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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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1. Introduction 1.1. Development ofMicrowave Heating In contrast to conventional heating, microwave heating has a com- pletely different heating mechanism, which is the so-called volumet- ric heating ability. Instead of the heating flow from the surface to the core, the microwave energy is directly transferred into heat inside the heated material. As shown in the figure 1.1, in conventional heating, the surface is always hotter than the core because the heat flows from the surface to the core. However, in microwave heating both the surface and the core of the product are heated simultaneously. Due to the heat convection on the surface, the maximum temperature of microwave heating occurs in the core of the product. It is the opposite to the temperature distribution of the conventional heating, therefore it is called the inverse temperature profile. This inverse temperature pro- file might be a problem for some applications, but it is helpful in cer- tain cases, such as in the microwave sintering of YBCO superconduc- tors [BADAC92]. Hot Cold Product Product Heat flow Conventional Heating Microwave Heating Figure1.1. Temperature profile comparison between conventional (left) and microwave(right)heating. Benefiting from the special heating mechanism, microwave heating hasseveral advantagesover theconventional heating. 1. Reduced energy andtime consumption Microwave heating generates the heat directly in the volume of a product, which saves significant energies from the unnecessary 2
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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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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources