Alternative Fuel Properties' Effects on Particulate Matter Produced in a

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Alternative Fuel Properties’ Effects on Particulate Matter Produced in a Gas Turbine Combustor Lukai Zheng, Chenxing Ling, Emamode A Ubogu, James Cronly, Ihab Ahmed, Yang Zhang, and Bhupendra Khandelwal Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b01442 • Publication Date (Web): 02 Aug 2018 Downloaded from http://pubs.acs.org on August 4, 2018

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Energy & Fuels

Alternative Fuel Properties’ Effects on Particulate Matter Produced in a Gas Turbine Combustor Lukai Zhenga,b,1, Chenxing Linga, Emamode A Ubogua, James Cronlya, Ihab Ahmeda, Yang Zhanga, Bhupendra Khandelwala a b

University of Sheffield, Sheffield, S20 1AH, United Kingdom Nanjing Institute of Technology, Nanjing 211167, China

1. Abstract Diversified fuel supplies and stringent environmental pollution regulations in the aviation sector have promoted the development of the alternative fuels industry. The chemical and physical properties of some of these diverse fuel substitutes lie outside of historical experience. Therefore, their combustion behaviour cannot be judged via research of petroleum-derived jet fuel. Particulate matter (PM) emissions are important for future alternative fuels, although extensive results in relations to combustors are not available in literature. Hence, large-scale experimental testing is essential for improving our understanding of alternative fuel effects on combustion performance and environmental impact. The aim of this study is to evaluate the impact of fuel properties and composition on the PM emissions characteristics and flame sooty tendency profile on a Rolls-Royce Tay gas turbine combustor. Extractive sampling and in-situ measurement methods have been used in this study. Sixteen types of alternative fuels have been tested under two different operating conditions. PM emissions were measured via a

1

Corresponding author: [email protected] (Lukai Zheng)

ACS Paragon Plus Environment

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Differential Mobility Spectrometer (DMS 500 fast particulate spectrometer), and the soot propensity profile was analysed via an innovative visual method based on flame luminosity high-speed imaging. The results indicate that a higher aromatic can be found as the main factor for insufficient burning and greater soot formation. In addition, for fuel properties, the density and surface tension were supposed to be key factors for soot formation. For chemical compositions, fuels with higher cyclo-paraffin content have the potential to induce soot promotion. In contrast, a fuel with a high hydrogen content can perform in much more environmentally friendly way. Furthermore, it was observed that the results of PM emission measured by DMS 500 and sooting tendency computed via imaging method (in-situ) correlated particularly well for all the tested fuels and conditions in this study. The in-situ soot emission monitoring method presented in this study can be used for detailed, instantaneous investigation of PM emissions within the combustor. Thus, this method can be considered an alternative evaluation method for measuring qualitative soot emissions.

Nomenclature CPC DCN DFCD DMS FAR LBO LII PM SAR SPK

Condensation Particle Counter Derived Cetane Number Digital Flame Colour Discrimination Differential Mobility Spectrometer Fuel/Air Rate Lean Blowout Laser-Induced Incandescence Particulate matter Sooty Flame Area Ratio Synthetic Paraffinic Kerosene


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γ

H Ar N-P I-P C-P R2 k

LBO Equivalence Ratio Density (Kg/M3) Viscosity (cst) Surface Tension (mN/m) Flash Point (°C) Smoke Point (°C) Initial boiling point (°C) End boiling point (°C) Distillation Slope 10%~50% recovery Distillation Slope 50%~90% recovery Net Heat Combustion Average Molecular Weight Hydrogen Content (wt%) Aromatic Content (wt%) Normal Paraffine Content (wt%) Iso-Paraffine Content (wt%) Cyclo-Paraffine Content (wt%) Correlation Coefficients Gradient of The Linear Trend Line

2. Introduction Along with the recent dramatic increase in demand in aviation market, the energy security [1] and environment deterioration problems have emerged unceasingly [2,3]. The statistics show [4–6] that emissions, especially for the particulate matters (PM) emissions [7], are regarded as among the greatest threats to the respiratory health of humans [8]. In addition, most pollution gases and PM emissions of aircraft are released at high altitudes and with temperatures [9] whereby the perniciousness of emissions is exacerbated [10]. Therefore, since the dawn of the 21st century, interest in industrial research into alternative fuel has boomed. The first alternative fuels certified for use by commercial aviation in the early 2010s included synthetic paraffinic kerosene (SPK) [11]. Since


