Diagnostic techniques for the monitoring and control of practical flames

Progress in Energy and Combustion Science 36 (2010) 375–411

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Progress in Energy and Combustion Science journal homepage: www.elsevier.com/locate/pecs

Diagnostic techniques for the monitoring and control of practical flames Javier Ballester a, *, Tatiana Garcı´a-Armingol b a b

Fluid Mechanics Group/LITEC, University of Zaragoza, Centro Polite´cnico Superior, Marı´a de Luna, 10, 50018-Zaragoza, Spain Laboratory of Research on Combustion Technologies (LITEC), CSIC, Marı´a de Luna, 10, 50018-Zaragoza, Spain

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 August 2009 Accepted 18 November 2009 Available online 9 February 2010

The development of diagnostic methods suitable for the monitoring of practical flames is an important objective, which is receiving a growing attention and significant research efforts. This is motivated by the need to achieve a more precise description of the process and, ultimately, implement efficient and reliable control and optimisation methods as a key step towards the development of more efficient, flexible, reliable and clean combustion systems. Many and interesting attempts have been proposed, involving widely different approaches in terms of the instrumentation utilized and the concepts proposed to convert sensorial information into various meaningful parameters. This article intends to review the techniques proposed in the literature for the monitoring of flames, either applied to or conceived for the monitoring of practical combustion equipment. It has been divided into four sections, dealing respectively with optical sensors, imaging techniques, pressure transducers and probing methods. A detailed analysis of the works published reveals that probably the main challenge in this field is the definition of the most representative flame signals and of their subsequent processing to derive the meaningful information required to diagnose the state of a flame or to drive a controller in an effective and safe manner. This together with the wide range of diagnostic needs and restrictions imposed by the different combustion situations probably explains the notable heterogeneity observed among the works published. In spite of the great efforts devoted, the techniques proposed for the advanced monitoring of practical flames are still at a development stage. However, significant advances in this field are expected in a near future, fostered by the urgent demands from the combustion industry and facilitated by the continuous progress in sensor technology, signal processing techniques and, not the least, in the understanding of combustion processes. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: Flame monitoring Flame diagnostics Combustion control Optical sensors Flame imaging Pressure fluctuations Gas sensors Ionization sensors

Contents 1. 2.

3.

4. 5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .376 Flame spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .378 2.1. Flame emission spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 2.1.1. Spontaneous flame radiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 2.1.2. Interpretation and behaviour of chemiluminescence signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 2.1.3. Equivalence ratio monitoring based on intensity ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 2.1.4. Spontaneous emission as a flame signature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 2.2. Laser absorption spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 Flame imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 3.1. Pyrometry-based temperature mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 3.2. Chemiluminescence imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 3.3. Broadband flame imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 3.4. Processing and interpretation of image data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 Pressure fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .397 Probing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .401

* Corresponding author. Tel.: þ34 976 762 153; fax: þ34 976 761 882. E-mail address: [email protected] (J. Ballester). 0360-1285/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.pecs.2009.11.005

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376

6.

5.1. Analysis of flue gases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 5.2. In-furnace measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 5.3. Ionization probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408

1. Introduction The combustion industry is continuously facing new challenges in order to increase the efficiency, reliability and flexibility (e.g., to new fuels) of combustion equipment and, not the least, to reduce their environmental impact. Besides the development of improved designs and new burner concepts, the permanent supervision and optimization of flames, which are the core of any combustion process, appears as an essential instrument to meet those demands. The importance of these aspects as one of the key technological objectives is recognised in surveys and roadmaps proposed for different combustion applications [1–4]. However, the possibilities for permanent optimization of combustion equipment are currently very limited. The monitoring and control of most practical burners is based, apart from the mandatory flame management system, on the analysis of flue gases. This suffices to achieve the desired excess air or to check the levels of undesirable emissions (e.g., CO, NOx, particulates). However, the information provided by the gas analysers constitutes a highly incomplete description of the combustion process and is clearly insufficient to act on the burner settings in an effective and safe way. This situation is in clear contrast with the increasing sophistication of control methods in many other industrial processes (or in other parts of combustion installations). In general, the primary obstacle is thought to reside in the lack of reliable flame monitoring tools, rather than in the need for very complex, multivariate control algorithms (in many cases, the objective can be reduced to optimizing one or a few burner settings, like the air-fuel ratio). In the authors’ opinion, the implementation of advanced control methods in practical combustion systems poses two main difficulties:  The lack of reliable flame models that could be used as an operation support tool to predict with sufficient accuracy the relevant combustion parameters as a function of operating conditions and burner settings, or even just the relative response to changes in the manipulated variables.  The risk of bringing the system into unstable regimes; a plant manager may accept the lack of efficacy of a controller in optimizing the process, but most probably will not take the risk of reaching unstable regions, which can bring the system out of control and may cause mechanical and thermal damages or unplanned shutdowns.

In the absence of reliable predictive methods, the permanent supervision of the flame seems the most rational alternative in order to develop efficient and safe strategies for the control and optimization of practical burners. The close relationship between combustion control and sensors is clearly implicit in the review by Docquier and Candel [5]. Like in any other industrial installation, sensors are necessary for control but also to provide information on the state of the process. Conventional instruments are customarily used to measure global variables such as input flow rates and flue gas

composition (e.g., O2, CO, NOx, SO2), but they afford a very limited description of the process taking place inside the combustion chamber. A precise diagnostic would require much richer information on the properties of the flame, as the core of the combustion process. This is even more evident in multi-burner combustors, where the behaviour of individual flames can be very different from that estimated from global values. As a result, drifts or malfunctions can in many instances go unnoticed until they become significant. Some sort of monitoring tools are needed to permanently evaluate whether the system is performing as expected, to identify the actual combustion state, to detect anomalous or off-design operation or even to diagnose potential causes for the observed behaviour. Such objectives can only be addressed through the collection and analysis of sensorial information captured directly from the flame. Therefore, the development of flame monitoring techniques suitable for practical applications appears as an important prerequisite for the design of ambitious supervision and control strategies of combustion equipment. Numerous and interesting attempts in this respect have been reported, especially during the last decade. This paper is aimed at reviewing the most relevant ideas and results published on the development of flame ‘monitoring’ techniques that are or might be applied in practical combustors. Flame monitoring: needs, constraints and approaches Since the word ‘monitoring’ may have different interpretations, it is deemed pertinent at this point to clarify the meaning (and, hence, the objective) selected here. This review is focused on sensing methods designed to collect direct information from the flame which may be used either by an operator, in order to diagnose the state of the flame or to manually adjust the combustion hardware, or by a control system to automatically regulate or optimize the process (see Fig. 1). Whereas this subject has obvious connections with both conventional and advanced combustion diagnostics, there are some fundamental differences, especially regarding the approaches applied to process and use sensorial information. On the one hand, the information needed to monitor a flame may or may not constitute a reliable measurement of meaningful combustion parameters of practical relevance. Whereas it is clear that some variables must be quantified in absolute terms with reasonable accuracy (e.g., excess air or pollutant emissions), qualitative information or relative variations with respect to known situations may also be perfectly valid for monitoring purposes, even if the measurement involves significant uncertainties in absolute terms. On the other hand, the objectives are clearly different from those of scientific flame studies. Nevertheless, it should be noted that the diagnostic techniques used in combustion research may constitute most valuable tools to characterise practical flames. In fact, some of the sensing methods described below are also used to investigate laboratory flames. The main difference resides in the subsequent processing and interpretation of the data. For example, OH* chemiluminescence imaging has been utilized in many research studies as a flame visualization technique and has also

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377

Evaluation Sensors Regulation (manual or auto)

Direct monitoring Quantification of meaningful parameters

Indirect monitoring Parameter estimation

Operator MONITORING

Flame Signals

SIGNAL PROCESSING

Flame

Controller

State identification Calibration Previous experience Fig. 1. Diagram showing possible approaches and information flow paths in a flame monitoring system.

been proposed in a number of works as an industrial sensing method. However, defining a procedure to use these data for online flame monitoring is not straightforward; this would require, for example, establishing the desired spatial distribution of OH* or the ability to interpret a particular chemiluminescence image in practical terms, and both are rarely possible in practice. An option (see the section on flame imaging) is to treat OH* images as a characteristic signature of a particular combustion regime as a means to identify different flame states. Similar approaches can be applied to other research diagnostic tools, which might serve perfectly (and, probably, would be advantageous to other simpler sensors) for monitoring purposes. Practical reasons (high cost, lack of ruggedness, need for highly-skilled personnel or optical access requirements), however, still prevent the use of advanced combustion diagnostics for industrial applications. Since sensor technology is in permanent evolution, the situation might be different in the future and some of the techniques that are currently restricted to laboratory studies might be transferred to industrial environments. In any case, the description of advanced combustion diagnostics is not the objective of this review and can be found in some comprehensive compilations on both intrusive and advanced optical techniques [6–10]. The general concept of flame monitoring actually includes diverse applications (in terms of the characteristics of the combustion equipment and monitoring objectives) and can be based on various types of sensors as well as on widely different approaches for the interpretation and use of sensorial information. Because of this, the methods found in the literature are notably heterogeneous. However, the different approaches applied in the works reviewed in the following sections can be fitted into the diagram shown in Fig. 1. The instruments issue some kind of flame signal (images, time records of radiation, pressure, etc), in the form of raw data that need to be processed in order to extract some characteristic features. In some cases, it may be possible to determine the absolute value of parameters having an intrinsic physical meaning and a direct practical relevance. Depending on the application, this might be the case of peak or average temperatures, stoichiometry or NOx emission of individual flames in multi-burner chambers, visible length determined from flame images (e.g., to avoid impingement on walls), etc In those cases, named as direct monitoring, the monitoring objective is completely fulfilled by the quantification of such parameters, which can be directly used to

inform the operators or to drive a control loop. This is the ideal situation, but direct monitoring is not feasible in many instances. First, it is difficult to obtain accurate quantitative estimates of important combustion parameters in industrial environments. Second, even when these parameters can be measured, the absolute value is not used as such but in most cases provides information on the system with respect to situations encountered previously (indirect monitoring). For example, as explained below, pyrometry techniques may yield spatial temperature distributions in flames; in spite of the importance of temperature in combustion processes, even such a detailed description may not be revealing by itself but, in order to diagnose the flame condition, it should rather be empirically compared with distributions previously obtained under known conditions. I.e., measured data still need to be further processed to extract information (e.g., estimates of air-fuel ratio or of NOx production in individual flames) that might be meaningful for an operator to evaluate the actual combustion conditions. The expression indirect monitoring will be used here to designate the methods based on the extraction of meaningful parameters of the combustion process from sensorial data not having a direct (or, at least, known) mathematical relationship with them. The result can be expressed in terms of an estimated magnitude or the classification of the monitored flame with respect to states previously defined. For example, as explained below, geometrical and/or luminous parameters (e.g., length) extracted from flame images may be correlated in some cases with pollutant emissions. Alternatively, the target could be to identify a flame with respect to a set of combustion regimes contained in a database for different operating conditions. In general, indirect monitoring involves as a key step the ‘calibration’ of the method by correlating flame signals with situations previously characterised. Hence, these methods have to rely on empirical knowledge specifically adapted to the particular combustion application; whether this is a serious problem or not will largely depend on how this empirical knowledge is developed and applied. For a method to become a feasible monitoring technique, it should ideally be of a generalizable nature in the sense that implementation in new applications does not require a costly design/adaptation procedure and includes as few as possible ad-hoc models of parameters. Actually, a vast majority of the monitoring techniques discussed in this work should be classified as indirect methods, involving the conversion of measured variables into meaningful parameters by means of a wide variety of correlation methods.

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An alternative, more ‘indirect’ approach consists of processing large amounts of plant data measured with conventional instruments as a means to diagnose and, ultimately, optimise the process, or a part of it, as a whole (see, e.g., Refs. [11–14]). Those methods do not perform a proper flame monitoring, but usually treat the combustion chamber as a black box characterised by its input and output variables and, therefore, will not be addressed in this review. It should be noted, however, that the algorithms applied (usually based on artificial intelligence techniques) might be extended to process flame data, which could be generated by any of the techniques discussed in this work. This review has been divided into four sections, dealing respectively with optical sensors, imaging techniques, pressure transducers and probing methods. It should be noted that the main emphasis here is not on the instrumentation and associated hardware but on the basic concepts and approaches proposed in the different works to extract meaningful information from the signals generated by the different sensors. An exception are the new developments for the measurement of gas composition (TDLAS and solid state gas sensors), which directly provide relevant combustion parameters (direct monitoring) and where the challenge is not the conversion of the signals into physical parameters but the design of the instrument. The description of the monitoring techniques includes in some cases reference to experiences of application for control and optimization of flames; although this review is not specifically oriented to combustion control, in many cases this is the ultimate objective as well as the driving force for the development of new diagnostic methods and, at the same time, their integration into closed-loop controllers constitutes an excellent test bench to assess the actual capabilities and limitations of advanced flame monitoring techniques. Some of the techniques described in this paper were already discussed in Section 4 of [5], which constitutes a key reference on combustion control and sensors. However, the overlap is relatively small and the present article complements the previous review. Also the orientation of the present survey is somewhat different as it is focused on monitoring strategies rather than on sensors. The reader is directed to [5] for a detailed description of those topics where there are only a few new results in recent years with respect to those already analysed in that reference.

2. Flame spectroscopy The analysis of light emitted (spontaneously or as a result of external excitation) or absorbed by flames is the basis of many diagnostic techniques, designed to determine a wide range of combustion variables, in some cases with very good spatial or temporal resolutions [8–10]. Besides being a most valuable tool for combustion research, a few of them may be suitable for the monitoring of practical flames and their review will be the objective of this section. Special attention is devoted to the methods based on the analysis of radiation spontaneously emitted by flames, in correspondence with the numerous works published and their potential for many different monitoring tasks. Laser absorption spectroscopy appears also as a promising technique for flame diagnosis in industrial environments and is discussed in the final section.

