Temperature control during microwave heating process by sliding mode neural network

Li, Jianshuo; Xiong, Qingyu; Wang, Kai; Shi, Xin; Liang, Shan; Gao, Min

doi:10.1080/07373937.2015.1037889

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…This article aims to explore the utilization of a neural network to regulate the heating process in a microwave oven [9] integrated within a solar-powered vending machine. Additionally, this article (research) will discuss the significance of food refrigeration through a refrigeration unit and the efficient management of energy consumption in both the refrigeration unit and the microwave oven.…”

Section: Introductionmentioning

confidence: 99%

Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations

Savaniu,

Chiriță,

Tonciu

et al. 2023

Energies

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This article presents novel research on the utilization of a neural-network-based time control system for microwave oven heating of food items within a solar-powered vending machine. The research aims to explore the control of heating time for various food products, considering multiple variables. The neural network controller is calibrated through extensive experimentation, allowing it to accurately predict optimal heating times based on input parameters such as food type, weight, initial temperature, water content, and desired doneness level. The results demonstrate that the neural-network-controlled microwave oven achieves precise and desirable heating durations, mitigating the risk of overheating and ensuring superior food quality and taste. Moreover, the solar-powered vending machine showcases a commitment to sustainable energy sources, effectively reducing dependence on non-renewable energy and minimizing greenhouse gas emissions. To maintain food quality and freshness, a food refrigeration unit is integrated into the vending machine, employing load-balancing technology to control the refrigeration chamber’s temperature effectively. Energy efficiency is prioritized in both the refrigeration unit and the microwave oven through intelligent algorithms and system optimization. The combination of a neural-network-controlled microwave oven, a solar-powered vending machine, and a food refrigeration unit introduces a novel and sustainable approach to food preparation and energy management.

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Section: Introductionmentioning

confidence: 99%

Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations

Savaniu,

Chiriță,

Tonciu

et al. 2023

Energies

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“…It resulted a mathematical model that could connect the mathematical relation between a microwave power signal as an input of microwave heating system and the measured temperature as an output. On the other hand, works about microwave heating system control mainly deal with the effort to obtain system response with a certain set point value for the measured temperature [15]. It could be developing a new control method or modifying the existing controller to perform better responses.…”

Section: Introductionmentioning

confidence: 99%

Microwave heating control system using genetic algorithm-based PID controller

Apriaskar

Prastiyanto

Malik

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The superiorities of microwave heating technology have attracted many researchers for further development. Designing a satisfying temperature control system for the microwave system is one of them. To confirm that the controller can work with good system performance, the adjustment of controller parameters is important. Most previous research about microwave control systems did not focus on the tuning problem which is presented in this work. This paper provides the use of a genetic algorithm to adjust the best parameters for a PID controller. A microwave heating system with a single microwave power input and a single output temperature probe which is identified using the ARX model is examined. Evaluation of the proposed controller was conducted in the simulation environment using MATLAB. The result shows that the genetic algorithm can obtain the most optimized parameter for PID controller which can follow a certain heating pattern. The parameters for Kp, Ki, and Kd are 998.94, 79.42, and 79.78, respectively. It can minimize the value of integral absolute error for the given setpoint to 446.11.

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Data-Based Online Optimal Temperature Tracking Control in Continuous Microwave Heating System by Adaptive Dynamic Programming

Liu

Liang

Xiong

et al. 2019

Neural Process Lett

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Temperature control during microwave heating process by sliding mode neural network

Cited by 8 publications

References 21 publications

Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations

Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations

Microwave heating control system using genetic algorithm-based PID controller

Data-Based Online Optimal Temperature Tracking Control in Continuous Microwave Heating System by Adaptive Dynamic Programming

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