1

Document Prepared for the US EPA by: 

UNISEARCH Associates Inc.

CEMTEK Environmental

June 2012

UNISEARCH ASSOCIATES INC.

www.unisearch-associates.com

CEMTEK ENVIRONMENTAL

www.cemteks.com

TDL Performance Specification 

Notes for In-situ HCl Monitoring 
2

Table of Contents

A.Introduction........................................................................................................................3

B. Detection Limit and Identification of Gasses by NIR Spectroscopy .............................3

B.1 Detection Limit Description..................................................................................3

B.2 HCl Minimum Detection Limits Determined in the Lab under Simulated 

Flue Gas Conditions ..............................................................................................4

B.3 NH3 Minimum Detection Limits determined in the Lab under Simulated Flue 

Gas Conditions as Well as in the Field ................................................................6

C.Background Subtraction and Zeroing Considerations in TDLAS 

Spectroscopy......................................................................................................................8

C.1 Background Subtraction .......................................................................................8

C.2 TDL Laboratory Evaluation for Measurement of HCl....................................10

C.3 Curve fitting Techniques for IR Spectroscopy..................................................11

C.4 In-situ zeroing of TDLAS for Direct Absorption Measurements....................15

C.5 Determination of In-situ Minimum Detection Limits.......................................18

D. In-situ SPAN Checks for NIR TDLAS Spectroscopy..................................................23

D.1 In-situ Span Checks.............................................................................................23

D.2 Audit Cell Verification ........................................................................................24

E. References.........................................................................................................................26
3

A. Introduction

This report is intended to address the technical issues posed by Ray Merrill and his team from

recent stakeholder conference calls conducted by the USEPA. This elaboration of the original 

comments made by Cemtek and Unisearch Associates is to augment the draft performance 

speciation for HCl CEMS. At issue is how do Integrated Path (IP) Continuous Emission 

Measurements (CEMs) perform a span and zero drift check in an in-situ measurement mode. 

Since there will be no practical way to remove or be certain that HCl gas is not present in the insitu measurement path the zero and span of the system need to be addressed differently. For the 

span, a spike test with certified gas through an inline cell can be used to verify the measurement 

range of the system. To obtain a measurement system zero, Unisearch has developed a method 

that we believe will meet the intent of the performance specification for evaluating zero drift. 

This method uses generally accepted curve fit techniques that are primarily used in FTIR and 

proprietary algorithms. The basis for these techniques is presented here for your evaluation. 

Another issue posed in the same stakeholder discussion was how we would conduct a field 

determination of the Limit of Detection (LOD). Using this zero check method developed and 

subsequently described here and then by taking replicates of zero checks, the zero numbers can 

be used to establish the LOD.

B. Detection limit and identification of gases by NIR TDLAS Spectroscopy

B.1 Detection Limit Description:

According to the accepted convention the limit of detection (LOD) is the lowest analyte 

concentration that is significantly different from the blank which is a known sample that does not 

have any of the analyte (basically zero). 

The signal intensity (yD) corresponding to the detection limit represents the size of the signal that 

is equivalent to the blank value. 

The signal is determined by the mean value μB and the standard deviation σb of a blank signal 

described in equation (1), (Grant et al, 1992)

yD = μB + KD σb (1)

where KD = 3.

Because the values μB and σb are not known, their estimates are used, i.e. the average blank value 

‹yb›and the corresponding standard deviation sb.

Although the LOD is defined in the signal domain, the main objective in trace analysis is to 

obtain a concentration value of LOD, [C]D. 
4

Because the values of the signal are to be corrected for the blank/zero experiment, the corrected 

linear calibration dependence should theoretically pass through zero. Thus the uncorrected 

dependence provides on the signal axis the value of the intercept μB, estimated as yb. 

Then the usual method of obtaining concentration [C]D is based on the use of equation:

[C]D = ((yD)-‹ybx›)b1 (2)

If we substitute into equation (2) equation (1) given above, with the replaced values of μB and

σb, we obtain for the concentration detection limit the following relationship

[C]D = 3sb/b1 (3)

Where

sb is the standard deviation

b1 is the slope of the calibration curve, analytically identified as sensitivity.

The estimate of the standard deviation is possible by the analysis of repeated measurements of 

the blank/zero experiments. 

This approach was adopted to evaluate the limit of detection for near infrared tunable diode laser 

spectroscopy (NIR TDLAS) at laboratories at the University of California at Riverside (UCR) 

under The Electric Power Research (EPRI) funding for HCl and NH3. (Dene et al., Himes et al.)

A blank signal was obtained by introducing zero gas with equivalent moisture levels as well as 

possible interfering gasses (CO2, NO, SO2) expected in typical flue gasses known at coal-fired 

power plants at temperatures typical of flue gas exhaust.

The true detection limit then is evaluated as possible interferences from flue gas constituents as 

well as any thermal bias was accounted for by simulating conditions expected in the sampling 

environment.

Linearity checks were also done on the instrument response to evaluate the intercept.

B2. HCl Minimum Detection Limits determined in the Lab under Simulated Flue Gas 

Conditions

The University of California at Riverside has built a laboratory specifically designed to evaluate 

spectroscopic techniques and instrumentation typically used for the measurement of trace gases 

emitted in combustion processes. This has been funded by EPRI; for a complete description of 

the laboratory as it pertains to HCl please refer to EPRI publication (Dene et al., EPRI, Palo 

Alto, CA: 2011 1022083).
5

For HCl the evaluation of minimum detection limits accounted for possible biases from 

interference gasses listed on table 1 as well as moisture (8%) and temperature.

Table 1: Interference testing gas concentrations

Interference NO= 1510 ppmV

Testing CO= 1220 ppmV

Gas CO2=11.9%

Concentrations SO2=2050 ppmV

NH3=100ppmV

The Unisearch S-series LasIR SM410 instrument was tested.

Linearity is expressed in terms of slope, intercept, and coefficient of determination (R2

) and is 

shown in figure 1.

Figure 1

Linear Regression, of the measured response, from the Unisearch S Series LasIR SM410 

as compared to the source calibration value. The measured responses are averaged from 

all 10 tests at each source calibration value that encompassed the range of moistures and 

temperatures used in this evaluation. 
6

Since the system was linear and the intercept was 0.0261 and the response to zero gas combined 

with all the possible interfering gasses with moisture at thermal levels typical of coal-fired flue 

gasses the minimum detection limit is reported at three times the average standard deviation of 

all the one-minute zero measurements made at each of the temperatures used in the evaluation 

when the instrument was evaluated over the entire range of tests and b1 is 1. 

The three temperatures were 25 oC (1 set of measurements, 15 points), 100 oC (3 sets of 

measurements, 45 points) and 200 oC (6 sets of measurements, 90 points). 

The numbers are reported in ppmV-meter, and are also shown with a 5 meter path, typical to 

power plant installations as detection limits were found to improve with longer path lengths.

Table 2: HCl MDL’s as a Function of Temperature

Temperature Minimum Minimum

Detection Detection

Limit Limit

Degrees °C 5-meters

ppmV-m ppmV

25 0.35 0.07

100 0.66 0.13

200 1.10 0.22

To evaluate minimum detection limits in the field for HCl would require a stable blank, either 

knowing that there is no HCl present or a value of HCl very low near the laboratory determined 

MDL so we could establish an average value for yb.

B.3 NH3 Minimum Detection Limits determined in the Lab under Simulated Flue Gas 

Conditions as Well as in the field

Prior to the HCl tests, similar tests were done on 6 TDLAS instruments from various 

manufacturers for NH3.

The goal was to determine minimum detection limits, instrument response; tests were also done 

in the field.

Refer to EPRI publications for results of these tests (Himes et al.).

The goal of these instruments is to measure as accurately as possible from 0.5 to 20 ppmV NH3

slip as observed from post SNCR (Selective non-catalytic) or SCR (selective catalytic) NOx 

reduction processes.
7

The test done in the field was to measure the NH3 blank response when no urea or NH3 was 

injected into the SNCR or SCR.

Field measurements corroborated the laboratory estimates of the minimum detection limits.

Typical results for a similar Unisearch instrument configured for NH3 instead of HCl were also 

at 3 times the standard deviation and are listed as a function of temperature in table 3.

Table 2: NH3 MDL’s as a Function of Temperature

Temperature Minimum Minimum

Detection Detection

Limit Limit

Degrees °C 10-meters

ppmV-m ppmV

20 2.12 0.21

200 2.56 0.26

300 2.78 0.28

400 3.33 0.33

C. Background Subtraction and Zeroing Considerations in TDLAS 

Spectroscopy

C.1 Background Subtraction

The objective of background subtraction is to remove a constant offset from the signal which 

may include an underlying spectral feature that may bias the measured signal. It is critical when 

doing background subtraction that in this process, we want to be consistent in our approach and 

be accurate in our methodology so as to avoid altering the true character of the measured 

spectrum.
8

There are three modes when considering the backgrounds observed in TDLAS: They are:

1. Empirical backgrounds which arise with no consideration to the theoretical rational 

behind them. Few if any background shapes used in TDLAS are purely empirical in 

nature.

