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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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5. ExperimentalResults ing the heating process, the central part of the aluminum plate is al- ways hotter than the corners and edges, because of less heat dissipa- tion to the ambiance. Therefore for the workpiece, the heat dissipated to the aluminum plate through thermal conduction in the central part is always lower than the other parts, leading to a lower cooling rate and higher temperature. In addition, the heat convection effect of the central part is also lower than that of the other parts, because of the inhomogeneous temperature distribution of the surrounding air. As a result of above impacts, the temperature of the central part of the workpiece is always higher than temperatures of corners and edges andcorrespondingly, this setupisnot fullycontrollable. When the aluminum plate is replaced by a teflon plate, the tempera- turedistributioniscompletelychanged,suchasshowninfigure 5.18. Since teflon has a much lower thermal conductivity (0.25 W/(m ·K) Figure5.18. Thesetupwith the teflonplate. [tef]) compared with aluminum (237 W/(m ·K)), the heat conduction effect caused by the teflon tool is weak. The temperature distribu- tion of the teflon plate is similar to the temperature distribution of theworkpiece. Thetemperaturedifferencesbetweendifferentpartsof the workpiece and their contacting parts of the teflon plate are simi- 156
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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