Temperature control during microwave heating process by sliding mode neural network. Issue 2 (25th January 2016)

• Record Type: Journal Article
• Authors: Li, Jianshuo; Xiong, Qingyu; Wang, Kai; Shi, Xin; Liang, Shan; Gao, Min
• Is Part Of: Drying technology. Volume 34:Issue 2(2016)
• Page Start: 215
• Page End: 226
• Publication Date: 2016-01-25
• Subjects: Convective heat transfer -- microwave heating -- neural network -- sliding mode; Drying -- Periodicals; Desiccation; 660.28426
• DOI: 10.1080/07373937.2015.1037889
• Languages: English
• ISSNs: 0737-3937

Abstract

ABSTRACT: In microwave heating applications, Lambert's law is a common way to calculate power distribution. However, because of the complex application environment, Lambert's law is not precise for the unknown power distribution on material surfaces. During the microwave heating process, the system process parameters can only be partly known by experience. Therefore, for such situations, to make the entire heating process safe, a sliding mode combined with a neural network algorithm is proposed. The algorithm is designed to calculate the suitable input power at each control period to make the material temperature follow the reference trajectory, which is determined by experience. The simulation and actual application results demonstrate that the proposed algorithm can commendably control the heating process. The difference between the reference trajectory and the material sampling temperature may exceed 1°C initially. However, as time progresses, the difference gradually decreases. Nonetheless, due to the low conduction coefficient, a single microwave heating process may take a long time. Therefore, many actual applications combine convective heat transfer with microwave. This article also discusses the control method of multiple inputs including microwave power and convective heat transfer with unknown model parameters. Another neural network is constructed to identify the unknown parameters. The algorithm is designed to obtain the suitable input power and input convective heat transfer at each control period. The simulation results show that the control algorithm can work well under multiple inputs. The material temperature on both the surfaces and the interior can follow the reference trajectory with a satisfactory difference, and suitable inputs can be obtained with few fluctuations during the learning process.