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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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3. ModelingMicrowave Heating BasedontheintroductiontomicrowaveheatingandHEPHAISTOSin thepreviouschapter, themathematicalmodelsofthemicrowaveheat- ingprocessinHEPHAISTOSarebuilt inthischapter. Abrief introduc- tionofmathematicalmodelingisgivenfirst, followedbytwodifferent modelingapproaches, thegrey-boxmodelingandtheblack-boxmod- eling. For each modeling approach, the corresponding system identi- fication(SID)algorithms,whichareusedtoestimatetheunknownpa- rameters in themodel,arealsoexplained. Bothapproacheshavebeen implemented, to identify the most suitable modeling scheme for the temperature control system of HEPHAISTOS. The performance com- parisoncanbefoundinchapter 5. In this dissertation, we are focusing on controlling and improving the surface temperature distribution of a rectangular dielectric foil setup, suchasshowninfigure 3.1. Twoassumptionshavebeenmade: 1. The loadreceivesmostofelectromagneticenergy(heatingenergy) fromthetopsurface. 2. The thickness of the load is much smaller than the wavelength of themicrowave( 12.5cm). These two assumptions guarantee that the surface temperature distri- butionof the loadisequivalent to thetemperaturedistributionwithin the load (similar assumptions are used in [CRB08] and [BRS01]). In other words, the temperature difference in the vertical direction (z- direction as in figure 3.1) is sufficiently small. Thus the heat conduc- tion on the vertical direction can be neglected, which simplifies the entire model and following derivations. If the surface temperature 37
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Titel
Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
Autor
Yiming Sun
Verlag
KIT Scientific Publishing
Ort
Karlsruhe
Datum
2016
Sprache
englisch
Lizenz
CC BY-SA 3.0
ISBN
978-3-7315-0467-2
Abmessungen
14.8 x 21.0 cm
Seiten
260
Schlagwörter
Mikrowellenerwärmung, Mehrgrößenregelung, Modellprädiktive Regelung, Künstliches neuronales Netz, Bestärkendes Lernenmicrowave heating, multiple-input multiple-output (MIMO), model predictive control (MPC), neural network, reinforcement learning
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Technik
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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources