Article pubs.acs.org/EF
Ultrasound Measurements of Temperature Profile Across Gasifier Refractories: Method and Initial Validation Yunlu Jia,† Melissa Puga,† Anthony E. Butterfield,† Douglas A. Christensen,‡ Kevin J. Whitty,† and Mikhail Skliar*,† †
Department of Chemical Engineering, and ‡Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, United States ABSTRACT: A gasifier’s performance and the longevity of its refractory are directly affected by the temperature of its reaction zone. One of the key technological challenges impacting the reliability and economics of coal and biomass gasification is the lack of temperature sensors capable of reliably performing in a harsh gasification environment over extended periods of operation. In this paper, we describe and experimentally validate a novel approach that uses noninvasive ultrasound to measure the temperature distribution across the refractory or other containments, including the temperature of the hot side of the refractory. This method uses an ultrasound propagation path across a refractory that has been engineered to contain multiple internal partial reflectors. Beginning with an ultrasound excitation pulse introduced on the cold side of the refectory, a train of echoes created by partial reflectors is acquired and used to determine the speed of sound in the corresponding segments of the refractory. By using an experimentally established relationship between the speed of sound in the refractory material and the temperature, the temperature distribution across the refectory is obtained. The initial validation of the proposed approach is reported for a model cementitious refractory heated up to 100 °C. The options for incorporating partial ultrasound refractors into the refractory, the achievable accuracy, and the spatial resolution of the measured temperature profile are discussed.
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INTRODUCTION Gasification involves the thermal breakdown of carbon-rich material in a hot, reactive environment to produce a so-called synthesis gas, or syngas, which is rich in hydrogen (H2) and carbon monoxide (CO). The produced syngas can then be used as an alternative feedstock for many chemical processes, such as those used in methanol, butanol, dimethyl ether, diesel, and gasoline production. When used in power generation, the syngas produced by gasifying biomass or coal is burned as a fuel in carbon neutral or carbon capture-ready power generation. Coal and biomass gasification are examples of processes with some of the most extreme temperatures, chemical aggressiveness, mechanical abrasion, and pressure conditions. Several technological challenges impact the reliability and economics of gasification, one of which is the complete lack of temperature and other sensors that perform reliably in the harsh gasification environment over an extended period of operation. The conventional approach of developing hardened conventional insertion sensors1 has proven to be largely unsuccessful. This is especially true for entrained flow slagging gasifiers since even the most hardened sensors are unlikely to survive for more than 1 or 2 months as the inner surface of the refractory wall degrades and recesses, exposing sensors directly to the corrosive slagging environment. Several reports describe how secondary measurements that are relatively easy to obtain such as temperatures, pressures, and compositions of streams into and out of a gasifiercan be used in conjunction with empirical or theoretical models and correlations to estimate inaccessible operating parameters inside the reaction zone. For example, Higman and van der Burgt2 conclude that the temperature of a dry-slurry feed gasifier can be monitored by measuring the concentration of methane in the product gas. © 2013 American Chemical Society
In fact, it was reported that this approach was used to estimate gasification temperature during the Tampa Electric Integrated Gasification Combined-Cycle Demonstration Project3,4 and is believed to be in common use by at least some operators of gasification units in the United States. An attempt to correlate a large number of routinely measured process variables to the composition of the produced syngas was also reported.5 A computational study by Sarigul6 showed close correlation between CH4 concentration and the adiabatic flame temperature of the gasifier. A promising approach is to obtain direct measurements of gasification temperature using methods that do not require the direct insertion of a fragile sensing element into the harsh environment. The most widely used techniques in this category are optical measurements, including combustion specific measurements of temperatures and reaction composition.7 Though minimally invasive (require a transparent access port), optical techniques are not suitable for temperature and composition measurements when an optically transparent line of sight is difficult or impossible to maintain, as in the case of slagging gasification or when high particle concentration in the reaction zone prevents light transmission. In this paper, we describe a noninvasive ultrasound approach to the measurements of the temperature distribution across refractories and report its initial validation. Special Issue: Accelerating Fossil Energy Technology Development through Integrated Computation and Experiment Received: December 19, 2012 Revised: March 3, 2013 Published: March 8, 2013 4270
