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A method based on infrared detection for determining the moisture content of ceramic plaster materials
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A method based on infrared detection for determining the moisture content of ceramic plaster materials E.V. Macias-Melo a, K.M. Aguilar-Castro a,n, M.A. Alvarez-Lemus a, J.J. Flores-Prieto b a Universidad Juárez Autónoma de Tabasco, División Académica de Ingeniería y Arquitectura (DAIA-UJAT), Carretera Cunduacán- Jalpa de Méndez km. 1, Cunduacán, Tabasco, CP 86690, México b Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET-TNM-SEP), Prol. Av. Palmira S/N. Col. Palmira, Cuernavaca, Morelos, CP 62490, México
art ic l e i nf o
a b s t r a c t
Article history: Received 8 September 2014 Received in revised form 30 March 2015 Accepted 30 March 2015 This paper was recommended for #?publication by Prof. A.B. Rad.
In this work, we describe a methodology for developing a mathematical model based on infrared (IR) detection to determine the moisture content (M) in solid samples. For this purpose, an experimental setup was designed, developed and calibrated against the gravimetric method. The experimental arrangement allowed for the simultaneous measurement of M and the electromotive force (EMF), fitting the experimental variables as much as possible. These variables were correlated by a mathematical model, and the obtained correlation was M ¼ 1:12 expð3:47 EMF Þ, 7 2.54%. This finding suggests that it is feasible to measure the moisture content when it has greater values than 2.54%. The proposed methodology could be used for different conditions of temperature, relative humidity and drying rates to evaluate the influence of these variables on the amount of energy received by the IR detector. & 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Keywords: Near infrared reflectance spectroscopy Moisture content sensor Ceramic–plaster IR detection Non-contact measurement
1. Introduction Currently, moisture content sensors are mainly based on gravimetry, psychrometry, electrical resistance, dielectric capacity, microwaves, fiber-optic techniques and near infrared spectroscopy (NIRS) [1–3]. Among these methods, gravimetry has been the approach most commonly used to measure moisture content and is considered as a reference method [4,5]. NIRS-based methodologies, which have been recently developed for industrial applications, allow one to correlate the moisture content with the spectral radiation reflected or, transmitted or the IR radiation emitted by the sample surface [6–8]. The NIRS method is attractive because it is fast and non-invasive, making the measurement easier than conventional techniques in which pre-treatment and/or destruction of the samples is required [5,9]. NIRS has been widely used to monitor the moisture content in drying and quality control by measuring the spectral optical properties for a variety of materials, including wood, paper, cellulose, peat, olives, lactose, mangos, potatoes and jaboticaba, among others [3,10–17]. In these reports, a correlation model between the moisture content and the optical property was obtained. Measuring optical properties, NIRS detection is usually carried out by the means of spectral emitting diodes and
n
Corresponding author. Tel.: þ 52 914 11 39 580. E-mail address: [email protected] (K.M. Aguilar-Castro).
photodiodes [2,9], which are chosen according to the most significant absorption bands of water (1450 and 1940 nm) [11]. Moreover, to implement this technique, additional accessories, such as an objective lens, optical chopper, collimating lens, beam splitter, and band-pass filter, among others, are required. This approach has also been reported for monitoring the moisture content in soil samples by considering the dependency between the energy emitted and the moisture content [18–20]. However, there are no reports of models that relate the IR radiation and the moisture content of plaster samples. There are very few investigations using thermal infrared (IR detection) to determine the moisture content by measuring the energy emitted by the sample. IR detection has been possible through transducers that convert IR radiation into an electromotive force (EMF) by photoelectric and Seebeck effects, among others. In most of the applications of infrared sensors, the measurements must be adjusted (calibrated) for each type of sample to optimize the sensor sensitivity [21–23]. In the case of the photoelectric effect, an additional light source is required. However, techniques based on the Seebeck effect using a thermopile, usually have been used as a single element or set that can be placed together in an array to improve the sensitivity of the transducer [22,24]. A thermopile has a similar sensitivity to that of pyroelectric detectors but retains an output voltage proportional to the temperature difference between the high and low absorption surfaces. Unlike pyroelectric detectors and photodiodes, thermopile detectors do not require the light source, mechanical
http://dx.doi.org/10.1016/j.isatra.2015.03.014 0019-0578/& 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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Subscript
Abbreviations EMF Electromotive force (V) Fa, Fb, Fc Fraction of radiant energy between 0 and (Dimensionless) RH Relative humidity (%) M Moisture content on wet basis (%) q Heat flux (W/m2) 2 R Coefficient of determination (Dimensionless) SE Standard error of estimate (%) T Temperature (1C) t Time (min) λ Wavelength (nm) ε Emittance (Dimensionless) ρ Reflectance (Dimensionless)
λT
chopping or compensation procedures of EMF. In addition, thermopiles have a wide spectral range, from 1 μm to beyond 20 μm [25], a potentially fast time response, low degradation and versatility of the absorption surface in each case (spectral window ranges). Moreover, thermopiles have the advantage of directly measuring temperature differences without any offset; if radiation does not strike the detector, the output voltage signal is zero. Thermopile applications are abundant and well known, including fire detectors, temperature sensors, gases analyzers, body human detectors, and moisture sensors, among others [22,26– 28]. However, the use of thermopiles as moisture sensors is quite limited, and there are only a few reports using soil samples [18– 20]. According to the report by Flores et al., [29], in the ceramic industry, the moisture content is measured by gravimetry with an uncertainty of 7 2.9%. However, the ceramic industry requires fast and non-contact measurements of the moisture content to facilitate the drying process of the ceramic–plaster and optimize the time and movements. To satisfy these requirements, IR detection by means of a thermopile would be suitable, and optimizations are desirable in terms of the range and uncertainty (standard error of estimate) considering the absorption of the thermopile surface according to the water absorption bands [11]. Considering the aforementioned factors, we propose a methodology based on IR detection by means of a thermopile in which a non-invasive sensor is designed, developed and calibrated to measure the moisture content for applications in the ceramics industry.
