Prediction of corn drying performance for a combined IRC dryer with a genetically-optimized SVR algorithm

  • Aini Dai
  • Xiaoguang ZhouEmail author
  • Zidan WuEmail author
Special Issue


Grain drying process is a complex nonlinear system which is characterized by long delay process, multi disturbance and strong coupling. In order to explore the modelling of an uncertain system, such as those used in grain drying, and to study the application of the support vector regress algorithm, a corn drying process conducted in a side-heat Infrared Radiation and Convection dryer was modelled by using a support vector regress algorithm combined with a genetic algorithm which is abbreviated as GA-SVR. The algorithm was trained by using the input and output data collected from the practical experiment of corn drying. The predicted performance comparisons between the GA-SVR modelling method and the other two modelling methods (the neural network of BP model and the SVR model based on the grid search algorithm) were also made. Moreover, we successfully used the method to design a model of concurrent-counter flow drying. The designed GA-SVR model has achieved higher modelling prediction accuracy according to the prediction results which have verified the feasibility of the proposed modelling algorithm for modelling the grain drying. The modelling method can also realize the performance prediction of different drying techniques and can be applied in the model prediction control of the grain drying.


Infrared radiation drying Grain drying Support vector regression Genetic algorithm Modelling 



Genetic algorithm


Support vector regress


SVR algorithm combined with a genetic algorithm


The SVR model by using a grid search method


Infrared radiation and convection


The artificial neural network model of back propagation


The SVR model by using a grid search method


Partial differential equation


Distributed parameter


Artificial neural network


Machine learning


Support vector machine


Support vector classifier


Wet basis


Moisture content


Radial basis function




Grid Search method based on a cross-validation


Mean squared error


Root mean squared error



The authors of this paper would like to thank the support of Qingdao Agricultural University High-level Talents Research Fund (1118037), and the support of National Key Research and Development Program of China under Grant (2016YFC0803206).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Science and Information CollegeQingdao Agricultural UniversityQingdaoChina
  2. 2.School of Economics and ManagementMinjiang UniversityFuzhouChina
  3. 3.School of AutomationBeijing University of Posts and TelecommunicationsBeijingChina

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