Opportunities for optical flame diagnostics in commercial and industrial furnaces

Conference paper

Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Opportunities for optical flame diagnostics in commercial and industrial furnaces † Charles E.A. Finney1,*, Christopher W. Kulp2, C. Stuart Daw1, Sandeep Alavandi3, Timothy A. Fuller4, Thomas J. Flynn4, Thomas Osborne2, Nathan Stewart2 1 Oak Ridge National Laboratory, Oak Ridge TN 37831 USA 2 Lycoming College, Williamsport PA 17701 USA 3 Gas Technology Institute, Des Plaines IL 60018 USA 4 The Babcock & Wilcox Company, Barberton OH 44203 USA Inefficient operation and control in commercial and industrial furnaces and boilers are responsible for wasting large amounts of fuel and releasing excess greenhouse gases (CO2 and N2O) and pollutants (CO, NOx). For instance, in the United States more than 80% of the energy used across all sectors is generated by fossilfuels combustion, and only a fraction is subject to advanced diagnostic and closed-loop control systems. It is now recognized that continuous monitoring and control of both individual burners and groups of burners in boilers is essential to meet and sustain ever more stringent greenhouse and pollutant emission limits. This has become especially true as incremental improvements in burner performance have become disproportionately more difficult, and this difficulty is exacerbated by variations in fuel properties. In this paper, we outline and demonstrate concepts for measurements and diagnostics of flame quality and stability using optical sensors and discuss how such techniques could be integrated into the next generation of boiler and furnace monitoring and control systems. We expect that the need for such capabilities will increase substantially as the diversity of boiler fuel sources and needs for tighter emissions and efficiency controls continue to grow globally. Motivation and overall objective‡ Utilities with coal-fired boilers are under increasing scrutiny and pressure to improve efficiency, thereby lowering greenhouse gas emissions, and to reduce pollutant emissions. The past two decades has seen significant progress in real-time monitoring of boiler flame and emissions performance, with several technologies currently in commercial implementation. In field testing during development of the Flame Doctor® system, it was demonstrated how just one flame out of dozens in a boiler could severely affect overall boiler emissions and that having simple diagnostics of flame stability and state could enable boiler operators to make targeted changes with significant improvements, even when they lacked direct visual access to the poor-performing flames [Fuller 2004]. While gas-fired combustion, considered cleaner than coal-fired combustion, has received less regulatory attention, it is now an opportune time to prepare for future constraints and the next † Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). * Corresponding author: [e-mail] or 865-946-1243. ‡ Note: Significant components of the introductory and methodology descriptive text herein are included verbatim from our paper from last year's AFRC meeting [Finney 2015]. We choose this approach of verbatim inclusion over reference to make this paper self-contained in its narrative, in case of limited availability of the cited paper. Compared with last year's paper, the present paper changes focus on application using different data, analytical methodology, results and conclusions. Page 1 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai generation of gas-fired systems. Domestically, with the conversion of many coal-fired boilers to natural gas, there is an opportunity to provide a means to "tune" a retrofitted natural gas combustion system to a boiler cavity originally optimized around pulverized coal combustion, and to maintain it at near optimum performance. Globally, countries that have not had strict emissions control regulations, such as China and India, are now implementing strict emissions regulations as their utility and industrial capacity in fossil fuel based units grows, and international pressure on greenhouse gas emissions strengthens. The state of the art in boiler flame monitoring has moved from time-averaged, usually linear, metrics to nonlinear metrics which account for the dynamical behavior of flames. Capturing both long- and short-timescale dynamical features of flames is important because many unstable and transient behaviors contribute disproportionately to poor combustion and emissions performance, and time-averaged metrics often do not quantify such events. With the continued development of hardware and software systems amenable for optical flame sensing in boiler furnaces, we are planning for the next generation of optical flame diagnostic systems. Continuous monitoring of burner operation has become increasingly important toward achieving and maintaining optimum performance as incremental hardware improvements in combustion system design have become more difficult to achieve. In this paper, we summarize highlights of some of the new approaches to boiler diagnostics that we expect to become prominent in the near future. Common features that these new approaches are likely to share include more spatially extensive optical flame access, utilization of complex and nonlinear dynamical systems theory for interpreting flame patterns, and increased integration of online flame diagnostics with active boiler controls. Here, we project these concepts should be applicable to gas- and oil-fired furnaces where fuel-air mixing and flame structure are key to energy efficiency and emissions control. Brief overview of prior work The following discussion focuses on optical measurements of flames for diagnostic purposes. Other systems using non-optical measurements, such as with pressure, acoustic, or ionic sensors, or which do not directly measure flame properties, such as with flue-gas spatial analyzers, are not included. Commercially implemented systems Most commercially offered flame-monitoring systems have focused on coal-fired flames (and we summarize these below), but their analytical methodology generally could be extended to oil- and gas-fired systems with appropriate measurements. While several ad-hoc schemes for measuring whole-flame images or signals from light-intensity sensors had been utilized informally for decades, in the mid-1990s commercial systems began to be developed which focused on statistical analysis of flame measurements. One such system was an optical system which used the Fourier (frequency-based) spectrum of flame flicker to assign a numerical quality metric [Khesin 1996a, 1996b, 1997]. The Fourier spectrum is linear and captures first-order effects on a time-averaged basis, but it does not capture all the nuances of dynamical variations seen in nonlinear systems, such as flames in certain unstable combustion regimes such as with staged combustion of pulverized coal burners. Concurrently, techniques