2. Material and methods The methodology for developing a mathematical model based on IR detection, as well as the detailed design and manufacturing of all of the components of the experiment, is presented below. 2.1. Mathematical model The proposed model for the energy emitted from the sample is shown in Fig. 1. M is the moisture content of the sample at t¼0, qy is the amount of energy emitted by the sample and qρb is the energy reflected from the incoming energy emitted by the surroundings (qb). At t a0 (t1), the moisture content decreases to M0 , and the amount of energy varies to qy0 ,qρb0 and qb0 . The proposed mathematical model assumes that: (1) the surrounding environment behaves as a gray body, (2) the moisture content in the surface represents the moisture content of the whole sample, and
Cal D b ent i Md o ref sc y 0
Calculated moisture content IR detector Surroundings Inlet air Ambient air Experimental moisture content Outlet Reference Dryer Sample Initial
(3) no significant changes in the surface rugosity occur during drying. In this array the energy change measured by the transducer inside the spectral window (ΔqD (λ2 λ1)) corresponds to the difference of the sum of all the energies (qy0 ,qρb0 and qb0 ), each multiplied by the portion of radiant energy inside the spectral window at t¼ n and t¼n 1, which can be expressed as follows:
ΔqD ðλ2 λ1 Þ ¼ qD ðλ2 λ1 Þt ¼ n 1 qD ðλ2 λ1 Þt ¼ n
ð1Þ
where the energy qD at t¼ n and t ¼n 1 is given by: h i qD ðλ2 λ1 Þt ¼ n 1 ¼ Faλ2 λ1 qy ðtÞ þ Fbλ2 λ1 qρb' ðtÞ þ Fcλ2 λ1 qb' ðtÞ h i qD ðλ2 λ1 Þt ¼ n ¼ Faλ2 λ1 qy ðtÞ þ Fbλ2 λ1 qρb' ðtÞ þ Fcλ2 λ1 qb' ðtÞ
t ¼ n1
t¼n
ð2Þ where qy ðt Þ ¼ f εy ðt Þ; T y ; qρb0 ðt Þ ¼ f ρy ; qb0 ; qb0 ðt Þ ¼ f εb0 ðT Þ; T b0 ; ρb0 ; qy Þ and Faλ2 λ1 , Fbλ2 λ1 ,Fcλ2 λ1 correspond to the radiant energy inside the spectral window λ2 λ1 . Assuming that variations in the sample temperature (Ty) and the surrounding temperature (Tb) are negligible during the experiment, M can be expressed as a function of ΔqD ð3Þ M ¼ f ΔqD
where ΔqD depends on the variations in both the properties, εy and ρy . In the transducer, ΔqD is differentially absorbed on the thermopile surface, leading to a temperature difference (ΔT) that directly acts on the transducer, which turns the IR radiation into an output voltage (EMF). Hereby, the determined values of M can be directly related to the measured EMF values. To adjust the experimental data to the most suitable fit, we propose a single correlation model, which assumes that variations in M depend only on the energy measured by the transducer in terms of the EMF. This model assumes that changes in both the sample temperature and the relative humidity of the air (inside the drying chamber) are negligible. Ordered pairs (M, EMF) are obtained from the initial moisture content until it reaches the equilibrium. A single correlation fitting is obtained from the minimum squares assuming no effects due to the surroundings (temperature and relative humidity of the air) because they are controlled during the measurements. The response of the sensor is fitted to Eq. (4) considering the exponential behavior of the moisture content during the drying process M ¼ a expðb EMF Þ
ð4Þ
Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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Fig. 1. Graphical representation of the energy components involved during the drying process: qy ¼ energy emitted by the sample, qρb ¼ energy reflected by the sample from the surroundings, and qb ¼ energy emitted by the surroundings.
