The results for FACTS allocations and sizes
\r\n\tThe aim of this book will be to describe the most common forms of dermatitis putting emphasis on the pathophysiology, clinical appearance and diagnostic of each disease. We also will aim to describe the therapeutic management and new therapeutic approaches of each condition that are currently being studied and are supposed to be used in the near future.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"278931ae110500350d8b64805c70f193",bookSignature:"Dr. Eleni Papakonstantinou",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7934.jpg",keywords:"Atopic eczema, Interleukin, Topical corticosteroids, Hand eczema, Blisters, Pruritus, Irritant contact dermatitis, Allergic contact dermatitis, Discoid eczema, Sebaceous glands, Inflammatory dermatitis, Facial rash",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 5th 2019",dateEndSecondStepPublish:"March 19th 2019",dateEndThirdStepPublish:"May 18th 2019",dateEndFourthStepPublish:"August 6th 2019",dateEndFifthStepPublish:"October 5th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"203520",title:"Dr.",name:"Eleni",middleName:null,surname:"Papakonstantinou",slug:"eleni-papakonstantinou",fullName:"Eleni Papakonstantinou",profilePictureURL:"https://mts.intechopen.com/storage/users/203520/images/system/203520.jpg",biography:"Dr. med. Eleni Papakonstantinou is a Doctor of Medicine graduate and board certified Dermatologist-Venereologist. She studied medicine at the Aristotle University of Thessaloniki, in Greece and she continued with her dermatology specialty in Germany (2012-2017) at the University of Magdeburg and Hannover Medical School, where she completed her dissertation in 2016 with research work on atopic dermatitis in children. During this time she gained wide experience in the whole dermatological field with special focus on the diagnosis and treatment of chronic inflammatory skin diseases and also the prevention and treatment of melanocytic and non-melanocytic skin tumors. Her research interests were beside atopic dermatitis and pruritus also the pathophysiology of blistering dermatoses. In addition to lectures at german and international congresses, she has published several articles in german and international journals and her work has been awarded with various prizes (poster prize of the German Dermatological Society for the project: 'Bullous pemphigoid and comorbidities' (DDG Leipzig 2016), 'Michael Hornstein Memorial Scholarship' (EADV Athens 2016), travel grant (EAACI Vienna 2016). Since 2017, she works as a specialist dermatologist in private practice in Dortmund, in Germany. Parallel she co-administrates an international dermatologic network, Wikiderm International and she writes a dermatology public guide for patients, as she is convinced that evidence-based knowledge has to be shared not only with colleagues but also with patients.",institutionString:"Private Practice, Dermatology and Venereology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"270941",firstName:"Sandra",lastName:"Maljavac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/270941/images/7824_n.jpg",email:"sandra.m@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Moreover, because of restructuring, the problem of power system security has become a matter of concern in deregulated power industry. Better utilization of available power system capacities by Flexible AC Transmission Systems (FACTS) devices has become a major concern in power systems too.
FACTS devices can control power transmission parameters such as series impedance, voltage, and phase angle by their fast control characteristics and continuous compensating capability. They can reduce flow of heavily loaded lines, resulting in low system losses, improved both transient and small signal stability of network, reduced cost of production, and fulfillment of contractual requirement by controlling the power flow in the network. They can enable lines to flow the power near its nominal rating and maintain its voltage at desired level and thus, enhance power system security in contingencies [1-6]. For a meshed network, an optimal allocation of FACTS devices allows to control its power flows and thus, to improve the system loadability and security [1].
The effect of FACTS devices on power system security, reliability and loadability has been studied according to proper control objectives [4-14]. Researchers have tried to find suitable location for FACTS devices to improve power system security and loadability [13-16]. The optimal allocation of these devices in deregulated power systems has been presented in [17-18]. Heuristic approaches and intelligent algorithms to find suitable location of FACTS devices and some other applications have been used in [15-21].
In this chapter, a novel heuristic method is presented based on Harmony Search Algorithm (HSA) to find optimal location of multi-type FACTS devices to enhance power system security and reduce power system losses considering investment cost of these devices. The proposed method is tested on IEEE 30-bus system and then, the results are presented.
In this chapter, we select three different FACTS devices to place in the suitable locations to improve security margins of power systems. They are TCSC (Thyristor Controlled Series Capacitor), SVC (Static VAR Compensator), and UPFC (Unified Power Flow Controller) that are shown in Fig. 1.
Power flow through the transmission line i-j namely
TCSC can change line reactance, and SVC can control the bus voltage. UPFC is the most versatile member of FACTS devices family and controls all power transmission parameters (i.e., line impedance, bus voltage, and phase angles). FACTS devices can control and optimize power flow by changing power system parameters. Therefore, optimal device and allocation of FACTS devices can result in suitable utilization of power systems.
Models of FACTS Devices (a) TCSC, (b) SVC and (c) UPFC
In this chapter, steady-state model of FACTS devices are developed for power flow studies. TCSC is simply modeled to modify just the reactance of transmission lines. SVC and UPFC are modeled using the power injection models. Therefore, SVC is modeled as shunt element of transmission line, and UPFC as decoupled model. A power flow program has been developed in MATLAB by incorporating the mathematical models of FACTS devices.
TCSC compensates the reactance of the transmission line. This changes the line flow due to change in series reactance. In this chapter, TCSC is modeled by changing transmission line reactance as follows:
where,
SVC can be used for both inductive and capacitive compensation. In this chapter, SVC is modeled as an ideal reactive power injection at bus
Two types of UPFC models have been studied in the literature; one is the coupled model [28], and the other the decoupled type[29-31]. In the first, UPFC is modeled with series combination of a voltage source and impedance in the transmission line. In the decoupled model, UPFC is modeled with two separated buses. The first model is more complex than the second one because the modification of the Jacobian matrix is inevitable. In conventional power flow algorithms, we can easily implement the decoupled model. In this chapter, the decoupled model has been used to model the UPFC as in Fig. 2.
UPFC controls power flow of the transmission lines. To present UPFC in load flow studies, the variables
Although UPFC can control the power flow but, it cannot generate the real power. Therefore, we have:
Reactive power output of UPFC,
Decoupled model for UPFC
The security index for contingency analysis of power systems can be expressed as in the following [32-33]:
Here we have:
Harmony Search Algorithm (HSA) has recently been developed in an analogy with music improvisation process, where music players improvise the pitches of their instruments to obtain better harmony [34]. The steps in the procedure of harmony search are as follows [35]:
Step 1: Initialize the problem and algorithm parameters
Step 2: Initialize the harmony memory
Step 3: Improvise a new harmony
Step 4: Update the harmony memory
Step 5: Check the stopping criterion
The next following five subsections describe these steps.