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then, there have been several breakthroughs related to alternative fuels in both military and commercial contexts. To meet the demand of fuel diversity and flexibility, research has continuously explored sources and technologies for alternative aviation fuel. In the aviation sector, to provide high-level reliability and stable combustion during operation, the alternative fuels are suggested to meeting the following requirements. Firstly, a desired alternative fuel is expected to guarantee the safety and security of aviation fuels supply. Additionally, an alternative fuel also should not have any negative impact on engine performance, maintenance, ground storage and transportation. Furthermore, from the environment angle, such fuel is required to be renewable, economical, sustainable, have a reasonable GHC life-cycle etc. [12]. PM emissions can be easily controlled for gaseous fuels, but the elimination of residual fuel particles in fossil fuels is still a challenging research goal. Many complicated and expensive diagnostic systems have been exploited to provide real-time monitoring of emissions. The AIR6241 standard reports several primary instruments [13] such as the Particle Counter Advanced for automotive nvPM number, Laser-Induced Incandescence LII-300, and the Micro Soot Sensor, which can provide real-time, high-resolution PM mass measurements. The DMS 500 used in this study can determine real-time PM size distribution information, as well as quantified concentration of exhaust PM over the full particle-size spectrum [14–16]. Petzold et al. [17] and Lobo et al. [13] observed that the results of PM number concentration measured by condensation particle counter (CPC) and DMS 500 are in good


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Energy & Fuels

consistency. The PM emissions results measured by the instruments mentioned in the previous paragraph are more exactly measuring the net result of exhaust soot emission and further soot oxidation after combustion [18]. Another soot formation is measured within the combustor, which more directly represents the fuel chemical reaction [19]. Soot is mostly made up of particles with irregular shapes and size, as well as different chemical compositions [20,21]. As fuel burns, these particles are heated, presenting as a bright yellow sooty flame at the end of the flame plume [22–24]. Many studies have utilised the visualisation of flame luminosity to characterise the combustion process and soot detection [25–28]. For instance, Fujino et al. [29] and Jiotode et al. [30] have provided time-resolved imaging data in the combustion chamber to analyse flame temperature distribution and combustion efficiency with visualisation techniques. Botero [31] correlated visual sooty flame height with soot particles mass to analyse the paraffin impact on soot tendency. After that, he also studied optical soot formation induced by aromatic fractions via luminous temperature and particles size number [32]. Ayoola et al. [33] used flame intensity with the imaging method for heat release rate

measurement.

The

results

correlated

well

with

traditional

OH*

chemiluminescence measuring in several types of flame. Later, Temme et al. [34] calculated the spatial and temporal heat release rate of the flame in a gas turbine

&

combustor with the Rayleigh Index, R x, y$ = ' (′*′ +,, where *′ is proportional & to flame intensity, p’ is the pressure, and T is time. The visualisation system also be used on flame stabilities near lean blowout monitoring by Giorgi et al, [35], who


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observed that the spectral results are in agreement with the results of flame emissions acquired by the analysis of the exhaust gas emissions (NOx and CO). He also affirmed the capability of imaging techniques for flame stability detection and for use as a predictive tool. Such optical diagnostics offer the advantage of minimised interference with the flame and avoid the complex test bench for sample collection and chemical analysis procedures. Yang et al. [19] has pointed out that fuel composition was found to a controlling factor on jet fuel soot generation after a comprehensive review. Research has reached a consensus [31,36,37] that aromatics content is a major cause of soot formation. Such experimental results regarding pure individual hydrocarbon fuel exhibit different soot tendency behaviours. Researchers have suggested the soot tendency of different molecules in the following order, ‘naphthalenes > alkylbenzenes > alkynes > alkenes > cycloparaffins ≈ iso-paraffins > n-paraffins’. In addition, the soot tendency is unlikely to be related to the increase of carbon number. Obviously, each kind of alternative fuel is composed of hundreds of hydrocarbons. Hence, the distinction of fuel components will have a huge impact on gaseous and PM emissions during combustion [38]. New feedstocks and pathways offer the possibility of the mass production of aromatic-free alternative fuels [39]. By blending these fuels with conventional fuels, emissions could be reduced [40]. Under this situation, other parameters related to soot formation tendency stand out; however, at the present, soot emission studies into alternative fuels are still limited. Saffaripour et al. [41] compared the jet performance with four alternative fuels and blends, demonstrating


