlet needle for the second pump
could access as much of the solution as possible, a camera/float
combination was able to measure the amount of intermediate in
the reservoir (Figure 11). A digital camera was positioned so
that it was observing the collection vessel. Regular snapshots
were taken and analysed using computer vision software to Two-step synthesis procedure
locate the green float within the image. This information was Finally, the previous procedure was modified to perform a two-
used to estimate the height of the float in the reservoir, which step process using the optimised parameters for each step
enabled the control protocol to make decisions based on the (Figure 12, Figure 13). The maximum flow rate to allow full
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Beilstein J. Org. Chem. 2014, 10, 641–652.
Figure 12: Flow set up for the automated machine assisted synthesis of (R,S)-piperidine-2-carboxamide.
Figure 13: Control sequence for the two-step process.
conversion in the H-Cube® was relatively low, which limited available; but in this case the use of a reservoir meant that the
the throughput of the material in the reduction step. This could flow rates did not necessarily have to be matched, resulting in a
be mitigated if larger-scale hydrogenation apparatuses were semi-continuous process.
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Figure 14: Chart of monitored parameters over a 15 hour reaction. The output from the hydrolysis step is directed into the reservoir as soon as the IR
absorption crosses a threshold. When enough material has been collected, the H-Cube® is powered on and the hydrogenation step is started. When
the reservoir fills up the first step is stopped. The hydrogenation continues until the intermediate has been used up.
Using the two different flow rates and having the hydrolysis on productivity, not just for traditional applications such as
step stop automatically when the reservoir filled up led to a total library synthesis, but also for one-off protocols for which auto-
running time of about 10 hours (Figure 14). However, we can mation may previously have required a much greater time
imagine that this could represent one cycle of a prolonged investment.
sequence where the first step is periodically stopped to allow
time for the collected intermediate to be processed in the second
Supporting Information
step. With a large reservoir (for example, our 50 mL reservoir
allows up to a 16 hour start/stop cycle) the proportion of ma-
Supporting Information File 1
terial wasted during start up and shutdown of the first step can
be reduced.
Experimental data.
[http://www.beilstein-journals.org/bjoc/content/
supplementary/1860-5397-10-56-S1.pdf]
We anticipate that the potential to rapidly realise complex
control sequences such as these will further broaden the scope
for the use of flow chemistry reactors in both the research and
scale-up environments. We hope that the increasing availability
of free and open software to enable such processes will help to
democratise the field of flow chemistry when applied as an
advanced enabling technology in the laboratory.
Supporting Information File 2
Control sequence for extended period hydrolysis
experiment with monitoring. Alternate web version:
http://gist.github.com/richardingham/0a58a291bad2e3b900
9f
[http://www.beilstein-journals.org/bjoc/content/
supplementary/1860-5397-10-56-S2.txt]
Conclusion
Supporting Information File 3
Control sequence for performing DoE experiments.
Alternate web version:
A machine assisted synthesis of pyrazine-2-carboxamide – a
component of Rifater®, used in the treatment of tuberculosis –
and its reduced derivative (R,S)-piperazine-2-carboxamide has
been demonstrated, using a new open-source software platform
for the simultaneous control of multiple devices. The protocol
developed here represents a valid example of how these tech-
nologies can be used to implement chemistry processes for
synthesis. Automated procedures can have a significant impact
http://gist.github.com/richardingham/83401127622036c6af
d0
[http://www.beilstein-journals.org/bjoc/content/
supplementary/1860-5397-10-56-S3.txt]
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Control sequence for performing DoE experiments using
intermediate from a reservoir. Alternate web version:
http://gist.github.com/richardingham/f2117b9dc7504d6e19
42
[http://www.beilstein-journals.org/bjoc/content/
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Flow chart representation of the control sequence for
performing DoE experiments using intermediate from a
reservoir.
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