Inverse estimation of fluid-to-particle heat transfer coefficient in aseptic processing of particulate foods

Vidyarthi, Sriram K.; Mishra, Dharmendra Kumar; Dolan, Kirk D.; Muramatsu, Yoshiki

doi:10.1016/j.biosystemseng.2020.08.012

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…The standard error assumptions apply for the sequential estimation of a parameter. This includes that errors have a zero mean, constant variance, normal distribution and are additive and uncorrelated [ 21 ]. The iterative sequential procedure from Beck and Arnold, [ 19 ] was used:

where A is the inversion matrix, P is the covariance matrix, X is the sensitivity matrix, ∆ is the sequential delta, K is the gain matrix, e is the error vector, b is the parameter index, and i is the iteration index.…”

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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Section: Methodsmentioning

confidence: 99%

“…The standard error assumptions apply for the sequential estimation of a parameter. This includes that errors have a zero mean, constant variance, normal distribution and are additive and uncorrelated [21]. The iterative sequential procedure from Beck and Arnold, [19] was used:…”

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

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“…Containers are usually pre-sterilized and quality checked. Then, filling and hermetic sealing follows in aseptic containers, as shown in Figure 2 [16,17]. In aseptic filling, the food product and the package are continuously sterilized separately and then meet in the aseptic filler, which has a sterile environment, and this makes it different to other traditional methods of food packaging.…”

Section: Thermal Technologies In Food Processingmentioning

confidence: 99%

“…The thermal process should be assessed by determination of the heat transfer coefficient (h fp ) between the fluid and particle in the continuous flow conditions. The fluid flow field around the solid particle, the thermal and rheological properties of the fluid, and the dimensions of the particle and pipe are key parameters affecting the fluid-to-particle convective heat transfer coefficient (h fp ) [17].…”

Section: Thermal Technologies In Food Processingmentioning

confidence: 99%

Advances, Applications, and Comparison of Thermal (Pasteurization, Sterilization, and Aseptic Packaging) against Non-Thermal (Ultrasounds, UV Radiation, Ozonation, High Hydrostatic Pressure) Technologies in Food Processing

2022

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Nowadays, food treatment technologies are constantly evolving due to an increasing demand for healthier and tastier food with longer shelf lives. In this review, our aim is to highlight the advantages and disadvantages of some of the most exploited industrial techniques for food processing and microorganism deactivation, dividing them into those that exploit high temperatures (pasteurization, sterilization, aseptic packaging) and those that operate thanks to their inherent chemical–physical principles (ultrasound, ultraviolet radiation, ozonation, high hydrostatic pressure). The traditional thermal methods can reduce the number of pathogenic microorganisms to safe levels, but non-thermal technologies can also reduce or remove the adverse effects that occur using high temperatures. In the case of ultrasound, which inactivates pathogens, recent advances in food treatment are reported. Throughout the text, novel discoveries of the last decade are presented, and non-thermal methods have been demonstrated to be more attractive for processing a huge variety of foods. Preserving the quality and nutritional values of the product itself and at the same time reducing bacteria and extending shelf life are the primary targets of conscious producers, and with non-thermal technologies, they are increasingly possible.

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“…The parameter estimation technique has been widely used in estimating the thermal properties of various food products [3,[5][6][7][8][9][10][11][12][13][14][15]. It has also been used to estimate the fluid-to-particle heat transfer coefficient during aseptic processing of particulate foods [16] and heat flux during baking [17]. Constant temperature boundary condition can lead to prolonged exposure of heat to the sample.…”

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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Inverse estimation of fluid-to-particle heat transfer coefficient in aseptic processing of particulate foods

Cited by 8 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

Advances, Applications, and Comparison of Thermal (Pasteurization, Sterilization, and Aseptic Packaging) against Non-Thermal (Ultrasounds, UV Radiation, Ozonation, High Hydrostatic Pressure) Technologies in Food Processing

Dynamic Thermal Properties Estimation Using Sensitivity Coefficients for Rapid Heating Process

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