Measurement of Zone Temperature Profile of a Resistive Heating Furnace Through RVM Model

Tripathy, Hara Prasada; Bej, Debabrata; Pattanaik, Priyabrata; Mishra, Dilip Kumar; Kamilla, Sushanta Kumar; Tripathy, Rajesh Kumar

doi:10.1109/jsen.2018.2826722

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…16(a). Finally, the flame 2-D emissivity matrix and the radiation luminance matrix are put into the temperature backprojection function (9). Then, the 2-D temperature distribution of flame can be calculated.…”

Section: -D Emissivity and Temperature Distribution Of Flamementioning

confidence: 99%

“…As the main form of non-contact temperature measurement, optical temperature measurement methods include laser spectroscopy and radiation spectroscopy method. Laser spectroscopy methods, such as laser interferometric holography [9], [10], planar laser-induced fluorescence (PLIF) [11] and coherent anti-Stokes Raman scattering spectroscopy (CARS) [12]- [15], rely on complex, high-precision optical instruments which are expensive and difficult to meet the testing requirements in extreme industrial environments. On the contrary, the radiation spectral method with better environmental adaptability is widely used in industry.…”

Section: Introductionmentioning

confidence: 99%

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Two-Dimensional Flame Temperature and Emissivity Distribution Measurement Based on Element Doping and Energy Spectrum Analysis

et al. 2020

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Most of the existing non-contact flame temperature measurement methods rely on the ideal thermal-optical excitation model, which has a great influence on temperature measurement accuracy. Therefore, based on element doping and energy spectrum analysis, this study proposes a novel twodimensional (2-D) estimation method for flame temperature and emissivity distribution. The element doping method and laser-induced breakdown spectroscopy (LIBS) are introduced into the temperature field test. The external doped element whose spectral radiation characteristics are easy to be analyzed, is regarded as the measured particles to describe the flame temperature distribution from the side. And LIBS is used to analyze and select the doped element, and further determine the effective working wavelength of the optical camera. Besides, the relationship between spectral radiance and emissivity (L -ε) of doped samples is obtained by the emissivity calibration experiment. Then, the 2-D temperature and emissivity distributions can be estimated. Infrared thermograph is used to verify the accuracy of temperature measurement, the measurement error between calculated and standard values is not more than 5%. The experimental results of the oxygen-ethanol combustion flame show that this method can be well applied to the similar temperature measurement.INDEX TERMS Temperature measurement, Spectral emissivity, Element doping, Spectral analysis.

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Section: -D Emissivity and Temperature Distribution Of Flamementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Flame Temperature and Emissivity Distribution Measurement Based on Element Doping and Energy Spectrum Analysis

et al. 2020

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“…Usually, the actual temperature of the hot material inside the furnace is measured intermittently by an operator using thermocouples or blackbody cavities with specialized equipment at a single point in the furnace (Tripathy et al, 2018, Wang et al, 2018. This is known as a measurement with contact with hot material, and although simple, it carries some level of risk for operators due to the high temperatures and dynamics of the furnace.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neuronal Network-Based Sensor for the Detection of Oxygen inside the Carbonization Furnace

Sarmiento

2022

etj

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Agricultural production generates biological surpluses that can be used to produce additional products through thermal and/or chemical transformations, such as the carbonization of vegetables. The oxygen on the vegetable material during the carbonization process inside a furnace is an important parameter that determines not only the success of the process but also the quality of the final product. It is difficult to measure the oxygen inside the furnace in real time, partly because of the working environment of the sensor, and partly because of the operating characteristics of the furnace (continuous rotation on its axis). The goal of this project is to develop a reliable measurement system capable of operating in real-time. For the continuous and precise detection of the temperature inside the furnaces of a carbonization plant, we propose a method based on the characterization of the image inside the furnace using a deep neuronal network. First of all, the images of the interior of the furnace are captured through a digital camera in front of the material and the axis of rotation of the furnace. Then, the area of interest in each frame of the video is determined by image processing. Oxygen content information is then obtained using a deep model trained for the same furnace with pattern oxygen level sensors. Experiments conducted on actual operating conditions of the furnace demonstrate that the proposed method for estimating the working oxygen provides reliable data for the control of plant operation. The proposed measurement scheme demonstrated high reliability against the many changes present inside the furnace, also, its low computational consumption makes it a viable strategy for embedded implementation and operation in real-time.

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“…At the present scenario, a few number of measuring devices have been developed to measure up to the high‐temperature 700°C. Out of those measuring devices, widely used devices are mercury thermometry [5], optical‐fibre thermometry [6], thermocouple [7], radiation pyrometer [8], pyroelectric thermometry [9] and ultrasonic thermometry [10]. In the real‐time environment to sense and achieve the accurate temperature is a very critical task.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work, we have designed a system to measure the temperature values at different zones of a resistive heating furnace. There, we have experimentally verified the ultrasonic principle based temperature measurement (up to 577°C) through a relevance vector machine model [10]. However, in this work, a model‐based approach is used to validate the effectiveness of 2D‐axisymmetrical ultrasonic MEMS transceiver with AlN as piezoelectric material to sense the temperature without any contact with the measuring environment.…”

Section: Introductionmentioning

confidence: 99%

Model‐based approach to validate the aluminium nitride material based ultrasonic MEMS transceiver for temperature sensing

Tripathy

Pattanaik

Mishra

et al. 2019

Micro & Nano Letters

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In this work, a model-based approach is proposed to validate the performance of aluminium nitride (AlN) piezoelectric material based ultrasonic MEMS transceiver for temperature sensing. This validation is performed based on the modelling of ultrasonic MEMS transceiver with AlN as piezoelectric material using COMSOL Multiphysics simulations. The output voltage values for different instances are evaluated by varying the input parameters of ultrasonic MEMS transceiver based temperature sensor. Moreover, the least square support vector machine (LSSVM) based regression model is used to estimate the output voltage from the input parameters ultrasonic MEMS transceiver. The results demonstrate that the LSSVM model successfully predicts the voltage with normalised correlation coefficient values of 0.9962, 0.9963 and 0.9780, respectively, using only transmitter section parameters, only receiver section parameters and, both transmitter and receiver sections parameters.

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Measurement of Zone Temperature Profile of a Resistive Heating Furnace Through RVM Model

Cited by 11 publications

References 24 publications

Two-Dimensional Flame Temperature and Emissivity Distribution Measurement Based on Element Doping and Energy Spectrum Analysis

Two-Dimensional Flame Temperature and Emissivity Distribution Measurement Based on Element Doping and Energy Spectrum Analysis

A Deep Neuronal Network-Based Sensor for the Detection of Oxygen inside the Carbonization Furnace

Model‐based approach to validate the aluminium nitride material based ultrasonic MEMS transceiver for temperature sensing

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