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METHOD Background. The physical basis of the proposed approach is the temperature dependence of the speed of sound (SOS) in solids. By measuring the time it takes an acoustic signal to travel a known distance between a transducer and a receiver (the time of flight, TOF), which can be the same device in the pulse-echo mode of operation, the indication about the temperature distribution along the path of the ultrasound (US) propagation may be obtained. The application of this idea to the measurements of the temperature in gases is known as acoustic pyrometry and is well-established.8,9 This technology is used in many high-temperature applications, such as combustion10 and cement production.11 One advantage of the approach is the ability to obtain real-time temperature measurements over an extremely large range of temperatures (from 0 to 3500 °F), which makes it applicable to process monitoring from a cold start up to normal high-temperature operation. Disadvantages include significant measurement uncertainties when temperature along the line of sight between the transducer and the receiver varies significantly and unknown changes in the adiabatic constant due to variability in gas composition in the reaction zone are present. The utilized acoustic frequency range is low (typically, ≤3 kHz) because higher-frequency sound does not propagate well through gases, the consequence being interference from combustion instabilities, sounds produced by turbulent flow, and other disturbances, collectively known as a passive acoustic signature. Such low frequencies also limit the achievable spatial resolution of measurements when multiple transducers−receivers are used in order to measure the temperature distribution inside of a containment.12 Unlike numerous studies on gas acoustic pyrometry, very little work has been done on acoustic temperature measurements in solids, though the speed of sound in solids is also a function of temperature. Two notable examples use acoustic thermometry to measure temperature in microelectronic and medical applications. Lee et al.13 reported the development of an acoustical temperature measurement system that uses TOF measurements of an acoustic wave introduced into a silicon wafer through an excitation quartz rod. The wave, partially reflected from the quartz−silicon interface, traveled through the wafer until reaching a second quartz rod through which the wave reached the receiver. The difference between an arrival time of the reflected wave and the wave reaching the receiver through the second rod gave the TOF through the wafer, which was used to estimate wafer temperature. Lee et al. reported that ±5 °C accuracy was achieved between 25 and 1000 °C. They further suggest that achieving an accuracy of ±1 °C is possible. Arthur et al.14 investigated the use of backscattered US energy in temperature measurements in order to monitor and control noninvasive thermal therapies of tumors. Using a 7 MHz linear US phased array transducer, they demonstrated temperature measurements in ex vivo phantom tissue from 37 to 50 °C in 0.5 °C steps. The project did not progress toward in vivo testing because the quality of temperature measurements was severely affected by subject motion, unavoidable in subjects due to breathing and other disturbances. Other related examples include ultrasound characterization of thermomechanical behavior of refractories15 and, specifically, the measurements of Young’s modulus of refractories using ultrasound.16 Physical Basis of the Method. To illustrate the approach in its simplest implementation, consider a sample of solid material of known thickness L maintained at a uniform
temperature. Assuming a pulse-echo method in which the same device is used as an ultrasound transducer and receiver, the measurement of the TOF (return delay) of an ultrasound pulse, created by an ultrasound transducer coupled to one side of the sample and reflected from its distal end, may be used to calculate the speed of sound as SOS =
2L TOF
(1)
The reflection from the distal end occurs due to a change in ultrasound impedances caused by changes in density and the speed of sound at the boundary of the sample. The dependence of the speed of sound on the temperature, SOS = f(T), obtained experimentally or theoretically, would then allow us to estimate the temperature of the sample. However, when the temperature of the sample is nonuniform, the overall time of flight depends on the temperature in a complex and unknown way rc
TOF =
∫ rh
2 dr f (T (r ))
(2)