Data dispersion is estimated in terms of the standard error of estimate (SE), Eq. (5), and the coefficient of determination (R2) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X SE ¼ ðM Md M Cal Þ2 =ðN 2Þ ð5Þ where N is the number of measurements, MMd is the measured moisture content of the sample and MCal is the moisture content calculated from the fit. The total uncertainty is determined by the error propagation method that determines the combined standard uncertainty [30]. 2.2. Experimental model In this model, M and the energy emitted from the sample were simultaneously measured. During the experiment, the sample temperature, the indoor air temperature and the air relative humidity were fixed through a controlled environment chamber. M was determined by gravimetry, whereas the energy emitted was measured using an infrared transducer. Fig. 2 depicts a schematic of the experimental model. The device included the sample, an IR detector, a balance, a drying chamber, and a heat exchanger. The IR detector delivered a potential difference according to the radiation received on the transducer surface. Before starting the experiments, the temperature of the thermal bath was set to 10 1C to provide a temperature of reference to the IR detector. The inlet air was heated from Ti to Tent before entering the chamber. The heated air was spread inside the drying chamber, reaching Tsc, with a relative humidity of RHsc. Then, a wet sample was placed inside the drying chamber and its temperature was set to Ty. In addition, the rate and temperature of the drying air were fixed in such a way that the energy emitted could be measured only as an effect of the variation in the moisture content while drying was achieved. Finally, the moist air exited the chamber with a relative humidity of RHo. During the experiment, the IR detector measured the energy emitted from the sample, whereas the weight loss was registered by the balance, and the corresponding moisture content was determined. 2.3. Prototype description Fig. 3 displays an instrumentation diagram of the experimental equipment. 2.3.1. Sample A mixture of 1:1.3 plaster:water was used to shape a disc of 12 cm diameter 1 cm thickness. Before the test, the sample was immersed in water for 24 h until the moisture content was 26.67%, which corresponds to the previous report by Aguilar et al. [31]. The sample was instrumented in such a way that both the surface temperature and the supplied energy for heating could be measured (Fig. 4). The energy for heating was supplied using an
Fig. 2. Experimental model with the main components, the airflow path and the measured variables.
electrical resistance powered by a programmable power supply (4 W average power).
2.3.2. Infrared detector The IR detector was designed using the structured design methodology reported in the literature [31] considering the need statement, main constraints and functions, design conditions and critical parameters. The need statement and the main constraints were the detection of radiosity from the sample during drying below 50 1C to avoid plaster decomposition, whereas the design condition was the minimization of the influence of the surroundings. The IR detector was composed by a thermopile and housing. The housing was designed and built to maximize the potential difference by using a cavity with water recirculation at a steady temperature, and a type T thermocouple was used for the thermopile (Fig. 5). On the front, the detector was divided into two sections: low and high absorptance. The selection of the high and low absorptance surfaces was made by testing several coatings, and their corresponding absorptances were obtained in the range from 780 to 2500 nm using a UV–visible–NIR spectrometer (Shimadzu 3100). The high-absorber surface was the hot junction, and the other surface was the cold junction.
Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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Fig. 3. Experimental setup instrumentation: (1) IR detector, (2) drying chamber, (3) sample, (4) balance, (5) thermal bath, (6) heat–air exchanger, (7) voltage supply with a symmetrical output, 12 V, (8) voltage amplifier, (9) nanomultimeter, (10) reversing amplifier, (11) data acquisition system, (12) PC 1 and (13) PC 2. The scheme includes two photos of the actual equipment.
Fig. 4. Ceramic–plaster sample coupled to electric resistance: (a) front view and (b) inner layers.
nanomultimeter with 7.5 digit resolution and a computer (PC1), respectively. The housing of the IR detector is shown in Fig. 6. This component was formed by four sections: 1) a polished aluminum section that minimized the surroundings effects, 2) a quartz window with a space full of dry air, 3) a thermopile, and 4) a space for water recirculation. Sections 2–4 were placed inside a housing built in Nylacero.
Fig. 5. Thermopile with rubber stamp: (a) measurement surface, (b) cross-section, showing the wires and the fixing material.
The temperature of the cavity was measured and monitored using a thermal bath with an uncertainty of 70.1 1C. The output signal of the thermopile was measured and monitored using a
2.3.3. Dryer The drying chamber was coupled to an air extractor, a heat–air exchanger and a balance, Fig. 3. The dimensions of the chamber were 20 30 21 cm3, and the chamber had a sample holder and a front window made of acrylic. At the entrance of the chamber, three aluminum deflectors were placed to improve the air distribution. The airflow from the bottom to the top and the rate (0.10–0.24 m/s, 70.015 m/s) were controlled by an extractor that varied the electric power supply.
Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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Fig. 6. IR detector: (a) cross-section and (b) experimental equipment.
The heat exchanger consisted of an isolated rectangular duct with an electric radiator inside with an equivalent surface area of 0.5 m2. An extruded polystyrene layer (2.5 cm) was used as an insulator (thermal resistance of 0.88 m2 1C/W at 25 1C). The heat exchanger heated the air from ambient temperature to 54 1C. The balance was designed according to the lever principle as a first-class lever and was formed by eight sections made of aluminum and steel. The lever of the balance had a tare weight with the same weight that the dry sample. A flexible force sensor (450 g) with spot metering was used to measure the weight. The voltage detected by the sensor was related to the weight through its calibration equation, shown as follows, in the range of 0–90 g. WeightðgÞ ¼ 68:29 Voltage 16:14
Table 1 Test conditions. Test Tref (1C)
M (%)
Ty (1C)
Tsc (1C)
Ti (1C)
Tent (1C)
RHsc (%)
RHo (%)
RHi (%)
1 2 3
22.54 42.8 26.67 45.4 25.68 47.7
51.5 53.5 50.6
23.6 25.6 24.4
61.3 62.8 60.8
6.7 7.9 8.2
17.5 22.0 19.8
50.6 56.3 51.3
10 10 10
Mwet_base ¼moisture content on a wet basis, Tref ¼ reference temperature, Ty ¼sample temperature, Tsc ¼temperature inside of the dryer, Ti ¼ ambient air temperature, Tent ¼ inlet air temperature, RHsc ¼relative air humidity inside the dryer, RHo ¼ relative air outlet humidity and RHi ¼ ambient relative humidity.