2. Semi-empirical backgrounds arise from some consideration of the underlying principles 

that give rise to a background signal in the spectrum. 

3. Analytical backgrounds arise from direct consideration of the underlying principles that 

give rise to a background signal in the spectrum and measure the background when a zero 

or null is present in the signal path.

One of the first considerations is to determine the range over which the background will be fit.

1. The Entire Spectrum: This is the usual choice when we have only one feature of interest 

(a peak) in the entire spectrum.

2. A Portion of the Spectrum This choice is made because we have a) multiple features in 

the spectrum, b) abrupt changes in background shape, or c) both

At the endpoints (where the background meets the data), we will typically want to decide 

whether to have the background match the values of the data points exactly or be the expected 

value of the signal at those points

For any data outside of the chosen range, we will typically want to decide whether to

1. Use the raw data values

2. Set everything to a pre-determined value

3. Extrapolate the behavior of the background

The background shape will typically be determined by

1. How easy it is to apply

2. How close it is to theoretical principles

3. How it compares to observed backgrounds during null or zero target gas present in the 

signal path.

A typical algorithm for background subtraction might be

1. Determine the range

2. Determine the shape

3. Determine the endpoint values

4. Apply the background

5. Consider whether your choices were suitable

Background subtraction can be an iterative process.
9

A constant background removes a constant value from the spectrum in the selected range. This is 

typically the standard approach with the background taken during a period of null or zero target 

gas in the path. This background typically arises from optical noise inherent to the laser based 

system which accounts for the following:

1) Laser based noise, typically very low

2) Optical noise from optical components as beam splitter, multiplexers, lenses, mirrors or 

other optical components that are in the signal path

3) Optical etalons which are sinusoidal in nature and are typically caused by transmission of 

the optical beam through lenses

4) Electrical noise which may be present from the detector back to the instrument

The major advantage for this is that all spectra typically have a constant underlying offset in the 

signal throughout their range and this technique is relatively easy to apply. The major 

disadvantage is that in many optical systems this is not always the end of the story and further 

background subtraction may be needed over time if the background deviates.

So we have discussed background subtraction as a way to determine the null or zero response of 

the instrument.

The major advantages to the TDLAS technique are:

1) That we are observing a signal response over a very narrow band (typically a few tenths 

of a nm or less). 

2) So making the assumption that where the backgrounds meet the signal on both sides of 

the response is fairly constant can be measured and evaluated and has merit as the 

bandwidth of the response is so narrow. 

3) Backgrounds are typically linear, this alludes to the background over the narrow range 

from points prior to where the measured signal is to points past the measured signal can 

be predicted absorption range mathematically.

4) Scanning the entire absorption feature leads to being able to measure the background 

response to where the point of signal as well as the range point 

5) The TDLAS technique is very fast, typically thousands of scans of the total response a 

second, so perturbations in the region where there is no target gas response can clearly 

be detected

6) The TDLAS technique scans the entire absorption feature as well as significant portions 

prior to and after the absorption feature.

7) Taking a background with the majority of TDLAS instruments is a relatively simple 

process.

8) Spectral analysis techniques developed primary for FTIR analysis can determine residual 

values that can be used to minimize spectral interferences from other gasses, as well as a 

methodology to deal with multiple lines of absorption of the same target gas. Such 

analysis can similarly be applied to TDALS spectroscopy, but since it is a single line it 

basically will observe the residual as a background to the measured signal and by 
10

subtracting this residual will improving the zeroing of the measurement and 

subsequently enhance the minimum detection limits.

C.2 TDL Laboratory Evaluation for Measurement of HCl

Work has been completed for the evaluation of TDLAS measurements of HCl, basically with 

methodology concurrent to extractive sampling. In this testing an inert (Teflon lined 1 meter 

sample cell) was used to investigate the primary response of the TDL instrument to HCl in 

environments that simulated coal fired power plants flue gasses. The funding of this work was 

through EPRI (Contact: Chuck Dene, Principal Investigator and Project Manager, 650-855-2425) 

at a laboratory specifically constructed to test spectroscopic techniques at the University of 

California at Riverside (Contact: John Pisano, Senior Research Engineer, 951-660-0585). An 

EPRI report describing all these tests and results was published and is available, the EPRI report 

and number is: A Continuous Emission Monitor For Hydrochloric Acid: Tunable Diode Laser 

Test Report, Unisearch LasIR S Series. EPRI, Palo Alto, CA: 2011 1022083, December 2011. 

Chuck Dene of EPRI is currently working with the legal department of EPRI to provide this 

document to EPA in support of this performance specification.

The instrument was tested for the following:

• Zero response

• Minimum detection limits

• Zero drift

• Linearity

• Precision

• Accuracy

• Possible interferences to known power plant flue gasses, as NO, SO2, CO, CO2, H2O and 

NH3.

Tests were conducted over a range of temperatures known to exist at typical coal fired power 

plant flue gas sampling locations for CEMS applications.

The overall testing clearly showed that the TDL technique is viable for low level, non-interfered 

measurements of HCl, in power plant applications of typical flue gasses for CEMS applications, 

but these tests were done in a sample cell.

The major advantage to the TDL technique is because it is an extremely narrow wavelength 

measuring technique in the near infrared, at wavelengths that are typically used for optical 

communication; it is easy to make a system that can measure the flue gas in-situ. However this 

opens up complexities on how to verify zero and span when the instrument is measuring in a 

cross duct configuration and not extractive into a sampling cell.

An examination the TDL technique for coal fired power plant applications follows. TDLAS 

(Tunable diode laser spectroscopy) makes use of very narrow wavelength measurements and was 
11

shown not to respond to the major known other gasses in the flue stream, this is significantly

different than FTIR or NDIR which being broadband techniques are known to be affected by 

these other gasses, and must inherently be corrected for perturbations caused by response to 

these possible interfering gasses.

It is our opinion that these techniques should be zeroed with gasses that are representative of 

actual flue gas constituents, especially at least H2O which has such a strong and wide infrared 

signature.

C.3 Curve fitting Techniques for IR Spectroscopy

Curve-fitting methods yield significant information of complex spectra provided that the bands 

measured in the system can be isolated (FTIR technique), or there are single line measurements, 

which is the case for HCl, used in this TDL approach.

Typically for FTIR analysis there are a number of bands and their positions can be accurately 

determined by fitting a highly deconvoluted spectrum. The procedure for FTIR used is in effect:

1. Fourier self-deconvolution of the experimental spectrum after base line correction,

2. Curve fitting of the (highly) deconvoluted spectrum, 

3. Curve fitting of the original spectrum using the data set obtained from the previous 

step,

4. Check for similarity between the deconvoluted fitted and deconvoluted experimental 

spectrum using the similar parameters.
12

Figure 2

Show the Hi Tran Database Spectra, for HCl, in the 5500 to 6000 cm-1 NIR spectral range, 

showing discrete absorption lines.

This technique results in minimizing the residuals which are primarily from the effect of having 

possible interfering gasses as well as looking at multiple lines, however in the TDL case if there 

are no interfering gasses and it is a single line measurement, then the residual is primarily noise 

from the detector, or optical noise which may include laser noise, etalon fringing from optical 

elements or other noise generating effects from inline optical elements as multiplexers, beam 

splitters lenses and mirrors.

As previously discussed, FTIR analysis requires the incorporation of curve fitting routines to 

minimize residuals that are likely from interfering gasses as well as having a spectral response of 

the target gas that incorporates many absorption lines.

In spectroscopy, the Voigt profile is a line profile resulting from the convolution of two 

broadening mechanisms, one of which alone would produce a Gaussian profile (usually, as a 

result of the Doppler broadening), and the other would produce a Lorentzian profile. Voigt 

profiles are common in many branches of spectroscopy 

All normalized line profiles can be considered to be probability distributions. The Gaussian 

profile is equivalent to a Gaussian or normal distribution and a Lorentzian profile is equivalent to 

a Lorentz or Cauchy distribution. 
13

Without loss of generality, we can consider only centered profiles which peak at zero. The Voigt 

profile is then a convolution of a Lorentz profile and a Gaussian profile:

(4)

where x is frequency from line center, is the centered Gaussian profile:

(5)

and is the centered Lorentzian profile:

(6)

The defining integral can be evaluated as:

(7)

where Re[w(z) ] is the real part of the complex error function of z and

(8)

Figure 3 shows the difference between a Gaussian, a Lorentzian and a Voigt profile using the 

same values for Y0, x0 and Δx (linewidth) (Bradley, TECO App. Note 50733). The sharper 

maximum and long wings of the Lorentzian are clearly seen. Typically the Lorentzian profile 

works best for gases, The Voigt profile is similar to the G-L, except that the line width Δx of the 

Gaussian and Lorentzian parts are allowed to vary independently. 
14

Figure 3

Show the difference between a Gaussian, a Lorentzian and a Voigt profile, using the same 

values for Y0, x0 and Δx (linewidth). 