and does not provide sufficient information to estimate the temperature distribution across the sample, T(r). By adding constraints on the feasible solution, an estimation of a unique temperature distribution based on measurements in the integral form (eq 2) may be possible.17 Examples of such constraints may include the requirement that T(rc) = Tc, where Tc is an independently measured surface temperature in the proximity of the ultrasound transducer and T(r) is monotonically increasing, as would be the case for a refractory of thickness L = rc − rh (with rh ≤ r ≤ rc, where rh and rc are the radial coordinates of the hot and cold surfaces of the annular refractory) heated from the inside, and where T must satisfy the heat conduction model ρCp
∂T 1 ∂ ⎛⎜ ∂ ⎞⎟ = T kr ∂t r ∂r ⎝ ∂r ⎠
(3)
where ρ, Cp, and k are refractory density, heat capacity, and thermal conductivity, respectively. It is expected that the measurements (eq 2) will be relatively insensitive to significant changes in temperature localized near the hot side of the refractory. The dependence of model parameters (the values ρ, Cp, and k in eq 3) on temperatures is also a concern with the described model-based method for estimating T(r) using ultrasound measurements. Concept. The central idea of the proposed method for direct ultrasound measurement of the temperature distribution in solids is to create an US propagation path inside the refractory (or material of interest), which incorporates partial ultrasound reflectors (backscatterers) at known locations. Figure 1 illustrates three different alternatives to creating such ultrasound backscattering in annular refractories. In this illustration, it is assumed that the same element serves as a transducer and receiver; modification for the case of a separate transducer and receiver and an angled US beam is straightforward. In Figure 1(A), partial US reflections are created by planes of scattering material embedded into the refractory. The second option is depicted in Figure 1(B), where the refractory material is layered, with slightly different acoustical impedance in each layer. Figure 1(C) shows an embodiment in which partial reflections are created by geometric changes in the US propagation path through an embedded refractory insert. Such an insert can have a geometry (e.g., as shown in Figure 1(C)), designed to produce distinct US reflections at predetermined 4271
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Figure 1. Acoustic measurements of temperature distribution in the refractory. (A) Refractory material contains embedded planes of scattering material. (B) Layered refractory. (C) Refractory insert with geometric changes in the ultrasound propagation path creates partial backscattering. Left panel shows an ultrasound excitation pulse and the train of partial echoes produced by internal partial ultrasound reflectors. Right panel illustrates an engineered ultrasound waveguide/insertwith internal backscatterers, layers structure, or geometrical changesembedded into the gasifier refractory.
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spatial positions, or layered properties, as in the case of Figure 1(A) and (B). Separately produced inserts can be introduced into the refractory during its replacement, service, or relining, as illustrated in the right panel of Figure 1. A measurement of the temperature distribution begins with a US pulse, generated by an ultrasound transducer. This pulse will be partially reflected from each scatterer in the US propagation path through the refractory or the refractory insert and return to the receiver as a train of partial echoes at times TOF1, TOF2, TOF3,..., as shown conceptually in Figure 1 (left panel). The TOF of the first echo gives an indication on the average temperature in the first zone of the refractory, between the cold side and the first internal scatterer. The next return echo will originate from the second scatterer. By subtracting the TOF of the second and the first echoes, the average speed of sound and the corresponding average temperature between scatterers 1 and 2 can be estimated, and so on until the estimate of the temperature distribution throughout the refractory is obtained. With that distribution known, the temperature of the last (distal) segment can be used to determine Th = T(rh), the temperature of the refractory’s interior hot surface. In the described approach, the sensitive electronic components are kept away from harsh gasification environments and it is only required that the US transducer be acoustically coupled to the cold side of the refractory, representing minimal modifications to the gasifier. The overall system for measuring temperature distribution across refractory consists of (a) the engineered ultrasound propagation path either embedded as an insert or incorporated into the refractory to provide partial ultrasound reflections from predetermined locations, (b) an ultrasound transducer and receiver, which can be implemented as single or distinct components, (c) the analog and digital ultrasound instrumentation used to generate the excitation pulse and then acquire and amplify the return echoes, and (d) the signal processing system that determines the time of flight for each echo and then uses this information to calculate the speed of sound in the corresponding segment of the refractory. The temperature in each segment is then obtained using SOS versus temperature correlation found experimentally or from theory.