Initialize the problem and algorithm parameters
In step 1, the optimization problem is specified as follows:
where, f(x) is the objective function, g(x) the inequality constraint function, and h(x) the equality constraint function. x is the set of each decision variable
Initialize the harmony memory
In step 2, the HM matrix is filled with as many randomly generated solution vectors as the HMS in the following:
Improvise a new harmony
A new harmony vector,
For example, an HMCR of 0.85 indicates that the HS algorithm will choose the decision variable value from historically stored values in the HM with 85% probability or from the entire possible range with (100–85) % probability. Every component obtained by the memory consideration is examined to determine whether it should be pitch-adjusted. This operation uses the PAR parameter, which is the rate of pitch adjustment as follows:
The value of (1-PAR) sets the rate of doing nothing. If the pitch adjustment decision for
where, bw is an arbitrary distance bandwidth and rand () is a random number between 0 and 1.
In step 3, HM consideration, pitch adjustment or random selection in turn is applied to each variable of the new harmony vector.
Update harmony memory
If the new harmony vector
Check stopping criterion
If the stopping criterion (maximum number of improvisations) is satisfied, the computation terminates. Otherwise, steps 3, and 4 are repeated.
Using database of [32], cost function for SVC, TCSC, and UPFC shown in Fig. 3 are modeled as follows:
For TCSC:
For SVC:
For UPFC:
Here,
Cost Function of the FACTS devices: SVC, TCSC, and UPFC
The goal of the optimization algorithm is to place FACTS devices in order to enhance power system security-level considering cost function of FACTS devices. These devices should be placed to prevent congestion in transmission lines and transformers, and to maintain bus voltages close to their reference values. Security index introduced in section III has been used in objective function considering cost function of FACTS devices and power system losses. Fitness function
The coefficient
Simulation studies are carried out for different scenarios in the IEEE 30-bus power system. Five different cases have been considered:
Case 1: power system normal operation (without installation of FACTS devices),
Case 2: one TCSC is installed,
Case 3: one SVC is installed,
Case 4: one UPFC is installed, and
Case 5: Multi-type (TCSC, SVC, and UPFC) FACTS devices are installed.
The first case is the normal operation of network without using any FACTS device. In the second, third, and fourth cases, installation of only one device has been considered. Each device is placed in an optimal location obtained by HSA. Multi-type FACTS devices installation is considered in the 5th scenario. In this case, three different kinds of FACTS devices have been considered to be placed in optimal locations to enhance power system security.
The performance index evolutions of implemented methods are shown in Fig.4, and Fig.5. The average, and maximum performance indices are shown in Fig.4. Tables 1, and 2 show optimal locations of devices for different cases. These results illustrate that the installation of one device in the network could not lead to improved security of power system and reduction in power system losses simultaneously, and that multi-type FACTS devices should be placed in optimal locations to improve security margins and reduce losses in the network.
DeviceType | \n\t\t\t\n\t\t\t\tUPFC\n\t\t\t | \n\t\t\t\n\t\t\t\tTCSC\n\t\t\t | \n\t\t\t\n\t\t\t\tSVC\n\t\t\t | \n\t\t|||
\n\t\t\t\tSize/Location\n\t\t\t | \n\t\t\tSize(MVA) | \n\t\t\tLocation(Bus No-Bus No) | \n\t\t\tSize(MVA) | \n\t\t\tLocation(Bus No-Bus No) | \n\t\t\tSize(MVA) | \n\t\t\tLocation(Bus No.) | \n\t\t
TCSC | \n\t\t\t- | \n\t\t\t- | \n\t\t\t90.6 | \n\t\t\t1-2 | \n\t\t\t- | \n\t\t\t- | \n\t\t
SVC | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t39 | \n\t\t\t1 | \n\t\t
UPFC | \n\t\t\t48.3 | \n\t\t\t12-15 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t
Multi-type | \n\t\t\t75.9 | \n\t\t\t12-15 | \n\t\t\t73.1 | \n\t\t\t2-5 | \n\t\t\t66.7 | \n\t\t\t1 | \n\t\t
The results for FACTS allocations and sizes
\n\t\t\t\tScenario\n\t\t\t | \n\t\t\t\n\t\t\t\tJP\n\t | \n\t\n\t\tJV\n | \n\n\t($) Cost\n | \n\n\tLosses (MVA)\n | \n
1 | \n\t3.45 | \n\t24.2 | \n\t- | \n\t28.36 | \n
2 | \n\t3.39 | \n\t19.6 | \n\t9197500 | \n\t22.51 | \n
3 | \n\t3.33 | \n\t19.1 | \n\t4521600 | \n\t26.3 | \n
4 | \n\t3.21 | \n\t16.4 | \n\t8501600 | \n\t23.09 | \n
5 | \n\t3.09 | \n\t10.5 | \n\t27897000 | \n\t20.49 | \n
Simulation results for different cases
Performance index evolution (average of fitnesses in every iteration)
Performance index evolution (maximum of fitnesses in every iteration)
Nowadays, dc-to-ac inverters are widely used in industry. All applications are mainly divided into two general groups; 1- Electric drives for all ac motors when dc supply is used, and 2- in systems including high voltage direct current (HVDC) transmission systems, custom power and flexible ac transmission systems (FACTS) devices, flexible distributed generation (FDG), and interconnection of distributed generation (DG) units to a grid. Several switching algorithm such as pulse width modulation (PWM), sinusoidal pulse width modulation (SPWM), space-vector modulation (SVM), selective harmonic eliminated pulse width modulation (SHEPWM), or programmed-waveform pulse width modulation (PWPWM) are applied extensively to control and determine switching angles to achieve the desired output voltage. In the recent decade, a new kind of inverter named multi-level inverter has been introduced. In various publications, this inverter has been used in place of the common inverters to indicate its advantages in different applications. Being multi-level, it can be used in high-power and high-voltage applications. In order to reach the desired fundamental component of voltage, all of various switching methods produce harmonics and hence, it is of interest to select the best method to achieve minimum harmonics and total harmonic distortion (THD). It is suggested to use optimized harmonic stepped waveform (OHSW) to eliminate low order harmonics by determining proper angles, and then removing the rest of the harmonics via filters. In addition, this technique lowers switching frequency down to the fundamental frequency and consequently, power losses and cost are reduced.
Traditionally, there are two states for DC sources in multi-level inverters: 1- Equal DC sources, 2- Non-equal DC sources. Several algorithms have been suggested for the above purposes. In [37] Newton-Raphson method has been used to solve equations. Newton-Raphson method is fast and exact for those modulation indices (M) that can satisfy equations, but it cannot obtain the best answer for other indices. Also, [38] has used the mathematical theory of resultants to find the switching angles such that all corresponding low-order harmonics are completely canceled out sequentially for both equal and non-equal DC sources separately. However, by increasing levels of multi-level converters, equation set tends to a high-order polynomial, which narrows its feasible solution space. In addition, this method cannot suggest any answer to minimize harmonics of some particular modulation indices where there is no acceptable solution for the equation set. Genetic algorithm (GA) method has been presented in [39] to solve the same problem with any number of levels for both eliminating and minimizing the harmonics, but it is not fast and exact enough. This method has also been used in [40] to eliminate the mentioned harmonics for non-equal DC sources. Moreover, all optimal solutions have used main equations in fitness function. This means that the fundamental component cannot be satisfied exactly.
