JSSSJournal of Sensors and Sensor SystemsJSSSJ. Sens. Sens. Syst.2194-878XCopernicus PublicationsGöttingen, Germany10.5194/jsss-6-155-2017Purity monitoring in medical gas supply lines with quantum cascade laser
technologyZimmermannHenrikzimmermann@neoplas-control.deWieseMathiashttps://orcid.org/0000-0003-0875-8732FioraniLuigiRagnoniAlessandroneoplas control GmbH, Walther-Rathenau-Str. 49a, 17489 Greifswald,
GermanyLoccioni Group, Via Fiume 16, 60030 Angeli di Rosora, Ancona, ItalyHenrik Zimmermann (zimmermann@neoplas-control.de)13April20176115516130September201618March201727March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://jsss.copernicus.org/articles/6/155/2017/jsss-6-155-2017.htmlThe full text article is available as a PDF file from https://jsss.copernicus.org/articles/6/155/2017/jsss-6-155-2017.pdf
Because of their direct impact on patients, medical supply lines are under
strict regulations and have to be monitored in terms of purity on a regular
basis. State-of-the-art measurement solutions do not allow for continuous bedside
monitoring. The aim of the presented project is to provide a compact
multispecies monitoring system based on the latest quantum cascade laser
technologies.
Introduction
Medical gas supply lines in hospitals are subject to strict regulations.
Besides the establishment of modern management techniques, continuous
monitoring of the distribution lines could avoid the fatal accidents that
unfortunately still happen to patients, especially after maintenance is performed on
older buildings (Weller et al., 2007; Cornwell et al., 2016). Concentration
limits are defined for every gas line by local or national regulations, such as the
European Pharmacopeia. The most important impurities are CO2, CO, H2S,
SO2, NOx, H2O, O2, N2O and oil. Currently, monitors
can be found on the market that are either compact, user-friendly mono-gas
systems used for discrete low-accuracy inspections or multi-gas, bulky and
quite expensive instrumentation developed to perform periodic checks on
gas access; however, these definitely do not allow for comfortable bedside
operation. A compact, cost-effective multi-gas sensing technology would
ensure higher safety potential as it could be installed directly at the
sampling point to perform distributed continuous monitoring.
In this context, sensors based on quantum cascade laser technology, which have proven their
applicability in demanding conditions (Anscombe, 2011) spanning from the
environmental monitoring sector to process monitoring and control
applications (Röpcke et al., 2012), may offer a suitable solution.
The present study introduces a new approach to the bedside purity monitoring of
medical gases in hospitals. The three main supply lines to check are the
oxygen line, the nitrogen protoxide line and the compressed air line.
As a first step, a single-species monitor focusing on SO2 detection is
presented (Q-MACS Trace), which is the milestone for the multispecies
solution based on the latest technology developments
(http://www.mirifisens-project.eu).
Experimental setup
Two sketches, one for the sensor assembly and the other for the
optical subsystem of the Q-MACS Trace compact sensor configuration, are
presented in Fig. 1.
The general sensor assembly with electronics, optics (a) and the
detailed optical sensor setup (b).
As a laser absorption spectrometer based on the direct absorption technique,
the Q-MACS Trace compact SO2 sensor acquires the transmission signal
with an infrared detector after passing a measurement cell of Herriott type
with a volume of 0.5 L which provides an absorption path of 0.5 L. A
portion of the laser beam, which is reflected at the coupling window of the
measurement cell, is guided through a gas cuvette with a fixed filling and
collected by a second detector. The absorption feature from the cuvette is
used to stabilize the spectral position of the laser by means of temperature
tuning. To address a suitable SO2 absorption feature while taking into
account possible cross sensitivities, a QCL emitting around 7.4 µm
is applied as a narrow-band light source providing an optical power of about
10 mW. The laser repetitively tunes over a spectral range of 0.7 cm-1
via a monotonic current ramp with a repetition rate of 2 kHz. To reduce the
heat impact of the laser and allow it to reliably achieve the laser
temperature of 7 ∘C via Peltier cooling, the intermittent scanning
regime introduced by Fischer is applied (Fischer et al., 2014). A duty cycle
of 20 % is selected. This driving scheme allows for the acquisition of a
complete SO2 absorption feature with very high resolution. In Fig. 2,
the processed portion of 0.2 cm-1 from the acquired detector signal and
the corresponding fit result are shown. This signal is taken at the chosen
full-scale value of 7 ppm at 60 mbar and a 56 m absorption path,
matching an absolute density of 1.03e+13 molecules cm-3.
The acquired detector signal with the SO2 absorption feature corresponding
to 7 ppm at 60 mbar and the fit result.