derived from the study of nonlinear dynamical and chaotic systems began to be applied to boiler flames [Fuller 1996a, 1996b]. Continued development under collaboration of the Electric Power Research Institute, The Babcock & Wilcox Company and Oak Ridge National Laboratory led to the development of the Flame Doctor system for commercial release in 2004 [Flynn 2003; Daw 2003b]. The Flame Doctor system utilizes flame-scanner signals, typically Page 2 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai focused on a relatively small volume of the flame seen through a 10-15 cm (4-6 inch) diameter and several meter long sight tube, near the burner nozzle, and applies a series of statistical analyses of linear and nonlinear metrics to determine the relative stability of the flame. By using existing flame scanners installed for safety purposes or using custom scanners, the system can be quickly implemented on most utility boilers. The Flame Doctor system has been used on a temporary or permanent basis on dozens of utility boilers in both the US and internationally and has seen a good degree of success in identifying poorly performing burners. Correction of the boiler operation, largely in supervised open-loop control, either on a burner or mill-group basis has yielded very favorable improvements in boiler emissions (such as NOx and CO) by improving combustion quality. While this experience has had considerable value in improving boiler performance, ever more restrictive Environmental Protection Agency (EPA) boiler emissions limits demonstrate that enhanced optical sensors and improved dynamical characterization of flame, mill-group and overall boiler dynamics, reflecting the latest understanding of nonlinear dynamical systems theory, will be worth the development effort to achieve even greater improvement in boiler performance. Additionally, the latest EPA regulations recognize the importance of advanced closed-loop control systems; therefore, the integration of advanced sensors into these control systems will continue to be a priority. Recent laboratory and modeling studies in the literature Using optical sensors and images of flames to analyze flame states is not new. Marques and Jorge [Marques and Jorge 2000] used infrared images of flames to characterize flame shape and monitor flame characteristics in a boiler. Lu et al. [Lu 2004] used digital images of flames to measure several flame characteristics including flicker, temperature, and spreading angle. Digital images have also been used to analyze flames from a model industrial burner [Hernández and Ballester 2008]. The aforementioned cases involved studying features of the flame (such as shape) from the images. In some of the work described here, we will focus on generating time series data from the images (as in frame-by-frame analysis of a movie). Therefore, time series analysis algorithms can be applied to the images in order to gain information about the dynamics of the flame. Time series analysis algorithms have been successfully applied to time series generated by nonoptical measurements of combustion systems. Gotoda et al. [Gotoda 2012] used time series analysis algorithms and information theoretic analyses to study complexities in combustion instability for flames produced by a gas turbine combustor close to lean blowout. Time series analysis algorithms have been used to show that thermoacoustic oscillations transition to chaos in lean premixed fuel-air burners [Kabiraj 2012, 2015]. The time series in the aforementioned studies are not generated from images, but rather from measurements made directly from the flame or combustor (e.g. pressure fluctuations). However, both image analysis and time series analysis studies provide useful information about the dynamics of the flame. Hence, we expect that time series and information theoretic analyses on time series generated from images of the flame should produce important insights into the flame dynamics. Theoretical basis for the next generation of optical diagnostics In our 2003 presentation to the AFRC [Daw 2003b], we reported on the recent development of a commercial boiler flame diagnostic system. We recognized then that that system involved compromises both in the sensors used and in the analysis algorithms employed. In this section, we outline the potential for an improved diagnostic system based on enhanced sensors and enhanced signal-analysis techniques. Page 3 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Enhanced sensors Potential for whole flame measurements As mentioned previously, a major limitation of many current optical sensors is their limited field of view of the flame, either through an off-axis sight tube or a limited side view. This limited view severely restricts the ability of current diagnostic systems to adequately capture spatial information about the flame. The importance of the spatial information has been observed in practice on coal-fired boiler flames where the structure of the flame within the optical sensor's field of view is dramatically different from the flame structure on the opposite side of the flame. In these situations, the Flame Doctor system incorrectly assesses the state of the flame. One way to address this limitation is to employ multiple optical sensors sighted on different parts of the flame. Another way to address this limitation is to image the entire flame using video cameras, where line of sight permits. Both approaches will not only provide flame structure information on different parts of the flame but will provide information on the correlation between different areas of the flame as well as the flow of information between those areas. As we discuss below, this type of information can be important in determining the onset of critical bifurcations. However, the relatively cleaner furnace-gas environment makes gas flames more amenable to whole-flame sensing than coal flames. One concern, with multiple simultaneous high-speed video measurements, is the computational processing required to analyze the data and diagnose flame states. Fortunately, the rapid development of general-purpose Graphical Processing Units over the past decade makes real-time processing and analysis of multiple video streams a possibility even on moderately capable hardware. Potential for multispectral measurements For coal and oil applications, the typical wavelength range of the scanners is from the upper visible spectrum to near infrared; approximately 600 to 1100 nm. For natural-gas flame applications, the scanners are sensitive to wavelengths in the ultraviolet spectrum, typically less than 300 nm. For the next generation of optical boiler diagnostic systems, improvements could be made by employing multispectral sensors. We anticipate that analyzing and comparing time series data from different wavelengths using advanced techniques like those described below will provide important information on the dynamics of the flame. Multispectral optical sensors