Here, a harmony search (HS) algorithm approach will be presented that can solve the problem with a simpler formulation and with any number of levels without extensive derivation of analytical expressions. It is also faster and more precise than GA.
The cascaded multi-level inverter is one of the several multi-level configurations. It is formed by connecting several single-phase, H-bridge converters in series as shown in Fig. 1a for a 13-level inverter. Each converter generates a square-wave voltage waveform with different duty ratios. Together, these form the output voltage waveform, as shown in Fig. 1b. A three-phase configuration can be obtained by connecting three of these converters in
Fig. 6b shows a 13-level inverter, where
where,
There are two approaches to adjust the switching angles:
Minimizing the THD that is not common, because some low order harmonics may remain.
Canceling the lower order harmonics and removing the remained harmonics with a filter.
The second approach is preferred. For motor drive applications, it is necessary to eliminate low order harmonics from 5 to 17. Hence, in this section, a 13-level inverter is chosen to eliminate low-order harmonics from 5 to 17. It is not needed to delete triple harmonics because they will be eliminated in three-phase circuits. Thus, for a 13-level inverter, Eq. (17) changes into (18).
a) Multi-Level Inverter b) Multi-Level waveform generation
Here,
It is necessary to determine six switching angles, namely
In order to optimize the THD, genetic algorithm (GA) that is based on natural evolution and population is implemented. This algorithm is usually applied to reach a near global optimum solution. In each iteration of GA (referred as generation), a new set of strings (i.e. chromosomes) with improved fitness is produced using genetic operators (i.e. selection, crossover and mutation).
4.75 | \n\t\t13.02 | \n\t\t30.26 | \n\t\t43.55 | \n\t\t87.36 | \n\t\t89.82 | \n\t
A typical chromosome
Chromosome’s structure
Chromosome structure of a GA is shown in table 1 that involves
Selection
The method of tournament selection is used for selections in a GA [41-42]. This method chooses each parent by choosing
Cross Over
Crossover allows the genes from different parents to be combined in children by exchanging materials between two parents. Crossover function randomly selects a gene at the same coordinate from one of the two parents and assigns it to the child. For each chromosome, a random number is selected. If this number is between 0.01 and 0.3 [42], the two parents are combined; else chromosome is transferred with no crossover.
Mutation
GA creates mutation-children by randomly changing the genes of individual parents. In this section, GA adds a random vector from a Gaussian distribution to the parents. For each chromosome, random number is selected. If this number is between 0.01 and 0.1 [42], mutation process is applied; else chromosome is transferred with no mutation.
Harmony Search Algorithm (HSA) has been implemented based on the algorithm described in section 4 of the first part of this chapter [34-35].
a) output voltage waveform b) harmonic spectrum
Harmony Search algorithm has been used to solve the optimization problem. The objective function has been chosen as follows:
where,
In the first part of the presented chapter, we presented a novel approach for optimal placement of multi-type FACTS devices based on harmony search algorithm. Simulations of IEEE 30-bus test system for different scenarios demonstrate that the placement of multi-type FACTS devices leads to improvement in security, and reduction in losses of power systems.
In the second part, the harmony search algorithm was proposed for harmonic optimization in multi-level inverters. Harmony search algorithm has more flexibility than conventional methods. This method can obtain optimum switching angles for a wide range of modulation indices. This advantage is of importance, especially when the number of switching angles goes up, where equation set may not have any solution, or when it is solvable only for a short range of modulation indices. Moreover, the implementation of the harmony search algorithm is very straightforward compared to the conventional methods like Newton-Raphson, where it is necessary to calculate the Jacobean matrix. In addition, one of the most attractive features of intelligent algorithms is their independency from case studies. Actually, intelligent algorithm can be imposed to a variety of different problems without any need for extensive manipulations. For example, the harmony search algorithm and GA algorithms are able to find optimum switching angles in order to cancel out low-order harmonics, and if it is not possible to completely remove them, they can suggest optimum switching angles so that, low-order harmonics will be reduced as much as possible. Furthermore, with a little manipulation in the defined objective function, one can use HSA and GA as a tool for THD optimization. Also, the results indicate that, harmony search algorithm has many benefits over GA such as simplicity in the implementation, precision, and speed in global convergence.
The Power Generation industry, one of the growth engines for a nation, today face a unique challenge of market volatility and uncertainty. The industry needs to depend on a diverse set of fuel mix and ensure reliable delivery of power while providing asset level visibility (state of asset performance in real-time). It has to provide electricity to over 1 billion people globally for stable growth while ensuring reduced environmental footprint and improved efficiency. The requirement for real-time demand adjustments in response to supply conditions requires integration of real-time measurements, predictability and operational process optimization. This in-turn requires smarter combination of monitoring devices and analytics. The successful transformation depends largely on merger of physical and digital technologies. Real-time, non-invasive and in-situ sensing technologies are the main connects with the network of assets providing powerful data driven insights – a single unified automation architecture for the utility operators and owners.
Apart from air pollution control, emission measurement provides a deeper insight and characterization of a combustion process and control. There are two distinct categories of emission species from a power plant. CO2, H2O, N2 and O2 are the major species of emission and are present in percent concentrations, whereas, NOx, CO, SOx, Unburned Hydrocarbons (UHC) and Particulate Matters (PMs) constitutes the minor species and are present in parts per million (ppm) concentrations [1, 2]. Nitrogen oxide (NOx), carbon monoxide (CO) and sulfur oxide (SOx) the three most important anthropogenic air pollutants are formed during the combustion process in power generation industry (gas fired, coal fired and oil fired). The emission levels from outlet of the treatment systems is important as it outlines few key parameters for selecting flue gas monitoring systems based on the target application: Measurement range, Measurement uncertainty and accuracy, Purpose of measurement and Interfering species.
Spectroscopy-based system is one of the most versatile technologies available for real-time, non-invasive and accurate measurement of trace gases in a combustion environment or a complex gas mixture.
