Feedback control procedure for energy efficiency optimization of microwave-heating ovens

Monzó‐Cabrera, Juan; Pedreño-Molina, J.L.; Toledo, Ana

doi:10.1016/j.measurement.2009.04.006

Cited by 11 publications

(7 citation statements)

References 5 publications

(11 reference statements)

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“…For example, they allow reactions in fast time, obtaining higher yields with potentially switchable selectivities. [22][23][24] Ethylene glycol (common solvent used as antifreeze agent in the automotive industry) was additionally employed as solvent and reducing agent. Wasted pig bristles were used as carbon and sulphur source while silver sulphide was employed as metal precursor.…”

Section: Introductionmentioning

confidence: 99%

Microwave-assisted preparation of Ag/Ag₂S carbon hybrid structures from pig bristles as efficient HER catalysts

Cova¹,

Zuliani²,

Santiago³

et al. 2018

J. Mater. Chem. A

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Section: Introductionmentioning

confidence: 99%

Microwave-assisted preparation of Ag/Ag₂S carbon hybrid structures from pig bristles as efficient HER catalysts

Cova¹,

Zuliani²,

Santiago³

et al. 2018

J. Mater. Chem. A

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“…This may be a low-cost approach but may not be effective. A cheap temperature controller [11] and optimum microwave oven power control techniques are discussed at [12,13]. Nevertheless, when the food temperature reaches the target temperature, they do not mention the automatic shutdown of microwave power.…”

Section: Related Workmentioning

confidence: 99%

“…It breakdowns the spatial dimensions of the input. The flatten layer in this model changes the (12,16,16) tensor to a one-dimensional vector of size 3072.…”

mentioning

confidence: 99%

An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

Khan

2020

ASI

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Whenever food in a microwave oven is heated, the user estimates the time to heat. This estimation can be incorrect, leading the food to be too hot or still cold. In this research, an intelligent microwave oven is designed. After the food is put into the microwave oven and the door is closed, it captures the image of the food, classifies the image and then suggests the food's target temperature by learning from previous experiences, so the user does not have to recall the target food temperature each time the same food is warmed. The temperature of the food is measured using a thermal camera. The proposed microwave incorporates a display to show a real-time colored thermal image of the food. The microwave automatically stops the heating when the temperature of the food hits the target temperature using closed-loop control. The deep learning-based image classifier gradually learns the type of foods that are consumed in that household and becomes smarter in temperature recommendation. The system can classify and recommend target temperature with 93% accuracy. A prototype is developed using a microcontroller-based system and successfully tested.

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“…This method could be low cost, but may not be accurate. An inexpensive temperature controller [19] and optimum power control strategies for microwave oven are discussed in [20], [21]. However, they do not discuss the automatic shutdown of microwave power when the food temperature reaches the desired temperature.…”

Section: Introductionmentioning

confidence: 99%

Smart Microwave Oven with Image Classification and Temperature Recommendation Algorithm

Khan¹

2018

IJECE

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When food is warmed in a microwave oven, the user guesses the estimated time for the heating. This cognitive process of guessing can be incorrect - resulting the final food temperature to be too hot or still cold. In this research, a novel closed-loop microwave oven is designed which automatically suggests the target temperature of a food by learning from previous experiences and the heating stops automatically when the food temperature reaches the target temperature. The proposed microwave captures and classifies the food image, and recommends the target temperature, thus the user does not need to remember the target food temperature each time the same food is warmed. The algorithm gradually learns the type of foods that are used in that household and becomes smarter in the recommendation. The proposed algorithm can recommend target temperature with an accuracy of 86.31% for solid food and 100% for liquid food. A prototype of the proposed microwave is developed using the embedded system and tested.

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Feedback control procedure for energy efficiency optimization of microwave-heating ovens

Abstract: -

Cited by 11 publications

References 5 publications

Microwave-assisted preparation of Ag/Ag₂S carbon hybrid structures from pig bristles as efficient HER catalysts

Microwave-assisted preparation of Ag/Ag₂S carbon hybrid structures from pig bristles as efficient HER catalysts

An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

Smart Microwave Oven with Image Classification and Temperature Recommendation Algorithm

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Feedback control procedure for energy efficiency optimization of microwave-heating ovens

Abstract: -

Cited by 11 publications

References 5 publications

Microwave-assisted preparation of Ag/Ag2S carbon hybrid structures from pig bristles as efficient HER catalysts

Microwave-assisted preparation of Ag/Ag2S carbon hybrid structures from pig bristles as efficient HER catalysts

An Intelligent Microwave Oven with Thermal Imaging and Temperature Recommendation Using Deep Learning

Smart Microwave Oven with Image Classification and Temperature Recommendation Algorithm

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Product

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Microwave-assisted preparation of Ag/Ag₂S carbon hybrid structures from pig bristles as efficient HER catalysts

Microwave-assisted preparation of Ag/Ag₂S carbon hybrid structures from pig bristles as efficient HER catalysts