Figure 4 shows the influence of altering the percent Lorentzian character of a G-L profile, 

leaving everything else unchanged. The wings grow with A, and the line narrows near the top. 

(Bradley, TECO App. Note 50733).

So these curve fitting routines minimizing the residual, similar to that used in FTIR analysis, can 

easily be applied to TDLAS as long as the signal analyzed is a direct absorption signal. 

Many TDL instruments look at derivative spectroscopy, employing modulation techniques as 

wavelength modulation (WMS) or two tone FM modulation (TTFM), which improve signal to 

noise but would obviate the use of this curve fitting technique.
15

Figure 4

Shows the influence of altering the percent Lorentzian character of a G-L profile, leaving 

everything else unchanged.

By applying these curve fitting techniques to a direct absorption TDL measurement, it is possible 

to approximate zero even if there is always some HCl present in the flue gas, which leads to a 

way to do in-situ zeroing as well as estimation of the minimum detection limits from the data 

recorded during the in-situ zeroing.

C.4 In-situ zeroing of TDLAS for Direct Absorption Measurements

In the case of a direct absorption TDL measurement the following can alter the zero response:

• Interfering gasses, but the TDL response has been shown to be unaffected by the other 

gasses in the coal-fired power plant stream

• Optical noise type 1, as etalons, which are inherently periodic. Such noise is fairly 

consistent to wavelengths just prior to the HCl absorption, through the HCl absorption 

and just past the HCl absorption. Therefore looking at the signal just prior and just after 

the HCl wavelength should approximate a background which can be subtracted from the 

whole spectrum, and yield a close approximation to zero.

• Optical noise type 2, as from the laser itself and the optical elements in the system, this 

noise is consistent from measurement to measurement, as the laser conditions are the 

same as are the physical properties of the optical elements including their location within 

the optical stream.

• Electrical noise, basically the detector itself and coaxial return line, however we know 

that the detector response is fairly flat over the narrow band that we are measuring at so 
16

looking at noise prior to the HCl absorption and after should also be consistent from the 

detector at the peak HCl absorption wavelength.

Spectroscopy can be defined as the investigation of the interaction that occurs between a defined 

source of electromagnetic radiation with a target sample. The way the emissive properties of the 

radiant source, usually some sort of light emitter, are perturbed depends upon the sample 

investigated. For example, molecules of gas can absorb electromagnetic radiation leading to 

molecular vibrations or rotations, with the frequencies that are absorbed by quantum theory 

being unique to each different type of molecule in the target sample. Therefore each molecule 

has its own characteristic absorption pattern over the electromagnetic spectrum. 

Spectroscopic methods basically evaluate the concentration of the molecule investigated through 

Beer-Lambert’s Law, which states that the fraction of light intensity transmitted through a gas is 

given by:

I(λ)/Io(λ) = exp (-φ(λ))NL) (9)

Where I and I o are the transmitted and incident powers respectively, L is the absorption path 

length (cm), and N would then be the concentration of the absorbing molecules in number of 

molecules per cubic centimeter. In this example φ(λ) is the wavenumber dependent absorption 

cross section in square centimeters per molecule. 

For direct absorption measurements, equation (9) is solved directly 

Tunable diode laser spectroscopy tunes the laser by locking the temperature of the laser and 

changes the emission wavelength by changing the current in small discrete steps (current ramp) 

y(I), that permit scanning the absorption feature prior to the absorption, into the absorption and 

after the absorption. In this manner the whole absorption feature is scanned. Since the laser 

output power changes as well as the emission wavelength with the current ramp, this requires 

that a function be applied to normalize the Io(λ) over the whole tuning range during each 

individual ramp cycle. This is achieved by fitting a polynomial f(λ) using a standard Least 

Squares Fit. This polynomial, however, will only fit for the I0 background of the laser, and to 

achieve a better fit when there is target gas present, ensuring the I(λ), measured in solving the 

beer-lambert law (equation 9) is normalized over the ramp scan cycle, both a polynomial for I0

and the Lorentzian component of the Voigt profile, are employed to generate a better fit. In this 

manner when solving the beer-lambert law we are employing a best fit for I(λ) (which requires 

the polynomial and Lorentzian) fit to Io(λ) (which has just the polynomial fit). The full 

mathematics showing how this is done is proprietary at this time but to elaborate, the relationship 

y(I)/f(λ) is minimized which results that the data is normalized for Io(λ). Subsequently the 

measured response I(λ) is then fit with the same polynomial and the Lorentzian function to yield 

comparable results used in the solving of the beer-lambert equation. What is significant from this 

