Deyong Yang

Dr. Deyong Yang


Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 February 2023 in Biosystems Engineering
Verified
Journal Article
Closed access
SWShuo Wei
Shuo Wei
WXWeijun Xie
Weijun Xie
ZZZhaohui Zheng
Zhaohui Zheng
LRLiuyang Ren
Liuyang Ren
DYDeyong Yang
Deyong Yang

24 April 2021 in Food and Bioprocess Technology
Verified
Journal Article
Closed access
PDF file
WXWeijun Xie
Weijun Xie
SWShuo Wei
Shuo Wei
ZZZhaohui Zheng
Zhaohui Zheng
YJYu Jiang
Yu Jiang
DYDeyong Yang
Deyong Yang

9 March 2021 in Drying Technology
Verified
Journal Article
Closed access
YJYi Jin
Yi Jin
Kok Wai WongKok Wai Wong
Kok Wai Wong
DYDeyong Yang
Deyong Yang
ZZZhongjie Zhang
Zhongjie Zhang
WWWenfu Wu
Wenfu Wu
JYJun Yin
Jun Yin
Due to the nonlinearity, strong coupling and hysteresis of the parameters used in measuring the grain drying process, it is always a challenge to perform accurate control of the drying system. The purpose of this paper is to describe the design of a suitable neural network model to control the grain dryer more effectively. First, the operating mechanism of a continuous grain dryer and the principle of the heat and mass transfer that can be used in the process of grain drying was analyzed before an intelligent control system was designed accordingly. Second, an intelligent control system based on the Back Propagation Neural Network (BPNN) was developed. The BPNN was the optimal model selected based on a series of comparative test results. According to the BPNN prediction of the moisture content of dried rice, the system could adjust the rate of grain discharge of the dryer, and then control the drying process accurately. Finally, the neural network control model was simulated using computer simulation technology, and was optimized by comparing analysis results with experimental results. The results showed that the optimized intelligent control system using BPNN has the advantage of strong stability and good noise handling, and could have great potential for future implementation studies.

9 April 2024 in International Journal of Biological Macromolecules
Verified
Journal Article
Closed access
LRLiuyang Ren
Liuyang Ren
ZZZhaohui Zheng
Zhaohui Zheng
HFHanyu Fu
Hanyu Fu
PYPei Yang
Pei Yang
JXJingshen Xu
Jingshen Xu
DYDeyong Yang
Deyong Yang

19 January 2022 in Postharvest Biology and Technology
Verified
Journal Article
Closed access
WXWeijun Xie
Weijun Xie
SWShuo Wei
Shuo Wei
ZZzhaohui zheng
zhaohui zheng
ZCZhaoli Chang
Zhaoli Chang
DYDeyong Yang
Deyong Yang

1 January 2018 in International Journal of Food Engineering
Verified
Journal Article
Closed access
PDF file
YZY. Zhao
Y. Zhao
KHKunlun Huang
Kunlun Huang
XCX.F. Chen
X.F. Chen
FWF.H. Wang
F.H. Wang
PCP.X. Chen
P.X. Chen
DYDeyong Yang
Deyong Yang
In order to study the effect of corn kernel composition and physical structure on moisture distribution and transfer process and obtain the optimal tempering-drying parameters of corn kernel, a physical model was constructed with four different components as follows: seed coat, horny endosperm, farinaceous endosperm and embryo. The drying model was established based on the assumption of different diffusion coefficients and same thermal conductivity for the four components. The software of COMSOL Multiphysics was used to simulate the heat and mass transfer process inside the corn kernel during the thin-layer drying. The results showed that the least total drying time and the best drying quality were achieved under the multistage circulating drying of 10 min hot air drying and 60 min tempering, and the tempering degree was up to 0.9207.