-

-

-

Solid bodies (e.g., soot, ash or char particles) produce the socalled black-body spectrum, which is continuous and exhibits a peak at wavelengths decreasing with the temperature. Gas molecules at high temperatures display rotation-emission bands. Radiation due to H2O and CO2 accounts for most of the radiative heat transfer in particle-free flames. Some chemical reactions generate excited species, a part of which reach their equilibrium ground state through the emission of light (chemiluminescence).

The spectral range varies for the different effects. Chemiluminescence occurs in the UV and VIS ranges whereas, at typical flame temperatures, thermal radiation by solid bodies and gases is found in VIS-IR and IR, respectively. By amount of energy emitted, black-body radiation is the main effect (in particle-laden flows) and chemiluminescence the weakest. This can be appreciated in Fig. 2, showing typical spectra for oil and gas flames. Therefore, the visible colour of a flame is dominated by the emission of particles (if present), and due to chemiluminescence in particle-free flames. The magnitude and spectrum of those three effects depend on the characteristics of a flame and, therefore, any of them might serve as a basis to develop flame monitoring techniques. However, the analysis presented here will concentrate on chemiluminescent radiation, as it has received the most attention for flame diagnostics. The detection of black-body emission has given rise to the different forms of radiation thermometers, described in many sources (e.g., [17]); the basic principles are outlined in the section dedicated to flame imaging. Infrared radiation by hot gas molecules has been applied for flame characterisation in a limited number of works (see [5] for a review). Excited radicals are not only formed in the flame by thermal excitation but also through chemical reactions, with concentrations much higher than their equilibrium values. Thorough descriptions on chemiluminescence mechanisms can be found, for example, in [15,18,19]. Chemiluminescence involves two reaction steps: (1) the formation of an excited radical from two parent species and (2) spontaneous loss of its excess energy to reach its ground state by the emission of one photon:

A þ B/R* þ others

(1)

R*/R þ hv

(2)

The wavelength of the electromagnetic radiation depends on the molecule R and the particular transition. Each radical R is

2.1. Flame emission spectroscopy 2.1.1. Spontaneous flame radiation Several phenomena make flames to spontaneously emit electromagnetic radiation [15]:

Fig. 2. Typical spectrum of gas and oil flames (note the different scales) [16].

J. Ballester, T. Garcı´a-Armingol / Progress in Energy and Combustion Science 36 (2010) 375–411

characterized by one or more emission lines, which can be grouped into characteristic bands, and the combined contribution of the various emitting radicals produces the chemiluminescence spectrum of a particular flame. For example, Fig. 3 represents the flame emission spectra in the range 250–500 nm for laminar methane-air flames with different equivalence ratios. The main chemiluminescence emitters in hydrocarbon flames are OH*, CH*, C2* and CO2*. Their formation reactions are detailed in Table 1, together with the bandhead wavelengths; it should be noted that, although the reactions listed in Table 1 are generally accepted as the main routes, research is still in progress to determine the detailed mechanisms. OH* emits ultraviolet radiation and its formation is ascribed mainly to reaction (R1) in hydrocarbon flames, whereas (R2) and (R3) are relevant for hydrogen flames. CH* and C2* emission is blue and green, respectively, and CO2* is characterised by a continuous emission, responsible of the ‘background’ that can be observed between 350 and 500 nm in Fig. 3. NO* and CN* radicals can also radiate but their contribution is only appreciable in flames with high nitrogen content (e.g., like those studied in [135]). Table 1 indicates that excited radicals are formed in reactions involving intermediate combustion species, whose concentration in the flame exceed by orders of magnitude their equilibrium value. Therefore, and since de-excitation reactions (2) are proportional to the concentration of the excited radicals and have very short characteristic times, chemiluminescence is mainly originated in thin reaction zones. This can be observed in Fig. 4, representing the spatial distribution of local emission intensities in a laminar premixed flame. A number of works have been devoted to predict chemiluminescence in flames. According to Eq. (2), the light emitted is directly proportional to the concentration of the excited radical, which is a result of its formation and destruction rates. The kinetic

Fig. 3. Flame emission spectra measured in laminar methane-air flames with different equivalence ratios (a) f ¼ 0.8, (b) f ¼ 1.0, (c) f ¼ 1.2 [20].

379

Table 1 Formation routes of excited radicals and characteristic wavelengths. Radical

Reactions

OH*

R1: R2: R3:

CH þ O2 / CO þ OH* H þ O þ M / OH*þM OH þ OH þ H / OH*þH2O

Wavelength (nm) 282.9, 308.9

CH*

R4: R5:

C2H þ O2 / CO2 þ CH* C2H þ O / CO þ CH*

387.1, 431.4

C2*

R6:

CH2 þ C / C2* þ H2

513, 516.5

CO2*

R7:

CO þ O þ M / CO2* þ M

Continuous spectrum 350–600

parameters of formation reactions like those included in Table 1 have been determined in some studies (e.g., [21–25]). Besides spontaneous photon emission (Eq. (2)), excited radicals are destroyed by quenching in collisions with other molecules, characterised by specific kinetic rates like those proposed by Tamura et al. [26]. The model must also include a detailed combustion mechanism in order to predict the concentrations of intermediate species leading to the generation of excited radicals (see Table 1). Chemiluminescence mechanisms have been compared with detailed spatially-resolved intensity measurements in a number of works [18,19,27]. For example, Fig. 4 shows modelling and experimental profiles across a laminar premixed flame. Ref. [27] is a recent and thorough review of published chemiluminescence mechanisms and evaluates the degree of agreement of different models with detailed experimental data. Positive results have also been reported in some works comparing predictions with line-ofsight measurements in laminar and turbulent flames [28–30]. In general, the models match better the experimental results for low equivalence ratios, whereas larger deviations between predicted and measured values are usually found for rich flames [18,19,28]. Besides their intrinsic scientific interest, the development of reliable chemiluminescence models is thought to be most useful for the interpretation of flame radiation signals for monitoring purposes.

Fig. 4. Intensity profiles of OH* (measured at 306  7 nm), CH* (431.4  0.75 nm) and C2* (516.5  1 nm) chemiluminescence in a laminar methane-air premixed flame (f ¼ 1.1) along the flame normal direction. (a) Simulation (b) Experiment [19].

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2.1.2. Interpretation and behaviour of chemiluminescence signals The reactions listed in Table 1 are a strong function of temperature and involve stable and intermediate fuel and oxidizing species. All of them are important combustion parameters and, therefore, the resultant chemiluminescence emission might be expected to be closely related to the properties of the flame. In fact, spontaneous flame radiation has been extensively used as a flame diagnostic method. The intensity of radiation at certain wavelengths, associated with specific excited radicals, has been used in many works as an indication of flame location or heat release rate. Experimental and modelling studies [18,19] support the reliability of local optical measurements to locate flame fronts or to analyze local flame structure, as applied, for example, in [31–36]. A few works have addressed specifically the existence of a relationship between chemiluminescence and heat release rate. One of the earliest studies is that of Price et al. [37], showing a linear relationship between mean C2* emission and volume flow rate of combustible, not influenced by the conditions of turbulence. Lawn [38] evaluated the spatial cross-correlation of chemiluminescent emissions and concluded that it might serve as a good indicator of instantaneous heat release rate. Comparison of spatially-resolved measurements of OH* and CH* chemiluminescence, flame surface density and heat release rate estimated as the product [CH2O][OH] revealed similar patterns and behaviour and, therefore, that either OH* emission or flame surface density serve to estimate heat release rate [39,40]. Measured trends of OH*, CH* and CO2* chemiluminescence intensity in [41,42] for variations in equivalence ratio and strain rate suggest that these are good markers for heat release rate whereas C2* is not a reliable indication. Nori and Seitzman [29,30] note, however, that due to the influence of equivalence ratio and pressure on OH* and CH* emission, their associated signals may not be fully reliable as heat release markers. Numerical results of Najm et al. [43] suggest that chemiluminescence due to OH*, C2* and CH* may fail as local markers of heat release in high curvature regions of flames; other studies, however, point out to the need for additional work and experimental evidences in order to verify that conclusion [41]. Ref. [44] also suggests that OH* and CH* may not be reliable indicators of unsteady heat release at some locations in a flame disturbed by standing waves (namely, in rarefaction zones). For the case of internal combustion engines, Kim et al. [31] found a good correlation between chemiluminescence in the range 350–390 nm and heat release rate in cool flames. A common application of chemiluminescence sensors is the measurement of instantaneous heat release rates for non-steady flames in works oriented to research on instability mechanisms [32,33,45–49], to the determination of transfer functions relating heat release rate with an external forcing (such as pressure, equivalence ratio or velocity fluctuations) [50–52] or to the development of active instability controls [53–59]. As an example, Fig. 5 displays the time evolution along an oscillation cycle of pressure, equivalence ratio, flame surface area and heat release rate in a premixed dump combustor. The emission spectrum is influenced by a number of variables, like equivalence ratio, turbulence, fuel properties or pressure. The effect of equivalence ratio has been analysed in many studies, mainly motivated by the interest in estimating flame stoichiometry from optical signals. Emission spectra captured in the premixed combustor sketched in Fig. 6 are shown in Fig. 7 for natural gas-air flames with different equivalence ratios. All chemiluminescence contributions (OH*, CH*, C2* and CO2* continuum) increase with equivalence ratio for the range explored, as it has been normally found in lean flames [28,41,61,62]. OH* and CH* intensities normalised with fuel mass flow rate have been reported to vary with f5.23 [62] and f2.72 [61], respectively. The case of rich flames is less

Fig. 5. Time evolution of equivalence ratio, pressure, total surface area of the flame and heat release rate along an oscillation cycle in a dump premixed combustor. Flame area and heat release rate were derived from CH* chemiluminescence data [32].

well documented but data indicate a different trend for f  1. Emissions of OH* and CH* have been found to peak at fw1 and w1.1, respectively [28,41]; the maximum emission due to C2* has been observed at f w 1.1–1.2 in some of the tests reported in [28,41], but this radical displayed a stronger dependence with the experimental conditions and kept increasing with f above those values at lower pressures [28] or strain rates [41]. Pressure also has a strong influence on the shape of the emission spectra, as shown in Fig. 8. The decreasing importance of the peaks due to CH* and C2* with respect to the continuum background is noticeable. This effect is most relevant in practice, since the use of those peaks for flame diagnostics might not be possible in pressurized combustors. The effect of pressure on absolute intensities due to OH* and CH* per unit of fuel mass flow rate has been quantified by Higgins et al. as p0.86 [62] and p0.22 [61], respectively.

Photo-Multiplier Tube

Si Photodiode Fiber Optic

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Fig. 6. Premixed combustor rig and instrumentation used to collect the data shown in Figs. 7, 9, 12, 22 and 23 [60].

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As it might be anticipated from Table 1, variations in flame chemistry associated to different fuel compositions lead to significant variations in the spectra. Fig. 9 displays the results for different blends of natural gas and hydrogen, where the decreasing importance of CH* and C2* peaks is clearly visible as the fraction of H2 increases. Those peaks completely disappear in hydrocarbon-free flames, such as flames of hydrogen [64] or syngas fuels (Fig. 10) [29], whose main components are hydrogen and carbon monoxide. Whereas most of the studies on flame chemiluminescence have been performed on laminar flames, an important objective is the development of diagnostic methods for turbulent flames. A fundamental difference between both regimes is the strain rate, which can lead to significant modifications in the structure of the reaction zone. A number of studies have analysed the influence of strain rate under controlled conditions (e.g., flat counterflow flames with variations in the injection velocity) [18,25,30,61]. All of them found that the intensity associated to the emitting radicals varies to some extent with strain rate, although its influence is less pronounced than the signal’s dependence on equivalence ratio and

Fig. 8. Emission spectra in methane-air flames at f ¼ 1.0 and different pressures (mass flow rates were increased proportionally with pressure to keep exhaust velocity constant) [63].

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pressure [30]. This reference also indicates that the effect of turbulence may diminish if the signal is averaged in space and time over the whole flame, as suggested by the fact that CH* intensity per unit of fuel mass flow rate was practically the same for laminar and turbulent flames (Karlovitz number w1) in the range f ¼ 0.6–1. A direct practical application of chemiluminescence sensing is the detection of the presence of a flame; i.e., a flame is assumed to exist only if the radiation collected in a certain spectral band exceeds a defined threshold. In fact, this is the basic principle behind the utilization of UV cells as flame detectors in many applications. More specialised uses have been explored in a few works, based on the analysis of transients in the radiation signals. Roby et al. [65] studied OH* and CH* emission for flame detection during light-off ignition in gas turbines, achieving response times of less than 2 ms. Muruganandam et al. [66,67] evaluated the risk of blowout by detecting the appearance of ‘blowout precursors’, identified as a drop in the OH* signal below a certain threshold (Fig. 11). A closed-loop control acting on the pilot fuel demonstrated

Fig. 10. Typical flame spectra in syngas and methane flames [29].

