A 2D non-linear “grey-box” model dedicated to microwave thawing: Theoretical and experimental investigation

Akkari, Elias; Chevallier, Sylvie; Boillereaux, Lionel

doi:10.1016/j.compchemeng.2005.09.008

Cited by 26 publications

(11 citation statements)

References 15 publications

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“…) is the thermal conductivity, which can be regarded as a function of temperature on each grid volume (Akkari et al, 2005). For a microwave heating process, the whole microwave power is absorbed by all the volumes.…”

Section: Thermodynamics Model Of Microwave Heating Processmentioning

confidence: 99%

“…The energy balance equation can be changed to a finite dimensional problem via 1-dimension (1D) finite volumes scheme (Akkari et al, 2005). The number of volumes is denoted as m in the direction (Oz).…”

Section: Thermodynamics Model Of Microwave Heating Processmentioning

confidence: 99%

“…Thermal run-away and the non-uniformity of heating are the particular phenomena that constitute the major challenges to the industrial development of microwave heating process (Akkari et al, 2005). These drawbacks are mainly due to the dielectric properties of materials and the non-uniformity of the electric field in the microwave heating cavity.…”

Section: Introductionmentioning

confidence: 99%

“…A "grey-box" model is semi-empirical model with a priori knowledge about heat transfer. Akkari et al (2005) proposed a non-linear "grey-box" model to handle the temperature distribution of the material in the microwave heating process. A dimensionless absorptivity function substituting Maxwell's equations or Lambert's law is used to describe the electromagnetic flux variation and calculate the temperature distribution.…”

Section: Introductionmentioning

confidence: 99%

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Thermodynamics model based temperature tracking control in microwave heating

Yuan

Liang

Xiong

et al. 2016

JTST

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Microwave heating technology has been widely used in both domestic and industrial applications. Temperature control technique is significant in improving the performance of microwave heating. A generalized numerical thermodynamics model associating with the temperature-dependent thermal and physical properties of material for the microwave heating process is proposed in this paper. Experimental data is applied to estimate the microwave power coefficients. Two controllers, sliding mode controller (SMC) and proportional-integral-differential (PID) controller, are presented as the easily implementable and efficient on-line controllers to track the desired temperature profile while acting on the microwave power and the heated material's temperature. The effectiveness of the proposed thermodynamics numerical model is verified by simulations and experiments, which shows that SMC controller has better dynamic control performance than PID controller.

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Section: Thermodynamics Model Of Microwave Heating Processmentioning

confidence: 99%

Section: Thermodynamics Model Of Microwave Heating Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Thermodynamics model based temperature tracking control in microwave heating

Yuan

Liang

Xiong

et al. 2016

JTST

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“…The term has however evolved to designate all types of models that combine white-box and black-box submodels. For instance, the grey-box models in Akkari, Chevallier, and Boillereaux (2005), Estrada-Flores, Merts, De Ketelaere, and Lammertyn (2006), and Worden et al (2007) combine first-principle based "white-box" models and empirical "black-box" models. In these cases both, white-and black-box models, are parametric models.…”

Section: Introductionmentioning

confidence: 99%

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Stosch¹,

Oliveira²,

Peres³

et al. 2014

Computers & Chemical Engineering

336

201

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Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry.In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scaleup and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.

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