are already being used in commercial utility boilers though none are currently being used to assess individual flame performance. Optical pyrometers use the ratio of two different wavelength bands to calculate the temperature within the sensors field of view. Optical pyrometers are typically deployed in the upper part of the furnace to measure the temperature of the gases leaving the primary combustion zone. More recently, tunable diode laser (TDL) systems have been deployed in the upper furnace to simultaneously measure the concentrations of important gas species such as oxygen and carbon monoxide. Although these multispectral systems have been successful in their current applications, they would be difficult to utilize for individual flame diagnostics due to inherent limitations in their designs. Potential for multivariate measurements The focus of this paper is on optically-based diagnostic systems; however, the use of other measurements in addition to optical measurements has the potential to greatly enhance the performance of advanced flame diagnostic systems (for instance, see [Sanz 2008]). Pressure measurements, acoustic measurements, electrical measurements, and microwave measurements all Page 4 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai have the potential to provide information about a flame's dynamics that cannot be gained from optical measurements alone. The potential benefits of a multivariate approach were demonstrated during the development of the Flame Doctor system in the 1990s. During pilot-scale testing of a single coal burner, we simultaneously collected pressure measurements in the coal transport line, pressure measurements in the air plenum, and acoustic measurements in the combustion chamber in addition to optical measurements of the flame. Analysis of these additional measurements revealed dynamical information similar to but not exactly the same as the dynamical information gained from the optical signals. More interestingly, correlating the different measurements with the optical measurements revealed additional insight into the combustion dynamics such as that information flow can actually be opposite to the physical flows of the fuel and air. Specifically, bivariate mutual information analysis was used to determine that ignition-extinction events as detected by an acoustic sensor in the combustion cavity were inducing pressure pulsations indicative of variations in the coal feed to the burner rather than vice versa. In gas flames, air and fuel variability is expected to be significantly less than with coal-fired flames, however, other types of instabilities, such as thermoacoustic oscillations ("burner rumble"), could be expected to be present at certain operating configurations and conditions. These thermoacoustic oscillations can damage structural components of the boiler and convection bank. Sometimes the root cause of the thermoacoustic oscillations can be difficult to identify. Enhanced signal analysis Anticipated improvements to the techniques currently implemented in the Flame Doctor system include exploitation of recent development of complex systems theory, the ability to characterize the flame dynamics behavior over spatial domains (such as described above), and the potential for adaptive learning for control systems. Complex systems often exhibit critical transitions (‘tipping points'), and these are often seen in the long-timescale shifts in pulverized-coal boiler flames, particularly in cyclone burners with their long ‘memory' but also with wall-fired burners; being able to detect the approach or onset of an adverse critical transition could allow predictive control intervention to avoid the transition. In spatially extended systems such as a staged-combustion burner flame, accounting for the behavior of the larger-scale structures should improve characterization and prediction over spatially limited measurements, so developing techniques to account for spatio-temporal effects is important. Additionally, adaptive learning systems to account for longer-period (on the week or month timescale) routine variations in boiler operation (for instance, fuel composition or seasonal effects) can improve the effectiveness of diagnostic and control systems. Detecting critical transitions in complex systems The emerging science of complex systems is revealing important new insights into systems (both natural and manmade) that are made up of multiple dynamic, spatially extended, and interacting components. One insight of particular interest here is that flames exhibit the key features of complex systems, including the occurrence of ‘tipping points' or critical transitions (also sometimes called global bifurcations), which typically involve a sudden shift in the dynamic state as one or more parameters pass through critical levels. In the case of flames (and more broadly all combustion systems), such critical transitions are often associated with changes in fuel/air ratio (e.g., lean and rich ignition limits) or mixing (e.g., burner swirl and staging). The diagnostic concepts we describe here for boilers are based on the well-recognized occurrence of multiple types of abrupt transitions in burner operating state as one or more key parameters are varied. By understanding the unique dynamic features associated with each critical transition and Page 5 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai how these features vary with distance from these transitions, previous work has already demonstrated that it is possible to use dynamic measurements of global burner flame luminosity variations to identify the proximity to one or more transitions. With this information, it is then possible to diagnose burner state and make appropriate control adjustments, both for individual burners as well as burner groups [Fuller 2014]. The key challenge in this process is to identify the most effective available burner measurements that are available and the most appropriate analytical algorithms for processing those measurements to detect the presence and proximity of critical burner states. A number of mathematical tools have been developed for understanding and diagnosing critical transitions in other fields (e.g., medicine [Liu 2012, 2014], ecosystems [Kéfi 2014; Lade and Gross 2012], climatology [Lenton 2008], and finance [May 2008]). The widespread applicability of these tools has been made possible by the underlying universality of the physical processes that drive critical transitions in complex systems [Scheffer 2009, 2012]. One general feature of these tools is that they are designed to detect specific temporal patterns in time series measurements of complex systems that are associated with critical transitions. Flames in particular tend to exhibit so-called catastrophic bifurcations, which are characterized by a significant hysteresis (different equilibrium trajectory) associated with whether the transition is approached by change in conditions from above or