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Fig. 11. Time record of OH* signal, showing a noise rejection approach based on double thresholding used to detect lean blowout precursor events. An event starts when the lower threshold is crossed and ends only when the upper threshold is crossed. Two precursor events are shown here [67].

that this technique may enable an increased operating safety while minimizing NOx emissions. 2.1.3. Equivalence ratio monitoring based on intensity ratios Air-to-fuel ratio is a key operating condition for any flame, with potentially significant effects on fuel conversion, pollutant emissions, heat losses or stability. Hence, permanent monitoring and control of global flame stoichiometry is an important objective in any combustion system. This aspect is of particular relevance in lean premixed combustors, which have become a common lowNOx technology in gas turbines. Fig. 12 shows CO and NOx emissions measured as a function of equivalence ratio in a lab-scale combustor, displaying the typical behaviour of real engines. The existence of optimum ranges, with simultaneously low emissions of CO and NOx, is apparent from the graph for the three natural gashydrogen blends shown. Flames leaner than the optimum are close to the lean stability limit, with high production of CO and a serious risk of becoming unstable or blown out. Therefore, a permanent

20

and precise control may greatly facilitate the efficient, clean and reliable operation of lean premixed combustors. Since conventional flow rate meters are difficult to implement (especially for the air) in a gas turbine, significant efforts have been devoted to develop stoichiometry sensors. Optical sensors appear as a convenient option (rugged, cheap, non-intrusive) which has been amply studied for this and other practical applications. Since, as shown in the previous section, chemiluminescence of OH*, CH* and C2* varies with equivalence ratio, the development of stoichiometry sensors based on this effect seems feasible. Instead of using a single signal, the common approach consists of calculating the ratio between two chemiluminescence bands (OH*/CH*, CH*/ C2*, etc), as it was initially proposed in [63]. On the one hand, an important advantage is that this greatly reduces interferences related to the geometrical or optical parameters of the medium and optical elements in the field of view of the detectors. For example, attenuation by particles or fouling of optical windows can dramatically modify the absolute levels of the energy detected [68], but demonstrated a negligible influence on ratio-based results [63]. On the other hand, this normalization makes the result much more stable against variations in heat release rate, due to load changes or arising from turbulent fluctuations. Finally, several studies demonstrate that OH*/CH* ratios are relatively immune to variations in strain rate [18,41,42,69] (also with little influence on C2*/ CH* ratio for f < 1.3), flame aerodynamics or turbulence intensities [70,71] (Figs. 13 and 14 display some representative results from those references); hence, the relationship between OH*/CH* and equivalence ratio might remain valid for a wide range of burner designs, flow rates or turbulence conditions. Different kinds of instruments can be used to collect flame radiation for equivalence ratio sensing. Since chemiluminescence emission varies in scales of the order of tenths of mm (see Fig. 4) detailed measurements oriented, for example, to validate kinetic models need to have a very good spatial resolution. A possibility is to reconstruct local distributions from radial line-of-sight profiles by means of tomographic deconvolution techniques [25,72]. Certain optical arrangements enable depth-resolved measurements with a single detector. A single-lens followed by a small aperture is compared with Cassegrain optics in [73], reporting advantageous performance of the latter, which has been used to collect local chemiluminescence in a number of research studies

2000 100% NG - 0% H2

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Fig. 13. Absolute measured OH*/CH* chemiluminescence intensity ratio as a function of equivalence ratio, with the strain rate, a, as a parameter, for premixed counterflow natural gas-air flames [18].

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Fig. 14. Dependence of CH*/OH* on equivalence ratio for various inlet axial Reynolds numbers in a premixed methane-air swirl combustor (solid line: 4th order least-square fit, dashed: 95% confidence). Measurements using sensors with different spectral resolutions are shown for Re ¼ 11700 [71].

[19,31,35,36,41,42,69,73–76]. Fig. 15 shows the optical arrangement used in [69] to collect spatially-resolved chemiluminescence of OH*, CH* and C2*. High spatial resolution is not necessary (and may even be counter-productive) when the goal is to achieve a global

Fig. 15. View of the optical head (a), cross-sectional view of the Cassegrain optics and (c) multi-wavelength detection unit [69].

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characterisation of a flame. In those cases, simpler optical arrangements are used to collect line-of-sight information from partial or total volumes of the flame. It is not evident, however, whether the fundamental knowledge gained from spatially-resolved data may be applicable to signals averaged over highly non-uniform fields. OH*/ CH* ratios were similar for local and line-of-sight measurements in [41] and [72] (fully-premixed and partially premixed flames, respectively) suggesting that the knowledge obtained from detailed experimental or modelling studies remains valid for spatially-averaged data. Nevertheless, it should be noted that, as shown in [36], due to the combination of uncorrelated contributions from different flame regions, line-of-sight signals do not reproduce actual turbulent fluctuations. Chemiluminescence sensing requires collecting light at specific wavelength bands. The most common option are single detectors (photomultipliers or, less commonly, photodiodes) fitted with bandpass filters at selected central wavelengths and with bandwidths usually of the order of a few nm. Arrangements with several sensors like that shown in Fig. 15c enable the simultaneous detection of different light bands from the same region of the flame. Spectrometers or monochromators can also be applied to analyze the full emission spectrum or to select any particular wavelength for subsequent processing [20,29,30,41,63,70,71]. Chemiluminescence imaging with intensified CCD cameras has also been accomplished in several works (discussed in the section on flame imaging). The relationship between equivalence ratio and the quotient between two chemiluminescence bands (different pairs among OH*, CH*, C2*, CO2*) has been explored in a number of works. If the objective is to develop an equivalence ratio sensor, ideally the intensity ratio should be a monotonic, strong function of f and little sensitive to other flame parameters. These are important criteria to select the most adequate pair of signals for a particular application. In general, the OH*/CH* ratio has been found to fulfil those requirements. As shown in Figs. 13 and 14, obtained in different premixed-flame configurations (laminar counterflow and swirlstabilised, respectively) display a behaviour that is similar between them (OH*/CH* increase with f for lean to slightly-rich flames) as well as for different Reynolds numbers or strain rates. More or less similar trends (monotonic decrease of OH*/CH* with f) have been reported for f > 0.6 in [19,20,29,30,70,72,77]; an inverse trend was found in [61] for the range f ¼ 0.65–0.9. On the other hand, peak values of OH*/CH* were observed in [63,68,77] at f w 0.6–0.7 (see Fig. 16) (it should be noted that, since these works explored leaner flames, their results do not necessarily contradict those reporting a monotonic decrease for f > 0.6). The C2*/CH* ratio has also been found to increase with f, but in general it is less linear [19,72,78] and in some cases it depicted a non-monotonic dependence [16,41] and a stronger influence of strain rate [41]. The background chemiluminescence due to CO2* has also been recorded in some works by selecting a spectral zone not influenced by the bands due to OH*, CH* or C2*. Different relationships have been observed between the ratio CH*/CO2* and f: direct in [41,63,77] and inverse in [68]. The CO2*/OH* ratio would be the only option for nonhydrocarbon flames (syngas), but [41] and [30] indicate a low sensitivity to equivalence ratio. Even though some common trends can be identified among a part of the works, they are not quantitatively identical and qualitative differences even exist among some experimental studies. As discussed in [71], this might be in part due to the influence of the continuum CO2* emission. It is important to note that the capture of light at a specific band, characteristic of a certain radical (e.g., w431 nm for CH*), also includes a ‘background’ component due to CO2*. This causes the curve intensity ratio/f to depend on the optical resolution of the detection system or on whether the actual emission due to the selected radical has been

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Fig. 16. OH*/CH* and CO2*/OH* as a function of f for a laminar premixed methane burner. This graph also displays a lookup-table procedure to calculate f. [63].

estimated by substracting the background. These aspects affect to some extent the observed variations of the intensity collected at specific wavelenghts with respect to the operating conditions and might explain, for example, the inverse behaviours found in [71] and [61] regarding the influence of the pressure on the curve OH*/ CH* vs. f. Although the most studied case is that of premixed methane flames, chemiluminescence signals have also been studied for other systems. The shape and/or values of the chemiluminescence-f curve vary with the composition of the fuel, as shown in [70], [35] and [78], where gaseous and liquid fuels were fired in the same rig (a swirl burner, a counterflow burner and an IC engine, respectively). OH*/CH* and CO2*/CH* showed some influence of the amount of CO2 added to natural gas in a premixed dump combustor [77]. Comparisons between premixed and diffusion flames display curves that are very close for lean flames but suddenly diverge when stoichiometric conditions are reached [20], as shown in Fig. 17 (consistent with the single data point reported in [18] at f ¼ 1). The trends reported in partially premixed flames for rich conditions up to f ¼ 10 are completely different from those typically obtained for f < 1.5 [72]. Some authors [30] suggest that a ‘universal’ relationship might exist between selected chemiluminescence ratios and f. The analysis of the published results (see above) reveals, however, different qualitative and quantitative patterns and more research would be needed to establish a sufficiently general law. A part of the differences might be explained by the particular method applied in the various studies [30]. In the current state of the art, ad-hoc calibration seems necessary to relate intensity ratios and f in a particular application as a previous step to estimate equivalence ratio from optical signals. The relationships observed have been quantified in the form of empirical correlations, relating chemiluminescence data with f and other parameters like pressure [20,41,42,61,68,72,78]. In cases of non-monotonic trends, more than one intensity ratio is needed to obtain an unambiguous estimate of f. The solution proposed in [63] (see Fig. 16) consists of building a lookup-table which combines an intensity ratio with a good dynamical range but non-monotonic behaviour (OH*/CH*) with another ratio (CO2*/OH*) displaying a lower sensitivity but monotonic with respect to f.

Fig. 17. Comparison of the dependence of local OH*/CH* ratio on equivalence ratio for three different spatial resolutions and with the results of [20].

The on-line detection of equivalence ratio opens interesting possibilities to develop operating-point control schemes able to regulate and maintain flame stoichiometry at the desired setpoint. This has been demonstrated in [63], where a calibrated optical sensor (curves shown in Fig. 16) guided the closed-loop controller sketched in Fig. 18. This work reports a notably good performance of the control system, which was able to respond in a few seconds to changes in f setpoint and to maintain the burner within a narrow band about the desired state. 2.1.4. Spontaneous emission as a flame signature As discussed in the previous sections, many works have analysed the relationships between selected flame variables and some features of the spontaneous flame radiation, essentially restricted to intensity levels at a few specific wavelengths. A more general viewpoint would be to assume that, in general, light emission can be taken as a distinct signature of a particular flame, without restricting the options to the relationships between the energy emitted at characteristic spectral bands and heat release rate or equivalence ratio. In principle, any combustion parameter might be correlated with any set of parameters derived from optical flame signals. Only a few of the possible combinations will be successful and the challenges include the selection suitable features of the signals that can be used as reliable indicators of a particular meaningful parameter of the flame as well as the processing and correlation methods needed to establish such a relationship. A number of works have explored this strategy, as explained below. In all cases, a calibration or ‘training’ stage is needed in order to reproduce the particular behaviour of the system under study. Therefore, these approaches are essentially empirical, although success critically depends on a judicious selection of parameters and fitting strategies, which should be based on a deep knowledge on combustion rather than on purely trial-and-error searches. In fact, although having sound physical roots, the methods for the estimation of equivalence ratio from intensity ratios discussed in the previous section actually can be considered as a particular case of this general approach: once the couple of spectral bands have

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Fig. 18. Operating-point control scheme of premixed combustion based on natural flame emission observation. The low-resolution monochromator and the fuel flow rate controller, respectively, serve as detector and actuator. The equivalence ratio estimate (fest) is determined using a spectral database acquired off-line (calibration curves shown in Fig. 16) and provided to a PID control algorithm [63].

been selected (based on knowledge of flame spectroscopy) the calibration curve must be established by on-site testing at different equivalence ratios; nevertheless, calibration might be replaced in the future, at least in part, by universal laws eventually available or by calculations with validated chemiluminescence models. The optical emission of a flame may be characterized in many different ways. The works published include narrow band [60,77,79– 85] and broadband sensors [60,77,81,86–93] with spectral response in the ultraviolet [60,77,79–84,87,89,91], visible [84,86,87,91] and infrared regions [87,91] or spanning over several of them [60,77,90,92,94]. Some authors have utilized the signal of conventional flame detectors, apparently not looking for a specific spectral range [95–97]. Optical access must be provided for the sensors to see a part of or the whole flame, which can be achieved by installation inside the burner or at openings in the walls of the combustion chamber. Cooling and air purges are necessary to keep the sensors and optical windows clean and cool. As an example, Fig. 19 displays the optical system used in [92,93] and its installation in the tangential furnace of a coal-fired utility boiler; Fig. 20 represents the temporal evolution of the intensity and frequency of the light collected with this system in a 0.5 MW pilot furnace along a start-up sequence with fuel-oil and subsequent variation in coal feeding rate [98]. Widely different approaches have been applied to relate the optical signals with meaningful combustion parameters, including flame stoichiometry [60,77,79,80,82,83,87,89,90], pollutant emissions [60,80,84– 88,90,92,93], stability [84,85,95], fuel properties [60,91], various operating conditions [87,88,94,96,97] or detection of anomalous operation [87,96,97]. Optical data have been processed to extract a number of selected features, like average intensity [60,77,79,80,82– 87,90,92–94,97], standard deviation [60,84,85,90,94], characteristic frequencies [91–93,95] or other frequency-related parameters [88,89,91,92,96,97]. It is first necessary to establish a link between the measured variables (i.e., features of optical signals) and the target combustion parameters. This requires, in the first place, the development of an empirical database, including signals recorded in a set of tests covering the operating range of interest of the combustion equipment. In the second place, the observed behaviour needs to be represented by some kind of ‘mathematical fitting’. Once established, this fitting will serve to estimate unknown combustion variables (e.g., equivalence ratio, pollutant emissions) from selected features of the optical signals (e.g., intensity, frequency). In many cases, the relationship is expressed in the form of calibration curves

or explicit empirical equations. Other approaches have been also applied that afford more complex, multivariate relationships and, therefore, might be more suitable to combustion applications, usually characterised by highly-nonlinear and rarely known functional dependences among the variables involved. Artificial intelligence (AI) techniques have demonstrated to be well suited for combustion applications [12] and have been used in several of the works dealing with flame monitoring based on flame signals. The description of AI methods is clearly beyond the scope of this review and may be found, for example, in [12,99]. Only a brief introduction to one of these algorithms is given, as it may help to understand some of the results reported in this and other sections. Artificial neural networks (ANN, thereafter) are a kind of AI algorithms, inspired on the structure of biological neural systems. Multilayer perceptrons are one of the many types of ANN, whose basic structure is shown in Fig. 21. The basic unit of the network is the perceptron or neuron (Fig. 21(a)); it yields a result, yi, calculated as a function of its inputs, xj, according to the general P expressionyi ¼ f ð wij xj þ bi Þ, where f is the activation function (e.g., sigmoid), wij and bi are the weight and bias factors. The result generated by the network from a particular input vector depends, therefore, on the values for wij and bi associated to each neuron and their interconnections. These parameters are adjusted at the ‘training stage’, in which a set of inputs (features of the optical signals) and the associated outputs (combustion variables) must be shown to the ANN (supervised learning) in order to adjust wij and bi so as to reproduce the input–output relations associated to the particular system (the flame). A common learning strategy is backpropagation: the error between the output given by the ANN and the known output is calculated and propagated towards the previous layer in the network while the biases and the weights in the connections between the neurons are recalculated in order to minimize this error. The training data set is shown to the ANN for a predetermined number of epochs until the error is below a predefined threshold. Once trained, the ANN can be used to predict the outputs corresponding to new sets of input data. A multilayer perceptron was applied in [60] to predict equivalence ratio, NOx and CO emissions in a premixed combustor from the OH* signal (see Figs. 6, 7 and 12). Besides the average intensity, its fluctuation level (Fig. 22) was also taken as a characteristic behaviour of the flame that could serve to estimate its stoichiometry or even to predict the pollutant emissions. As shown in Fig. 23, this strategy yielded good results in estimating equivalence ratio, NOx and CO

