A novel instrument for rapid measurement of temperature-dependent thermal properties of conduction-heated food up to 140 °C

Mishra, Dharmendra Kumar; Dolan, Kirk D.; Beck, James V.; Ozadali, Ferhan

doi:10.1016/j.jfoodeng.2016.06.028

Cited by 15 publications

(15 citation statements)

References 25 publications

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“…Sequential estimation of the parameters was done by using the matrix inversion lemma and Gauss minimization functions. This was done by the following equation [ 20 ]:

where S is the sum of squares, Y is the experimental response variable,

is the predicted response, μ is prior information of parameter β , W is the inverse of the covariance matrix of errors, U is the inverse of covariance matrix of parameters.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Detection of Fouling-Layer with a Non-Intrusive Continuous Sensor (NICS) during Thermal Processing in Food Manufacturing

Rivera

Mishra

Ozadali

et al. 2021

Sensors

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The fouling of indirect shell and coil heat exchanger by heavy whipping cream (HWC) and non-fat dry milk (NFDM) was studied at aseptic Ultra-High Temperature (UHT) processing conditions (140 °C) using a novel non-intrusive sensor. The sensor emitted a heat pulse intermittently throughout the duration of the process causing an incremental increase in temperature at the tube external surface. The temperature response of the sensor varied due to the radial growth of the fouling layer formed by certain components of the products. Each heating pulse and the temperature response was studied to estimate the thermal conductivity of the fouling layer using inverse problems and parameter estimation. The changes in thermal conductivity were used as an indication of the fouling layer development during food processing at UHT temperatures. The estimated parameters from experimental results showed a decreasing trend in the thermal conductivity of HWC and NFDM from 0.35 to 0.10 and 0.63 to 0.37, respectively. An image analysis tool was developed and used to measure the fouling layer thickness at the end of each trial. The measured thickness was found to be 0.58 ± 0.15 for HWC and 0.56 ± 0.07 mm for NFDM. The fouling layer resistance for HWC and NFDM was 5.95 × 10−3 ± 1.53 × 10−3 and 1.53 × 10−3 ± 2.0 × 10−4 (m2K)/W, respectively.

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“…Sequential estimation of the parameters was done by using the matrix inversion lemma and Gauss minimization functions. This was done by the following equation [ 20 ]:

where S is the sum of squares, Y is the experimental response variable,

is the predicted response, μ is prior information of parameter β , W is the inverse of the covariance matrix of errors, U is the inverse of covariance matrix of parameters.…”

Section: Methodsmentioning

confidence: 99%

“…Sequential estimation of the parameters was done by using the matrix inversion lemma and Gauss minimization functions. This was done by the following equation [20]:…”

Section: Inverse Problemmentioning

confidence: 99%

Real-Time Detection of Fouling-Layer with a Non-Intrusive Continuous Sensor (NICS) during Thermal Processing in Food Manufacturing

Rivera

Mishra

Ozadali

et al. 2021

Sensors

Self Cite

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“…Given this, innovative product and process development in a very competitive market demands the development of challenging products, which will require the determination of their thermal properties under realistic processing conditions. The inverse problems technique is an effective tool which can be used to solve emerging challenges in food manufacturing [1][2][3][4][5]. Due to the lack of rapid methods, estimation of thermal properties is usually performed from experiments in a constant temperature environment [1].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Thermal Properties Estimation Using Sensitivity Coefficients for Rapid Heating Process

Muniandy

Benyathiar

Mishra

et al. 2021

Foods

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Thermal conductivity determination of food at temperatures >100 °C still remains a challenge. The objective of this study was to determine the temperature-dependent thermal conductivity of food using rapid heating (TPCell). The experiments were designed based on scaled sensitivity coefficient (SSC), and the estimated thermal conductivity of potato puree was compared between the constant temperature heating at 121.10 °C (R12B10T1) and the rapid heating (R22B10T1). Temperature-dependent thermal conductivity models along with a constant conductivity were used for estimation. R22B10T1 experiment using the k model provided reliable measurements as compared to R12B10T1 with thermal conductivity values from 0.463 ± 0.011 W m−1 K−1 to 0.450 ± 0.016 W m−1 K−1 for 25–140 °C and root mean squares error (RMSE) of 1.441. In the R12B10T1 experiment, the analysis showed the correlation of residuals, which made the estimation less reliable. The thermal conductivity values were in the range of 0.444 ± 0.012 W m−1 K−1 to 0.510 ± 0.034 W m−1 K−1 for 20–120 °C estimated using the k model. Temperature-dependent models (linear and k models) provided a better estimate than the single parameter thermal conductivity determination with low RMSE for both types of experiments. SSC can provide insight in designing dynamic experiments for the determination of thermal conductivity coefficient.

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“…Research work regarding the estimation of temperature-dependent properties is available in literature as Refs. [7,9]. Then, if the thin heater can be modelled as a lumped capacitance body, the addressed transient heat conduction problem concerns a one dimensional (1-D) finite single-layer rectangular body (sample) subject to a boundary condition of the sixth kind at the heated boundary and insulated at the backside.…”

Section: Introductionmentioning

confidence: 99%

Effect of Heat Source and Imperfect Contact on Simultaneous Estimation of Thermal Properties of High‐Conductivity Materials

D'Alessandro

Monte

Amos

2019

Mathematical Problems in Engineering

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In the current paper a novel methodology accounting for both the heater heat capacity and the imperfect thermal contact between a thin heater and a specimen is proposed. In particular, the volumetric heat capacity of the heater is considered by modelling it as a lumped capacitance body, while the imperfect thermal contact is considered by means of a contact resistance. Thus, the experimental apparatus consisting of three layers (specimen-heater-specimen) is reduced to a single finite layer (sample) subject to a “nonclassical” boundary condition at the heated surface, known as sixth kind. Once the temperature solution is derived analytically using the Laplace transform method, the scaled sensitivity coefficients are computed analytically at the interface between the heater and the sample (heater side and sample side) and at the sample backside. By applying the proposed methodology to a lab-controlled experiment available in the specialized literature, a reduction of the thermal properties values of about 1.4% is observed for a high-conductivity material (Armco iron).

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A novel instrument for rapid measurement of temperature-dependent thermal properties of conduction-heated food up to 140 °C

Cited by 15 publications

References 25 publications

Real-Time Detection of Fouling-Layer with a Non-Intrusive Continuous Sensor (NICS) during Thermal Processing in Food Manufacturing

Real-Time Detection of Fouling-Layer with a Non-Intrusive Continuous Sensor (NICS) during Thermal Processing in Food Manufacturing

Dynamic Thermal Properties Estimation Using Sensitivity Coefficients for Rapid Heating Process

Effect of Heat Source and Imperfect Contact on Simultaneous Estimation of Thermal Properties of High‐Conductivity Materials

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