EXPERIMENTAL SECTION
The feasibility of the proposed approach hinges on two questions: 1. Is the speed of ultrasound propagation in the refractory temperature dependent? 2. Is it possible to create partial internal reflections along the path of the ultrasound propagation and what are the methods that can be used to create such reflections? In this study, cementitious samples obtained by form casting of Portland Type I/II cement (standard and Rapid Set formulations and Portland cement with fortifier) were selected as a model of castable refractory. A 2 in. I.D. PVC tubing was used as a mold, and the water− cement mixture was poured into a vertically oriented mold. The mold was vibrated by an external vibrator after each pouring to ensure uniform setting of each layer and to remove air bubbles. The samples were cured and aged at ambient temperature until their ultrasound properties stabilized. The ultrasound tests of cementitious samples were carried out using a Panametrics pulser/receiver (model 5072PR) and a Panametrics immersion transducer with a central frequency of 1 MHz (model V302), coupled to a sample using ultrasound gel. The data were acquired using a Tektronix oscilloscope (model MSO 2024) interfaced to a computer. Figure 2 illustrates typical ultrasound waveforms
Figure 2. Ultrasound waveforms acquired at different temperatures. acquired in this configuration from the same sample maintained at different uniform temperatures. The echoes are produced at the distal end of the sample. The first echo seen in the figure corresponds to the ultrasound pulse that traveled the length of the sample and back (a single round trip), while the second measured echo corresponds to the same pulse after it made the second round trip through the sample. 4272
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Figure 3. (A) The pulse-echo ultrasound response of two samples fabricated from the identical cementitious material. One of the samples (sample B, shown in inset photograph) contains embedded ultrasound scatterers at the midpoint of ultrasound propagation path, which produce partial reflection (waveforms in red). (B) Ultrasound pulse-echo response of the sample with two internal interfaces obtained by sequentially casting three layers of identical formulation and allowing time for a partial cure to occur prior to pouring the next layer. The direct inspection of waveforms in Figure 2 indicates that the speed of sound in the model refractory indeed depends on temperature, decreasing as the temperature goes up, leading to longer time of flight of ultrasound pulse at higher temperatures. Creating partial internal ultrasound reflections from known spatial locations inside the sample is the key prerequisite for the proposed approach to work. Two solutions, illustrated in Figure 1(A) and (B), were investigated. Figure 3A compares the ultrasound echo waveforms from two similar 2 in. long cementitious samples, one of which (waveforms in red) contains a few 0.5 mm steel shots placed in the middle of the sample during its casting. The result clearly shows a partial echo from inside of the sample created by embedded scatterers, confirming the viability of the concept depicted in Figure 1(A). The range of other material has been investigated in order to find the most appropriate selection for internal scatterers. An ideal choice for partial
reflectors would be a material with identical thermal expansion, and chemical and mechanical resistances similar to that of the surrounding refractory material; steel clearly does not satisfy these specifications. We, therefore, investigated if the concept depicted in Figure 1(B) can be implemented by using small variations in the composition of the layered cementitious materials, creating partial internal reflections at the interface between the layers. This indeed was found to be the case. In fact, it was found that, by casting multiple layers of the same composition and allowing for a partial curing before casting the next layer, enough variation in acoustic impedance is introduced to create partial US reflections at the interface. Such implementation of the refractory with an embedded partial internal ultrasound reflectors is particularly appealing since each layer will have essentially identical thermal, chemical, and mechanical properties. Figure 3B illustrates this approach. It depicts the results obtained with the 3 in. long 4273
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cementitious sample (shown in the inset) obtained by casting three 1 in. thick layers of identical cement mixture and allowing for a partial cure before the next layer is cast. Note three distinct echoes, produced at the two internal interfaces and the distal end of the sample. In this study, the relationship between the temperature and the speed of sound was obtained experimentally for a 4 in. sample created by sequentially casting four layers (each 1 in. thick) of a standard Portland Cement mixture into a PVC form and allowing a 30 min curing time between consecutive layers. After removing the solidified sample from the form, it was allowed to cure at room temperature for over 1 month. To establish SOS versus T correlation, the sample was placed inside the fabricated heating fixture depicted in Figure 4,
measured by a thermocouple and controlled by a PID controller. The surface temperature of the sample was measured by four surface thermocouples attached with high-temperature adhesive tape in the middle of each layer of the model refractory. Two additional thermocouples were used to measure the temperature of the top and bottom surfaces of the sample. The ultrasound transducer was coupled to the surface of the top layer of the sample (Layer 1, L1). To prevent the damage to the transducer, the top surface of Layer 1 extended above the fixture to allow for partial cooling of the sample; in this arrangement, Layer 1 is effectively used as a delay. The test temperatures were changed in 10 °C increments, from 20 to 100 °C. After each temperature change, sufficient time was allowed for thermal equilibration to occur before attempting the time-of-flight measurements. The sequence of temperatures for which the SOS measurements were conducted was randomized. The randomization included all repeat experiments for each temperature. Such randomization avoids measurement bias from one experiment to the next, which we observed in experiments without randomization when all temperatures were visited consecutively (during heating up or cooling down of the sample) and in fixed 10 °C increments. To calculate the 95% confidence interval, tests at each temperature were repeated at least six times in random order. We noticed that the interface between the consecutive layers is not entirely flat or smooth (consequence of coning, partial penetration, and/or mixing between layers). To account for unevenness of the interfaces, during the repeat experiments, the positioning of the transducer was slightly shifting relative to the centerline of the sample.
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RESULTS AND DISCUSSION
The physical basis of the proposed approach is the temperature dependence of the speed of sound propagating in solids. Since the speed of sound is calculated as the distance traveled by an ultrasound pulse divided by the time of propagation (time of flight, TOF), a method for precise measurements of the time of flight is essential for accurate measurements of temperature distribution across the refractory. Several methods for determining the TOF from the waveforms typified in Figures 2, 3, and 5 were investigated. Our initial approach was to use a delay line and the echo signal from the sample−delay interface as a reference “zero time” from which the time of flight is calculated. The time of flight is then
Figure 4. Experimental setup. which consisted of a thermally insulated steel container and an internal heating blanket (silicon rubber blanket by BriskHeat) that tightly surrounded the sample. The temperature of the heating blanket was
Figure 5. ΔTOF between echo waveforms at different temperatures is calculated by cross-correlation with a reference waveform acquired at 20 °C. 4274
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Figure 6. Comparison of the estimated ΔTOF at different temperatures obtained by cross-correlating the waveforms (panel a) or envelopes of the waveforms (panel b).
titles of each subfigure. The two methods give a similar trend of increasing time of flight with temperature but differ in values of ΔTOF. In further analysis, the envelope cross-correlation method is used as an approach less sensitive to the waveform distortion. The overall procedure for the data analysis using the envelope cross-correlation method and the speed of sound calculation is summarized in the following steps: 1. The reference zero time (trigger) is maintained the same for all measurements. 2. The reference waveforms, which include four echoes from the three internal interfaces and the distal end of the sample, are acquired at the reference temperature, selected to be 20 °C. 3. The temperature tests are conducted in random order, with at least six repeats for each temperature. During each test, 20 waveform sequences are collected and averaged. 4. The envelope cross-correlation between the reference and the averaged waveforms is applied to find ΔTOF at a given temperature relative to the reference temperature of 20 °C. 5. The speed of sound SOS1,...,SOS4 in each layer of the sample is calculated at each temperature using the following equations
calculated by matching single-point features (e.g., peak value or zero crossing) in the reference waveform from the sample− delay interface and the waveform of the reflection produced by internal interfaces and the end of the sample. Though this approach is standard, we encountered difficulties in its applications. Cementitious refractory materials are dissipative and have higher absorption of higher frequency components of the ultrasound pulse, which leads to distortion and broadening of the echo waveform and thus errors in determining the time of flight based on a single-point feature matching. We, therefore, opted to use the cross-correlation between the echo waveforms obtained at different temperatures to determine the difference in the time of flight at two different temperatures, ΔTOF. In this approach, ΔTOF between two echoes is obtained as the shift (offset) needed for the best possible match of the entire normalized shape of the two waveforms, as illustrated in Figure 5. This method uses both the phase and the amplitude information to find ΔTOF. It was furthermore suggested by Le18 that, for dissipative samples, a higher accuracy may be obtained if crosscorrelation is performed between the analytical envelopes of the waveforms, rather than the acquired waveforms themselves. To test the potential improvements, we implemented Le’s method and compared its performance with the TOF measurements based on the waveform cross-correlation. Figure 6 shows the comparison of the results obtained using the cross-correlation between the waveforms and the crosscorrelation between the envelopes of the waveforms. The scaled reference echo waveform (panel a) obtained at 20 °C and its envelope (panel b) are shown as green lines. The offsets of echo signals acquired at different temperatures (blue lines in panel a) and their envelopes (blue lines in b) needed for the best possible match with the reference are indicated in the
SOS1 =
SOS2 = 4275
2L1 TOFref1 + ΔTOF1
(TOFref2
2L 2 − TOFref1) + (ΔTOF2 − ΔTOF) 1
(4)
(5)
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2L3 − TOFref2) + (ΔTOF3 − ΔTOF2)
(6)
2L4 (TOFref4 − TOFref3) + (ΔTOF4 − ΔTOF3)
(7)
(TOFref3
each echo. On the basis of the calibration data, the thus obtained SOS of each layer provided the corresponding temperature of each layer. In establishing SOS−temperature correspondence, both the linear fit and the data specific to each layer were used (see Figure 7). Note that the confidence interval expands for the temperatures further away from the reference. The consequence of this is the lower uncertainty in measured temperatures close to the cold end of the sample and the higher uncertainty close to the heated surface. This suggests that the reference conditions must be selected carefully, perhaps by choosing them to be close to the temperature values of primary interest, which, for gasification, is the hot temperature of the reaction zone. The comparison of the thermocouple measurements of the surface temperature in the middle of each layer and the apparent temperature of each layer obtained with the proposed method is shown in Figure 8. Both methods show a similar
