Comparison of heat transfer rates during thermal processing under end-over-end and axial modes of rotation

Dwivedi, Mritunjay; Ramaswamy, Hosahalli S.

doi:10.1016/j.lwt.2009.08.018

Cited by 23 publications

(14 citation statements)

References 21 publications

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“…It can be seen that non-agitated mode of thermal processing has the least values of heat transfer coefficients (Table 6). Amongst agitated systems, biaxial mode of agitation has the highest heat transfer coefficient, followed by EOE mode and then by fixed axial mode of rotation (Dwivedi and Ramaswamy, 2010a). For oscillatory mode of rotation, when the direction of rotation was reversed every 15-45 s, Hotani and Mihori (1983) reported that heating rates and uniformity were increased and that there was no significant difference between EOE rotation and axial rotation.…”

Section: Mode Of Agitationmentioning

confidence: 99%

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Heat transfer phenomena during thermal processing of liquid particulate mixtures—A review

Pratap-Singh

Singh

Ramaswamy

2017

Critical Reviews in Food Science and Nutrition

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During the past few decades, food industry has explored various novel thermal and non-thermal processing technologies to minimize the associated high-quality loss involved in conventional thermal processing. Among these are the novel agitation systems that permit forced convention in canned particulate fluids to improve heat transfer, reduce process time, and minimize heat damage to processed products. These include traditional rotary agitation systems involving end-over-end, axial, or biaxial rotation of cans and the more recent reciprocating (lateral) agitation. The invention of thermal processing systems with induced container agitation has made heat transfer studies more difficult due to problems in tracking the particle temperatures due to their dynamic motion during processing and complexities resulting from the effects of forced convection currents within the container. This has prompted active research on modeling and characterization of heat transfer phenomena in such systems. This review brings to perspective, the current status on thermal processing of particulate foods, within the constraints of lethality requirements from safety view point, and discusses available techniques of data collection, heat transfer coefficient evaluation, and the critical processing parameters that affect these heat transfer coefficients, especially under agitation processing conditions.

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Section: Mode Of Agitationmentioning

confidence: 99%

“…b Deniston et al (1987); Dwivedi and Ramaswamy (2010a); Fernandez et al (1988); Hassan (1984);. Lenz and Lund (1978); Meng (20 06); Meng and Ramaswamy (2005, 2007a; Sablani andRamaswamy (1995, 1999); c Stoforos et al (1997).…”

Section: For Non-newtonian and Viscous Fluids Fmentioning

confidence: 99%

Heat transfer phenomena during thermal processing of liquid particulate mixtures—A review

Pratap-Singh

Singh

Ramaswamy

2017

Critical Reviews in Food Science and Nutrition

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“…On the other hand, during thermal sterilization of canned liquid foods, time and temperature data are generally used to evaluate process efficiency. For this reason, theoretical models can be applied for the process design, optimization and validation (Dwivedi and Ramaswamy ). An advanced prediction model for F value prediction can reduce the costs and time involved in F value evaluation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lethality by Nonlinear Artificial Neural Network Modeling

Guldas

Kurtulmuş

Gürbüz

2016

J Food Process Engineering

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In this research, the aim was to predict F value (lethality or sterilization value) of canned peas by using a nonlinear auto-regressive artificial neural network model with exogenous input (NARX-ANN). During the model testing, training, validation and reliability steps were followed, respectively. It was found that the model tested was a useful tool to predict the F value for the canned foods with high reliability. Cross-validation rules were performed for training and testing of the model. F value of the 5 kg canned peas could be predicted with a high degree of accuracy (R 2 5 0.9982, mean square error (MSE) 5 0.1088) using training the data yielded from 0.5 kg canned peas despite huge mass differences between cross-validated data sets. When the same data sets were trained and tested inversely, a high degree of prediction accuracy (R 2 5 0.9914, MSE 5 0.6262) was also observed. The model is also significant in terms of reducing the operational costs due to the fact that higher temperatures and longer process times lead to increased energy costs. PRACTICAL APPLICATIONSIn this research, it was found that nonlinear auto-regressive artificial neural network model with exogenous input is a reliable model for the prediction of lethality rate (F value) in canned food factories. It also provides the advantage of estimating process time more accurately in the retort and thus, reducing operational costs.

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“…Agitation as a mixing mechanism is used in a wide variety of applications such as in the food industry for improved heat and mass transport in stirred mixtures [1,2]. In industrial mixing, agitation is used in stirred tanks to mix fluid and solid phases.…”

Section: Introductionmentioning

confidence: 99%

“…The merits of agitation as a mixing technology are known [1][2][3][4][5][6]. As a step towards improving air cooling technology, our research group employed piezoelectric technology in a different way, using piezostacks to translationally oscillate blades that agitate the flow inside heat sink channels to thoroughly mix the flow, thus improving convective heat transfer.…”

Section: Introductionmentioning

confidence: 99%