Optimal dynamic heat generation profiles for simultaneous estimation of thermal food properties using a hotwire probe: Computation, implementation and validation

Scheerlinck, Nico; Berhane, Nahor Haddish; Moles, Carmen G.; Banga, Julio R.; Nicolaı̈, Bart

doi:10.1016/j.jfoodeng.2007.05.019

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Optimization-based approaches can be used for more systematic parameter estimation (Rodriguez et al, 2007;Mariani et al, 2008;Scheerlinck et al, 2008;Dasilva et al, 2009). The idea is to compute the parameter values that minimize the distance between the experimental data and the corresponding model predictions.…”

Section: Introductionmentioning

confidence: 99%

“…The uncertainty related to the "true" parameter values will play a major role in the capability to predict a new data set. Therefore, previous works paid special attention to optimal experimental design as a means to obtain accurate parameter estimates and thus improve model validation (Nahor et al, 2001;Balsa-Canto et al, 2007;Scheerlinck et al, 2008;Vanderlinden et al, 2013;Garcia et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Toward predictive food process models: A protocol for parameter estimation

Vilas

Arias-Méndez

García

et al. 2016

Critical Reviews in Food Science and Nutrition

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Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward predictive food process models: A protocol for parameter estimation

Vilas

Arias-Méndez

García

et al. 2016

Critical Reviews in Food Science and Nutrition

View full text Add to dashboard Cite

show abstract

“…Thermal properties are essential for modeling and evaluation of food processing operations involving heat transfer, especially when energy costs, food quality and safety are the main considerations (Marcotte et al, 2008). Knowledge of the thermal properties is crucial for the design, modeling, simulation and evaluation of different thermal food process scenarios and heating strategies (Scheerlinck et al, 2008). For heating and cooling operations thermo-physical properties (density, thermal conductivity, specific heat and thermal diffusivity) are essential requirements for the solution of the governing differential equations.…”

Section: Introductionmentioning

confidence: 99%

“…Nahor et al, 2001 used a hotwire probe to estimate volumetric heat capacity and thermal conductivity using different heat generation profiles. Scheerlinck et al, 2008 used a hotwire probe coupled with optimal heat generation profiles as well as an optimal location for measurements of temperature response to simultaneously estimate thermal conductivity and volumetric heat capacity.…”

Section: Introductionmentioning

confidence: 99%