below. One particularly useful diagnostic feature in time series measurements of such systems is referred to as ‘critical slowing down' [Scheffer 2009]. In simple terms, this means that as a critical point is approached, the system state (as revealed by time series measurements) becomes increasingly slow in responding to small perturbations (e.g., in flames, this can be the luminosity or flame shape changes associated with flow turbulence or small, deliberate changes in fuel or air flow). Theoretical studies have shown that such slowing down typically starts relatively far from critical points and grows stronger as a criticality is approached. Thus the degree of slowing can provide an indication of distance from a criticality well before that critical point is actually reached. One widely used statistical indicator of slowing down is an increase in longer timescale autocorrelations. Other useful statistical indicators are increased variance and skewness [Scheffer 2009]. The former results when the system responses to small perturbations do not decay (because of the slowing) and thus the accumulating changes increase the state variance. The latter occurs because of the asymmetry in the system responses to perturbations toward or away from the critical point. One other notable feature of complex systems approaching a critical transition is referred to in the complexity literature as ‘flickering'. This use of the term is distinct from what is typically meant in describing flames, although there probably is a more direct correspondence in some cases. In the literature, flickering refers to a condition where there is the potential for bimodality in the statistical distribution of the system state, as one would expect to observe when a system is pushed back and forth across a critical transition (e.g., separating two very different regimes) by stochastic perturbations. So critical combustion transitions that result in two distinct flame states (e.g., lifted vs. attached flames) could be classified in this context as flickering. Besides distinctive temporal patterns, the approach to critical transitions is also often associated with the development of non-uniform spatial patterns. Many complex systems (and flames clearly) can be seen as consisting of numerous local but coupled units or zones, each of which assumes a state influenced by the states of the zones around it. The resulting spatial coherencies can take the form of localized patches or fronts that move or oscillate in characteristic ways as the critical point is approached. These spatial features can take various forms in flames, including oscillatory shifts in flame shape or size, alternating bright and dark zones, and variations in color [Maxworthy 1999; Biagioli 2007; Annunziato 2000; Huang and Yang 2004]. Because the spatial patterns for each type of critical transition are typically very system specific, there is no ‘one size fits all' that applies to all Page 6 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai transitions and all systems. For burner diagnostics, however, this is actually advantageous, because it provides a mechanism to discriminate between the different possible flame types (i.e., different criticality regions) and thus improve the ability to resolve particular burner conditions and potentially diagnose the root cause of a combustion stability problem. As explained in the following discussion, we believe that the addition of spatial pattern characterization to the suite of tools used to interpret optical signals is one of the most promising new developments in the field of boiler diagnostics. Algorithms for characterizing spatio-temporal information Spatio-temporal information can be obtained from spatially extended systems such as a flame by combining time-series measurements from different spatial regions. One example is based on using optical sensors to record the color of different parts of a flame as a function of time, as we demonstrated earlier [Finney 2015]. As a first step, very simple geometric measures such as flame length and width, spatial centroid, and shape convexity can be calculated in real time and used to monitor flame dynamics; these measures will be defined and demonstrated below. Additionally, using the tools of symbolic time series analysis [Daw 2003a], spatio-temporal information can then be obtained from the multiple time series generated from various regions of the system. While the application of symbolic analysis is not a requirement, it is often useful in complex or noisy environments. In symbolic time series analysis, a time series is transformed into what is called a symbolic time series whose elements consists of a few discretized symbols (sometimes called an alphabet). For example, a binary symbolization would transform a time series into a series of 0s and 1s. The resulting symbolic time series can then be analyzed to obtain information about the underlying dynamics generating the series. See Figure 1 for an example symbolization of a scalar time series. Of particular interest are information-theoretic quantities such as the Shannon entropy [Shannon 1948] and the transfer entropy [Schreiber 2000]. Page 7 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Figure 1. Illustration of (a) partition-based binary symbolization and grouping of sequences into coded words, and (b) resulting frequency distribution of symbol sequences. (Figure from [Daw 2000]). Symbolization can be done using a variety of means. One of the simplest techniques is by binning the data using thresholds (as demonstrated in Figure 1). There are many different techniques for choosing and handling thresholds. For example, with binary symbolization, one can use the mean or median of a time series as the threshold and any element greater (less) than or equal to the threshold would be assigned the symbol 1 (0). Ideally, the chosen threshold would be a generating partition which would guarantee that the resulting symbolic time series accurately captures the underlying dynamics producing the time series [Bollt 2000]. However, identifying a time series' generating partition is not possible in engineering practice, because real time series contain noise. An alternative method of symbolization is known as the Bandt and Pompe (BP) methodology [Bandt and Pompe 2002]. The BP methodology is a dynamic, difference-based symbolization which partitions the time series into subsets of length d (with overlap d - 1) and then maps those subsets to permutations of the set {1, 2, … d}. The permutations are called ordinal patterns and each ordinal pattern can be assigned an integer from 1 to d! in order to simplify the symbolization. BP symbolization is robust to noise, takes into account causal relations in the data, and avoids the aforementioned issues of threshold dependencies. In the work described below, thresholds were used for symbolization, however an extension of the work using the BP methodology is a planned avenue of future