where L 1 ,...,L 4 are the thicknesses of each layer, TOFref1,...,TOFref4 are the times of flight of reference echoes originating from the three internal interfaces and the distal end of the sample, and ΔTOF1,...,ΔTOF4 are the differences between time of flights at reference and test temperatures. The overall length of the sample, L = ∑4i=1Li, was measured using a micrometer, and the speed of sound at the reference temperature was calculated using eq 1, where TOF is equal to TOFref4, which is the time of flight of the echo produced at the distal end of the sample. With the known speed of sound at the reference conditions, the thicknesses of each layer Li were calculated at reference conditions using the measurements of TOFref1,...,TOFref4. The speed of sound versus temperature results for all four layers of the sample, obtained using the described procedure, are shown in Figure 7. The obtained SOS on the vertical axis is
Figure 8. Comparison of thermocouple and ultrasound measurements of temperature distribution in the sample heated from the bottom.
trend in temperature distribution and an excellent agreement in the estimated axial thermal fluxes, which are proportional to (∂T)/(∂r). Two factors likely contribute to the observed difference in the measured temperature. First, it is reasonable to expect that the surface temperatures of the sample are indeed lower than the internal temperature measured noninvasively by the ultrasound, explaining some of the observed differences. Second, the thermocouples provide essentially pointwise measurements of temperature, while the ultrasound measurements depend on axial temperature distribution along the entire sample. In this first validation of the proposed method, a stepwise constant temperature distribution was assumed for each layer. A much more accurate estimate of the temperature distribution will be obtained if a more realistic “subgrid” parametrization is used. For example, we expect that, by requiring that the temperature distribution satisfies the realistic heat transport model (e.g., the conduction model of eq 3 supplemented with the boundary temperature condition at the transducer location), the accuracy of the ultrasound measurements of the temperature distribution and the estimation of the hot boundary temperature will improve. Though the coefficient for thermal expansion for cementitious materials is relatively small, the change in the ultrasound propagation length is yet another potential source of errors.
Figure 7. Calibration curves for the SOS as a function of temperature for all four layers of the sample were obtained using envelope crosscorrelation data analysis methods. The shown linear fit SOS = SOS(T) is based on data for all four layers. The shaded area shows the 95% confidence interval for the obtained linear fit.
plotted as a function of the temperature measured by a thermocouple located at the bottom of the model refractory sample. The data for all four layers were used to obtain a linear fit of the speed of sound as a function of temperature. The obtained correlation is plotted with the 95% confidence interval, shown as the shaded area. The proposed method for measuring nonuniform temperature distribution in the refractory was tested next. The sample was placed inside the fixture shown in Figure 4 and was heated using the base heater only. After the temperature measurements provided by surface thermocouples stabilized at constant values, an ultrasound excitation pulse was applied to the sample and the four return echoes were acquired. Using the envelope crosscorrelation method, the TOF of each echo was determined, and the result was used to estimate the apparent speed of sound in each layer needed to produce the observed time of flight for 4276
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Using a typical value for the coefficient of linear thermal expansion for cementitious materials as 6 × 10−6 in./(in °F), for the 4 in. sample, a conservative estimate (assuming 80 °C temperature change), is less than 0.09% (0.0035 in.) change in the sample length. A simple way to reduce errors caused by thermal expansion (which are small to begin with for the case reported in this paper) is to select the reference temperature somewhere between the lowest and the highest values in the sample (e.g., 50 °C in our case). Note that, if thermal expansion were taken into the account, slightly lower temperatures than those shown in Figure 8 would have been obtained. The thermal expansion errors will become more significant in the realistic gasification conditions, characterized by large temperature changes across the refractory, and will require a careful selection of the reference conditions. The errors caused by thermal expansion can be further reduced by directly accounting for the change in the ultrasound propagation length due to changing temperatures. One possible way to accomplish this is given by the following algorithm: 1. Measure the time of flight in different segments of the refractory. 2. Obtain piecewise constant values of the speed of sound needed to match the measured TOF values. In this calculation, initially use the length of each segment measured at the reference conditions. 3. Using the SOS versus temperature calibration curve, obtain the corresponding piecewise constant temperature distribution. 4. Obtain continuous temperature distribution that satisfies an appropriate heat transport model (e.g., eq 3) and results in the calculated time of flight (obtained from eq 2) that matches the measured values for each segment. 5. Update the length of each segment by accounting for thermal expansion at the estimated temperature distribution and return to step 2. 6. Continue to iteratively execute steps 2−5 until further iterations do not lead to significant changes in the estimated temperature distribution within the selected tolerance. These and other improvements needed to develop the proposed approach to its fullest potential, as well as its validation at high temperatures and gasification conditions, are the subjects of ongoing research.
