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Adaptive and Intelligent Temperature Control of Microwave Heating Systems with Multiple Sources
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1.1. DevelopmentofMicrowaveHeating Advantages Drawbacks Autoclave highqualityworkpiece expensive; slow Electron- beam highquality workpiece; low costs; short time very large temperature difference for thick workpieces Ultraviolet (UV) veryshort time special resinmatrix needed; relativelyhigh costs X-rays largepenetrationdepth longcuringtime; safety issues Microwave Heating short time; low costs; large penetrationdepth; highheating efficiency temperaturecontroland non-uniform distributionproblems Table1.1. Comparisonofdifferentcuringmethods (accordingto [BRS01]). [LLG14] and [KMS01], quality failures caused by the non-uniform temperature distribution have become the major problem associated withmicrowaveheatingapplications. It is easy to control a single temperature under microwave heating, by using a temperature sensor and adjusting the microwave power based on the feedback temperature value [SZ06]. Nevertheless, it is way more complicated and difficult to control the entire temperature distributionof theworkpiece. Because the temperaturedistribution is determined not only by the properties of the workpiece, but also by theelectromagnetic (EM)fielddistributionwithinthemicrowavecav- ity. The EM field and temperature distributions are governed jointly by two partial differential equations (PDEs) [BBBB95], which are the Maxwell’s equations [Mer98] and the forced heat transfer equation [Met96]. On the one hand, the EM field distribution influences the 5
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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