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emissions using ANNs with a topology like that shown in Fig. 21. Similar results were reported in [60] with parameters derived from broadband radiation, for flames of natural gas-hydrogen blends and even in predicting the volume fraction of hydrogen in the fuel. Similar approaches have been applied in [77,87,94]. The dynamics of the plant was also accounted for in [92,93], where a Neural Network Finite Impulse Response (NNFIR) model yielded good results in predicting pollutant emissions from present and past optical data (Fig. 24). Fuzzy logic appears also as a suitable method for monitoring purposes and was successfully applied in [91] to identify the type of coal being burned from the frequency content of the optical signals emitted by the flame. Predictions based on optical flame data always involve some uncertainty, whose magnitude may be acceptable or not depending on the application sought. An effective method to evaluate this aspect is to test the results by means of control trials, where the burner is automatically adjusted by a closed-loop controller whose only inputs are predictions based on the optical sensors. This has

been accomplished in a number of works, including widely different control objectives, optical signals and processing approaches [60,82–84,86,90,93]. The good results reported in all those cases suggest that this might be a promising approach for the permanent and automatic optimization of combustion equipment, offering important advantages over conventional instrumentation (e.g., gas analysers) derived from the much faster response of optical sensors or from their ability to inform on the state of individual flames in multi-burner combustion chambers. 2.2. Laser absorption spectroscopy Absorption spectroscopy is a well-known technique for the measurement of gas composition, based on the attenuation of a light beam across a gas sample at specific wavelengths, which are characteristic of the different chemical species (absorption spectrum). Non-dispersive infrared or ultraviolet (NDIR or NDUV) gas analysers, commonly used in the industry for the analysis of

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combustion products, are based on this principle. However, extractive sampling has several limitations such as slow time response as well as practical problems associated with the use of probes (condensation, corrosion, pluggage), which make the continuous, real-time measurement in harsh combustion ambient very difficult.

As shown in the early work of Hanson and Falcone [100], tunable diode lasers offer very interesting possibilities for absorption measurements in high temperature flows. In-situ laser spectroscopy presents many advantages, permitting non-intrusive, in-situ measurements and a very fast response, what makes them attractive for real-time diagnostic and control of practical combustion equipment. Besides being fully established as a flue gas analysis technique [101], its suitability for in-furnace measurements in practical combustion plants has also been demonstrated in a number of works. For this reason, laser absorption spectroscopy appears as one of the most promising alternatives for the direct determination of meaningful variables (namely, concentrations and

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Fig. 23. Predictions of equivalence ratio, NOx and CO using the average and normalised standard deviation of OH* signal as the only inputs to the ANN, for premixed natural gas flames [60].

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Fig. 24. Measured and estimated NOx emissions in a coal-fired, 500 kW pilot plant with an NNFIR model applied to optical signals captured with the optoelectronic device shown in Fig. 19 [93].

temperature) inside combustion chambers. The main purpose here is to review some representative examples of application for infurnace measurements, in order to illustrate its potentiality as an on-line monitoring method in practical combustion installations. In the first place, a brief overview of the principles of TDLAS is included. Interested readers should seek for more detailed information in publications from the groups specialized on this technique (see the references quoted below) or in the reviews [9,10]. Laser spectroscopy is based on the attenuation of a laser beam propagating through an absorbing medium, according to the Beer– Lambert’s law:

In ¼ In;0 exp½  SðTÞgðn  n0 ÞNlTrðtÞ þ EðtÞ

(3)

where I is the monochromatic laser intensity at frequency n, measured before (In,0) and after (In) propagating a path length l through a medium with a density N of absorbing species. The attenuation depends on the absorption cross section, calculated as the product of the temperature line strength, S(T), and the line shape function g(nn0), as well as on disturbances like thermal background radiation from particles or furnace walls, E(t), and additional absorption effects, Tr (t), caused by broadband absorption from soot or dust particles, scattering by particles or beam steering due to fluctuations of the refractive index on the gas [102,103]. Due to these effects, the basic design (a fixed-wavelength laser source plus a detector) needs to be modified in order to achieve reliable measurements. One way to compensate for the influence of E(t) and Tr(t) is by tuning the laser at a pace much faster than the fluctuations, in order to assure that they are almost constant during the scan, so that the results can be corrected by post-processing of the intensity signals. This strategy can be easily implemented with tunable diode lasers, which also offer many attractive characteristics: robustness and a reliable commercial technology, capability for sensing of multiple target gases, high spatial and temporal resolution and low maintenance. The rapid and versatile continuous wavelength tunability of diode lasers permits the development of advanced wavelength or frequency modulation techniques to reduce noise and to increase sensitivity [10,104–107]. Modulation produces a broadening of the spectrum, by creating sidebands around the timeaveraged value, equally spaced in frequency, what permits a detection displacement into a regime where laser amplitude noise is negligible. Fig. 25 displays the basic configuration of a TDLAS instrument; in wavelength modulation mode, a reference signal from the laser is communicated to the signal processing electronics to track the characteristics of the illumination source along wavelength scans. Laser spectroscopy can be adapted for the measurement of species concentration, temperature, velocity and pressure. Typical

species that can be measured by using TDLAS are oxygen, carbon monoxide, water vapor, selected hydrocarbons or alkali species [102–105,108–119]. If several species must be monitored simultaneously, several lasers must be combined in a multiplexed configuration [10,108,110]. Time-domain multiplexing consists in sweeping each laser at different phases of a ramp function with a single detector which measures sequentially the absorption at different wavelength bands (see Fig. 26) [120]. Time multiplexing takes advantage of the rapid amplitude and wavelength tuning properties of a diode laser in order to achieve quasi-simultaneous measurements of different species. Other multi-gas sensors are based on wavelength-domain multiplexing which consists of separating the laser onto multiple detectors by using a reflection grating or dichroic beam splitters, as in the system shown in Fig. 27 [111]. Temperature measurements [108,109,111,113,115,119] can be obtained by probing two different transition lines and calculating the ratio of the integrated absorbance of each transition. TDLAS can be also adapted to extract a flow velocity from a Doppler shift applied to a laser beam [108,120,121] and pressure measurements from the collision broadening of the line shape [10,107,122,123]. An important advantage of TDLAS with respect to conventional gas and temperature sensors (e.g., extractive sampling or thermocouples) is its much faster response, with temporal resolutions ranging from a few Hz to several kHz [109,115,124–126]. Therefore, diode-laser based sensors are suitable for the study of flame dynamics and combustion instabilities [115,126] or unsteady pulsed combustors [127] as well as for the development of fast-

Fig. 25. Basic architecture of a TDLAS sensor [104].

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& Housing

Amplifier

Laser In

50 cm

Mirror

Absorber (FlueGas)

Reference Cell

Diode Laser

Photodiode

389

Detection Spher. Mirror Photodiode

Amplifier

PC(DAQ)

Diode Laser Driver Pulse Generator Function Generator 1&2

Fig. 26. Simplified optical setup for time multiplexing [120].

response controllers (the performance of a laboratory combustor was optimized within 100 ms in [125]). TDLAS is a line-of-sight technique, providing an integrated measurement along the laser path. In some applications, this can be an advantage with respect to point measurements as it provides directly an averaged value in cases with highly non-uniform fields [102] (see also the 2nd comment at the end of this reference). Nevertheless, it is also possible to determine spatial distributions by the application of tomographic reconstruction techniques to multiple line measurements [108,115]. The suitability and advantages of TDLAS for the measurement of gas concentrations and temperatures in real combustion plants have been demonstrated in a number of studies. Some of them have been performed in waste incinerators [10,102,103,118], where the fast response of TDLAS is an important advantage in order to detect fluctuations in stoichiometry and harmful emissions due to the variations in the properties or feeding rate of the waste, either in continuous- or in batch-fired plants. Fig. 28 shows the measurement system used in [118], consisting of four TDLAS sensors for the measurement of O2 and CO at different locations in the furnace of a 300 ton/day waste incinerator. TDLAS can also be applied to determine the actual distribution of residence time in high temperature regions, an important parameter subject to legal regulations in waste incineration; as shown by Schlosser et al. [120], this can be accomplished by detecting the concentration-time

history of alkali atoms at the furnace exit after pulse injection of alkali salts at the fuel feeding ports. The feasibility of TDLAS sensors for in-furnace measurements of gas composition and temperature in large coal-fired plants has been demonstrated in various works [110,115,119,128]. In these applications, the laser beam has to travel long distances across a highly radiating medium with non-uniform properties and high concentrations of particles, resulting in significant background radiation, very high attenuations and beam steering effects. Even under those difficult conditions, the results reported indicate that CO concentrations could be measured within an accuracy of 200 ppm [119] and the possibility to detect changes of 25 K in gas temperatures due to changing ambient conditions [115]. Teichert et al. [119] demonstrated the good performance of a motorized setup for the automatic alignment of lasers and detectors under conditions of near-zero visibility (strong background radiation and transmission w103–104). TDLAS is particularly attractive as a non-intrusive technique for the measurement concentrations and temperatures in pressurized combustors. Several works by Hanson et al. [115,122] demonstrate the feasibility of this diagnostic method in combustors of gas turbines, scramjet and pulse detonation engines. TDLAS can also offer interesting opportunities for time-resolved monitoring of different relevant parameters in internal combustion engines, as depicted in Fig. 29.

Fig. 27. Wavelength-multiplexed diode laser absorption arrangement [111].

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Fig. 28. Multipoint TDLAS measurement of O2 and CO in a waste incinerator: (a) O2 and CO measurements points, (b) apparatus setup in the furnace [118].

3. Flame imaging Visualization methods have been historically an invaluable diagnostic tool in fluid mechanics and combustion, since information on the spatial distribution of the relevant variables (even just as qualitative patterns) can be most helpful to describe, or even understand, important features of a flow or a flame. A wide range of

Fig. 29. Vision of diode-laser based sensors for the intake manifold, in-cylinder and the exhaust manifold of an internal combustion engine [115].

laser-based imaging techniques are nowadays available for combustion research (see, for example, [8–10]). In principle, some of those diagnostic methods might be also adapted for the monitoring of industrial flames for supervision or control purposes, but practical reasons still prevent their use for routine operation in industrial plants. In the current state-of-art, the recording of radiation naturally emitted by the flame appears to be the most feasible method; in particular, it avoids the need for seeding or external illumination and is amenable to low-cost, rugged CCD cameras. The use of vision-based methods for the monitoring of practical flames has been explored in a number of works, whose results confirm its suitability for industrial applications. Therefore, although laserbased techniques (e.g., LIF) are thought to offer a high potential for the development of industrial combustion sensors in the foreseeable future, they will not be treated in this review, which will concentrate on passive imaging techniques. The hardware needed to record self-illuminated flame images includes, basically, CCD cameras and frame-grabbers. If narrowband radiation has to be recorded, bandpass filters must be installed and, due to the dramatic drop in the intensity of the optical signal, intensified CCD sensors may be required. Most of the difficulties with the hardware are associated with installation in the combustion chamber and protection of instruments from high temperatures and fouling. The image must be collected through the chamber wall, which usually is relatively thick and hot. The usual solution consists of inserting through a wall opening fibre optics or endoscopes, protected with cooling jackets and air purges. For example, Fig. 30 shows the configuration of the measurement system used in [129]. Vision methods consist actually of analysing the emission spectra of flames which, as noted in the previous section, includes different components. Depending on how the imaging system is designed, the signal will contain different kinds of information, giving rise to the various approaches analysed in the following sections. Pyrometry equations can be applied to radiation bands dominated by black-body emission in order to derive temperature information. Chemiluminescence peaks may be selected to detect the presence of excited radicals intimately related to combustion reactions. Alternatively, non-filtered flame images can be taken as a signature of the process rather than attempting to look for

Fig. 30. Schematic of the installation of the CCD camera in a combustion chamber [129].

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a specific physical interpretation. A final section is devoted to image processing methods suitable to convert images into practical information for monitoring purposes. 3.1. Pyrometry-based temperature mapping The amount of energy emitted by solid bodies depends, among other factors, on its absolute temperature. Therefore, it is possible to calculate the body temperature from the radiation collected by an optical sensor, which is the basic idea behind radiation thermometry or pyrometry. This is a well-known non-contact temperature measurement technique, described in many sources (like the thorough book [17]). For convenience, the basic equations and concepts are briefly introduced here. The spectral distribution of energy emitted is described by the Planck equation,

Jðl; TÞ ¼ 3ðlÞ



c1

l5 ec2 =lT  1



(4)

where J(l,T) is the power emitted at wavelength l per unit area per unit wavelength by a solid surface at absolute temperature T with emissivity 3(l) and c1, c2 are the first and second Planck’s constants. Fig. 31 represents J(l,T) for a black body at different temperatures. The radiation (watts) collected by a detector may be expressed as R(l,T) ¼ f(l)J(l,T), where f(l) includes geometrical parameters (distance between the receiving optics and the body, and solid angles subtended by the detector with origin at the emitting surface and vice versa) and optical properties of the medium and the instrument. Therefore, the temperature of the body can be derived from the signal of the radiation detector if l, 3(l) and f(l) are given. In many situations, however, f(l) and emissivity are not known with sufficient accuracy. Measurement errors due to these uncertainties are greatly reduced through the ‘two-colour’ method, based on the ratio between the radiative powers R(l1,T) and R(l2,T) detected simultaneously at two different wavelengths, l1 and l2:

Fig. 31. Emission spectra of a black body (3 ¼ 1) at different temperatures, according to Eq. (4).

391

!

   3ðl2 Þ f ðl2 Þ l51 Rðl2 ; TÞ c2 1 1 exp  ¼ 3ðl1 Þ f ðl1 Þ l5 T l1 l2 Rðl1 ; TÞ

(5)

2

where the Wien’s approximation (ec2 =lT >> 1) has been adopted, since c2/lT>>1 for the wavelengths and temperatures of interest in combustion. Eq. (5) includes the ratio of emissivities at the two wavelengths, which can be known with much higher accuracy than their absolute values, especially if l1 and l2 are relatively close. If the same receiving optics is used for both wavelengths, the ratio f(l2)/f(l1) does not depend on the geometrical parameters (e.g., emitting surface area –equivalent to number of particles in a flame- seen by the detector) but is only affected by relative changes between l1 and l2 of the spectral properties of the medium, optical elements and detectors. Temperature determination using Eq. (5) is called two-colour or ratio pyrometry. Since each pixel of a CCD sensor is actually a radiation detector, bandfiltered images of the radiation spontaneously emitted by a body can be converted into 2-D temperature distributions. Commercial thermographic cameras, based on the application of Eq. (4) to infrared radiation, are used in many technical fields. Pyrometry imaging might constitute a most valuable diagnostic tool for combustion applications, where temperature information is of particular relevance. The possibilities in this respect have been investigated in a number of works aimed at measuring temperature distributions in practical combustion equipment. Those experiences also reveal that obtaining meaningful temperature values from radiation collected in combustion chambers is not straightforward at all. Besides, in common with any application of pyrometry, the results may be significantly affected by uncertainties in emissivity values, especially for non-grey bodies like soot particles. In principle, pyrometry can be more easily applied to radiation originated in solid boundaries than in flames, where each pixel sees emitters at different temperatures and distances. Refs. [130,131] used infrared thermographic cameras to measure the surface temperature of the fuel bed in incineration plants. Fig. 32 shows the location and field of view of the camera used in [130]. Soot and gases existing between the fuel bed and the camera are a potentially significant interference, as they absorb a part of the radiation emitted by the bed and their own emission also reaches the sensor. A careful analysis of the most suitable spectral band is required to minimize those error sources, like that presented in [130,131], where 3.9 mm was finally selected. At this wavelength the major gaseous products are practically transparent and it is large enough to limit the influence of soot particles, whose emissivity and absorptivity decrease with wavelength. In [130], the temperature distribution calculated was used for the automatic adjustment of the amount and distribution of underfire air, reporting significant improvements (in terms of stability and pollutant emissions) with respect to the conventional control method based on the concentration of oxygen in flue gases; these benefits are ascribed to the much faster response of the pyrometry sensor and to its ability to yield spatially resolved information. In a very similar application, [131] accomplished a detailed analysis of the interferences causing the radiation seen by the camera to be different from that actually emitted by the bed surface: absorption and emission due to the flame and products, reflection of radiation originated in the gas stream and heat-transfer walls. A comprehensive zonal method was applied to resolve those mutual interactions, yielding a map of the temperature of the fuel bed, the walls and the combustion gases. This work may be taken as an example of how sensors and models can be combined to interpret signals not having a real meaning (called ‘dirty images’ in [131]) in terms of actual physical parameters.

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(4 and 8 cameras where used in [134] and [137], respectively). As an example, Fig. 33 displays the location of 8 CCD cameras in a 200 MWe coal-fired boiler (Fig. 33(a)), a set of captured images (Fig. 33(b)) and cross sections of a reconstructed 3-D temperature distribution (Fig. 33(c)) (from [137]). Temperature distributions in sooty gas flames were estimated in [136] by applying two-colour pyrometry to images collected at 800 and 900 nm, whereas [134,137] made use of the red and green channels of RGB video cameras installed in a coal-fired boiler. Comparisons with thermocouple probes indicate that absolute temperatures can be estimated with uncertainties <50 K [135–137]. As mentioned for the various studies, the wavelengths are generally selected in the upper half of the visible region or in the infrared, always avoiding emission bands of the main gaseous emitters (usually, H2O and CO2) which may lead to significant deviations from the black-body spectrum. Shorter wavelengths are usually avoided since, due to the strong dependence of radiation intensity on l (see Eq. (4) and Fig. 31), the amount of energy collected may become too small to be measured with accuracy. An exception is the work of Leipertz et al. [135], where temperature was derived from spectrally-resolved UV emission. In this case, instead of solving Eq. (5) for two of wavelenghts, the spectrum collected was fitted to Eq. (4), equivalent to averaging two-colour results for many pairs of wavelengths. 3.2. Chemiluminescence imaging

Fig. 32. Arrangement of the thermographic camera in an incineration plant [130].

More commonly, the objective is the measurement of flame temperatures. Obviously, pyrometry can only be applied to particleladen flames, with radiation dominated by continuous, Planck-type spectrum, which excludes, for example, blue flames. Shimoda et al. [132] and Huang et al. [129] applied the two-colour technique to bandfiltered images (at 600/700 and 650/700 nm, respectively) to estimate temperature distributions in radiating flames. Ref. [132] used the estimated temperatures to predict unburnt carbon in a 175 MWe coal-fired boiler using a simplified model, reporting good agreements with respect to measured unburnt fraction. The results described in [129] were obtained in coal and sooty gas flames in a 500 kW test facility, and also compare well with reference data from a pyrometer and a thermocouple. Wang et al. [133] estimated NOx emissions in a 300 MWe coal-fired utility boiler from temperatures derived from the green and red channels (centred at 546 and 700 nm, respectively) of an RGB video camera. It should be noted that pyrometry is a line-of-sight technique and each pixel accumulates contributions from all the emitters included in its field of view. As a result, the temperature calculated from the radiation detected by a pixel should be interpreted as some kind of average along the line-of-sight. Due to the steep variation of emitted energy with temperature (especially for wavelengths shorter than that of peak emission in Fig. 31), this average is biased towards peak temperatures; however, determining the precise meaning of this average is very difficult as it is also strongly influenced by the spatial distribution of the concentration of emitting particles over the flame. In order to obtain richer and more precise data, a number of works have applied tomographic reconstruction techniques to estimate spatial temperature distributions in industrial plants, in the form of 2-D maps in a cross section [134,135] or 3-D maps in the whole flame volume [136,137]. This requires processing at least two simultaneous images with cameras installed at different locations [135,136], although complex flow patterns are better resolved if more flame views are available

As it has been already discussed in a previous section, the detection of chemiluminescent emission associated to specific excited radicals can yield relevant information on the combustion process. Chemiluminescence maps can be even more revealing than point or global data in order to describe the structure of the flame or heat release patterns. This is commonly accomplished with intensified CCD cameras fitted with bandpass filters at selected wavelength bands. Conventional RGB cameras might also be used to estimate chemiluminescent emissions due to CH* and C2* from the blue and green channels, respectively, as shown in [138]. Refs. [23,25,27] are some examples of research studies where chemiluminescence imaging has been used as reference data for the development and validation of kinetic models in simple flame configurations. Since the images captured perform a line-of-sight integration, they need to be deconvoluted in order to obtain local values, which depict a notably different flame pattern, as shown in [72,139,140] and illustrated in Fig. 34. Chemiluminescence mapping has also been a most useful tool for the analysis of the changing flame pattern in oscillating flames. Vortex-driven acoustic instabililities were studied in [141] by means of phase-averaged C2* images; in this early work, a single detector was displaced on a 2-D grid instead of using a CCD sensor. Fig. 35 [140] displays three Abeltransformed OH* images captured at different phases with respect to the dynamic pressure signal and shows clearly the dramatic change in the configuration of a lean premixed flame along a pressure cycle. If the goal is the development of an industrial combustion sensor, the interest of selecting a particular chemiluminescence wavelength might not be so obvious. In fact, most of the works analysed in the next section use non-filtered visible flame images (many of which, nonetheless, mostly consist of chemiluminescent radiation). However, it should be noted that the selection of the spectral band may have an important impact on the characteristics of the images acquired. For example, Fig. 36 displays visible images of turbulent premixed flames at various equivalence ratios together with their corresponding OH* chemiluminescent emission maps [68]. Although differences are not dramatic, filtered images

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393

Fig. 33. Reconstruction of 3-D temperature maps in a 200 MW coal-fired boiler (from [137]). (a) Furnace and location of the eight CCD cameras, (b) Eight simultaneous images and (c) cross sections of a reconstructed 3-D temperature map.

highlight specific zones where heat release rates are supposed to be highest; therefore, although not specifically proven in any of the works reviewed, it might be argued that spectrally-selective imaging may be advantageous because it produces patterns that are more intimately related with the combustion process than broadband radiation. Filtered images at specific chemiluminescence bands have been used in a few works oriented to industrial combustion sensing. Ref. [142] treated intensity maps associated to OH* (band 250–400 nm) as a fingerprint of spray flames at different operating conditions. A control loop based on geometrical features of images filtered at 430 nm (related to CH*) was developed in Ref. [143]. The intensity of chemiluminescence signals due to OH* and CH* from the flame root was correlated with NOx emission from diffusion gas flames in [144]. It should be noted that none of these studies derived physically-meaningful information from the captured images, which instead were taken as characteristic signatures of different combustion regimes. The approaches applied to derive practical information from images in these and other works are reviewed in a subsequent section.

3.3. Broadband flame imaging Video cameras are a common instrument in some industrial combustion facilities, such as large utility boilers. However, their use is normally limited to on-line display in monitors installed in the control room and interpretation of the images is left to subjective analysis by the operators, based on their previous experience [145,146]. This situation demonstrates that flame images do contain relevant information for the monitoring of industrial flames, but also that suitable processing methods are needed to convert visual data into some kind of quantitative, meaningful information in an automatic and systematic manner. So, it is not surprising that a significant number or works oriented to this objective have been published, especially during the last decade. This section briefly analyses the studies based on nonfiltered images, not addressed in previous sections. Most of the studies use RGB video cameras with response in the visible range of the spectrum. Therefore, in general, the images combine chemiluminescent radiation due to CH*, C2* and CO2* plus black-body emission from char, ash or soot particles. The only

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Fig. 34. Comparison of a line-of-sight OH* image of a partially premixed CH4-air flame at f ¼ 1.36 (left hand side) and its tomographic reconstruction (right hand side) (from [72]).

exceptions detected are Refs. [147] and [148], where infrared images were captured; [148] had to look for the H2O emission bands in the near infrared because the hydrogen flames studied had a very low intensity in the visible range. The huge amount of information contained in an image is thought to constitute the main difficulty for their interpretation. One of the problems is the high demand of computing power needed to process the large volume of data generated by the CCD sensor. Advanced processing techniques, like Principal Component Analysis [149–151] or Principal Value Decomposition [152], have been applied to convert the original image into a lower dimensional matrix or to efficiently extract selected parameters representative of flame dynamics from high-speed records [153]. Another, more fundamental issue is the difficulty to quantitatively characterise a flame in terms of 2-D data for monitoring or control purposes. This is probably the main motivation behind the usual approach of reducing the image to a few selected features calculated from the matrix of intensities (1 or 3 data per pixel, for monochrome and RGB images, respectively). The features analyzed consist of geometrical parameters [147,148,154–157] or different variables related to the level and spatial distribution of luminosity [150,152,155,157] or colour [150,158,159]. As an example, Fig. 37(a) contains the definition of some geometrical parameters defined for coal flame images, whose variation with burner load is shown in Fig. 37(b). Temporal evolutions or characteristic frequencies of flame parameters have also been derived from high-speed image records, as relevant features of the process either by themselves or combined with other image properties [149,152,153,157,160]. As it has been already mentioned for bandfiltered imaging, vision methods collect line-of-sight information without depth resolution and may yield spatial luminosity distributions that are very different from the actual ones. Tomographic reconstruction techniques can be applied to broadband images to obtain spatiallyresolved luminosity distributions [139,157,161], which may be more

Fig. 35. Sequence of Abel-transformed OH* chemiluminescence images along a cycle of dynamic pressure in an unstable premixed flame. The images correspond to the following points in a pressure cycle: minimum (ph1), zero-crossing (ph3) and maximum pressure (ph5) (from [140]).

representative of the real flame pattern (see Fig. 38). Stereoscopic reconstruction has also been used successfully to describe three dimensional flame structures from two images acquired with a single CCD camera fitted with a special optical adapter [163].