In a spectroscopic measurement system, one down-selects a target molecular transition (based on a “selection” criteria) of the gas species for analyzing the line strength, line shape (and effect of gas temperature and pressure) and estimation of concentration of the species in the gas mixture. Accurate information about the transition will provide users symptoms of any machine/process health issues, possible causes (when evaluated along with operating parameters), possible consequences (impact on service schedules) and possible mitigation methods. Like any measurement system we have the challenge of mitigation of systematic errors (biases) and random errors (white noise). Systematic errors or biases tend to shift the result (the target molecular transition) to one side. This is particularly important as a molecular transition of any gas species is always closely stacked along with the transitions of moisture (a common product from any combustion process). An error in this case will lead to merger of multiple transitions (or targeting a wrong transition!). Proper laser tuning is probably the most critical step in minimizing this error. Random errors are mostly contributed by noise from the detector circuit (
Near-Infrared (Near-IR) (wavelength range: visible to ∼3 μm) Tunable Diode Laser Absorption Spectroscopy (TDLAS) is a promising technology for real-time trace gas detection without intruding the flow field. This has applications in multiple fields like, environment monitoring, medical diagnostics, defense and law enforcement. TDLAS technique using Near-IR laser at room temperature monitors the overtones of the molecular transitions which have much weaker line strength than the fundamental transitions [3].
Scanned wavelength Direct absorption spectroscopy (DAS) typically involves irradiating the sample with a laser whose wavelength is periodically changed across a fixed range that is larger than the range at which the gas absorbs. The output intensity is measured as a function of wavelength. Figure 1a, shows a schematic of DAS. In DAS, the measured output is a dip in intensity at the absorption wavelength of the sample. This becomes difficult for samples with weak absorption lines, or very low gas concentrations (in which the change in fractional absorbance can be as low as 10−5), where one needs to measure a very small change in intensity riding on a large background intensity.
(a) Schematic representation of scanned wavelength direct absorption spectroscopy (DAS). (b) Schematic representation of wavelength modulation spectroscopy (WMS).
Wavelength Modulation Spectroscopy (WMS) or second harmonic detection is a way of increasing the sensitivity of absorption spectroscopy. It involves modulating the laser wavelength and detecting the signal at the second harmonic of the modulation frequency. Figure 1b shows a schematic of WMS technique. The benefits of using WMS as opposed to DAS are:
increased sensitivity,
insensitivity to interfering species that have broad absorption features in the region of interest,
Insensitivity to input intensity fluctuations,
Insensitivity to vibrations.
In WMS, the input wavelength is simultaneously scanned (ramp or triangular waveforms are typically used) and modulated with a sinusoidal waveform. The output intensity is demodulated at the modulation frequency (for first harmonic) and at twice the modulation frequency (for second harmonic). The gas concentration is calculated from the amplitude of the second harmonic signal.
Due to weaker line-strengths and therefore low absorption coefficients of gas species in Near-IR region, absorption spectroscopy in Near-IR has limitations in presence of complex gas mixtures, high pressure and high temperature environment. Availability of industrial grade Interband Cascade Lasers (ICLs) and Quantum Cascade Lasers (QCLs) has enabled application of TDLAS at Mid-Infrared (Mid-IR) for an accurate and real-time measurement of trace gas content at high temperature in a complex gas mixture environment.
The first generation QCLs (Quantum Cascade Lasers) operated only in pulsed mode and at ∼90 K temperature. Significant advancements in epitaxial layer growth processes using Molecular Beam Epitaxy (MBE) and Metallorganic Chemical Vapor Deposition (MOCVD) technologies, bandgap engineering opened the Mid-IR spectral region (wavelength range: 3–24 μm). Subsequently, the continuous single-mode tunability of the QCLs were achieved using tuning schemes like active and simultaneous tuning of grating angles, external cavity length and optical length of the laser chip (drive current tuning and or chip temperature tuning) [4, 5, 6, 7].
Gas detection and measurement at trace concentration levels like ppbv (parts per billion in volume) and sub-ppbv or pptv (parts per trillion in volume) requires targeting of strong fundamental roto-vibrational transitions (and hence large absorption coefficient) of the molecules of gas species in the Mid-IR spectral region [8]. Availability of compact, solid-state, high performance and low dissipation single-mode QCLs (Example: Output power 25 mW at 2226 cm−1, dissipated power 1 W [9]) enabled cost-effective usage in several industrial applications without the constraint of heavy packaging in the measurement system. Apart from the QCLs, improvement in detector technology using multi-stage Peltier cooled HgCdTe (Mercury Cadmium Telluride or MCT) Mid-Wavelength Infrared (MWLIR) and Long Wavelength Infrared (LWIR) detectors paved way for stable, fast response (time constantτ <2 ns [10]) and low noise detection in the entire Mid-IR spectral range (upto≈13 μm) [11, 12].
To isolate the QCL devices from any kind of inadvertent exposure to high temperature of the process gas, ease of servicing of the laser modules and ensuring Gaussian beam delivery, Chalcogenide glass (ChGs), Flouride glass, Sapphire and Silver Halide fibers were initially used for the transmission of Mid-IR lasers as it has wide optical transmission windows in the IR region. A major challenge with the fibers was their brittle nature and laser feedback due to back reflections from the fiber end which reduces signal-to-noise ratio [13, 14]. Last decade has witnessed rapid development of low-loss Hollow Core Waveguides (HCW) for transmission of the Mid-IR beams from the QCLs [15, 16, 17, 18, 19]. These are essentially a glass capillary tube with dielectric/metallic structure deposited inside the bore of the tube. Apart from possessing high coupling efficiencies (>95%) and high-power handling capabilities, the hollow core waveguides propagate single-mode [20].
Beam divergence, astigmatism are some of the common challenges one needs to address for all practical applications of Mid-IR spectroscopy [21]. In case of multiple gas species detection, beams from multiple QCL sources are combined along with a red laser (this will be discussed later in Section 4.3). A reflecting beam expander with silver mirror (Wavelength: 450 nm–20 μm, for example see Thorlabs Product Catalog [22]) is typically used in these cases. A reflective beam reducing optics is similarly used at the detection end to avoid the chromatic aberrations.
As mentioned previously, the wavelength tuning of QCLs consists of two methods: (1) temperature tuning, (2) injection current tuning. Temperature tuning is slow process and generally used for coarse and slow frequency sweeps [23, 24, 25]. Wavelength change through injection current tuning is a much faster process with bandwidth
A simplified schematic of the Mid-IR spectroscopy system is shown in Figure 2.
A simplified schematic representation of the Mid-IR spectroscopy system (HCW: hollow core waveguides; DAQ: data acquisition; QC: quantum cascade).
