Intelligent control of a grain drying system using a GA-SVM-IMPC controller. Issue 12 (10th September 2018)

• Record Type: Journal Article
• Authors: Dai, Aini; Zhou, Xiaoguang; Liu, Xiangdong; Liu, Jingyun; Zhang, Chi
• Is Part Of: Drying technology. Volume 36:Issue 12(2018)
• Page Start: 1413
• Page End: 1435
• Publication Date: 2018-09-10
• Subjects: Genetic algorithm -- grain drying -- IMC -- mixed flow drying -- SVM; EMC, equilibrium moisture content -- GA, genetic algorithm -- GA-SVM-IMPC, Internal Model PID Controller (IMPC) based on SVM and the genetic algorithm -- IMC, internal model controller -- IMPC, internal model PID controller -- IRC, infrared radiation-convection -- MC, moisture content -- MPC, model predictive control -- PID, proportional integral derivative -- PLC, programmable logic controller -- RMSE, root mean squared error -- SVM, support vector machine -- SVR, SVM regression -- TDL, time delay length -- Wb, wet basis; Drying -- Periodicals; Desiccation; 660.28426
• DOI: 10.1080/07373937.2017.1407938
• Languages: English
• ISSNs: 0737-3937

Abstract

ABSTRACT: The grain drying is difficult to control because of its characteristics of long delay, strong nonlinearity, and uncertainty parameters. The aim of the paper is to design a suitable controller for a newly designed grain dryer. First, a nonlinear math model of the wheat mixed flow drying was established and analyzed based on the fundamental laws of simultaneous heat and mass transfer. The simulations for the batch circulating drying process and the continuous grain drying process have been made and the simulation results show that it fits well with the actual drying process. Second, an internal model proportional integral derivative (PID) controller (IMPC) based on the support vector machine (SVM) algorithm and the genetic algorithm (GA-SVM-IMPC) was proposed from the view of the energy loss and the dried grain quality. The structure of the GA-SVM-IMPC controller consists of a SVM prediction model, a SVM inverse model controller, a PID controller and a genetic optimization algorithm. Finally, the effectiveness of this controller was demonstrated by computer simulations, and the comparative study with the other controllers further confirmed the superiority of the proposed dryer controller.