3.4. Processing and interpretation of image data The previous sections describe widely different approaches to collect direct information from flames in the form of 2-D or 3-D maps using CCD cameras. However, in many cases this is only a first step and sensorial information must still be converted into some kind of meaningful information suitable for the diagnostic or the control of a combustion process. In some particular applications, the data directly derived from the images may be enough to characterise a flame for practical purposes; for example, the knowledge

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395

Fig. 36. Visible images (top) and OH* chemiluminescence maps (bottom) of swirling premixed flames of methane at various equivalence ratios (from [68]).

of peak temperatures or their distribution in a cross section may serve to directly evaluate the process, or the visible flame length can be sufficiently revealing in some cases (e.g., in glass furnaces or as an indicator of staged combustion). In most instances, quantitative image data are not meaningful by themselves but, before they can be used for practical purposes, need to be related to known behaviours or correlated with relevant combustion parameters (e.g., unburnt or pollutant emissions). This is still a challenging objective and, in the authors’ opinion, constitutes the most critical aspect that needs to be solved in order to develop advanced monitoring and control methods based on flame images. Different approaches have been applied in the past for the processing and interpretation of image data. Many of the works are based on the analysis of some parameters extracted from the flame images, related to their geometry, luminosity or colour. The existence of relationships between combustion conditions and selected image features has been demonstrated in a number of parametric studies [147,148,150,152,154–159] which may enable the development of empirical correlations designed to estimate meaningful process information from flame images. For example, Yu and MacGregor [150] used partial least squares to predict NOx and SO2 emissions and heat losses in an industrial boiler from a set of image features, and Rua˜o et al. [144] found a defined correlation between NOx emissions and chemiluminescence signals due to OH* and CH* in diffusion methane flames. In other cases, image-derived parameters where coupled with simplified physico-chemical models to estimate NOx

emissions [154] or unburnt carbon [132] in coal-fired utility boilers. Artificial neural networks (ANN) have been applied as a powerful fitting tool to relate image features with relevant combustion parameters. Feed-forward ANN have been used to identify combustion states from a few geometrical/luminous parameters [143,155]. Wang et al. [133] applied a back-propagation ANN to relate a set of image parameters (average and deviation of temperature, ignition distance) with NOx emissions. Bae et al. [164] developed a neural network to distinguish flame on/off conditions from the analysis of luminosity distributions. The use of features extracted from flame images for control or optimisation purposes has been examined in a few works (all in laboratory rigs). Lu et al. [155] coupled an ANN-based state predictor and a closed-loop control to bring the system to the desired state. Tao and Burkhardt [143] combined ANN and a fuzzy controller to reach optimal air and fuel flow rates, based on flame brightness and length. Soot formation was avoided in [159] using a fuzzy controller whose inputs were the levels of blue and orange in flame images. A different, relatively little explored approach consists in treating the image as a data set that can be processed as a whole in order to ‘classify’ or ‘identify’ the image as representative of a particular combustion state. The main drawback with respect to feature extraction is the large amount of data to be processed; however, this approach may be advantageous and more generalizable, as it avoids information loss due to data reduction and does not require

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Fig. 37. Study of image features in a pulverised coal flame [156]. (a) Definitions of geometrical parameters of the flame; (b) Variations of geometrical and luminous parameters with furnace load. Rf is the fraction of cross-sectional area of the viewing field occupied by the flame; Bf and Uf define the average intensity and its degree of uniformity (100% means fully uniform), respectively, in the luminous region of the image; the other parameters as defined in (a).

a careful design stage to select the most representative parameters or to develop the empirical rules needed for flame monitoring in a new application. Allen et al. [142] developed a feed-forward neural network to identify combustion states in a spray flame rig using the whole set of pixel data as input. This procedure was

validated by using the ANN state predictor to manipulate in closedloop the atomizing air in order to bring the system to the desired combustion regime (Fig. 39). Full flame images were processed in [165] using self-organising feature maps, a kind of neural network capable of classifying and grouping images according to their visual

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397

Fig. 38. Grey-level image collected with a video camera in the visible range for a swirling premixed natural gas flame (f ¼ 0.8) (a) and its tomographic reconstruction (b) [162].

characteristics. Fig. 40(a) displays the image space map built from 49 tests with an air-staged burner, including variations in three burner settings: the distribution of air between two separate injectors and the swirl numbers of both air streams. Once the feature map has been developed by training with known combustion states, unknown flames can be readily classified by direct processing of pixel data. It should be noted that a basic assumption behind any of the methods mentioned in this section is that their geometrical/luminous properties maintain some relationship with operating conditions or system performance. The type of classification provided by self-organising feature maps can be particularly useful to evaluate this hypothesis. Fig. 40(b) represents the NOx emissions associated to each of the flame images shown in Fig. 40(a), whose relative position was solely determined by their visual characteristics; the automatic grouping of the different NOx levels in Fig. 40(b) is thought to support the strong correlation between the aspect of a flame and its physico-chemical properties. In general, flame images are a good candidate for processing with pattern-recognition algorithms (self-organising feature maps fall in this category), currently utilized to analyse complex data in a wide range of applications. Cepstral analysis, commonly used for speech recognition, was applied in [165] to identify flames from visible images recorded in a lean premixed combustor (Fig. 38(a) is one of them), yielding good predictions for important combustion parameters (Fig. 41). The applicability of this procedure to other combustion situations was partially validated by the good results achieved by using exactly the same algorithm to tomographyreconstructed images (Fig. 38(b) is an example image) [162] and to air-staged diffusion flames (case illustrated in Fig. 40a) [165]. Finally, it should be noted that although the methods mentioned in this section have been applied to self-illuminated flame images, any of them is also expected to be suitable for any flame imaging technique, such as planar laser techniques. Moreover, the performance might still improve, since the information generated with those methods is usually more intimately related to flame properties and exhibits stronger gradients and sensitivity to operating conditions than, for example, broadband luminosity in the visible range. 4. Pressure fluctuations Flames are known to produce noise, in amounts that usually are far higher than that due to the corresponding isothermal flow. Pressure fluctuations are primarily generated due to localized heat release fluctuations, causing a sudden expansion of fluid parcels which act as elemental monopole sources. With some simplifying assumptions, the pressure wave equation in a combusting flow can be written as (see, e.g., [166])

g  1 vh0 1 v2 p0  V2 p0 ¼ 2 2 c vt c2 vt

(6)

where p’ is the fluctuating component of pressure, c is the speed of sound, g is the ratio of specific heats and h’ is the fluctuation of heat release rate. Eq. (6) is a non-homogeneous wave equation with a source term proportional to the time derivative of heat release, representing local noise generation due to unsteady combustion rates. This is, obviously, a simplified formulation (more rigorous studies are quoted below), but expresses adequately the close relationship between noise generation and flame dynamics. Aerodynamic noise due to fluctuating turbulent stresses is a strong function of velocity and, in most cases, its magnitude is much smaller than that of the right-hand term in Eq. (6) in subsonic flames [167,168]. Unsteady heat release rates can result from different effects. Turbulence is one of them, present in most practical flames. According to Eq. (6), the spatial distribution of turbulent heat release rate fluctuations represents a distributed noise source. The global sound emitted due to this effect is sometimes called ‘combustion roar’ and is normally characterised by a broadband spectrum, typical of large turbulent flames [169,170]. The equations describing noise generation in turbulent flames and analyses of the associated frequency spectra can be found, among others, in [168,171–173]. The noise radiated by a flame would be expected, therefore, to be closely related to the distribution of unsteady heat release rates. This general idea was further developed by Strahle et al. [174,175] to propose a flame diagnostic method based on the analysis of the acoustic signature of turbulent open flames. As confirmed by measurements of ionization and C2* chemiluminescence, a good agreement was observed between the actual spatial distribution of h’ and their predictions from pressure spectra. Although no subsequent application of this technique has been found in the open literature (optical techniques are nowadays advantageous for this purpose), these studies clearly demonstrate that the information contained in acoustic flame signals is intimately related with the very nature of the combustion process and, therefore, may offer interesting possibilities for flame monitoring. Other effects can contribute to the sound emitted by a flame. Heat release fluctuations can be generated by other, non-turbulent flow instabilities, like vortex shedding or precessing-vortex core [176]. In enclosed flames, the noise spectrum is not only determined by the flame emission but it can be strongly influenced also by the acoustic characteristics of the combustion chamber [177]; therefore, the acoustic signal is the result of the particular flameenclosure combination and may not be an intrinsic characteristic of the flame. This is relevant for flame monitoring applications, as the

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Fig. 39. Schematic diagram of the controller system (a) and closed-loop response to cyclical step input (b) with a neural network state predictor based on chemiluminescence images of spray flames. [142].

acoustic signature of a burner may change when it is installed in different combustion chambers. Under certain circumstances, pressure fluctuations can modify heat release rates in such a way that, combined with the effect described by Eq. (6), a positive feed-back is established and perturbations grow rapidly leading to the onset of combustion instabilities. This is a serious and well-known issue in practical combustion equipment, of particular relevance in lean premixed combustors, which keeps motivating very important research efforts. Among others, an important difficulty in this field is the wide variety of triggering and coupling mechanisms involved in combustion instability phenomena (see, for example, [33,166,177–181]). It may be said that the most common motivation for the measurement of dynamic pressures in combustors is, precisely, the detection, characterisation and/or correction of combustion instabilities. Microphones and fast-response transducers (e.g., piezoelectric) are the sensors normally used to measure pressure fluctuations in combustion chambers. Ideally, the sensible element should be flush-mounted on the wall of the combustion chambers. However,

in many cases, this is not possible and the sensor must be protected from high gas temperatures or radiative heating. The usual solutions are the connection of the transducer to the probed volume through tiny orifices (sometimes, water-cooled) or sound guides. In those cases, the installation must be carefully designed, since the acoustic waves are transmitted across the medium between the combustion chamber and the sensible surface, and the geometry of this volume may have a non-negligible influence on the magnitude, spectrum and phase of the pressure signal finally detected [182,183]. In many cases, the monitoring of pressure fluctuations is a means to detect instability problems, either due to self-sustained oscillations or to the proximity of the stability limit of the burners. For example, dynamic pressure transducers are becoming increasingly common in industrial gas turbines with lean premixed combustors, as this technology is relatively prone to combustion instabilities. Fig. 42 shows a dynamic pressure transducer installed in the burner flange of a large commercial gas turbine. When the pressure signal exceeds a defined threshold, a safety procedure may

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Fig. 40. Pattern flame images associated to the neurons of a self-organising feature map (a) and associated NOx emissions (greyscale: white indicates the highest value, 44 ppm, and black the lowest, 14 ppm) (b), from tests with a gas burner and variations of three burner settings: distribution of air between two independent injection ducts and swirl number of both air streams [165].

be started to bring the system to stable operation (e.g., by increasing equivalence ratio). Of particular interest is the diagnostic of stability problems for the implementation of automatic combustion control procedures; in fact, in the authors’ opinion, the risk of reaching an unstable region or the blowout limit is probably the main single obstacle for the implementation of advanced flame optimization strategies. A robust control procedure was demonstrated in commercial gas turbines in [185] based on the permanent monitoring of dynamic pressures during modifications of operating

Fig. 41. Comparison between predictions with cepstral analysis applied to flame images and measurements of equivalence ratio, CO and NOx emissions (divided by 3000 and 50 ppm, respectively) in premixed flames of natural gas [165].

conditions to minimize NOx emissions. Even though the control actions favouring pollutant reduction may have a deleterious effect on flame stability, this strategy allowed achieving the minimum NOx levels compatible with a safe operation. A similar idea, although at laboratory scale, was evaluated in [81,90], where burner settings were automatically modified in searches for optimal operation. The setpoint issued by a NOx-minimization algorithm was altered when the standard deviation of pressure exceeded a defined threshold in [81]. Fig. 43 displays the results reported in [90] for tests with a control procedure designed to attain the lowest NOx emission compatible with acceptable levels of pressure fluctuations during changes in the composition of natural gas/CO2 blends burned in a premixed combustor. Prevention and/or correction of combustion instabilities can be achieved by means of the so-called ‘active instability control’ methods, which use some type of external excitation (such as acoustic forcing or fuel modulation) to counteract self-induced oscillations. Refs. [178,181,186,187] are some thorough surveys on

Fig. 42. Schematic of the Siemens model Vx4.3A heavy-duty gas turbine with active control setup [184].

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Fig. 43. Control tests to minimize NOx emissions and keep pressure fluctuations below a defined threshold of 0.17 kPa during ramps of ascending (left) and decreasing (right) volume % of CO2 in natural gas-CO2 mixtures burned in a swirl premixed combustor. The volume fraction of CO2 in the fuel was changed in 1% increments along the time (toothed curves). The evolutions of the standard deviation of pressure, sP, and of NOx are shown, respectively, in the top and bottom graphs. Dotted lines correspond to the evolution at fixed equivalence ratio (control off) and solid lines are the results when equivalence ratio is automatically corrected to minimize NOx emissions while limiting sP below 0.17 kPa. [90].

the fundamentals and the different versions of active controllers. Closed-loop designs require detecting some sort of dynamic signal representative of flame oscillations. Fast-response pressure sensors are the most common instrument for this application [181,187], with the advantage over other sensing methods (e.g., chemiluminescence) of simultaneously quantifying the magnitude of the pressure fluctuations, as an important quantity directly related with the severity of the problem. An example of active control setup can be appreciated in Fig. 42, where the pilot fuel is modulated with a high-speed valve, commanded by a controller connected to a pressure transducer. Different works have explored the possibilities of extracting relevant information on the combustion process from fluctuating pressure signals. Ref. [188] postulated that flame stoichiometry might be estimated from the noise level of premixed flames. Changes in fuel/air ratio were observed to modify the spectrum of noise in a high pressure burner rig [189], although no further analysis is reported. Rea et al. [190] demonstrated that a detailed analysis of frequency spectra can yield information of practical relevance for the operation and health monitoring of large gas turbines. They compared pressure spectra measured during normal operation with that recorded after a turbine overhaul and, from previous experience, interpreted changes in the amplitude at selected frequencies in terms of variations in ambient conditions, fuel composition, operating conditions and the onset of component damage. In [191], different features of acoustic signals were related with the proximity to blowout in premixed flames. The analysis of time records and spectra of dynamic pressures revealed that flames near blowout were characterized, respectively, by higher kurtosis values (indicative of intermittent events) and by stronger fluctuations at specific frequency bands. Lieuwen [192] proposed an original approach to evaluate the margin to instability from dynamic pressure data. He evaluated the decay in the envelope of

the autocorrelation of the pressure signal and interpreted the result as an indicator of a damping coefficient. High/low levels of acoustic damping are expected to characterise stable/unstable operation, whereas its relative decrease due to changes in operating conditions is interpreted as a displacement towards the limits of the stability range (see Fig. 44). The enormous amount of data contained in time records of pressure data, or in derived frequency spectra, make them susceptible to be treated as a fingerprint of a particular flame condition. As Boyd and Varley [193] concluded in their review on the application of passive acoustic techniques to various chemical processes, this approach has the advantage of enabling the use of pressure data in problems where the physics behind acoustic emission is not completely understood and/or quantitatively described. Nevertheless, the lack of physical reasoning makes these methods critically dependent on the appropriate selection of the representative features, the development of a sufficient and reliable database and on the design of a robust fitting procedure. A few works have explored the use of acoustic signatures for flame monitoring. Tan et al. [194] evaluated the influence of burner slag deposits in a pilot coal burner on the characteristics of noise signals. This study demonstrated that the existence of such deposit could be predicted with very high success rates by applying a neural network-based classification procedure (selforganising maps) on the acoustic signals recorded. The different combustion regimes of a natural gas diffusion burner with airstaging were characterised in [195] in terms of their associated acoustic spectra. As a means to assess the existence of a defined relationship between combustion state and noise signature, a multilayer perceptron network was trained to predict NOx emissions taking as inputs the areas under six frequency bands of the power spectral density function (see Fig. 45). This strategy yielded a relatively good agreement with actual measurements

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Fig. 44. Influence of premixer velocity in a gas turbine combustor simulator on the amplitude and damping coefficient of the 430 Hz mode of the pressure signal. This mode was extracted from the time record of pressure by bandfiltering the raw data with a fourth-order Butterworth filter and the damping coefficient was evaluated from the decay in the envelope of the autocorrelation of pressure [192].

and, as reported in [94], NOx predictions based solely on the analysis of acoustic spectra guided successfully a closed-loop controller designed to iteratively adjust the burner settings in order to minimize pollutant emissions. 5. Probing techniques Probing techniques have been used for many years to characterize combustion processes with different purposes. A standard

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application in the industry is the measurement of different variables (gas composition, temperature, particulates, etc.) in flue gases. However, probe-based methods have also been, and still are, a most valuable tool in flame research. Refs. [7,196,197] are some comprehensive reviews on in-flame probing techniques. Special probe designs have been used for the detection of different variables (temperature, velocity) and for extraction of gaseous and condensed samples for subsequent characterisation, including specialised applications like the measurement of radicals or intermediate species [198]. Intrusive measurement techniques are, in principle, suitable for installation and use in practical combustion chambers. Probes can be designed to withstand the harsh environments of industrial flames, using adequate materials and, probably, a protective cooling jacket. For example, Refs. [199,200] report local temperature and composition data acquired inside large coal-fired furnaces in specific test campaigns. However, the use of probes for routine monitoring of combustion equipment is mostly limited to the characterization of flue gases. Two main reasons are thought to explain this fact. On the one hand, the permanent insertion of probes inside a flame pose a number of practical difficulties, especially in the presence of particles or drops (build-up of carbonaceous or mineral deposits, corrosion, pluggage of suction line). On the other hand, the difficulty in deriving useful information from point measurements is probably a more fundamental shortcoming, given the large spatial variations of any combustion parameter over the flame volume. Only a few examples of probe-based monitoring of practical flames have been found. One of them is the assessment of imbalances of air-to-fuel ratio in multi-burner furnaces. The other one is the use of ionization probes for real-time monitoring of flames. Both are described in the next subsections, preceded by a short reference to sensors for flue gas analysis since, although not

Fig. 45. Acoustic spectra for different air staging ratios in a gas diffusion burner indicating the defined frequency bands (left) and comparison between actual NOx emissions and predictions with a neural network (right) (based on [94]).

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properly a flame monitoring technique, they constitute an integral part of any combustion equipment. 5.1. Analysis of flue gases The analysis of flue gases is, by far, the most extended method for the monitoring of practical combustion equipment. A wide range of measurement techniques is available to quantify the oxygen excess (i.e., air-to-fuel ratio) as well as the emission of a number of undesirable species representing unburnt fuel (CO, unburnt hydrocarbons) or polluting agents (e.g., NOx, SO2). These are important combustion parameters and, differently to the other sections in this article, their interpretation does not pose any particular difficulty but research efforts are centred on the development of new devices. The most traditional method is extractive sampling, i.e. the analysis of a gas stream aspirated through a gas sampling probe inserted into the exhaust. A wide range of commercial solutions exist for both gas sampling and analysis (see, e.g., [201]); usually, the probes are installed in low temperature zones and do not require special materials or designs. New developments in this area are mainly oriented to on-line detection of different pollutants, like PAH or heavy metals. In-situ detection by means of solid state sensors is an alternative probing method, which is receiving much research effort and already offers a number of mature technological solutions. In particular, zirconia sensors have become a common choice for the measurement of oxygen concentration in combustion products. Although the technological development of solid state gas sensors has been mostly associated with the automotive industry, their applications now include a wide range of technical areas: combustion (industrial and domestic), ambient monitoring, biological systems, etc. Refs. [5,202–206] are some reviews on the fundamentals of solid state sensors and their use for the analysis of combustion products. Only a brief summary on the basic aspects and current possibilities is given in what follows. The zirconia oxygen sensor is based on the lambda probe, developed in the 60’s and initially restricted to on-off control of air/ fuel ratio around the stoichiometric point in automotive applications. Modifications of this original design are nowadays used in the exhaust of industrial combustion equipment to measure oxygen concentrations, with designs suitable for temperatures up to 1400  C. Fig. 46 displays the basic configuration of a zirconia cell. Differences in oxygen partial pressure across the electrolyte (usually, yttria stabilised zirconia, YSZ) cause a flow of O2 ions towards the low partial pressure side, creating a voltage difference between the two platinum electrodes. This electromotive force obeys the Nernst law,

Fig. 46. Basic configuration of a potentiometric zirconia sensor. [203].

so that Us is proportional to the logarithm of the ratio between the partial pressures of oxygen in the reference chamber (normally, 0 , and in the exhaust gas, P 00 . For a fixed P 0 at the filled with air), PO O2 O2 2 reference electrode, the unknown oxygen concentration can be readily derived from the measured electromotive force, Us (potentiometric mode). However, the response is highly non-linear and in practice this device is only used to distinguish fuel-lean and rich mixtures and to resolve oxygen concentrations close to stoichiometric conditions. The cell must be operated at controlled and relatively high temperatures, because oxygen diffusion through the ceramics only takes place above w350  C, the signal of the electrochemical cell is temperature dependent and its response time decreases with its temperature, with a limit at about 900  C due to degradation of the ceramic material; internal heaters usually are adjusted to keep the sensor at some fixed point in the range 700– 800  C. Alternatively, the cell can be operated in the amperometric mode. In this case, oxygen is electrochemically pumped by setting a voltage difference between the two electrodes. A diffusion barrier (a small orifice or a porous layer) limits the flow of oxygen molecules from the gas sample to the electrode and, hence, the current in the circuit. For a given voltage difference, this limiting current is proportional to the transport rate of oxygen molecules across the barrier, which depends on the concentration of oxygen in the gas outside the cell. This is the basic working principle of wide-band sensors, whose output varies gradually with oxygen concentration, avoiding the highly non-linear behaviour of potentiometric devices. Wider measuring ranges are achieved with more sophisticated designs in which both modes are combined, like that shown in Fig. 47. An electrochemical oxygen pump regulates the concentration of oxygen in an inner cavity and a potentiometric cell senses the difference in oxygen partial pressure between this cavity and a reference gas (usually, air). The current, which is controlled according to the output of the potentiometric cell, gives a measure of the oxygen concentration for a wide range of conditions, from fuel-rich mixtures to pure air. Commercial solutions exist also for the detection of other species, like CO and NOx. Selective catalytic electrodes are used to oxidize (CO) or reduce (NOx) those species, generating a so-called mixed potential which also depends on the amount of oxygen in the mixture. Since oxidizing catalysts can also act on hydrocarbons, the output of many designs of CO cells actually include the contribution of CO and HC, as a measure of total unburnt matter. Research is also under way to develop specific designs for CO2 and SO2 sensing [206,207]. Some devices are built as sensor arrays, either for simultaneous detection of several species or to perform a preconditioning of the sample. Both objectives are achieved in the NO sensor shown in Fig. 48. After crossing the diffusion barrier, oxygen molecules are removed with an electrochemical pump. This first

Fig. 47. Schematic diagram of a wide-band oxygen sensor. [202].

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Fig. 48. Amperometric array sensor for simultaneous detection of NO and O2.[203].

stage measures oxygen concentration and, at the same time, avoids interferences due to oxygen molecules in the second stage. NO is reduced catalytically at the second electrode and its concentration is detected amperometrically in terms of the oxygen ions produced and diffused across the solid electrolyte. Another category of solid state sensors is that of metal oxides, whose electrical conductance is modified due to interactions with some molecules. As reported in refs [208,209], there is a wide range of semiconductor materials suitable for detection of oxygen and other species like CO, hydrocarbons, SO2 or NOx. Optimal operating temperature ranges are of the order of those usually found in exhaust ducts, making this technology suitable for combustion applications. As a result of the important research efforts in this area, a rapid evolution of this technology and the appearance of new commercial devices should be expected in the near future. In fact, this is a priority subject for the ceramic industry [210] and will benefit from the continuous progress in materials science, as a key enabling technology in this field, in order to develop sensors with improved sensitivity and selectivity. Since automotive and domestic applications are the most important markets [211,212], miniaturisation is an important objective. This can be achieved, for example, by thick- or thin-film manufacturing or through designs like that described in [213], which avoids the need for a reference gas in zirconia sensors. Another field of research is the use of sophisticated signal modulation and/or processing techniques, like patternrecognition or multivariate algorithms, in order to reduce response times or to improve sensor accuracy and selectivity and to enable simultaneous detection of several species [204,205]. 5.2. In-furnace measurements In principle, probes are perfectly suitable for in-furnace measurements in many practical applications. However, and in spite of being a well established and relatively cheap diagnostic option, probing techniques are not used for routine monitoring of practical flames. The authors are only aware of one application in this respect, oriented to the detection of imbalances among flames in multi-burner facilities. Burner settings can be adjusted according to the gas composition measured in the stack in order to achieve optimal performance in terms of combustion efficiency (CO, UHC), sensible heat losses (O2) or pollutant emissions (e.g., NOx or particulates). However, the problem becomes more complex in multi-burner combustion chambers, due to eventual imbalances in fuel and air feeding rates or in the geometry of the different burners. A correct global excess air in the exhaust does not necessarily mean that all the burners are operated with the same air-fuel ratio; for example, in near-

stoichiometric operation, some of them can receive a too high excess air (which can lead to increased NOx formation) whereas some others could produce high CO amounts due to insufficient oxygen availability. Attempts to avoid CO emissions in such instances would result in a too high air-fuel ratio, leading to higher heat losses and, possibly, increased NOx emission. Individual monitoring of the different burners would be needed to achieve a correct burner adjustment. Burner-resolved measurements are possible in some configurations, like the arch-fired boiler shown in Fig. 49. In this case, combustion products from neighbour flames follow parallel average streamlines and remain largely unmixed for path lengths of several inter-burner distances. The gas composition downstream of each flame is indicative of air-to-fuel ratio at each burner as well as of their associated CO and NOx levels. Individual burner settings may be used to balance air and fuel distribution or to adjust secondary air injections in order to minimise CO and NOx emissions. The so-called MEIGAS system, developed by Indra, Union Fenosa and LITEC, was designed to achieve this objective and is currently installed in several coal-fired utility boilers in Spain. Each sampling probe is automatically inserted to extract a gas sample from each flame for a short period of time and then retracted. An automatic sequence is followed to insert each probe, adjust the manifold connecting with the gas analyser (one system at each side of the boiler), retract the probe and then repeat the procedure for the next probe/flame. Water-cooled sampling probes are used, with a special design that enables reaching insertion lengths of several meters while keeping their thickness at 10 mm so that they can be inserted through holes in the membrane between the tubes forming the water wall. Since this avoids having to modify the tubing, a large number of probes (one per burner) can be installed with very low retrofitting effort and costs. The results reported in [214] with a similar system (with one travelling probe instead of one per burner) demonstrate that individual flame monitoring can afford significant benefits in terms of NOx reduction and efficiency improvement. A similar approach might be applied to other multi-burner systems where combustion products from neighbour flames follow parallel paths (e.g., can-annular combustors), but it is not suitable otherwise (e.g., in wall-fired or tangential boilers). Other magnitudes (e.g., temperature) might also be measured to characterise and/or compare individual flames. Conventional suction pyrometers or (their industrial version) high-speed aspiration thermocouples are bulky and unsuitable for routine in-flame temperature measurement. The suction pyrometer described in [215] is a miniaturised design (thickness of 12 mm) and, hence, adequate to implement strategies equivalent to that of MEIGAS by periodically recording in-flame temperatures in coal-fired units.

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Fig. 50. Typical curves showing positive ion concentration and temperature across the flame front of a propane-air flame at equivalence ratio of 0.8. (based on [220]).

Fig. 49. Installation of MEIGAS probes in an arch-fired coal boiler.