research. Once a time series has been symbolized, various information theoretic analyses can be applied to the resulting symbolic series in order to better understand the system's underlying dynamics. One such algorithm is the Shannon entropy [Shannon 1948]: Page 8 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai 𝐻" = − 𝑝 𝑠 log (𝑝 𝑠 ) - where p(s) is the probability of the symbol, s, appearing in symbolic series, and the sum is done over all symbols in the series (or, p can represent the symbol-sequence histogram of temporal patterns as defined above). The Shannon entropy gives a measure of the information content of a series and can be thought of as the average information gained with each new measurement. The base of the logarithm provides the units of information. For example, a base 2 would have units of bits. In general, series with high Shannon entropies are considered to correspond to more complicated dynamics than series with a lower Shannon entropy. A series of all one symbol would have HS = 0 (p(s) = 1 and log(1) = 0); whereas a random series, where all symbols are equally probable, would have HS = log(N), with N being the number of symbols in the series. In the case of spatially-extended systems, it is possible to obtain temporal and spatial information simultaneously using an extension of the techniques described above. First, the system must be spatially-partitioned, i.e. distinct non-overlapping regions of the system are identified. Then, from each region, a time series is generated using the local measurements. Then, each time series can be symbolized and information theoretic metrics computed to quantify the temporal patterns. Continuing our earlier example of a flame, a windowed Shannon entropy for each region of the flame can be used to characterize how the dynamical complexity changes for various regions of the flame. Furthermore, computing the transfer entropy between time series generated from neighboring regions can reveal how information is shared or transported between those regions. When all the regions in a flame are considered simultaneously, it is possible to quantify the global flow of information throughout the flame. Adaptive learning and integration with control systems Dynamical control systems with static parameters or set points often cannot adapt to normal baseline shifts in the system behavior. Utility boilers can experience such shifts quite routinely, for instance, with changes in fuel composition, load or environmental variations. Additionally, subtle changes in air distribution or burner settings can result in flame dynamical changes, and control systems trained on the patterns seen at one baseline might not respond optimally or even accurately enough to diagnose the boiler state, at another baseline condition. Adaptive learning systems have been employed in various forms for decades. Examples of these include expert systems and neural networks; however, the framework of these schemes are often not suited to accommodate the types of multidimensional metrics associated with some of the more advanced signal-analysis techniques described above. A type of diagnosis system useful for boiler flame measurements as described above should be selfaware. That is, not only should it accurately quantify the flame behaviors, it should constantly assess the stationarity of the boiler ensemble and re-learn the system as needed. There are many ways to achieve this end; here are two of interest: • Using the ensemble of burner statistics in relation to a previously benchmarked state, particularly in the context of overall performance metrics (such as emissions data) • Using the statistics of burners relative to each other compared with previously benchmarked inter-burner relations In the first case, knowing when the ensemble ‘normal' (but acceptable) has changed is important. In the second case, tracking the interrelation of individual burner behavior can help highlight instances and perhaps causes of shifts in boiler operation. Especially important is monitoring shifts Page 9 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai in the context of critical transitions (as described above) and doing so in time to avoid a transition into an adverse condition. This could be particularly effective in diagnosing an egregiously unstable burner within a group of burners. Example experimental observations In this section, we focus on a simple laboratory gas burner to demonstrate certain concepts which could be relevant to boiler flame measurements. These concepts include how to characterize multispectral measurements (using visible color planes as examples), how to generate and numerically analyze time series from these measurements, and how to quantify information flows in the flame (either within a single flame or among adjacent flames). Our group has performed preliminary experiments in order to understand how to characterize spatio-temporal information in a flame. These experiments are on the level of "proof-of-concept" and are intended to illustrate the application of the information theoretic algorithms described above. In this section we describe our experiment, discuss how to generate time series from video, and demonstrate some calculations that can be done with the resulting time series. In the end, the goal is to provide information about the dynamics of the flame. Experimental apparatus The experimental facility is located at the Lycoming Complex Systems Research Laboratory at Lycoming College. The experiment used a High Temperature Blast Burner (H-5020) sold by Humboldt Mfg. Co. The burner combusts a premixed fuel-air mixture with a confined flame. The fuel was laboratory-supplied natural gas, and an air compressor supplied the air. Air and fuel flows were measured by analog flow meters and pressure gauges. It is important to note that since laboratory-supplied air is used, exact air-fuel ratios (AFR) are unknown since the composition of the natural gas is unknown. Because the experiment was intended only to demonstrate the application of the algorithms, knowledge of the specific AFR was not critical. Future experiments will involve better fuel sources and hence better AFR measurements. Our data for this experiment comes from video recordings of the flame. The flame was recorded using a Canon EOS Rebel T3 DSLR with an EFS 18 - 55 mm lens in QuickTime MOVIE (.mov) format at a rate of 30 frames/second. Generating time series from video recordings Video recordings of the flame were used to generate time-series data directly from each frame of the video. An image is typically stored as a matrix of pixels, such that, for example, an image with a resolution of C ´ R pixels is stored as a matrix with C columns and R rows. Each matrix element (or pixel) contains a pixel value, a number or triplet that is used to store information about the part of the image within that pixel. If we think of an image as a matrix of values (either constants or triplets), then we can produce a