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REFERENCES
(1) Dogan, C.; Kwong, K.-S.; Bennett, J.; Chinn, R. Improved Refractories for Slagging Gasifiers in IGCC Power Systems; OSTI ID 835687, DOE Report DOE/ARC-2003-01; U.S. Department of Energy: Washington, DC, 2003. (2) Higman, C.; van der Burgt, M. Gasification, 2nd ed.; Gulf Professional Publishing: Burlington, MA, 2008. (3) Hornick, M. J.; McDaniel, J. E. The Tampa Electric Integrated Gasification Combined-Cycle Project; Final technical report for project under Cooperative Agreement DE-FC-21-91MC27363; U.S. Department of Energy: Washington, DC, 2002. (4) Bennett, J.; Kyei-Sing, K. Refract. Appl. News 2004, 9, 20−25. (5) Guo, R.; Cheng, G.; Wang, Y. Texaco Coal Gasification Quality Prediction by Neural Estimator Based on Dynamic PCA. In Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation, 2006. (6) Sarigul, I. Model-Based Estimation of Adiabatic Flame Temperature Dusing Coal Gasification. M.Sc. Thesis, University of Utah, Salt Lake City, UT, 2012. (7) Rieker, G. B.; Jeffries, J. B.; Hanson, R. K. Appl. Opt. 2009, 48, 5546−5560. (8) Green, S. J. Acoust. Soc. Am. 1985, 77, 765−769. (9) Kleppe, J. J. Acoust. Soc. Am. 1996, 99, 2535−2574. (10) Bramanti, M.; Tonazzini, A.; Tonazzini, A. Meas. Sci. Technol. 1996, 45, 87−94. (11) Kychakoff, G.; Hollingshead, A.; Boyd, S. Use of Acoustic Temperature Measurements in the Cement Manufacturing Pyroprocess. In Proceedings of the IEEE-IA/PCA 2005 Cement Industry Conference, Kansas City, MO, May 2005. (12) Lu, J.; Takahashi, S.; Takahashi, S. Meas. Sci. Technol. 2000, 11, 692−697. (13) Lee, Y.; Khuri-Yakub, B.; Saraswat, K. IEEE Trans. Semicond. Manuf. 1996, 9, 115−121. (14) Arthur, R.; Trobaugh, J.; Straube, W.; Moros, E. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 2005, 52, 1644−1652. (15) Gault, C.; Huger, M.; Auvray, J.; Soro, J.; Yeugo Fogaing, E. Contribution of High Temperature Ultrasonic Measurements to Investigations of Thermomechanical Behaviour of Refractories. In Proceedings of the 10th International Conference and Exhibition of the European Ceramic Society, Berlin, Germany, June 17−21, 2007. (16) Baudson, H.; Debucquoy, F.; Huger, M.; Gault, C.; Rigaud, M. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 1999, 19, 1895−1901. (17) Zhang, H.; Basin, M.; Skliar, M. IEEE Trans. Autom. Control 2007, 52, 401−416. (18) Le, L. H. Phys. Med. Biol. 1998, 43, 2295−2308.
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare the following competing financial interest(s): M. Skliar and A. Butterfield co-founded a start-up company, Clovis Point Innovations LLC, to commercialize the technology described in the manuscript.
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ACKNOWLEDGMENTS The authors acknowledge support from the U.S. Department of Energy’s National Energy Technology Laboratory, under award number DEFG2611FE0006947. M.P. contributed to this work while a senior at the Highland High School in Salt Lake City, UT; she is currently a Chemical Engineering freshman at the University of Utah. 4277
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