5.3. Ionization probes Chemical reactions in flames are known to produce some amount of ions (chemiionization), when the energy involved is high enough to ionize one of the product species. Although a number of chemiionization routes have been identified, the following reaction is generally accepted as the dominant primary step for ion formation in hydrocarbon flames [216–218]:

CH þ O/HCOþ þ e

However, the accurate prediction of ion concentrations or electrical currents in flames is still a challenging objective, even for simple cases, and correlations need to be established empirically. The simplest approach is to interpret the circulation of an electrical current as an indication of the presence of a flame; this is the principle of ionization flame detectors, commonly used in small-scale burners (e.g., domestic gas appliances). However, since measuring weak DC currents may be problematic (especially in low-cost burners), commercial devices are usually based on the ‘flame rectification effect’: due to the different mobility of ions and electrons, a rectified current is obtained when an AC voltage is applied between two electrodes of different sizes (e.g., a rod immersed in the flame and the metallic burner body).

(7)

Due to the very high energy needs for (7) to occur, the yield of ions is very low (of the order of 2.5  106 ion–electron pairs per aliphatic carbon atom [219]). The large amount of water vapour in combustion products favours a rapid reaction to produce H3Oþ:

HCOþ þ H2 O/H3 Oþ þ CO

(8)

The spatial distribution of ions is highly non-uniform and is a combined result of ion generation, diffusion and recombination processes, also affected by self-induced or external electrical fields. In laminar premixed flames, the highest concentrations are located in the thin luminous zone (Fig. 50) [220], decaying steeply and leaving a much lower but measurable amount in post-flame products. An electrical current can be established between two polarised electrodes installed in contact with the flame (i.e., an ionization probe). Since the rate of reaction (7) varies with the temperature and the concentrations of CH and O, the magnitude of the current should be expected to be closely related, among other factors, with the instantaneous properties of the flame. The existence of such a relation has been confirmed in many works, both in laminar, labscale flames and in practical, turbulent flames. The electrical current generated by an ionization probe can be used, therefore, as a ‘flame signal’ which may be analyzed to derive relevant information on the combustion process. For example, Fig. 51 [221] reveals the strong relationship between equivalence ratio and ion current or concentration in a propane-air flame, with peak values at slightly rich conditions and a steep decay at both sides (similar results have been obtained in other studies with different fuels).

Fig. 51. Ion current, ionization density and flame temperature as a function of the air/ fuel ratio for a propane-air flame. The points are experimental while the curves are a best fit to the data. (based on [221]).

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Fig. 52. Relative FID response for different hydrocarbons. (based on [219]).

Another traditional application of chemiionization is the measurement of hydrocarbon concentrations with flame ionization detectors (FID), either as individual analyzers or connected to gas chromatographs (see, e.g., [201,219]). The gas sample to be analyzed is injected into a small hydrogen diffusion flame, where ions are generated from hydrocarbons contained in the sample. The total ion formation rate is monitored using two electrodes, one of them operated in a saturation voltage mode that ensures a withdrawal of all flame charge as it is formed. A linear relationship can be established between the electrical current and the molar concentration of hydrocarbons in the gas sample. The proportionality constant, as shown in Fig. 52, has been found to be almost exactly linear with the number of carbons in the hydrocarbon molecule [218,219]. Some deviation to this general rule is observed,

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however, for acetylene or other organic molecules like alcohols or ketones [218,219]. Special designs of the FID detector have been developed that greatly reduce the response time, of particular interest for cycle-resolved measurements in internal combustion engines [219]. Ionization probes have been extensively used as a research diagnostic tool. As reported in the reviews by Heitor and Moreira [7] and Fialkov [217], ionization signals can be processed to derive detailed information on movement of flame fronts (location, speed, direction), self-ignition, reaction rates, reaction dynamics, turbulent properties of flames or reaction–turbulence interactions. Electrical capacitance tomography was applied in Refs. [222,223] to determine the instantaneous 2-D flame position in configurations relevant for internal combustion engines and porous burners, respectively. Concepts similar to those applied for research purposes have been utilized to develop flame monitoring methods suitable for practical applications. Most of the studies have been oriented to spark-ignition engines, where the spark plug can be used directly as ionization detector (Ref. [5] includes a more detailed review of this application). An important goal has been the detection of misfiring and knocking, based respectively on the absence of ionization current or the appearance of anomalous spikes at late combustion stages [224–227]. Ignition and fuel injection timings at each cylinder can be automatically controlled by an electronic unit fed with the ionisation signal, by means of a control module like that shown in Fig. 53. Some attempts have been also made to determine equivalence ratio in automotive engines. For example, [224] reports positive results by estimating the air-to-fuel ratio from the duration of the ion current. As noted in [224–226,228], the reliability of these techniques may be hindered by the low magnitude of the current (especially for lean mixtures, see Fig. 51), which may be alleviated by improved designs of the electronic circuitry, the processing method or the electrodes [225,226]. In order to obtain reliable results in leanburn engines, Bellenoue et al. [228] proposed analysing the timecurrent history during the spark discharge (as opposed to the usual procedure based on post-discharge signals), demonstrating that equivalence ratio can be related to the time derivative of the current (detected as an inductive voltage in a coil). Although applications to burner flames are more scarce and recent, some results reported in the open literature demonstrate

Fig. 53. Architecture of the Ion Current Combustion Control System (ICCS) for knocking and misfire control. (based on [225]).

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Fig. 54. Prototype fuel injector with CCADS electrodes [229].

that ionization detectors can be used successfully to address highly relevant issues such as flashback detection, monitoring of equivalence ratio or characterisation of combustion dynamics. The performance of the so-called CCADS system (Combustion Control and Diagnostic Sensor) for those three tasks in gas turbine combustors has been analysed in several publications [229–232]. Ionization currents are measured between the grounded burner body and two electrically-insulated electrodes installed at the tip of the fuel nozzle, as shown in Fig. 54. An equal-potential positive voltage is applied to both electrodes, resulting in an electric field between the electrodes and the surrounding walls such that flux lines starting at the guard electrode cover the combustion chamber and the tip of the nozzle, whereas those associated to the sense electrode (ring behind the guard electrode) are restricted to the upstream section of the premix nozzle. Hence, significant ionization currents can only be detected in the sense electrode when the flame enters the premixing region of the fuel injector, due to autoignition or flashback. As shown in Fig. 55, the lack of signal in the sense electrode confirms the absence of flashback events in stable combustion conditions, whereas strong pressure fluctuations can cause the flame to periodically enter the injection nozzle. Some insight into the characteristics of the flame can be gained from the analysis of the ionization current detected at the guard electrode. As shown in [229,230], the magnitude of the guard signal varies with equivalence ratio, which might serve as the basis to develop a stoichiometry sensor. The results reported in [229–232] for different configurations demonstrate that the frequency content and the amplitude of the guard current and of the pressure in the combustion chamber are closely related. Therefore, the CCADS system might be used to characterise combustion dynamics, with the possibility of replacing the dynamic pressure transducers normally used. Some similarity was also found between OH* chemiluminescence emissions and ionization currents detected at the guard electrode and at spark plugs installed at different axial distances from the burner, which suggests that ionization signals might serve as an indicator of fluctuations in heat release rates. Additional evidences on the similarity of ionization current and chemiluminescent emission (due to CH* and C2*, in this case) are given in [233] and [234]. In those works, the sensing electrodes are not installed near the flame but in a supersonic flow of combustion products, downstream the combustor and separated from it by an M ¼ 3 nozzle. The ionization current, obtained in voltage saturation mode, is shown in [233] to be well correlated with equivalence ratio. This is further confirmed by the good results achieved in the closed-loop control of equivalence ratio by manipulating the fuel flow rate, using the ionization signal as the only input. Domestic gas burners appear as a most suitable candidate application, since the ionization electrodes commonly installed for flame detection might be converted into a low cost flame sensor.

The authors are aware of other initiatives in this respect, but the only work found in the open literature is related to the SCOT system developed by Ruhrgas AG [235]. The configuration normally used for flame detection is utilized as the ionization sensor; i.e., detection of a voltage differential between a rod immersed in the flame and the metallic burner. They found, however, that the current detected is dominated by the thermionic emission of electrons from the rod, rather than due to chemiionization. Nevertheless, the shape of the curve of current vs. equivalence ratio is similar to that shown in Fig. 51, as thermoionization is directly related to flame temperature. This sensing technique can be applied for closed-loop

Fig. 55. Time records of pressure fluctuations in the combustion chamber, guard and sense currents in a pressurized premixed combustor during (a) a period of strong dynamics and (b) a period of weak dynamics [230].

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control of equivalence ratio (e.g., for changing gas compositions) in different boiler types (including ceramic burners) and demonstrated a correct performance throughout long-term tests [235]. 6. Conclusions Like any other industrial process, combustion equipment needs to be supervised, controlled and optimized. These requirements are becoming increasingly critical, in order to achieve the objectives to reduce fuel consumption and environmental impact of energygenerating plants, which nowadays are possibly the main challenges of the industry at a global level. However, the considerable importance of combustion processes strikingly contrasts with the poor capabilities of current control systems. This paradoxical situation mainly results from the difficulties in predicting the behaviour of practical flames. This constitutes a fundamental obstacle to the implementation of automatic controllers capable of adjusting the burner settings in an efficient and safe way. In this context, the progress in this field depends critically on the availability of sensorial information that might be used to diagnose the actual state of the processes taking place inside combustion chambers. Thus, it is not surprising that significant efforts have been devoted, especially in recent times, to the development of flame monitoring techniques suitable for the diagnostic and, ultimately, the control of practical combustion equipment. Widely different approaches have been applied, in terms of the type of sensors, the information finally obtained and the rationale behind the whole monitoring procedure. Non-intrusive techniques have been utilized in most cases, due to their unquestionable advantages with respect to probes for routine use in the harsh conditions prevailing inside combustors. Different forms of optical sensors (narrow- and broadband, single sensors and CCDs) are the most common options; although passive methods are the usual choice in the works oriented to practical applications, laser absorption spectroscopy has also been included in this review as a promising option for measurement inside industrial combustors. The recording and analysis of pressure fluctuations is another noninvasive technique with good prospects for the supervision of flames. Solid state gas sensors, although not initially conceived for measurements inside combustion chambers, offer some important advantages (fast response, high temperatures) which may enable their use for advanced combustion monitoring (including low cost versions for small burners). The analysis of the ionization currents has also been applied as a flame diagnostic technique in different applications; although it is in essence a probing method, ionization detectors can be designed as non-intrusive sensors by using burner parts as the measuring electrodes. In a few cases, direct monitoring is possible: the instrument provides meaningful data that can be directly used for combustion monitoring (e.g., in-furnace measurements of gas composition with TDLAS or extractive sampling, pyrometry-derived temperature data, amplitude of pressure fluctuations). In those instances, flame monitoring is basically a problem of instrumentation engineering: the challenges are mainly related with the design of the instrument and its installation in the combustion chamber. The situation, however, is very different in the (much more common) cases of ‘indirect monitoring’; i.e., when the state or the performance of the flame are not determined directly, but they need to be estimated by converting the sensorial information collected into meaningful combustion parameters (e.g., stoichiometry or pollutant emissions). The success of these methods strongly depends on the adequate selection of measurable variables and of the strategies used to derive meaningful combustion parameters from the measured quantities. This requires imaginative solutions that can only be based on a good knowledge of combustion, both regarding

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its fundamental aspects (needed to relate flame signals with the underlying physico-chemical processes) as well as a sufficient insight on the behaviour, monitoring objectives and constraints of the particular combustion application. In spite of the significant efforts in this field, it should be noted that the development of monitoring techniques for practical flames still remains as an elusive objective. Apart from a few exceptions (mainly, in the area of ‘direct monitoring’), most of the methods proposed have been evaluated at lab or pilot scale and/or for a limited range of combustion situations. Further efforts are still needed to demonstrate their ruggedness, reliability in a particular application (also, in the long term) or versatility as general-purpose tools that may be easily adapted to different situations. Significant progress in the area of flame monitoring should be expected from the continuous advances in the sensor industry, as a key enabling technology that will afford novel as well as more robust, accurate, selective and cheap instruments and, presumably, will also facilitate transferring to industrial environments some of the sophisticated diagnostic techniques (e.g., LIF) currently restricted to laboratory research. The implementation of advanced processing methods is also foreseen to contribute to the development of improved and new monitoring concepts; for example, artificial intelligence algorithms offer a notable plasticity that can be most beneficial in this respect, as it facilitates the development of multivariate correlations with arbitrary functional forms as well as the design of nearly-automatic training procedures. Not less important are, in the authors’ opinion, the new and very interesting possibilities in this area that can stem from the development of reliable predictive tools for industrial flames. The main shortcoming of most diagnostic methods is thought to be the high dose of empiricism needed to relate measured (but not directly meaningful) variables with the meaningful (but not measurable) parameters required to evaluate the state of the flame. Without any doubt, there is a physical relationship between both; but current predictive capabilities do not allow establishing the connections that would be needed to describe the properties of a flame on the basis of signals captured in the combustion chamber. For example, time records and/or images of spontaneously emitted radiation contain very rich information that could be exploited to identify the actual characteristics of the flame (or even just a single parameter, like air-fuel ratio) with the aid of comprehensive models describing with sufficient accuracy flame dynamics, turbulence–chemistry interactions and chemiluminescence mechanisms. The efforts devoted to model the relationship between flame properties and selected signals (e.g., in the area of chemiluminescence sensing), although still of limited scope, are thought to show promising perspectives towards the development of flame monitoring techniques much less dependent on empirical calibrations and, therefore, more reliable and universally applicable. Acknowledgements The support of the Spanish Ministry of Science and Education through grant ENE2007-63641 is gratefully acknowledged. Financial support for T. Garcı´a-Armingol was provided by CSIC (Spanish National Research Council, Spanish Ministry of Science and Education) through grant JAEPre_076. The authors wish to acknowledge the contributions of other current and past members of the research group (R. Herna´ndez, A. Sanz, R. Ichaso, M.A. Gonza´lez, J. Barroso, A. Pina and A. Smolarz) to several of the studies mentioned here as well as the help of S. Lipari with the editing of some parts of this work. We are most grateful to many other authors for granting permission to use their work and for providing original illustrations.

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