As discussed in the introductory section proper down-selection of the roto-vibrational transition lines is a critical part of the process for ensuring accuracy of the measurement system. Several spectroscopic databases, like HITRAN (High Resolution Transmission) Molecular Absorption Database [29], HITEMP (High Temperature Molecular Spectroscopic Database) [30], GEISA (Gestion et Etude des Informations Spectroscopiques Atmospheriques) Spectroscopic Database [31], PNNL (Pacific Northwest National Laboratory) Vapor Phase Infrared Spectral Library [32], ATMOS (Atmospheric Trace Molecule Spectroscopy) Database [33], NIST (National Institute of Standards and Technology) Infrared Spectroscopy Database [34], BT2 (A high accuracy computed water line list) Spectroscopic list [35] and CDSD (Carbon Dioxide Spectroscopic Databank) [36] exists for analyzing the spectroscopic parameters and simulate the transmission spectra. As Mid-IR spectroscopy for industrial applications is the recurrent theme of this chapter, the focus will be on the dominant roto-vibrational transitions in the Mid-IR region for those gas species playing a major role in industrial applications. A summary of rotational-vibration bands and their line-strengths for anthropogenic pollutants NO, NO2, CO, SO2 and a major interfering gas (H2O) in the Mid-IR region is given in Table 1.
Gas species | Mode | Wave number (cm−1) | Line-strength (cm−2/atm) | Absorbance | Ref. |
---|---|---|---|---|---|
H2O | 1594.746 | 1.723 × 10−1 | 12 | [37, 41] | |
3151.629 | 2.172 × 10−3 | 0.18 | |||
3657.629 | 2.665 | 1.8 × 102 | |||
3755.928 | 2.773 × 10−2 | 64 | |||
4666.790 | 1.961 × 10−2 | 0.00013 | |||
5234.976 | 8.004 × 10−2 | 7.0 | |||
5331.267 | 2.380 × 10−1 | 18 | |||
CO | 2115.625 | 9.213 | 4.3 × 103 | [38, 41] | |
2145.999 | 7.374 × 10−4 | 36 | |||
4204.664 | 3.778 × 10−2 | 0.43 | |||
4260.063 | 2.866 × 10−6 | 0.012 | |||
SO2 | 517.75 | 1.635 × 10−2 | 45 | [38, 39, 41] | |
1155.920 | 1.033 × 10−1 | 98 | |||
1360.791 | 5.119 × 10−1 | 2 × 103 | |||
2492.444 | 2.019 × 10−2 | 27 | |||
2498.444 | 1.334 × 10−2 | 38 | |||
NO2 | 741.599 | 7.862 × 10−3 | 28 | [38, 41] | |
1490.77 | 1.430 × 10−4 | 0.35 | |||
1616.152 | 9.861 × 10−1 | 2.4 × 103 | |||
1605.497 | 2.478 | 3.8 × 103 | |||
2805.512 | 7.041 × 10−6 | 0.0089 | |||
2898.193 | 8.516 × 10−2 | 1.5 × 102 | |||
2906.069 | 4.967 × 10−2 | 89 | |||
NO | (1875.959) | 3.179 × 10−2 | 2.7 × 102 | [40, 41] | |
1875.898 | 1.908 × 10−2 | 5.0 × 102 | |||
3723.526 | 2.476 × 10−3 | 6.1 | |||
1678.184 | 1.207 × 10−5 | 0.00028 | |||
(1846.568) | 4.177 × 10−1 | 1.3 × 103 |
Summary of some major roto-vibrational spectra for anthropogenic pollutants in Mid-IR spectral region.
Table 1 also contains the absorbance values for the gases at temperature (T) = 300 K, Pressure (P) = 1 atm, Length (L) = 100 cm and gas mole-fraction (X) = 1, computed using SpectraPlot [41] tool. The line-strength and hence absorbance for H2O in Mid-IR (
An overview of the roto-vibrational spectra of the above gases in Near-IR and Mid-IR region is shown in Figure 3.
Roto-vibrational spectra of H2O, NO, CO and SO2 in NIR and Mid-IR region simulated using spectral data from HITRAN database [29].
It can be noticed that the Mid-IR spectra contains the fundamental vibration modes for H2O. H2O has very strong bands in both Near-IR and Mid-IR region and careful line selection strategy needs to be adopted for accurate spectral analysis of the target gases.
The study of the spectroscopic properties and down-selection of proper transition for the target gas species is extremely important as the sensitivity and accuracy of the gas species measurement in a gas sensor depends primarily on the line selection process. It is the first step towards designing an accurate sensor. The basic criteria for the selection of a transition are existence of strong absorbance and minimal spectral interference from other combustion products (like water vapor). For example, consider the NO2 transitions in Table 1. The transitions at around 1600 cm−1 are the strongest among all the transitions highlighted for NO2. In most industrial applications, water vapor is the major interfering species. A simulation study using SpectraPlot [41] tool for NO2 (mole-fraction = 0.1) and H2O (mole-fraction = 0.8) is shown in Figure 4.
Roto-vibrational spectra of H2O and NO2 in Mid-IR region simulated using spectral data from HITRAN database and SpectraPlot tool.
Figure 4 shows strong absorption band of NO2 in 1600 cm−1 region with a well-defined and less-structured water vapor spectrum. This is particularly important as it allows the use of wavelength modulation spectroscopy technique to properly differentiate the spectral features of NO2 and H2O completely removing the interferences.
The theory of laser absorption spectroscopy has been widely discussed in several literatures in details [42, 43, 44]. Some of the key equations will be highlighted here to set the stage for further discussions.
The basic equation relating the incident laser intensity and transmitted laser intensity through a gas medium is given by Beer-Lambert’s law
Here
The spectral absorbance depends on specific gas properties like mole-fraction (
Lineshape model | FWHM, Parameters | Mechanism and Ref. | |
---|---|---|---|
Gaussian | Doppler broadening [45] | ||
Lorentz | Radiation damping, collision broadening [45] | ||
Pseudo-voigt | Parameters: | Convolution of Gaussian &Lorentzian [45, 46] | |
Asymmetry | Complex molecules in Mid-IR [45] |
Summary of line-shape models used for spectral analysis in absorption spectroscopy along with the parameters characterizing the profiles.
FWHM: full width at half maximum.
From Table 2, we can see that the Doppler broadening has strong ν dependence and weak T (temperature) and m (molecular weight of the gas species) dependence. On the other hand, the Lorentzian profile changes with the life-time of the transition state. Doppler broadening is the dominant factor at low pressure and pressure induced shift
The models are developed to provide a ‘best fit’ to the experimental data and to quantify the parameters of interest as described in Table 2. Derived using theoretical equations, the parameters provide us specific physical interpretation of the underlying process. The ‘Sum of Squared Residuals’ (Sum of Squared Residuals: square of the difference between a model estimate and the corresponding data point) is often used for estimating the measurement error [49]. At very low-pressure regime (<20 Pa), the Voigt distribution fit of the profile does not reproduce accurately the observed spectral line shape of the gas species. ‘W-shaped’ residuals have been observed in these cases [50]. Galatry profiles (for soft collisions) and Rautian profiles (for hard collisions) have been developed to minimize the observed residuals [51, 52].