ð6Þ
The temperatures A1–A4 were measured using a type T thermocouple with an uncertainty of 70.5 1C. The relative humidity was measured in the 0–90% humidity range with an uncertainty of 7 3%. These variables together with the voltage were monitored using aNI-PXI 32-bit data acquisition system with graphic-visual software on a computer (PC2).
3. Results and discussion In this work, three drying tests were carried out at different sample temperatures in the range of 42–48 1C while maintaining the temperature inside of the dryer as far below 50 1C as possible to avoid sample degradation. The moisture content and the energy emitted were measured at one-minute intervals and they were analyzed to identify the best correlation of M vs EMF in terms of the standard error of estimation. The tests were carried out under the experimental conditions shown in Table 1. Each test was carried out in duplicate to show repeatability, and the differences in the EMF were 3.10% (Test 1), 2.96% (Test 2) and 3.20% (Test 3). Insignificant observed variations were also considered within the total uncertainty. During the tests, the temperatures Tent, Tsc and Ty ranged from 2.0 to 4.9 1C, whereas the environmental conditions Ti and RHo remained with an average variation of 2.0 1C and 5.7%, respectively. Fig. 7 shows the behavior of the moisture content of the sample during the drying process. The drying was very similar for all of the tested conditions, and the sample was dried from 25.0 7 2% to 1.072% with a 0.07 h 1 average drying constant in 76 min. The drying kinetics showed no significant changes in the temperature ranges used and showed an exponential behavior, as previously reported by several authors [4]. The EMF measurements of each test during the drying process of the ceramic–plaster are shown in Fig. 8. M decreased as the EMF diminished, showing a similar behavior to the drying kinetics. The differences between the EMF values measured in Tests 1, 2 and
Fig. 7. Drying kinetics of the ceramic–plaster sample.
3 were due to differences in the sample temperature (42.8 1C, 45.4 1C and 47.7 1C, respectively) during the test. When M was below 2.0%, negligible changes were observed in the EMF, which indicated that the IR detector was unable to detect variations when the sample was almost dry. These findings indicated that the moisture content could be monitored by measuring the energy emitted from the sample. Considering the measurements shown in Figs. 7 and 8, the correlation model of M vs EMF for each test was found considering Eq. (4). The M vs EMF correlation for each test is shown in Table 2. Considering a single test, it can be observed that the SE was less than 1%, whereas for the overall tests the SE was 2.52%. The total uncertainty was estimated considering the measurement uncertainty of the EMF ( 70.015 V), the M (70.2%) and the SE (2.52%). The total uncertainty was estimated as 7 2.54%. Therefore, the
Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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The standard error of estimate calculated was 2.52% and R2 ¼0.95. This correlation fit the behavior of the drying kinetics obtained by gravimetry with a total uncertainty of 72.54%. These results suggest that it is feasible to determine the moisture content using IR detection. The correlation model obtained fit properly; therefore, the proposed methodology is feasible for use.
4. Conclusion
Fig. 8. Relationship between M and the energy emitted by the sample (EMF) during the drying process at different sample temperatures: Test 1, 42.8 1C; Test 2, 45.4 1C; Test 3, 47.7 1C.
Table 2 Correlation model for each test. Test (sample temperature)
1 (42.8 1C) 2 (45.4 1C) 3 (47.7 1C) Total (1, 2, 3)
Correlation model: M ¼ a exp (b EMF) a
b
0.90 0.74 0.69 1.12
3.44 4.72 4.20 3.47
SE
R2
A non-invasive infrared sensor was designed, developed and calibrated to measure moisture content during the drying process of ceramic–plaster materials by means of IR detection using a thermopile as the transducer. The calibration of the device was performed using a correlation model between the energy emitted (in terms of the EMF) and the moisture content. The proposed model was obtained under the following conditions: 26.67% maximum moisture content, sample temperature of 42.8–47.7 1C, drying temperature of 50.6–53.5 1C and drying velocity of 0.24 m/ s, with an acceptable total uncertainty of (72.54%). These results suggest that the methodology used is suitable for the measurement of the moisture content of a ceramic–plaster material over 2.54%. Due to the ease of the measurements, as well as the simplicity of the design, this methodology has the potential to be applied to other experimental conditions for similar samples in which the physical and chemical characteristics remain unaffected during the drying process.
Acknowledgments 0.85 0.82 0.66 2.52
0.97 0.98 0.99 0.95
The authors would like to acknowledge financial support from Centro Nacional de Investigación y Desarrollo Tecnológico, CENIDET-TNM-SEP and give special thanks to the Union of Morelos-Mexico Potters for their valuable experience and suggestions. Finally, the authors would like to acknowledge Universidad Juárez Autónoma de Tabasco for the facilities to develop this work. References
Fig. 9. Comparative graphs of the moisture content estimated by gravimetry and the moisture content obtained by the IR radiation measurement (EMF).
moisture content could be measured in the range from 42.7 to 47.7 1C with an uncertainty of 72.54%. Fig. 9 presents a comparison of the moisture content obtained by gravimetry and the moisture content obtained by EMF measurement using the correlation model. Considering the total tests, the correlation model obtained was given by M ¼ 1:12 exp ð3:47 EMFÞ
ð7Þ
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Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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A method based on infrared detection for determining the moisture content of ceramic plaster materials E.V. Macias-Melo a, K.M. Aguilar-Castro a,n, M.A. Alvarez-Lemus a, J.J. Flores-Prieto b a Universidad Juárez Autónoma de Tabasco, División Académica de Ingeniería y Arquitectura (DAIA-UJAT), Carretera Cunduacán- Jalpa de Méndez km. 1, Cunduacán, Tabasco, CP 86690, México b Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET-TNM-SEP), Prol. Av. Palmira S/N. Col. Palmira, Cuernavaca, Morelos, CP 62490, México
art ic l e i nf o
a b s t r a c t
Article history: Received 8 September 2014 Received in revised form 30 March 2015 Accepted 30 March 2015 This paper was recommended for #?publication by Prof. A.B. Rad.
In this work, we describe a methodology for developing a mathematical model based on infrared (IR) detection to determine the moisture content (M) in solid samples. For this purpose, an experimental setup was designed, developed and calibrated against the gravimetric method. The experimental arrangement allowed for the simultaneous measurement of M and the electromotive force (EMF), fitting the experimental variables as much as possible. These variables were correlated by a mathematical model, and the obtained correlation was M ¼ 1:12 expð3:47 EMF Þ, 7 2.54%. This finding suggests that it is feasible to measure the moisture content when it has greater values than 2.54%. The proposed methodology could be used for different conditions of temperature, relative humidity and drying rates to evaluate the influence of these variables on the amount of energy received by the IR detector. & 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Keywords: Near infrared reflectance spectroscopy Moisture content sensor Ceramic–plaster IR detection Non-contact measurement
1. Introduction Currently, moisture content sensors are mainly based on gravimetry, psychrometry, electrical resistance, dielectric capacity, microwaves, fiber-optic techniques and near infrared spectroscopy (NIRS) [1–3]. Among these methods, gravimetry has been the approach most commonly used to measure moisture content and is considered as a reference method [4,5]. NIRS-based methodologies, which have been recently developed for industrial applications, allow one to correlate the moisture content with the spectral radiation reflected or, transmitted or the IR radiation emitted by the sample surface [6–8]. The NIRS method is attractive because it is fast and non-invasive, making the measurement easier than conventional techniques in which pre-treatment and/or destruction of the samples is required [5,9]. NIRS has been widely used to monitor the moisture content in drying and quality control by measuring the spectral optical properties for a variety of materials, including wood, paper, cellulose, peat, olives, lactose, mangos, potatoes and jaboticaba, among others [3,10–17]. In these reports, a correlation model between the moisture content and the optical property was obtained. Measuring optical properties, NIRS detection is usually carried out by the means of spectral emitting diodes and
n
Corresponding author. Tel.: þ 52 914 11 39 580. E-mail address: [email protected] (K.M. Aguilar-Castro).
photodiodes [2,9], which are chosen according to the most significant absorption bands of water (1450 and 1940 nm) [11]. Moreover, to implement this technique, additional accessories, such as an objective lens, optical chopper, collimating lens, beam splitter, and band-pass filter, among others, are required. This approach has also been reported for monitoring the moisture content in soil samples by considering the dependency between the energy emitted and the moisture content [18–20]. However, there are no reports of models that relate the IR radiation and the moisture content of plaster samples. There are very few investigations using thermal infrared (IR detection) to determine the moisture content by measuring the energy emitted by the sample. IR detection has been possible through transducers that convert IR radiation into an electromotive force (EMF) by photoelectric and Seebeck effects, among others. In most of the applications of infrared sensors, the measurements must be adjusted (calibrated) for each type of sample to optimize the sensor sensitivity [21–23]. In the case of the photoelectric effect, an additional light source is required. However, techniques based on the Seebeck effect using a thermopile, usually have been used as a single element or set that can be placed together in an array to improve the sensitivity of the transducer [22,24]. A thermopile has a similar sensitivity to that of pyroelectric detectors but retains an output voltage proportional to the temperature difference between the high and low absorption surfaces. Unlike pyroelectric detectors and photodiodes, thermopile detectors do not require the light source, mechanical
http://dx.doi.org/10.1016/j.isatra.2015.03.014 0019-0578/& 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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Subscript
Abbreviations EMF Electromotive force (V) Fa, Fb, Fc Fraction of radiant energy between 0 and (Dimensionless) RH Relative humidity (%) M Moisture content on wet basis (%) q Heat flux (W/m2) 2 R Coefficient of determination (Dimensionless) SE Standard error of estimate (%) T Temperature (1C) t Time (min) λ Wavelength (nm) ε Emittance (Dimensionless) ρ Reflectance (Dimensionless)
λT
chopping or compensation procedures of EMF. In addition, thermopiles have a wide spectral range, from 1 μm to beyond 20 μm [25], a potentially fast time response, low degradation and versatility of the absorption surface in each case (spectral window ranges). Moreover, thermopiles have the advantage of directly measuring temperature differences without any offset; if radiation does not strike the detector, the output voltage signal is zero. Thermopile applications are abundant and well known, including fire detectors, temperature sensors, gases analyzers, body human detectors, and moisture sensors, among others [22,26– 28]. However, the use of thermopiles as moisture sensors is quite limited, and there are only a few reports using soil samples [18– 20]. According to the report by Flores et al., [29], in the ceramic industry, the moisture content is measured by gravimetry with an uncertainty of 7 2.9%. However, the ceramic industry requires fast and non-contact measurements of the moisture content to facilitate the drying process of the ceramic–plaster and optimize the time and movements. To satisfy these requirements, IR detection by means of a thermopile would be suitable, and optimizations are desirable in terms of the range and uncertainty (standard error of estimate) considering the absorption of the thermopile surface according to the water absorption bands [11]. Considering the aforementioned factors, we propose a methodology based on IR detection by means of a thermopile in which a non-invasive sensor is designed, developed and calibrated to measure the moisture content for applications in the ceramics industry.
2. Material and methods The methodology for developing a mathematical model based on IR detection, as well as the detailed design and manufacturing of all of the components of the experiment, is presented below. 2.1. Mathematical model The proposed model for the energy emitted from the sample is shown in Fig. 1. M is the moisture content of the sample at t¼0, qy is the amount of energy emitted by the sample and qρb is the energy reflected from the incoming energy emitted by the surroundings (qb). At t a0 (t1), the moisture content decreases to M0 , and the amount of energy varies to qy0 ,qρb0 and qb0 . The proposed mathematical model assumes that: (1) the surrounding environment behaves as a gray body, (2) the moisture content in the surface represents the moisture content of the whole sample, and
Cal D b ent i Md o ref sc y 0
Calculated moisture content IR detector Surroundings Inlet air Ambient air Experimental moisture content Outlet Reference Dryer Sample Initial
(3) no significant changes in the surface rugosity occur during drying. In this array the energy change measured by the transducer inside the spectral window (ΔqD (λ2 λ1)) corresponds to the difference of the sum of all the energies (qy0 ,qρb0 and qb0 ), each multiplied by the portion of radiant energy inside the spectral window at t¼ n and t¼n 1, which can be expressed as follows:
ΔqD ðλ2 λ1 Þ ¼ qD ðλ2 λ1 Þt ¼ n 1 qD ðλ2 λ1 Þt ¼ n
ð1Þ
where the energy qD at t¼ n and t ¼n 1 is given by: h i qD ðλ2 λ1 Þt ¼ n 1 ¼ Faλ2 λ1 qy ðtÞ þ Fbλ2 λ1 qρb' ðtÞ þ Fcλ2 λ1 qb' ðtÞ h i qD ðλ2 λ1 Þt ¼ n ¼ Faλ2 λ1 qy ðtÞ þ Fbλ2 λ1 qρb' ðtÞ þ Fcλ2 λ1 qb' ðtÞ
t ¼ n1
t¼n
ð2Þ where qy ðt Þ ¼ f εy ðt Þ; T y ; qρb0 ðt Þ ¼ f ρy ; qb0 ; qb0 ðt Þ ¼ f εb0 ðT Þ; T b0 ; ρb0 ; qy Þ and Faλ2 λ1 , Fbλ2 λ1 ,Fcλ2 λ1 correspond to the radiant energy inside the spectral window λ2 λ1 . Assuming that variations in the sample temperature (Ty) and the surrounding temperature (Tb) are negligible during the experiment, M can be expressed as a function of ΔqD ð3Þ M ¼ f ΔqD
where ΔqD depends on the variations in both the properties, εy and ρy . In the transducer, ΔqD is differentially absorbed on the thermopile surface, leading to a temperature difference (ΔT) that directly acts on the transducer, which turns the IR radiation into an output voltage (EMF). Hereby, the determined values of M can be directly related to the measured EMF values. To adjust the experimental data to the most suitable fit, we propose a single correlation model, which assumes that variations in M depend only on the energy measured by the transducer in terms of the EMF. This model assumes that changes in both the sample temperature and the relative humidity of the air (inside the drying chamber) are negligible. Ordered pairs (M, EMF) are obtained from the initial moisture content until it reaches the equilibrium. A single correlation fitting is obtained from the minimum squares assuming no effects due to the surroundings (temperature and relative humidity of the air) because they are controlled during the measurements. The response of the sensor is fitted to Eq. (4) considering the exponential behavior of the moisture content during the drying process M ¼ a expðb EMF Þ
ð4Þ
Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i
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Fig. 1. Graphical representation of the energy components involved during the drying process: qy ¼ energy emitted by the sample, qρb ¼ energy reflected by the sample from the surroundings, and qb ¼ energy emitted by the surroundings.