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Energy & Fuels

that the soot levels in flames were proportional to benzene concentrations. However, Xue et al. [42] argued that ATJ has a lower aromatic content than FT-SPK, but with a higher sooting propensity. They concluded that, for the sooting behaviour of the alternative jet fuels, the n-paraffinic content and molecular size should be taken into consideration. Further investigation noted that the soot propensities for different hydrocarbon types follow a decreasing order, aromatics, cyclic alkenes, branched alkenes and linear alkanes [43]. Moreover, researchers have determined that different physical properties of aviation fuels will affect combustion performance as well as soot formation. Roquemore and Litzinger [44] found that blended fuel with high initial and end boiling points can contribute to high particle numbers. Calcote and Manos [45] defined an equation of a threshold sooting index (TSI) to rate the soot formation tendency based on smoke point and molecular weight of the fuel. Later, Yang et al. [19] claimed that the TSI increased with the smoke point and molecular weight. These results were further confirmed by Xue et al. [42], who claimed that the soot volume fraction decreased with the smoke point and hydrogen content. Recent experimental results from Christie et al. [46] indicate that, by blending two fuels with different proportions, the nvPM emission linearly increased with the smoke number. Zhang et al. [47] conducted a review of the latest developments in alternative jet fuels, highlighting the importance of fundamental research into fuel properties per combustion, which can provide guidance for fuel development and assess the feasibility of aviation applications. The diversity of fuel composition, feedstock and


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production pathways make it more difficult to predict the combustion behaviour based the research of petroleum-derived jet fuel [48]. For any widespread application of alternative fuels, it is essential to guarantee that reliable combustion performance and acceptable emission profiles of the alternative fuels are compatible with infrastructure and operating conditions. Thus, large-scale experimental testing is essential to improve our understanding of alternative fuel effects on combustion performance and environmental impact [49]. Within the above discussion, several important relative parameters for soot formation have been mentioned. However, there remains a lack of comprehensive analysis of the impact of fuel properties on exhausted PM emissions and the order of significance. In addition, most analyses are based on the data concerning further oxidation exhaust soot emissions. No effective measurement has been introduced evaluating in-combustor instantaneous soot performance. Therefore, it is necessary to combined detailed investigations on sooty tendency results from both novel in-combustor soot measurement techniques and traditional PM emission equipment (DMS 500). The objective of this study, therefore, is to investigate the effect of physical and chemical properties of fuel on soot formation and PM emissions in a Rolls-Royce Tay combustor. For more in-depth analysis of the combustor performance, 16 different types of candidate fuels have been tested at two different operating conditions (stable condition and lean-burning condition). The exhaust PM emissions were measured by an extractive sampling method (Differential Mobility Analyser, DMS 500). The


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in-situ measurement was done by high-speed camera to measure in-combustor soot formations from a novel aspect. The correlation between results from the extractive (DMS500) and in-situ optical combustion monitoring has been further evaluated.

3. Experiment setup A schematic of the experimental rig design for the Rolls-Royce Tay combustor test can be observed in Figure 1, and a photo is shown in Figure 2. The design contains an air supply system, a fuel supply system and an optic data acquisition system. In the air supply system, air is delivered to the combustor from an atmospheric pressure fan through a process line, which has been designed and manufactured according to the industrial design standard BS:5167. This line is capable of measuring flow with an uncertainty of ±2% of the measured value. The atmospheric pressure, fan-driven motor is controlled by a speed controller, providing analogue control of the fan speed subject to the execution of custom-written LabVIEW code. A Tay combustor research rig provided by Rolls Royce was designed to house a single can combustor, igniter and fuel spray nozzle. The configuration provides a platform by which to investigate the performance of the Tay combustor at various flight conditions. Given the development purpose of the research rig, the housing provided multiple temperature and pressure sampling points. Figure 3 provides a diagrammatic illustration of the can combustor, with photographs showing internal and external views of the experimental hardware.