is the residual component over the region where there is no target gas absorption is similar in 

both size and nature to the region where there would be target absorption. 
17

Basically by monitoring the residual as a function of wavelength Ri(λ) and subtracting this from 

the next residual measured Ri+1(λ) which is currently done as with each scan a residual is 

obtained, than this previous residual can be removed from the next scan and values can be 

calculated from this. By removing the previous residual from the current spectrum after the 

values have been calculated, systemic errors that are introduced by keeping a running average of 

the perceived background are prevented. This in effect leads to having a zero measurement for 

each scan which is just a subtraction of the measured residuals. This has been shown to be 

similar whether there is HCl present or not, simply by comparing residuals when there is no HCl 

(true zero) to when there is HCl (residual over the range where there is no HCl absorption only). 

Since the major source of noise which can bias the zero is optical noise, and this tends to be more 

random, and is a sum of differing sine waves, then the source of any perturbation of zero will be 

minimized by looking at these residuals.

Applying the testing to establish how close the residuals from background subtraction of the 

observed response at nominal values of HCl for a typical measurement path in a power plant 

compares to having a sample with zero HCl only have been performed and are shown in figure 5. 

If anything when there is indeed signal present the residuals are smaller. This is likely an artifact 

of the fitting routine but does indicate that the residuals may be employed as a measure of the 

background variation due to optical and electronic components of the noise. Results have shown 

an enhanced minimum detection limit as defined in section A of this report.
18

Figure 5

Shows the case of applying the residual determination using the described technique and

how close the residuals from background subtraction of the observed response at nominal 

values of HCl for a typical measurement path in a power plant compares to having a 

sample with zero HCl only. 

C.5 Determination of In-situ Minimum Detection Limits

As discussed in section B1, the limit of detection we obtain for the concentration detection limit 

has the following relationship

[C]D = 3sb/b1 (10)

Where

sb is the standard deviation
19

b1 is the slope of the calibration curve, analytically identified as sensitivity.

The estimate of the standard deviation is possible by the analysis of repeated measurements of 

the blank/zero experiments. 

For the way described to measure the zero in-situ, this approach was adopted to evaluate the 

limit of detection for near infrared tunable diode laser spectroscopy (NIR TDLAS) but not just 

for zero values of HCl but over the range of expected HCl concentrations. In this way we can 

present results of zero with and without HCl present, noting how they compare and in summation 

yield minimum detection limits. This is the only way to validate whether in-situ zeroing as 

described achieves consistent levels whether HCl is present or not.

The experimental procedure was to inject standard calibration gas through a dilution system into

a sample cell to generate four levels of HCl gas as listed in Table 3 below. The temperature was 

ambient 25°C into a 3-meter sample cell. 

Table 3: HCl Levels Chosen for In-situ Zeroing Analysis

HCl HCl measured Reason for 

generated measured level chosen

(ppmV) (ppmV)

0.0 -0.03 Zero

0.4 0.39 Low Level

3.8 3.77 Mid-level

15.0 14.97 High Level

Four values, 0.0 ppmV (zero gas), 0.4 ppmV (approximating low level measurements observed 

at a power plant site), 3.8 ppmV (approximating high levels observed at a power plant site), and 

15 ppmV (approximating the high end of the range of measurements used to verify calibration at 

a power plant site) were selected to do the evaluation.

Figure 6 shows the measurement of the 15 ppmV value along with the residual measured 

concurrently at the 15 ppmV value, so in effect it shows the instrument response to 15 ppmV of 

HCl in the sample cell and the residual value after doing the curve fitting described in C4.

Looking at the plots, the blue spectra represents the measured HCl spectrum on the left side, and 

on the right shows the residual of the measured spectra after doing the curve fitting routine.

As can be seen we see a strong response to the 15 ppmV sample cell value, measured to be 14.97 

ppmV, the correlation coefficient R, which is how close the measured sample fits via a least

squares analysis over the entire measured response to that of the reference (which is in red) is 

1.00 indicating a virtually perfect fit. Also indicated is the full width height maximum, FWHM, 
20

of the measured spectrum for HCl (7.81 in this case). Also note the parameters for the reference 

signal in red, are also listed for each measurement. The reference cell has a known amount of 

HCl to yield an average ppmV level, it is concurrently measured with the signal as a check, this 

is easily done in a fiber coupled system as the laser output is beam split 5% to the reference and 

95% to the signal path. The reference parameters are laser temperature 26.705-26.709 °C, laser 

current 66.958-66.966 mA, reference cell value (the reference has a known value of HCl and is 

always cross checked concurrently with the measured signal) 8.96 to 8.98 ppmV. The slight 

differences in the reference parameters shown are that the residual shown included the previous 

measurement of the residual to the measured signal, basically if the sampling interval is 1 

second, then when the signal is measured at time (t1), then the residual shown would be at time t1

as well but includes the subtracting of the residual at time t1-i where i is the sampling interval.