time series by recording the value of each pixel in each frame of the video. Hence each pixel provides a time series. A 10 second video recorded at a resolution of 400 x 600 would give 24,000 time series (400 times 600), each of which are 300 elements long (10 seconds times 30 frames/second). Regarding the previous discussion on spatio-temporal information, in our work, the image was divided into regions (pixels) and a time series was generated from each region (pixel values). Pixel values can be stored in a variety of ways using different color spaces. A common color space is RGB where each pixel is represented by a triplet of integers, {R, G, B}, that range from 0 to 255. For example, the color red would be {255, 0, 0} while white would be {255, 255, 255}. So an RGB image is a matrix of triplets. Another color space is grayscale where each pixel is represented by an integer ranging from 0 to 255 where 0 is black and 255 is white. It should be noted that some Page 10 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai software use a normalize grayscale in which black is given the value 0 and white is given the value of 1. The advantage to working with grayscale is that each pixel is represented by only one number, so generating a time series from each pixel is easy. The disadvantage of grayscale is that one loses color information in the transformation from RGB to grayscale. However with grayscale, information on the light intensity is retained. Deciding how and when to use RGB or grayscale is an avenue for future research. While it is possible to generate a time series for each pixel, we are often only interested in the pixels that contain the region of interest. In this case, we are video recording a flame. Focusing only on the flame pixels makes the calculation more computationally efficient. However, it also leads to the challenge of having the computer distinguish between the background and the flame. To aid in this distinction, the flame is recorded in front of a black background, which should have the RGB value {0, 0, 0} or grayscale value of 0. Noise in the camera's CCD can prevent one from simply filtering out all pixels whose value is {0, 0, 0} or 0. Hence, a threshold has to be established in order to distinguish between the flame and background. The choice of threshold is difficult because if it is set too high, the algorithm may mistakenly identify parts of the flame as background, especially for dim flames. In addition, flames flicker and sometimes pixels do not remain in the flame or in the background for the entire video. In our preliminary experiments using grayscale, we disregarded noise effects, which were small, and identified any pixel with a value of 0 as a background pixel. Any pixel whose value is 0 for the entire video is considered part of the background and is not included in calculations. For RGB-based calculations, we disregarded any pixel whose maximum value in the triplet was less than 10, based on the intensity histogram. Studying the effects of the choice of background threshold is an avenue of future research. Once the time series data has been obtained from each pixel, the time series needs to be symbolized in order to perform information theoretic calculations. In the work described here, we performed information theoretic calculations only on grayscale data. A threshold-based symbolization was used in which pixels with a grayscale level of 0 were assigned the symbol, 0. Pixels that had grayscale values greater than or equal to 0.2 were assigned the symbol 2 and values greater than 0 but less than 0.2 were assigned the symbol 1. Note that a normalized grayscale was used for pixel values. Three symbols were chosen in order to distinguish the background from flame pixels. The threshold value of 0.2 was chosen because it was the mean of the pixel data for most of the flame pixels. However, future work is needed in better determining thresholds, especially when dealing with the RGB color space. Video recordings provide a method of obtaining spatio-temporal information about a flame. Because a video is a series of frames, a video of a flame can be thought of as a series of optical measurements of the flame's state as a function of time. A variety of image analysis tools can be used on each frame in order to characterize the state, providing both spatial and temporal information. Two video analysis methods will be discussed here. The first approach, geometricbased methods, relies on analyzing contours of the flame's image and provide information on the spatial structure of the flame as a function of time. The second approach, symbolic methods, uses information theoretic quantities and can provide additional insights into spatio-temporal information. The results of the geometric and symbolic methods will be illustrated on three flames. The first flame shown in the left of Figure 2 has an approximate fuel-air equivalence ratio of 0.74, this flame will be referred to as the "lean flame". The second flame, shown in the right of Figure 2, has an approximate fuel-air equivalence ratio of 4.0, and will be referred to as the "rich flame" in this paper. A third flame, shown in Figure 3, had a fuel-air equivalence ratio that increased from 0.70 to 4.0 in five steps over a duration of one minute, and is referred to as the "variable flame". There were approximately 10 seconds between increases in the equivalence ratio of the variable flame to Page 11 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai allow transients in the flame to decay before the next increase was performed. In each frame, the total combustion area was contained within frames of 270 pixels wide and 911 pixels high. Figure 2. Two of the three flames used in the analyses presented here. The left (right) flame has an approximate fuel-air equivalence ratio of 0.74 (4.0). The left image has been zoomed for detail. Page 12 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Figure 3. Snapshot sequences of the variable flame at seven different equivalence ratio steps, starting with leanest fueling (top row) and progressing to richest fueling (bottom row). In each row, successive frames are separated by 0.2 sec, for an elapsed time of 3 sec, with all images displayed on the same scale. Oscillations in flame height and shape become more pronounced with increasingly rich fueling. Page 13 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Geometry-based methods Information about the spatial structure of the flame can be obtained by identifying the contour of the flame in each frame. By studying multiple frames of the video, it is possible to identify how the spatial structure of the flame changes with time. A stable flame will have little change in its spatial structure compared to an unstable flame. The contour of a flame can be found using the Open Source Computer Vision (OpenCV) [OpenCV 2016] package, which provides many tools for analyzing images. The OpenCV package is available for use in the