Improved mathematical model of fixed-bed infrared dryer process under different water flux expression

The use of infrared radiation in the drying industry has been increased in recent years due to its advantages over other drying methods. High intensity, precise control and ability to penetrate the product are some of the advantages of infrared radiation. Although infrared radiation has unique advantages over conduction and convection heating methods, the lack of visual feature data related with the radiant process and the complicated mathematics have hampered wide numerical simulation for optimization and process design. The objectives of this research are to develop a mathematical model of fixed-bed infrared dryer by include suitable form of water mass flux ( ) for the simulation purpose, modification of the model to be valid and pplicable for different kind of materials, numerical solution of the model and finally control the system using PID controller. New approaches with modifying of the model are presented in three different methods. In method 1 the Equation of water flux is modified and simplified to be substituted in the main model. In method 2, the Equation of the dimensionless water content is added to the main model, as the model becomes a system of 5 partial differential Equations. In the last method, water flux Equation is modified by increases the effect of the emitter temperature on the system performance, as it can be applicable for more variety of food materials. Nonlinear regression is used to fit the experimental data to the models. Coefficient of determination ( ) is calculated in order to measure the proportion of variability qualified to the mathematical model, where mean bias error (MBE) and root mean square error (RMSE) were used to evaluate the goodness of fit of each method. In order to have more efficient and accurate drying performance, all developed models are controlled using PID controller. The moisture content values of three methods are compared together before and after connecting the models to the controller. Resulting curves show that the first method has the best fitting values with the experimental curves while method 3 reaches the desire moisture content faster than method 1 and 2.All methods show faster drying performance after controlling the process. It may consider as energy and time saving with efficient performance of the system. In this work Method 3 show the best performance for IR dryer that reach desired moisture content of 0.5 (Kg water/Kg dry mater) in 3 hours where it is 3.5 and 5 hours for method 1 and 2 respectively. Developing three methods and designing control strategy are considered as the contributions of this work while the Main contribution is presenting new water flux Equation in method 3.

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