Data dispersion is estimated in terms of the standard error of estimate (SE), Eq. (5), and the coefficient of determination (R2) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X SE ¼ ðM Md M Cal Þ2 =ðN 2Þ ð5Þ where N is the number of measurements, MMd is the measured moisture content of the sample and MCal is the moisture content calculated from the fit. The total uncertainty is determined by the error propagation method that determines the combined standard uncertainty [30]. 2.2. Experimental model In this model, M and the energy emitted from the sample were simultaneously measured. During the experiment, the sample temperature, the indoor air temperature and the air relative humidity were fixed through a controlled environment chamber. M was determined by gravimetry, whereas the energy emitted was measured using an infrared transducer. Fig. 2 depicts a schematic of the experimental model. The device included the sample, an IR detector, a balance, a drying chamber, and a heat exchanger. The IR detector delivered a potential difference according to the radiation received on the transducer surface. Before starting the experiments, the temperature of the thermal bath was set to 10 1C to provide a temperature of reference to the IR detector. The inlet air was heated from Ti to Tent before entering the chamber. The heated air was spread inside the drying chamber, reaching Tsc, with a relative humidity of RHsc. Then, a wet sample was placed inside the drying chamber and its temperature was set to Ty. In addition, the rate and temperature of the drying air were fixed in such a way that the energy emitted could be measured only as an effect of the variation in the moisture content while drying was achieved. Finally, the moist air exited the chamber with a relative humidity of RHo. During the experiment, the IR detector measured the energy emitted from the sample, whereas the weight loss was registered by the balance, and the corresponding moisture content was determined. 2.3. Prototype description Fig. 3 displays an instrumentation diagram of the experimental equipment. 2.3.1. Sample A mixture of 1:1.3 plaster:water was used to shape a disc of 12 cm diameter 1 cm thickness. Before the test, the sample was immersed in water for 24 h until the moisture content was 26.67%, which corresponds to the previous report by Aguilar et al. [31]. The sample was instrumented in such a way that both the surface temperature and the supplied energy for heating could be measured (Fig. 4). The energy for heating was supplied using an
Fig. 2. Experimental model with the main components, the airflow path and the measured variables.
electrical resistance powered by a programmable power supply (4 W average power).
2.3.2. Infrared detector The IR detector was designed using the structured design methodology reported in the literature [31] considering the need statement, main constraints and functions, design conditions and critical parameters. The need statement and the main constraints were the detection of radiosity from the sample during drying below 50 1C to avoid plaster decomposition, whereas the design condition was the minimization of the influence of the surroundings. The IR detector was composed by a thermopile and housing. The housing was designed and built to maximize the potential difference by using a cavity with water recirculation at a steady temperature, and a type T thermocouple was used for the thermopile (Fig. 5). On the front, the detector was divided into two sections: low and high absorptance. The selection of the high and low absorptance surfaces was made by testing several coatings, and their corresponding absorptances were obtained in the range from 780 to 2500 nm using a UV–visible–NIR spectrometer (Shimadzu 3100). The high-absorber surface was the hot junction, and the other surface was the cold junction.
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Fig. 3. Experimental setup instrumentation: (1) IR detector, (2) drying chamber, (3) sample, (4) balance, (5) thermal bath, (6) heat–air exchanger, (7) voltage supply with a symmetrical output, 12 V, (8) voltage amplifier, (9) nanomultimeter, (10) reversing amplifier, (11) data acquisition system, (12) PC 1 and (13) PC 2. The scheme includes two photos of the actual equipment.
Fig. 4. Ceramic–plaster sample coupled to electric resistance: (a) front view and (b) inner layers.
nanomultimeter with 7.5 digit resolution and a computer (PC1), respectively. The housing of the IR detector is shown in Fig. 6. This component was formed by four sections: 1) a polished aluminum section that minimized the surroundings effects, 2) a quartz window with a space full of dry air, 3) a thermopile, and 4) a space for water recirculation. Sections 2–4 were placed inside a housing built in Nylacero.
Fig. 5. Thermopile with rubber stamp: (a) measurement surface, (b) cross-section, showing the wires and the fixing material.
The temperature of the cavity was measured and monitored using a thermal bath with an uncertainty of 70.1 1C. The output signal of the thermopile was measured and monitored using a
2.3.3. Dryer The drying chamber was coupled to an air extractor, a heat–air exchanger and a balance, Fig. 3. The dimensions of the chamber were 20 30 21 cm3, and the chamber had a sample holder and a front window made of acrylic. At the entrance of the chamber, three aluminum deflectors were placed to improve the air distribution. The airflow from the bottom to the top and the rate (0.10–0.24 m/s, 70.015 m/s) were controlled by an extractor that varied the electric power supply.
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Fig. 6. IR detector: (a) cross-section and (b) experimental equipment.
The heat exchanger consisted of an isolated rectangular duct with an electric radiator inside with an equivalent surface area of 0.5 m2. An extruded polystyrene layer (2.5 cm) was used as an insulator (thermal resistance of 0.88 m2 1C/W at 25 1C). The heat exchanger heated the air from ambient temperature to 54 1C. The balance was designed according to the lever principle as a first-class lever and was formed by eight sections made of aluminum and steel. The lever of the balance had a tare weight with the same weight that the dry sample. A flexible force sensor (450 g) with spot metering was used to measure the weight. The voltage detected by the sensor was related to the weight through its calibration equation, shown as follows, in the range of 0–90 g. WeightðgÞ ¼ 68:29 Voltage 16:14
Table 1 Test conditions. Test Tref (1C)
M (%)
Ty (1C)
Tsc (1C)
Ti (1C)
Tent (1C)
RHsc (%)
RHo (%)
RHi (%)
1 2 3
22.54 42.8 26.67 45.4 25.68 47.7
51.5 53.5 50.6
23.6 25.6 24.4
61.3 62.8 60.8
6.7 7.9 8.2
17.5 22.0 19.8
50.6 56.3 51.3
10 10 10
Mwet_base ¼moisture content on a wet basis, Tref ¼ reference temperature, Ty ¼sample temperature, Tsc ¼temperature inside of the dryer, Ti ¼ ambient air temperature, Tent ¼ inlet air temperature, RHsc ¼relative air humidity inside the dryer, RHo ¼ relative air outlet humidity and RHi ¼ ambient relative humidity.