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Figure 1. Experimental apparatus of the Tay combustor

Figure 2. Picture of the Tay combustor .


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Figure 3. Tay combustor configuration The fuel injector is an air-blast type. The atomisation pressure was maintained at 0.5 bar +/- 0.003. This pressure was chosen to provide the best atomisation with minimum air flow. The diagram shown in Figure 4 illustrates the injector’s cross section. Compressed dried air is supplied at a controlled pressure. It enters the separation disk and then goes into the air swirler. In contrast, the fuel is supplied through a counter-rotating swirler to increase kinetic energy and help the atomisation.

Figure 4. 3D sketch and cross-section of the Rolls-Royce Air blast fuel injector Fuel is supplied to the combustor using an electronically controlled fuel system. The fuel supply bomb is pressurised using a regulated head filled with nitrogen and


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provides a steady fuel pressure. Fuel exiting the bomb is then controlled by a twin series (coarse and fine) of air-actuated needle valves and an air-actuated fuel shut-off solenoid.

Table 1. Tay Experimental Uncertainty for Control Variables Measured Quantity Air Mass Flow (g/s) Fuel Mass Flow (g/s) Exhaust Gas Temperature (°C)

Measured Range 0 – 320 0–5 0 – 500

Total Measurement Uncertainty % ±2% ±3% ±1%

Each of the air-actuated fuel control values, as well as the air-fan motor speed controller, are controlled using an LV analogue voltage controller at a frequency of approximately 5 Hz via the NI SCXI chassis and control PC. Data from the atmospheric pressure airline orifice flow meter and the fuel flow Coriolis meter are read into this low-speed system and displayed on the screen for monitoring by the test engineer. The measurement uncertainty is presented in Table 1. The operating parameters for the lean-burning and stable-burning conditions were determined empirically in an effort to generate a measurable range of PM. The air mass flow rate was set as 200 g/s for both conditions. The fuel flow rates were 1.8 g/s for stable-burning conditions and 0.5g/s for lean-burning conditions. The PM number concentration was measured by DMS 500 [50]. A Photron-SA4 high-speed colour camera was employed for optical data collection. In order to provide optical access to the burner, a quartz window was mounted on the exhaust duct, facing the combustor chamber axially. The camera was placed at the exhaust end of the combustion chamber, and the images were acquired at


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the tail of the flame through the exhaust vent. Images for sooty flame profile were captured at both lean- and stable-burning conditions. The shutter speed was 1/1000s for lean-burning conditions and 1/10000 for stable-burning conditions.

4. Methodology 4.1. PM Emissions Measurements For particle size distribution measurements, a combustion differential mobility spectrometer (DMS 500) was employed for the experiment, as this instrument can provide real-time measurements of soot particle size distribution [51,52]. The exhaust sample was directed through a corona discharge charger, wherein particles gain charges according to their size; thereafter, they are transported to a classifier. On the application of high voltage to the central electrode, the charged particles are deflected toward the electrometer rings. Particles with a higher charge/lower drag will be deflected further and land on an electrometer ring closer to the sample inlet. As charged particles land on the metal electrometer rings, their charge will flow to the ground via an electrometer amplifier. This amplifier is capable of measuring small currents caused by groups of particles landing on the metal rings, and these form the basis of particle detection. The average number-weighted particle mobility diameter (Dp) distribution, n(Dp) = dN/dlogDp, for each size bin recorded, was used to generate a particle size distribution, which was then integrated to produce the total number of concentration per volume of the exhaust. The soot particles collected at the exhaust vent were delivered to the analyser


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through a five-meter sample line [53]. For each candidate blend, PM emissions were repeated three times to guarantee accuracy.