The parameters listed then include the average of both in that short interval on the right side.

Figure 6

Shows the case of applying the residual determination using the described technique and

comparing it to a real-time measurement of HCl. The concentration for this case of HCl 

is at the high end, being approximately 15 ppmV.

The residual value is not shown on the display, only the HCl measured in the sample cell is 

shown, but the residual is recorded in the software.

For this measurement the residual was measured to be 0.017 ppmv.

Figure 7 shows typical residual plots for all concentrations used in this analysis, again the blue 

numbers indicate the measured concentration observed and do not list the residual values, but 

these are stored in the data. As can be observed in the plots the magnitude and form of the 

residuals are very similar independent of the amount of HCl that is present, the standard zero 

measurement (top left, with zero air injected) was of the same order as the residual 

Measured 

14.97 ppmV 

signal (blue)

Measured 

0.017 ppmV 

residual (blue)

Measured 

reference 

8.96 ppmv

(red)

Measured 

reference 

8.98 ppmv

(red)
21

measurements taken when HCl was present, (top right 0.4 ppmV, bottom left 3.8 ppmV and 

bottom right 15 ppmV). This proves a strong argument that by looking at the residual for these 

very narrow single line TDL direct absorption measurements, zeroes can be evaluated 

independently of the amount of HCl within the prescribed dynamic range of the system.

Figure 7

Shows the case of applying the residual determination using the described technique and 

comparing it to a real-time measurement of HCl, over the four levels of HCl chosen, 

these being the standard zero measurement (top left, with zero air injected) 0.4 ppmV 

HCl (top right), 3.8 ppmV HCl (bottom left) and 15 ppmV HCl (bottom right). 
22

Since most power plant applications require 1-minute averages, two tests are included in this 

report.

The first shows one-minute averages for each of the four levels of HCl chosen to investigate for 

one-hour periods, an example of this test for the 0.4 ppmV is included in figure 8. From the data 

compiled from these tests, with 60 one-minute samples, table 4 was generated. The second 

shows normal zero drift data taken during the EPRI evaluation which was for ten tests over a 

one-week period.

Figure 8

Shows the case of applying the residual determination using the described technique and 

comparing it to a real-time measurement of HCl, over the 0.4 ppmV HCl for one-hour 

period taking measurements every minute. This was done for all four HCl measured 

values with results listed in Table 4. 
23

Table 4: HCl Results for Minimum Detection Limit Determination With and Without 

HCL present over the Four Chosen HCl Values.

Average Average Minimum Maximum St. Dev. MDL MDL

HCl HCl measured HCl residual HCL residual HCl residual HCl residual from from

generated measured measured measured measured measured St. Dev. St. Dev.

(ppmV) (ppmV) (ppmV) (ppmV) (ppmV) (ppmV) (ppmV) (ppmV-m)

0.0 -0.17 -0.17 -0.22 -0.14 0.02 0.06 0.18

0.4 0.41 0.02 0.00 0.07 0.02 0.06 0.18

3.8 3.83 0.01 0.00 0.04 0.02 0.06 0.18

15.0 14.93 0.02 -0.01 0.04 0.02 0.06 0.18

The since the tests were done in a 3-meter sample cell at 25 °C, the results are listed in the last 

column of the table as ppmV per meter, basically as the pathlength of the sample is extended, the 

system becomes more sensitive. These results show that independent of the HCl concentration, 

the minimum detection limit is 0.18 ppmV-meter of path sampled, the same as arrived at with the 

zero sample gas. These results also show by using this technique it almost doubles the 

sensitivity at the low end as compared to the laboratory tests (refer back to table 2) where the 

MDL’s at 25 °C per meter were determined to 0.35 ppmV which did not incorporate the residual 

algorithm. .

The zero drift results over a 1-week period, for 10 independent tests of zero, showed no greater 

drift than 1.7% on an average zero at each of the temperature ranges measured, these being at 25 

°C, 100 °C and 200 °C (refer to the EPRI HCl report).