Python, C, C++, and Java programming languages. The basic process of identifying the flame's contour using OpenCV is as follows. After the image is imported, it must be converted to grayscale. Next, the grayscale image must be transformed into a binary image by choosing a threshold. Pixels with grayscale values greater (less) than or equal to the threshold are reassigned the symbol, 1 (0). In the work done here, the threshold was chosen simply by visually comparing the original image to the binary image to ensure that most of the flame was identified in the binary image. Developing a better, possibly automated, method of identifying the threshold is a topic of future research. Once the binary image has been captured, the OpenCV package has tools to identify contours of each of the connected components of the image. As parts of the flame may break off, there may be multiple components in the binary image. For the purpose of automation, the contour with the largest area in the image was identified as being "the flame". The OpenCV package has tools for computing the area of each contour in the binary image. This process is then repeated for each frame of the video. Figure 4 shows an example of the contour of a flame overlaid on the grayscale image. Figure 4. An example of the contour found using the OpenCV package. The geometry of the flame's contour can then be used to obtain information about the spatial structure of the flame. If such calculations are repeated for each frame of the video, one can create a time series that illustrates how the spatial structure of the flame changes in time. Using the Page 14 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai contour of the flame in each frame, the following geometric properties were found: flame height and width, the centroid, and the convexity. Flame height and width The flame height and width can be found from the contour by finding a bounding rectangle for the contour. An example of such a bounding rectangle is shown in Figure 5. The bounding rectangle is found using the boundingRect() command as part of the OpenCV package in Python. The output of the boundingRect() command contains the height and width of the rectangle, which are then used as the height and width of the flame. Figure 5. A bounding rectangle around a flame. The height of the flames as a function of time are shown in Figure 6. From Figure 6 it is clear that the lean flame has a fairly stable height. As expected, the rich flame has more variability in its height. Furthermore, the step increases in equivalence ratios are clearly evident in the variable flame height graph. For the variable flame, the top of the flame leaves the frame of the video near the end, hence its height graph being cut off around t = 40 seconds. Page 15 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Figure 6. The height of each flame (in pixels) as a function of time. Figure 7 shows the width of each flame as a function of time. The lean flame's stable shape is apparent. The variability of the lean flame's width is small mainly because the flame's width never exceeds the width of its base. The rich flame's width is greater than that of its base for most of the video. While not as evident as the height data, the variable flame's changing equivalence ratio is also demonstrated in its width data, as the flame gets wider with increasing equivalence ratio, and in addition, the width's variance also increases. Page 16 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Figure 7. The width of each flame (in pixels) as a function of time. Centroid The centroid of the flame can also be obtained using the OpenCV package. The function moments() can be used to obtain the pixel coordinates of the centroid. Figure 8 shows a plot of the ycoordinate vs. the x-coordinate of the centroid. Tracing the paths in Figure 8 shows how the centroid moves in space as time progresses. Notice in Figure 8 that the lean flame's centroid moves less than that of the other flames, due to the stability of the lean flame's shape. The rich flame's shape is more unstable and its centroid moves more both vertically and horizontally as compared to the lean flame. The variable flame's changing equivalence ratio is also apparent in Figure 8. The variable flame's centroid graph consists of five elliptical structures. Each ellipse represents one of the states of the flame. Figure 8 also shows that the frame rate of the camera is not sufficient to smoothly track the motion of the centroid. The jagged lines connecting one point to the next would be made smoother if the camera's frame rate were higher. Page 17 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Figure 8. The location of the flames' centroid (in pixels) as a function of time. Note the highly disparate ranges in the abscissa axis for the lean and rich flames (top left and right). Convexity The final geometric quantity discussed here is convexity. Convexity is a simple measure which related to how confined the flame is and could be related to combustion efficiency and emissions. To understand convexity, one must first understand the convex hull of an image. The convex hull of an image can be thought of as the smallest convex set that contains the image. For clarity, an example is shown in Figure 9, where the border of the flame's convex hull appears as a white curve. The convex hull is a convex shape that contains the entire flame. The convex hull shown in Figure 9 was found using the OpenCV command, convexHull(). Figure 9. The white curve is the border of the flame's convex hull. Page 18 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai The convexity of the flame is the area of the contour that bounds the flame divided by the area of the convex hull. The convexity is a dimensionless quantity that provides a means of quantifying the shape of the flame. A convexity close to 1 suggests that the flame has a convex shape, while a small convexity (<1) suggests that the flame has some concaveness to its shape. For example, in Figure 9, the upper right section of the flame "bows in", as does a large fraction of the left side. The convexity of the flames is shown in Figure 10. In Figure 10, the convexity of the lean flame has a low variance and is relatively constant throughout the video. The lean flame's convexity is generally higher than that of the rich flame. The rich flame's convexity also has a higher variance, due to the instability of the rich flame's shape. The variable flame starts with a generally high convexity with low variance, but as the fuel equivalence ratio is increased, the variance of the convexity gets larger and its mean decreases. In other words, the variable flame's convexity starts similar to that of a lean flame and ends similar to a rich flame's. However, after a certain critical equivalence ratio, seen around 30 sec elapsed time, the convexity is less sensitive to fueling changes. Either the flame structure stays the same with different dynamics, or the flame