4.2. Optical Flame Morphology Analysis The digital flame colour discrimination (DFCD) approach developed by Huang and Zhang [54,55] shows that the instantaneous flame combustion performance can be directly represented via flame colour. The optical detection method and post-processing imaging provide a non-intrusive and alternative approach for monitoring the instantaneous soot formation behaviour of liquid fuel combustion [30,32,56,57].

4.2.1. Stable Burning Condition

Figure 5. Sample images of different types of fuels at stable burning condition Figure 5 shows the sample images of the candidate fuels. The two-dimensional flame image can be considered the integration of flame luminosity along the axis of


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Energy & Fuels

the combustion chamber. More dynamic flame details can be observed in a video clip2. For each fuel, the flow pattern of the bluish and yellowish luminosity generally exhibited a clockwise-swirl motion encircling the central recirculation zone. The video is recorded in 0.1 seconds with a shutter speed of 1/10000 s and a frame rate a 1000 f/s. Soot emission is one of the main parts of the fuel evaluation of air pollution control. Sooty flame consists of unburned, impure carbon particles, which have been heated up, manifested by a bright yellow colour owing to incomplete combustion. Hence, the sooty flame is a direct indicator of soot emission and the insufficient burning of fuel components. The soot radiation concentration analysis method introduced by Huang and Zhang [22] was successfully applied to various combustion performance evaluations [30,58]. The sooty flame region and blue chemiluminescence included regions that can be identified and separated based on the post-processing image method introduced by Wang et al. [59]; hence, the blue flame of the captured images was filtered within the hue-value band ranging from 180° to 252°, while the sooty flame was filtered within the range of 10° to 70°, as shown in Figure 6.

2

Dynamic flame of different types of fuels at stable burning condition: https://youtu.be/88nSF1C-uag


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Figure 6. The sample flame image and its corresponding colour distribution histograms in hue space. The spatial contour plot of intensity from the R layer (RGB matrix) in the hue range of 1° to 70° expresses the local distribution of the soot concentration, as shown in Figure 7. The soot concentration rate displayed in the colour bar ranges from 0 to 255, in arbitrary units, and with varied colours from dark blue to red. The red regions in a contour represent the highest soot concentration.


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Figure 7. Soot concentration contour plot of different types of sample fuels Further digital image processing was applied to integrate the power spectrum of the soot radiation concentration, in order to gain a more quantifiable understanding of sooty flame morphology. With the aid of consecutive images recorded by the high-speed camera, the time-resolved dynamic properties of soot radiation emitting can be calculated by integrating relative R intensity. PSD was applied to extract the spectrum and density amplitude of the soot radiation from the time-domain image data. For ease of comparison, the results of soot radiation power are presented in the form of RMS stacked histograms, generated by integrating the energy-containing spectra for fuels.


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4.2.2. Lean Burning Condition

Figure 8. Sample images of different types of fuels at lean burning conditions As shown in Figure 8, the sample flame images of tested fuels demonstrate a lean burning behaviour. The bluish flame area was enhanced five times for the facilitation of visual observation. More dynamic flame details at lean burning condition can be observed in a video clip3. The sooty flame area and intensity varied among candidate fuels, indicating that the fuels still undergo different levels of unclean and insufficient burning, even at very lean combustion conditions. The combustion performance is based on the properties and compositions of the fuels. Correspondingly, further quantitative analyses will be conducted on the sooty flame profile analysis. In a lean burning condition, the whole ring shape of the flame can be observed. Therefore, the combination analysis of the sooty flame area ratio over the whole flame area and local soot concentration profile can provide a more accurate sooty tendency

3

Dynamic flame at lean burning condition: https://youtu.be/TcJbwl1CGu4


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Energy & Fuels

index. The 2D contour plot of soot concentration result is shown in Figure 9.

Figure 9. Soot concentration contour plot of different types of sample fuels at lean burning conditions. According to the recorded 2000 images, the sooty flame concentration ratio (SCR) for one image is defined by Equation 1 and calculated through MatLab. The result for the further discussion in the following section is the average of the 2000 relevant images for each fuel. 3°

SCR = / 012 6 125° 3°

Equation 1

= / 012 6 125°

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Alternative Fuel Properties' Effects on Particulate Matter Produced in a

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