D. In-situ SPAN Checks for NIR TDLAS Spectroscopy

D.1 In-situ Span Checks

Span is relatively easy, since the laser output is fiber coupled, inserting a flow through cell 

(calibration cell) in the path is a viable procedure, the cell having a pathlength xc, at a 

temperature Tc can trivially be ratioed to the pathlength xs of the in-situ measurement (stack) 

which is at a temperature Ts by spiking the cell over the typical measurement range with the 

introduction of certified HCl gas. A background would be taken just prior to the spike test, 

eliminating any external HCl contribution, since the response is extremely fast the contribution 

from the flow through cell would only be measured. This also tests the integrity of the whole 

optical system.
24

D.2 Audit Cell Verification

Sealed audit cells are available for most gasses, which are designed to be added as an in-line 

audit module to typically verify calibration, but are typically not used for daily span checks. The 

reason is it is difficult to make an audit cell better than 5% accuracy.

Here is an example of an audit cell used for NH3 verification of calibration. A Unisearch Model: 

LAS-AM-NH3 was used in this project to conduct on-site verification of calibration. The audit 

module was designed to allow the user to check the sensitivity of the analyzer without the need 

for calibration gas cylinders and flowing gas streams. The audit module concentration was 

warranted to be within +/- 5% of the stated value for a period of at least one year from date of 

shipment. The cell was expected to be stable for a minimum of five years for ammonia. 

As shown in Figure 9, the audit module has two FC/APC ports on the front panel. In order to 

perform an in-line audit of the system the laser beam was directed through both the audit module 

and the measurement path in series. The ammonia concentration measured on the monitor 

display represents both the ammonia concentration in the measurement path plus the ammonia in 

the audit cell. Thus, the audit module introduces an ammonia ‘spike’ to the measurement path, 

which can then be compared against a known response based on measurement and audit cell 

temperature, pressure, measurement path length, and ammonia concentration within the audit 

cell. 

Figure 9

Audit cell Linked in Series with LasIR Ammonia Monitor 

The ammonia concentration value displayed on the ammonia monitor with the audit cell inserted 

in series with the measurement path will then be the sum of the concentration of the gas in the 

flue gas stream plus the audit cell value, corrected to the same temperature, pressure, and path 
25

length. Thus, if the flue gas ammonia concentration was 1 ppmV, the total ammonia 

concentration displayed with the audit cell inserted used for this example would be 30.6 ppmV. 

The insertion of the audit cell in line does cause a significant loss in signal return power of 

approximately 30%.

A background was taken prior to insertion of the audit cell, with background subtraction used to 

correct resultant ammonia slip readings with measured ammonia slip values of 0.40 ppmV. 

The insertion of the audit cell showed high confidence in the calibration of the system, as it went 

to a value of 29.43 ppmV (Figure 4-2), which means the audit cell added 29.03 ppmV of NH3.

This is well within the 5% stated accuracy value of the audit cell, which was calculated to add 

29.6 ppmV of NH3.

Although this is not a substitute for a continuous in-line module that can do spans by simply 

adding calibration gas, it has been shown to be accurate within 5% in numerous applications and 

may be worth considering as an alternative for systems where having certified calibration gas on 

site may be difficult.
26

E: References:

Bradley M., Curve Fitting in Raman and IR Spectroscopy: Basic Theory of Line Shapes and 

Applications, Thermo Fisher Scientific Application Note 50733, Madison, WI, USA

Dene C., J. Pisano, K. Bumiller, T. Durbin, “Continuous Emission Monitor For Hydrochloric 

Acid: Tunable Diode Laser Test Report - Unisearch LasIR S Series” (EPRI, Palo Alto, CA: 2011 

1022083)

Grant WB, Kagann RH, McClenny WA. “Optical remote measurement of toxic gases” J Air 

Waste Manage Assoc. 1992 Jan;42(1):18-30.

Himes R., J.Pisano and T. Durbin, “Ammonia Lab Test Verification – LTG Lightwise TDL 

Monitor” (EPRI, Palo Alto, CA: 2009 1019372) 

Himes R., J.Pisano and T. Durbin, “Ammonia Lab Test Verification – Siemens LDS 6” (EPRI, 

Palo Alto, CA: 2007 1014745) 

Himes R., J.Pisano and T. Durbin, “Ammonia Lab Test Verification – Norsk Electo Optik’s 

(NEO) LaserGas II” (EPRI, Palo Alto, CA: 2007 1015315) 

Himes R., J.Pisano and T. Durbin, “Ammonia Lab Test Verification – Opsis LD500”(EPRI, Palo 

Alto, CA: 2007 1016116) 

Himes R., J.Pisano and T. Durbin, “Ammonia Lab Test Verification – Unisearch LasIR SM410” 

(EPRI, Palo Alto, CA: 2006 1014664)