The acquired signal is analyzed with respect to the well-known molecular
parameters by means of the Levenberg–Marquardt algorithm, which ensures
nearly calibration-free measurements. In terms of the introduced SO2
measurements, these parameters are taken from the HITRAN molecular
absorption database (Rothman et al., 2013).
The gas dilution system consisted of two units (MF1; MKS Instruments, Inc., Andover, MA, USA) of mass flow
controllers and a diaphragm pump (Thomas 7011Z DC; Gardner Denver, Milwaukee,
WI, USA). One mass flow controller provides a range of 0 to 200 sccm and
another one comes with a range of 0 to 10 slm. This set of flow controllers
allows for reasonable accuracy in the dilution of gas standards over a very
wide range of dilution ratios with the selection of the appropriate settings
on the mass flow controllers. The distinct concentration steps, which were
alternatingly tuned as a continuous flow through the measurement cell, are
reflected in Table 1.
A list of the dilution steps, the resulting SO2 mixing ratios
and the
achievable accuracy.
This dilution system provides the test atmospheres for the analyzers that are
undergoing
testing.
The pressure in the sample cell is monitored via an HPS Series 902 piezo
transducer (MKS Instruments, Inc., Andover, MA, USA), which provides a range of application of 0 to 1300 mbar.
Using this pressure gauge, the pressure in the cell was maintained at a
fixed value of ∼ 60 mbar during the tests. No active pressure
control was applied during this test.
Statistical methods
The statistical methods used to evaluate the quantitative performance
factors are presented in this chapter. Since no alternative concept for the
detection of SO2 was available during the test, the evaluation of the
performance parameters for the sensor had to be based on the calculated
mixing ratios, which depend on the settled flow rates. This approach limits
the types of statistical comparisons that could be applied. Qualitative
observations were also used to evaluate the verification test data.
The linearity factor is assessed by linear regression with the calibration
concentration as the independent variable and the analyzer response as the
dependent variable. The calibration model is given in Eq. (1):
YC=hc+errorC,
where YC is the analyzer response to a challenge concentration c,
h(c) is a linear calibration curve and the error term is assumed to be
normally distributed. The variability σ in the measured concentration
values c was modeled by the relationship expressed in the following
equation:
σC2=α+kcβ,
where α, k and β are constants to be estimated from the data.
After determining the relationship between the mean and the variability,
the appropriate weighting is determined by Eq. (3):
wC=1σC2.
The form of the regression model to be fitted is expressed in Eq. (4):
c=h-1YC=(YC-α0)α1.
The concentration values were calculated from the estimated calibration curve
using the following formula:
hc=α0+α1c.
A test for the departure from linearity is carried out by comparing the residual
sum of squares to a chi-square distribution with 6-2=4 degrees of
freedom, as given with Eq. (6):
∑i=16(Y‾ci-α0-α1ci)2nCiwCi,
where nC is the number of replicates at concentration c.
The response time of the analyzers to a step change in the analyte concentration
was calculated by determining the total change in the response due to the step
change (either an increase or a decrease) in concentration, and then determining
the point in time at which 95 % of that change was achieved. Both the rise and
fall times were determined. Using data taken at intervals of 1 s, the calculation is
carried out by Eq. (7):
RTotal=Ra-Rb,
where Ra is the final response of the analyzer to the test gas after
the step change, and Rb is the final response of the analyzer before
the step change. The analyzer response that indicates the response time is then
calculated by applying Eq. (8):
R=0.95RTotal.
The point in time at which this response occurs is determined by inspecting
the response time data, and the response time is calculated according to
Eq. (9):
TimeResponse=Time95%-TimeI,
where Time95% is the time at which R occurs, and TimeI is the
time at which the step change in concentration was imposed. Since only one
determination was made, the precision of the rise and fall time results
could not be estimated.
The detection limit (LOD) was defined as the smallest true concentration at
which the analyzers expected the response to exceed the calibration curve at
zero concentration by 3 times the standard deviation of the analyzer
zero reading. The LOD is then determined by applying Eq. (10):
LOD=[α0+3σ0-α0]α1=3σ0α1.
Here, σ0 is the estimated standard deviation at zero
concentration. Note that the validity of the detection limit estimate and
its standard error depend on the validity of the assumption that the fitted
linear calibration model accurately represents the response down to zero
concentration.
The statistical procedures for assessing the zero and span drift were similar to
those used to assess the interrupted sampling. The zero (span) drift was calculated
as the arithmetic difference between the zero and span values respectively
obtained before and after the sampling of the source emissions. During this test, no
estimate of the precision of the zero and span drift values was made.
Results and discussion
A Q-MACS Trace analyzer prototype was tested for the most highly sensitive
online monitoring of SO2 traces. The laboratory tests were designed to
challenge the analyzer over its full range under a variety of conditions.
These tests were performed using certified standard gases and a gas dilution
system. The gas standards were diluted with high-purity gases to produce the
desired range of concentrations with known accuracy. The laboratory testing was
conducted primarily by supplying known gas mixtures to the Q-MACS Trace
analyzer from the gas delivery system. The linearity of the response of the
Q-MACS Trace analyzer was tested with 30-point calibrations of the
SO2 gas filling. Prior to this check, the analyzer is provided with the
appropriate zero gas (N2) and then with a span gas concentration of 7 ppm of SO2, which is defined in this verification test to be the nominal
range of the analyzer. After any necessary adjustments to the analyzer to
match that span value, the 30-point check proceeded without further
adjustments. The 30 points consisted of three replicates each at 70,
170, 350 and 690 ppb and 1.7, 3.5, 5.2 and 7 ppm in random
order interspersed with six replicates of zero gas. Following
the completion of all 30 points, the 0 and 100 % spans were repeated
without adjustment of the analyzer. The zero and span drift will be evaluated
using the data generated in the linearity and the accuracy tests. The zero and
span drift is determined as the difference in the response to the zero and span
gases in these two tests. This comparison will be made for all zero and span
responses using data from the linearity and the accuracy tests.
Figure 3 shows the linearity results obtained from
the linearity tests for the Q-MACS Trace analyzer, which was configured
for the SO2 measurements.
The linearity results for the Q-MACS Trace setup with a 1 s acquisition time.
In Table 2, the linear equations for the system configuration developed from
these data are shown.
The quite low value for the regression coefficient in the linearity tests is
caused by the rather high uncertainty that the chosen dilution system provides
for the N2 dilution gas at lower flow rates. The uncertainties in the
dilution system are listed in Table 1 and visualized by the
error bars in Fig. 3.
The results of the linear fit for the measured values at 0 and 1.67 % of the range.
For the linear fit, the concentration values are weighted with the
uncertainties of the dilution system to respect the deviation of the finally
settled concentrations from the targeted ones.
The detection limit determined according to the SO2 measurements.
Table 3 shows the detection limits for each configuration of the Q-MACS
Trace analyzer tested, determined from the detection limit procedure
described in the previous section. Figure 4
visualizes the results of the linear fit valid for 0 to
1.67 % of the range. The calculated detection limit for 1 s of acquisition time
is 22.99 ppb, corresponding to a density of 3.36e+10 molecules cm-3.
The response time for the sensor based on a step change in the analyte
concentration was determined to about 14 ± 1 s with the acquisition time limited to 1 s.
The trends in the SO2 concentration during five measurements of the zero and span test.
Figure 5 shows the
trend in the SO2 sensor signal while the SO2
concentration in the gas stream is changed between 0 and 7 ppm (span) for seven independent measurements.
The decrease in the concentration level after about 20 s is due to the
control behavior of the chosen mass flow controller.
The zero and span data taken at the start and end of the linearity test are
shown in Table 4.
The data used to assess the zero and span drift of the Q-MACS Trace compact
SO2 analyzer.
The observed drift values are shown in Table 5 as the differences between the
pretest and the posttest concentration measurements. Furthermore, Table 5 also
presents the zero and span drifts as a percent of the span gas concentrations.
The zero drift for the tested sensor was less than 0.04 % of the
respective span gas concentration. The span drift was less than 0.22 % of
the respective span concentration.
The results of the zero and span drift of the SO2 analyzer.
ComponentDifference (ppb)Zero2.49Span15.26Drift in % of spanZero0.04 %Span0.22 %
The noise behavior of the Q-MACS Trace system is characterized by means
of an Allan variance analysis. In Fig. 6, the
resulting Allan–Werle plot, as introduced by Werle as a powerful tool in the
characterization of noise performance from laser-based sensors (Werle et
al., 1993), is shown for a 3 h run of the sensor at a constant SO2
concentration. The plot shows a minimum detection limit of 4 ppb (1σ) with 70 s averaging.
An Allan–Werle plot from 3 hours of continuous measurement by the Q-MACS SO2 sensor.
Operation under real conditions
The developed sensor module was applied in a measurement campaign at Ancona
Hospital in Italy (Ospedali Riuniti di Ancona). In this campaign,
benchmarking was performed
against an established FTIR-based measurement system: the Loccioni GIGAS
10M. The QCLAS sensor system was configured to support sulfur
dioxide (SO2) and methane (CH4) as the target gases to be monitored in
the compressed air supply line.
A comparison of the SO2 measurements by FTIR (blue) and QCLAS
(red)
as well as the CH4 measurements by QCLAS (green).
In Fig. 7, the results of a continuous
measurement of SO2 by the FTIR and the QCL-based monitoring systems are shown.
Also shown are the results for CH4 measured exclusively by the QCL absorption
spectrometer.