The ‘fit’ of the models to the experimental data becomes extremely critical when we try to estimate the gas concentration with ppbv (parts per billion in volume) or pptv (parts per trillion in volume) accuracy levels (in
In most real-world applications, it is desirable to measure the gas species concentration at elevated temperatures. Temperature dependency of line-strength and line-shape leads to complications in species concentration estimation in combustion gas flow field. A thorough understanding of ‘fit’ of the models (described in Section 2.3) with the acquired spectra at higher temperatures is important, as it lowers the accuracy levels of the species concentrations.
The temperature dependency of line-strength of a transition is given by Eq. (3):
where kB (Boltzman’s constant), c (speed of light), h (Plank’s constant) are the constant terms and ν0 is the line-center frequency, E″ is the lower energy state and Q is the partition function. Eq. (3) is given in terms of reference temperature T0 (296 K) [41, 53].
The accuracy of line-strength and absorbance depends on the accurate knowledge of high temperature partition function
Gas species | Frequency (cm−1) | Line strength (cm−2/Atm) | Absorbance | |||
---|---|---|---|---|---|---|
300 K | 600 K | 900 K | References | |||
H2O | 2060.48 | 8.272 × 10−4 | 0.65 | 0.94 | 0.7 | HITEMP 2010, SpectraPlot [30, 41] |
CO | 2013.35 | 1.118 × 10−5 | 0.02 | 0.62 | 1.20 | |
2059.91 | 8.753 × 10−4 | 1.4 | 3.6 | 3.7 | ||
NO | 1927.27 | 1.183 × 10−4 | 1.0 | 1.3 | 1.0 | |
1929.03 | 1.828 × 10−4 | 1.6 | 1.7 | 1.2 | ||
NO2 | 1599.01 | 2.324 × 10−3 | 16 | 6 | 2.3 | HITRAN 2012, SpectraPlot [29, 41, 54, 55] |
1599.91 | 2.802 × 10−3 | 15 | 5 | 1.8 | ||
1600.08 | 1.014 × 10−5 | 2 | 0.77 |
Variation of absorbance at different temperatures with conditions as mole-fractions (xGas = 0.001, xH2O = 0.08), path-length (L) = 250 cm, pressure (P) = 1 atm, balance N2.
The condition at 900 K is the typical case one would likely get in a combustion environment. The absorbances for H2O, CO and NO has been estimated using HITEMP 2010 and Spectraplot tool [30, 41]. Absorbance for NO2 has been estimated using HITRAN 2012 and Spectraplot [41, 55]. An experimental determination of broadening parameters
A HITRAN simulation of CO line at two different temperatures (300 and 900 K) for 2059.91 cm−1 (E” = 806.4 cm−1) line using Spectraplot tool is given in Figure 4.
From Figure 5, we can see that at 300 K, the CO transition lines at 2058.4 cm−1 (corresponding to
(a) HITRAN simulation using Spectraplot tool of a CO and a water transition line at 300 K, (b) HITRAN simulation using Spectraplot tool of a CO and a water transition line at 900 K.
Considering the two transitions at 2059.91 and 2060.3 cm−1, we can infer the gas temperature from the ratio of absorbances at both the transitions of CO. The ratio of absorbances is shown in Eq. (4):
The temperature sensitivity is given by
The
Two categories of laser-based absorption techniques are available for measurement of trace gas species concentration: direct absorption spectroscopy (DAS) and wavelength modulation spectroscopy (WMS). These techniques are further sub-divided into two categories based on fixed wavelength and scanned wavelength techniques. Though these techniques were initially developed for spectroscopy in the Near-IR spectral region, it has found widespread application in Mid-IR spectroscopy.
In direct absorption spectroscopy, the laser wavelength is tuned such that it is resonant with the absorption transition of interest of the gas species. Fixed wavelength direct absorption spectroscopy is rarely used as it contains very limited spectral information and non-absorbing losses (scattering, vibrations, beam-steering) are negligible. In case of scanned-wavelength direct absorption spectroscopy (SW-DAS), the laser injection current is tuned to scan across an absorption transition of interest and the trace gas species properties are estimated using Eq. (2). In this case, the laser frequency tuning range is ≈0.1–10 cm−1. Hyperspectral direct-absorption spectroscopy techniques exist for range greater than 10 cm−1.
In a complex gas mixture, like in case of combustion gas or in natural gas, one of the major challenge is to identify an absorption line of the trace gas species isolated from background gases. Also, at elevated temperature, the Boltzmann distribution results in redistribution of molecules among energy states thereby increasing the relative strength of absorption of the far wings of the fundamental bands. This increases the chances of overlap of the trace gas spectra with the background gases. To address this challenge, Wavelength Modulation Spectroscopy (WMS) technique of detection of trace gas has been developed to estimate the concentration in presence of complex background gas mixtures (e.g. CO2, H2O, Hydrocarbons).
Several literatures exist discussing the details of the WMS technique [42, 43, 53]. In brief, the harmonics (nf or nth-derivative) of the wavelength modulation provides several answers to the absorption spectra of the trace gas. The second-harmonic (WMS-2f) is used for trace gas species concentration estimation when the absorbance is quite low. In WMS-2f/1f technique, the WMS-2f signal is normalized with the 1 f signal to minimize the non-absorption losses like, beam-steering, scattering, window fouling. This is particularly useful in using the laser-based technique for a robust trace gas species measurement in an industrial environment.
In fixed-WMS, the injection current modulation is used to modulate the laser’s wavelength on an absorption transition of interest. Whereas, in case of scanned-WMS, the laser’s wavelength modulation is accompanied by a minor amplitude wavelength scan to resolve the peak of the WMS-2f signal normalized with 1 f.
Optimization of the modulation depth parameter
In WMS model, the tuning frequency is a superposition of laser scanning and modulation terms around a mean optical frequency
Wavelength modulation Spectroscopy scheme discussed above is widely used in Mid-IR region. The suitability and subsequent modification of the technique depends largely on the knowledge of spectral features of the target gas and its variations with local operating conditions. In Mid-IR region, the transitions of single gas species are sometimes severely convoluted. This requires characterizations of all the components of transition parameters for accurately modeling the profiles [54].
This becomes further complicated during the detection of trace gas in presence of strong spectral interference from species like water. In these cases, one also must consider the effect on sensitivity of the WMS-2f signal due to fluctuations in interference by the interfering species. An optimization method to be used in this case has been discussed in detail by Sur et al. in Ref. [54].
For any measurement system, noise, repeatability and reproducibility plays an important role in determining three critical points: (1) factors influencing the measurement process, (2) whether measurement system variability is small compared to process variability and (3) the capability of measurement system to distinguish between parts (or part-to-part variation). “Part-to-part” variation in the present case will be variations in industrial processes. For any laser-based measurement system, noise and repeatability are the major contributors of measurement errors. Reproducibility is the variability in measurement system due to differences in operators and cannot be considered in this case.
The availability of industrial grade QCLs for trace gas measurement has enabled sub-ppm and sub-ppb level measurement. The measurement error due to noise and repeatability play an extremely crucial role as we go down to the low concentration and high specificity measurement regime. Various factors like, 1/f-noise, pressure (P) and temperature (T) effects, etalon effect contribute to the measurement system noise. Wavelength stability of successive scans leads to short- and long-term drifts in measurement which is mostly a repeatability challenge with the system.
In Direct Absorption Spectroscopy (DAS) technique, the noise is usually dominated by 1/f-noise of the laser source. This limits the lowest detectable limit to a higher value. Development of high frequency modulation techniques, substantially reduced the 1/f-noise in the TDLAS technique. This was further improved by using Mid-IR spectral region for measurement purpose.
Figure 6 gives an overview of the noise regimes and the contributing factors in a laser-based technique.
Noise regimes at various sections of the measurement system and the contributing factors to the overall error budget.
As discussed above, the 1/f-noise is the primary contributor in Section 1, due to the laser source. In Section 2, the gas pressure and temperature are the major factors contributing the deviations from Voigt-only distribution fit (giving the distinct “W-shape” residuals for the best fit) [60].
The detection limit of a trace gas species is calculated as [61, 62],
In the above equation,
Another important contributor of the noise are the Etalon fringes due to non-uniform transmission through optical windows, lens [64]. This appear as oscillations in 2f signals. The transmittance of laser through two windows with the distance between the windows as L is given by:
Where F is the coefficient of finesse, given as,
The Normalized Noise Equivalent Absorbance (NNEA) is calculated using Eq. (12):
In the above equation
A summary of optical techniques and their normalized noise equivalent absorbance is given in Table 4.
Measurement technique | NNEA (Wcm−1 Hz−1/2) | Spectral region (cm−1) | Ref. |
---|---|---|---|
Open path tunable diode laser absorption spectroscopy | 6.32 × 10−8 | 3778–3780 | [65] |
6 × 10−9–8.419 × 10−11 | 1246–1250 | [66] | |
Cavity ring down spectroscopy | 2.0 × 10−13 | 9397–9399 | [67] |
4.25 × 10−14 | 6135–6369 | [68] | |
3.0 × 10−16 | 6350–6380 | [69] | |
Cavity enhanced spectroscopy | 2.0 × 10−15 | 6472–6693 | [70] |
6.0 × 10−14 | 6490–6555 | [71] | |
Tunable diode laser photo-acoustic spectroscopy | 2.2 × 10−9 | 6525–6529 | [72] |
5.19 × 10−10 | 4038.8–4039 | [73] | |
1.2 × 10−7 | 2310–2313 | [74] | |
3.2 × 10−10 | 2310–2313 | [75] |
Summary of optical techniques for trace gas concentration measurement using laser absorption in both near IR and Mid-IR spectral region and its normalized noise equivalent absorbance.
NNEA: normalized noise equivalent absorbance.
Another important source of measurement error in spectroscopic systems is the repeatability of the measurement process. In trace level measurement, integration time of the measurement or time-binning is an important parameter that helps us to study the repeatability of the system when laser frequency stability is a factor (not the systematic errors). The bandwidth of the measurement system becomes critical when one tries to measure multiple trace gases in a single measurement system.
Allan variance is defined as [76]:
and the deviation is given as
A good discussion on using of Allan deviation for calibration of laser absorption spectrometer using QCLs is given in Ref. [77] by Smith et al. Optimization of sampling rates plays a major role in enabling high sensitivity measurement of QCL-based laser absorption spectrometers.
Some of the challenges of using Quantum Cascade Lasers for industrial applications have already been highlighted in previous sections in a different context. Few those challenges along with some new ones will be discussed here to avoid the desultoriness.
Some of the common window materials used for Mid-IR applications are Sapphire, Calcium Fluoride (CaF2), Barium Fluoride (BaF2), Magnesium Fluoride (MgF2) and Zinc Selenide (ZnSe). The transmittance from un-coated wedged windows (wedge angle: 30 ± 10 arcmin) is shown in Figure 7.
Transmission % from wedged windows of Sapphire, CaF2, BaF2 and ZnSe with wavelength (μm). Data Source: Thorlabs catalog for optical windows [78].
Though CaF2 and BaF2 can transmit over a broad wavelength range (around 10–14μm), it is not suitable for combustion or high temperature applications in presence of moisture. These materials are extremely hygroscopic and degrade in presence of moisture. Due to their large thermal expansion coefficients they are not suitable for combustion applications. ZnSe has much broader transmittance and has lower thermal expansion than the fluoride crystals. Sapphire is the most suitable material for high temperature and high-pressure combustion applications but it has a lower transmittance window compared to the Fluoride crystals and ZnSe (upto 4.0 μm). Careful selection of windows is required keeping in cognizant the following parameters: application conditions (temperature, pressure), gas species, etalon effects. A summary of the properties for the window materials is given in Table 5.
Window material | Refractive index range | Wavelength range (μm) | Thermal expansion coeff. (/°C) | Melting point (°C) |
---|---|---|---|---|
SAPPHIRE | 1.9–1.62 | 0.2–5.0 | 5.3 × 10−6 | 1800 |
Calcium fluoride | 1.58–1.3 | 0.2–10 | 18.85 × 10−6 | 1418 |
Barium fluoride | 1.65–1.3 | 0.2–15 | 18.4 × 10−6 | 1368 |
Magnesium fluoride | 1.43–1.3 | 0.2–6.7 | 13.7 × 10−6 | 1255 |
Zinc selenide | 2.75–2.35 | 0.5–16 | 7.1 × 10−6 | 1520 |
Summary of optical and thermal properties of some common window materials used for Mid-IR applications.
Data Source: Thorlabs catalog for optical windows [78].
It is always desirable in an industrial application to isolate the lasers from the operating environment to protect the lasers. Several types of fiber cables exist for delivery of Mid-IR laser beams. A summary of optical fibers available for Mid-IR range is given in Table 6.
Fiber type | Attenuation (dB/m) | Core diameter (μm) | Wavelength range (μm) | Ref |
---|---|---|---|---|
Zirconium (IV) flouride | 0.20 | 100, 200, 400 | 1.5–3.7 | [79] |
Zirconium (iv) fluoride | 0.25 | 600 | 2.0–3.5 | [79] |
Indium (iii) fluoride | 0.45 | 100 | 1.0–4.5 | [79] |
Hollow core waveguides | 0.10 | 700 | 3.0–14 | [17] |
Hollow core waveguides | <1.0 | 300 | 7.6–11 | [15] |
Chalcogenide (as-s type) | 0.12 (0.6) | 100 | 2–3.5 (4.0) | [80] |
Chalcogenide (As-se type) | 0.2 (0.5) | 100 | 2–8 (4.5) | [80] |
Mid-IR optical fibers with core diameters, attenuation and wavelength range.
The wavelength range covered by hollow core waveguides is widest among all the fibers available for Mid-IR laser beam transmission.
For all practical applications, the laser beams need to remain in continuous alignment with the detector during all operational conditions. As discussed in Section 2.4, 1 f -normalization of WMS-2f is used to reject the background emissions (beam-steering, transmission losses due to dust particle scattering, window fouling) that vary much slowly with respect to 1 f. But for coarse alignment of the beam (due to thermal misalignment, etc.) one needs to use a different technique for automatic misalignment correction. In this case, a 633 nm laser is multiplexed with the Mid-IR lasers and transmitted through the gas flow path. The transmitted beam is de-multiplexed and a quadrant photodetector is used as the position sensitive detector. The technique has been discussed in detail in Ref. [81] by Mitra et al.
Simultaneous detection of multiple gases in combustion process is an unique challenge for a spectroscopy-based measurement. For example, one needs to measure NO, NO2, CO, O2 and H2O simultaneously in the combustion gas mixture. This implies multiplexing of Mid-IR and Near-IR wavelengths for measurement. The de-multiplexing process involves using CaF2 (1–6μm) or ZnSe (1–12 μm/7–14 μm) beam splitters to separate the Mid-IR and Near-IR beams [82].
Sequencing the laser operation and the detection is the key factor for the optimizing the sensor performance when we have multiple source and single detector. Mukherjee et al. [83] discussed about using scanning galvanometer to switch between the lasers (in <1 s) for multispecies trace gas detection. Time-division multiplexing (TDM) method has been reported by Dong et al. [84] where a stepper motor coupled to detectors were used for measurement of multiple gases.
For process control applications, the measurement time is usually between 1–10s. As highlighted in Section 3.2, optimization of integration time is required for each species to minimize the noise and drifts while carrying out all the measurements within the time window required for process control applications. A hybrid approach of wavelength and frequency multiplexing needs to be developed in accordance to the process requirement.
For all on-site measurements, periodic calibration of the measurement process is required for accurate estimation of concentration. Usually, a certified standard gas mixture in fiber-coupled gas cell is used for the calibration purpose [85]. A detailed calibration process steps have been discussed by Werle et al. in Ref. [86]. The “Zero” reading of the sensor is established using Nitrogen, Synthetic Air or local clean ambient air. Pre-mixed calibration gases of various concentrations are used for sensor “span” calibration. The “dynamic” calibration is finally carried out using a known test gas with the full sensor system in operational condition.
Presently most of the commercially available TDL-based sensors contains fiber coupled gas cells of high concentrations (also known as reference cell) of the target gas. A part of the laser beam is transmitted through the cell and used for line-locking of the transmitted beam. In this technique, a prudent selection of spectral region is required which is isolated from any neighboring transitions. Though in Mid-IR spectroscopy there is a good chance of locating these transitions, the implementation is quite challenging in applications which require trace level measurement in complex gas mixtures.
There are broadly two versions of “Calibration-free” WMS technique reported in literatures. The first version discusses about using residual amplitude modulation (RAM) from the 1 f signal for normalizing the incident laser intensity [87, 88, 89]. Like above, in this case too, the spectral region of interest should be well isolated from neighboring transitions. This limits the application in high gas pressure regime as the line broadening might blend the neighboring transitions. In addition to this, incident laser intensity fluctuations due to high vibrations, window fouling, beam steering are some of the major challenges for implementations of this technique.
In the second version of the “calibration free” technique, the WMS-2f/1f signal obtained from actual measurement is compared against a model WMS-2f/1f signal developed using laser tuning parameters and transition parameters used in the absorption feature under probe. The model is continuously updated based on the process parameters (T, P and gas concentration). A detailed explanation of the technique has been discussed by Rieker et al. and Sun et al. in Refs. [90, 91]. The same approach can be used for QCL-based measurements.
One of the major challenge with above method is the uncertainties with HITRAN spectral line parameters. Figure 8 shows the uncertainty distributions for CO, NO and H2O spectral parameters in the Mid-IR region [92].
Uncertainty distribution of ν0, S and γair for (a) CO (1900–2300 cm−1), (b) H2O (1500–2300 cm−1) and (c) NO (1600–2200 cm−1).
It becomes a key to understand the impact of these uncertainties of the line parameters on absorbance and concentration estimation when measuring trace gas levels. Using the above details, a Monte-Carlo simulation was carried out to check the percentage contribution to variance at 600 K (Figure 9).
Monte-Carlo simulation of absorbance at 296 and 600 K with percentage contribution to variance.
Figure 9 shows that in general the absorbance distribution (with 80% confidence interval) remains Gaussian at both 296 and 600 K temperatures. The major contribution for variance is due to variations from errors in measurement of temperature 296 K (63%) and uncertainties in line-strength (22%).
Since its discovery in 1994 by Capasso et al. [93], Quantum Cascade Lasers has come of age from being a laboratory tool to industrial application. It has opened the Mid-IR spectral region for industrial usage. Maturation of near-IR TDLAS techniques over last 20 years and its applications using QCLs have opened a new vista for trace gas sensing for efficiency of combustion processes, environmental sensing for leak detection, emission and air quality monitoring. It also enabled explosives, chemicals and bio-hazard detection for law enforcement and defense agencies.
Availability of Mid-IR spectral region and improvements in WMS measurement scheme has enabled our ability to measure, CO, CO2, NO, NO2 accurately. Availability of High Heat Load (HHL) packages for QCLs, HCWs and robust detectors with multi-stage cooling, auto-alignment and calibration-free techniques has led to the application of QCLs in combustion process monitoring and controls.
Two broad categories of challenges remain to be addressed by Mid-IR spectroscopy.
First, the accuracy of spectroscopy databases in Mid-IR spectral region needs to be improved for high temperature applications. Only five species are covered in HITEMP (H2O, CO, CO2, NO, OH). High temperature parameters are based on quantum mechanical computations with uncertainties ranging from 5–20% and that limits the sensitivity of the measurement. Accurate HITEMP database is required for other combustion gases like NO2, SO2 and hydrocarbons.
Second, a major drawback with the Mid-IR fibers (HCWs) is their bending loss [17]. Also, the length of the HCWs available is not more than 1 m without significant transmission loss (<1 dB/m). For true remote application we need to have fibers >10 m. The Mid-IR lasers with improved performance and stability without additional cooling mechanisms are required for a robust sensor system.
IntechOpen publishes different types of publications
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