ð6Þ
The temperatures A1–A4 were measured using a type T thermocouple with an uncertainty of 70.5 1C. The relative humidity was measured in the 0–90% humidity range with an uncertainty of 7 3%. These variables together with the voltage were monitored using aNI-PXI 32-bit data acquisition system with graphic-visual software on a computer (PC2).
3. Results and discussion In this work, three drying tests were carried out at different sample temperatures in the range of 42–48 1C while maintaining the temperature inside of the dryer as far below 50 1C as possible to avoid sample degradation. The moisture content and the energy emitted were measured at one-minute intervals and they were analyzed to identify the best correlation of M vs EMF in terms of the standard error of estimation. The tests were carried out under the experimental conditions shown in Table 1. Each test was carried out in duplicate to show repeatability, and the differences in the EMF were 3.10% (Test 1), 2.96% (Test 2) and 3.20% (Test 3). Insignificant observed variations were also considered within the total uncertainty. During the tests, the temperatures Tent, Tsc and Ty ranged from 2.0 to 4.9 1C, whereas the environmental conditions Ti and RHo remained with an average variation of 2.0 1C and 5.7%, respectively. Fig. 7 shows the behavior of the moisture content of the sample during the drying process. The drying was very similar for all of the tested conditions, and the sample was dried from 25.0 7 2% to 1.072% with a 0.07 h 1 average drying constant in 76 min. The drying kinetics showed no significant changes in the temperature ranges used and showed an exponential behavior, as previously reported by several authors [4]. The EMF measurements of each test during the drying process of the ceramic–plaster are shown in Fig. 8. M decreased as the EMF diminished, showing a similar behavior to the drying kinetics. The differences between the EMF values measured in Tests 1, 2 and
Fig. 7. Drying kinetics of the ceramic–plaster sample.
3 were due to differences in the sample temperature (42.8 1C, 45.4 1C and 47.7 1C, respectively) during the test. When M was below 2.0%, negligible changes were observed in the EMF, which indicated that the IR detector was unable to detect variations when the sample was almost dry. These findings indicated that the moisture content could be monitored by measuring the energy emitted from the sample. Considering the measurements shown in Figs. 7 and 8, the correlation model of M vs EMF for each test was found considering Eq. (4). The M vs EMF correlation for each test is shown in Table 2. Considering a single test, it can be observed that the SE was less than 1%, whereas for the overall tests the SE was 2.52%. The total uncertainty was estimated considering the measurement uncertainty of the EMF ( 70.015 V), the M (70.2%) and the SE (2.52%). The total uncertainty was estimated as 7 2.54%. Therefore, the
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The standard error of estimate calculated was 2.52% and R2 ¼0.95. This correlation fit the behavior of the drying kinetics obtained by gravimetry with a total uncertainty of 72.54%. These results suggest that it is feasible to determine the moisture content using IR detection. The correlation model obtained fit properly; therefore, the proposed methodology is feasible for use.
4. Conclusion
Fig. 8. Relationship between M and the energy emitted by the sample (EMF) during the drying process at different sample temperatures: Test 1, 42.8 1C; Test 2, 45.4 1C; Test 3, 47.7 1C.
Table 2 Correlation model for each test. Test (sample temperature)
1 (42.8 1C) 2 (45.4 1C) 3 (47.7 1C) Total (1, 2, 3)
Correlation model: M ¼ a exp (b EMF) a
b
0.90 0.74 0.69 1.12
3.44 4.72 4.20 3.47
SE
R2
A non-invasive infrared sensor was designed, developed and calibrated to measure moisture content during the drying process of ceramic–plaster materials by means of IR detection using a thermopile as the transducer. The calibration of the device was performed using a correlation model between the energy emitted (in terms of the EMF) and the moisture content. The proposed model was obtained under the following conditions: 26.67% maximum moisture content, sample temperature of 42.8–47.7 1C, drying temperature of 50.6–53.5 1C and drying velocity of 0.24 m/ s, with an acceptable total uncertainty of (72.54%). These results suggest that the methodology used is suitable for the measurement of the moisture content of a ceramic–plaster material over 2.54%. Due to the ease of the measurements, as well as the simplicity of the design, this methodology has the potential to be applied to other experimental conditions for similar samples in which the physical and chemical characteristics remain unaffected during the drying process.
Acknowledgments 0.85 0.82 0.66 2.52
0.97 0.98 0.99 0.95
The authors would like to acknowledge financial support from Centro Nacional de Investigación y Desarrollo Tecnológico, CENIDET-TNM-SEP and give special thanks to the Union of Morelos-Mexico Potters for their valuable experience and suggestions. Finally, the authors would like to acknowledge Universidad Juárez Autónoma de Tabasco for the facilities to develop this work. References
Fig. 9. Comparative graphs of the moisture content estimated by gravimetry and the moisture content obtained by the IR radiation measurement (EMF).
moisture content could be measured in the range from 42.7 to 47.7 1C with an uncertainty of 72.54%. Fig. 9 presents a comparison of the moisture content obtained by gravimetry and the moisture content obtained by EMF measurement using the correlation model. Considering the total tests, the correlation model obtained was given by M ¼ 1:12 exp ð3:47 EMFÞ
ð7Þ
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Please cite this article as: Macias-Melo EV, et al. A method based on infrared detection for determining the moisture content of ceramic plaster materials. ISA Transactions (2015), http://dx.doi.org/10.1016/j.isatra.2015.03.014i