Methane is generally not regulated by the pharmacopeia, but as one of
the most volatile compounds in oil released at temperatures of around
35 ∘C, it is one of the promising candidates for oil contamination
in the compressed air supply line. Because of the natural fluctuation of the
CH4 concentration and the varying technologies used in hospital gas
supplies, CH4 concentration measurements were analyzed with the aim of
deriving unambiguous statements about the oil contamination of gas probes.
While the QCL sensor system is connected to a bedside compressed air supply
outlet, medical compressed air is generated through the mixing of pure gases.
Therefore, impurities introduced by SO2 should be expected through leakages only. As the
SO2 concentration in the ambient air is currently far below the detection
limit of both systems, it is expected that no SO2 signal will be
detectable. Furthermore, it should be noted that no specific procedures were
applied to avoid water in the gas-handling system, which would react with
the SO2 and reduce the concentration to be detected. Taking
into account these considerations, the sensor signals are consistent as
illustrated in the graph. An issue in the calibration data caused the
observable irregularity in the SO2 concentration measured by the FTIR-based system. This illustrates the drawback of the multispecies-capable FTIR
technique, as eminent cross sensitivities have to be respected carefully
during the calibration process. Nevertheless, the false measurement is very
close to the SO2 detection limit of the FTIR-based system. A
reasonable correlation between the measurements of the different sensor
types as well as good reproducibility can therefore be seen.
Summary and outlook
The test results, which are summarized in Table 6, confirm that the Q-MACS
Trace analyzer provides a linear response over wide operating ranges. The
compact prototype configuration, as used in this preliminary study, provides
very good results with respect to sensitivity, selectivity and
stability.
The results from the performance analysis for the Q-MACS Trace SO2
sensor.
The system is rugged and portable, and the necessary setup time is minimal.
The fast sensor response times and measurement stability allowed for the
verification testing to proceed smoothly. Its design incorporates a sample
probe and a sample conditioning system, making it adaptable to a wide range of
measurement applications.
Although the aim of 15 ppb and below for the limit of detection was not
achieved with this Q-MACS Trace compact configuration, the positive
results show that it is possible to design a system that will fulfill this
specification. Recently, long path cells with more than 150 m of optical
path length have become available. Unfortunately, such a cell would not allow
for the
maintaining or even the further optimization of the compactness of the resulting sensor
system. Moreover, the much higher volume of such a cell would lead to a
significant and unwanted increase in the response time. A more practical
approach is the selection of stronger absorption lines, which could be
possible depending on the gas matrix to be analyzed in the specific
application. Developments for the further optimization of the gas handling, e.g.
through the integration of an active pressure control, and the extension
of the analytical methods are currently in progress. Applying these drafted
optimizations in a future configuration will allow for a decrease in the achievable
detection limit by approximately a factor of 5; SO2
concentrations below 5 ppb could therefore be detected by a continuous monitoring system.
During an on-site measurement campaign, it was possible to convincingly
demonstrate the applicability of QCL-based concentration monitoring
solutions in hospital gas supply lines. With respect to the monitored
CH4 concentration, it became obvious that this species is not sufficient
as a unique marker for the determination of the oil contamination in gases.
Therefore, efforts were made to incorporate additional target species into
the QCL sensor system. In this context, it was possible to incorporate water
vapor (H2O) as another target species of the sensor, which is relevant
in the purity monitoring of the compressed air supply line as well. The
measurements allow for the determination of the concentrations for selected
impurities clearly below the threshold levels given by regulations such as those in
the various
pharmacopeia. This performance is mainly caused by the intrinsic, narrow
laser-line width of the QCL and the capability to scan across the
respective target spectrum with the highest spectral resolution, which makes it
preferable under lower pressure conditions of a few hundred mbar as
implemented in the solution developed within the project. This could be a
disadvantage in the targeted application because it could result in the need
for
additional external gas-handling devices. This fact and the need for
measuring even more species with a single sensor will be the basis for
further developments to improve the device.
In pending development steps, the single-QCL source will be exchanged by a
QCL array to address several molecular species in parallel while maintaining
the compactness of the system. First introduced by Lee et al. (2009), QCL arrays will now make the step from the lab to the market
as their technological relevance and applicability has already been
demonstrated
(Geiser et al., 2016).
Research data are available upon request to the authors.
The authors declare that they have no conflict of interest.
Acknowledgements
This research was made possible thanks to funding from the European Union
Seventh Framework Programme (FP7, 2007–2013) under grant agreement
no. 17884 through the collaborative integrated project MIRIFISENS.
A special thank you also goes to the plasma diagnostic group at the Leibniz
Institute for Plasma Science and Technology in Greifswald for the professional
support during the final verification tests of the sensor configuration
(http://www.inp-greifswald.de).
Edited by: J. Wöllenstein
Reviewed by: two anonymous referees
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