dynamics do not change much in this span. A more complicated spatial measure might be required to account for this insensitivity. Figure 10. The convexity of the flames as a function of time. The geometric methods described above give insight into the structure and shape of the flame. The general trend is that the geometric properties of lean flames tend to have low variance in time, while rich flames have geometric properties with a higher variance. Monitoring the variance of the above quantities may serve as a means of detecting a change of state in the flame. Next, symbolic methods are presented that will provide spatio-temporal information about the flame. Page 19 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Shannon entropy Next, we demonstrate some results of applying the Shannon entropy calculation to the three flames studied using the geometric methods. The first result relies on calculating the Shannon entropy of each frame. By finding the Shannon entropy of each frame, we are essentially using the largest possible spatial coarse graining, the entire frame. Each frame is symbolized and "flattened", the rows of each image are appended to another, and the Shannon entropy of the resulting series is computed. By repeating the process for all frames, the resulting "Shannon entropy time series" provides a measure of the complexity of the flame. Figure 11 shows the normalized Shannon entropy time series for each flame. The lean flame has a generally low entropy with small variance. The rich flame has a much higher entropy and variance. These results are consistent with the geometric methods, where the rich flame had a higher variance in its quantities. The rich flame's less stable flame shape results in a more complex behavior in the video, yielding a higher entropy. The variable flame used a slightly different symbolization scheme than was described above. Instead of choosing one threshold for the background and one threshold for the flame for the entire video, the thresholds changed as the fuel equivalence ratio changed. Changing the thresholds was necessary since the brightness and color of the flame changed with the equivalence ratio. However, the results of the Shannon entropy video are as expected, the flame's entropy starts similar to the lean flame (in terms of variance), and ends similar to the rich flame. Figure 11. The entropy of each flame as a function of time. For the next analysis, the image is coarse grained using the finest possible graining, pixels. Each pixel produces a time series and is symbolized as described above. The normalized Shannon entropy is found for each pixel. Using the value of the normalized Shannon entropy, a grayscale Page 20 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai image, called a Shannon entropy image, is produced. The Shannon entropy image shows regions of high variability, and therefore more complex dynamics, in the flame. Figure 12 shows Shannon entropy images for the lean and rich flames. Figure 12. Shannon entropy images for the lean (left) and rich (right) flames. Figure 12 identifies regions of high complexity; lighter pixels are very high entropy and black pixels are zero entropy. The lean flame has two regions of complex dynamics, along the outer perimeter of the flame and along the perimeter of the inner cone. The rich flame has an unstable flame shape and therefore a higher complexity along its perimeter. Furthermore, the inner region of the rich flame shows more complexity in its dynamics than the lean flame. It should be noted that the normalization for each image is the same, hence the brighter white in the rich flame is indicative of an overall higher level of complexity than the lean flame. The entropy images for the variable flame are not shown; however, the variable flame starts in a state similar that of the lean flame in Figure 12 and ends in a state similar to that of the rich flame in Figure 12. Simple geometric measures are suitable as metrics of flame-structure and can assist in tracking dynamical variability of flames over time, either within zones of the flame or over its whole spatial extent. As a first step, these metrics could help form the basis for a diagnostic system. However, to capture subtle effects such as drift in fueling, more complicated spatial measures might be required. Conclusions and recommendations Currently implemented boiler diagnostic systems for coal flames have shown that real-time monitoring of combustion performance, on a per-flame and/or mill-group basis, can yield significant improvements in combustion quality and thus improve emissions and increase efficiency. Extension to natural-gas flames in some respects offers greater opportunities because whole-flame optical measurements are more easily made than within coal-fired furnaces. Page 21 of 24 #12;Finney/Kulp/Daw/Alavandi/Fuller/Flynn/Osborne/Stewart - Opportunities for optical flame diagnostics in commercial and industrial furnaces - AFRC 2016 Industrial Combustion Symposium - Kauai Continued improvement to optical systems should reflect recent advances in hardware, software, and dynamical-systems theory. Taking advantage of the current state of the art in imaging and computational hardware, the next generation of optical sensors for boiler flames may incorporate some of the following features: • Wider field of view (whole flame preferred) • Greater spatial resolution, providing images for analysis rather than overall flicker intensity signals • Wider or multiple spectrum, incorporating visible and UV in multiple channels, to take advantage of each detector's sensitivity to different aspects of the combustion physics • Multivariate sensors, for instance, to consider effects of pressure/acoustic fields in addition to the optical flame qualities, or using multiple optical sensors with different views of the flame • Employment of recently developed affordable computational capabilities, such as Graphical Processing Units, which can process and analysis significant amounts of data in real time. Both empirical experience and dynamical systems theory strongly suggest that it should be possible to go beyond simple classification of individual flame states to predictive identification of global boiler performance trends, either in response to imposed plant operational changes or as a result of inherent system transitions. Implicit in the notion of prediction is that there is some sort of causal model (either physical or statistical) based on which the likelihood of future boiler states can be deduced from the present and/or past states. There are a variety of such model-based predictive approaches, each with different qualities depending on their focus, but approaches which account for the system's global behavior should be favored over those that focus only on local flame assessments. In doing so, integration into automatic boiler control systems will be easier and more effective. 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