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In this study, innovative, intelligent software based on Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to effectively estimate the corn drying process parameters using data from getting experimental study. For this purpose, laboratory-scale corn drier experimental setup was established to get corn drying parameters (air temperature in the different locations, air humidity, and capacitance value). The twelve sensors measure the drying parameters (s1, s2, s3, ..., s12) such as inlet air temperature T1 with ±0.5 °C sensitivity, relative humidity RH1 with ±0.1 RH, inlet temperatures T2 and Tw2 with the ±0.5 °C, dryer chamber temperature Ti (i = 3,...,7) with the ±0.5 °C, outlet temperatures T8 and Tw8 with the ±0.5 °C, product moisture content with ±0.05 gr. Finally, depending on the twelve sensor parameters, the capacitance value was measured using a capacitive sensor with ±0.05% sensitivity. Accordingly, an Adaptive Neuro-Fuzzy Inference System architecture was developed with twelve inputs and one output. For the training of the Adaptive Neuro-Fuzzy Inference System, 52 data sets from laboratory studies were used, and Adaptive Neuro-Fuzzy Inference System was successfully trained with absolute fraction of variance 0.998 and mean squared error 0.8007. For the testing the performance of the Adaptive Neuro-Fuzzy Inference System 51 dataset was used and testing performance of the ANFIS getting with the absolute fraction of variance 0.9963 and mean squared error 1.47. Finally, the performance of the Adaptive Neuro-Fuzzy Inference System compared with the artificial neural network (ANN), and it is clearly seen that the performance of the fuzzy-neuro hybrid algorithm is higher than the core neuro algorithm with %0.97 for the training and %31.5 for the testing. By forecasting the moisture content of corn under various operating conditions, the Adaptive Neuro-Fuzzy Inference System model can be used to improve the operation of the corn dryer, resulting in increased efficiency, lower energy usage, and higher-quality dried corn. [ABSTRACT FROM AUTHOR]

Tower type grain dryers are widely used in corn drying process and are manufactured as cylindrical structures with a height of about 30 m. Grain is filled into the annular section between the perforated walls and dried with exhaust gas from the combustion chamber. In terms of drying quality and energy efficiency, high capacity drying systems need a reliable control system. In the current study, a laboratory scale test prototype of this type of industrial grain dryer is installed to determine the most suitable model to be used in the control system. A fixed bed design has been made that allows hot exhaust gases to enter the drying chamber horizontally, as in industrial tower dryers. Corn drying experiments were carried out experimentally to reveal and model the drying behavior of corn, to examine the effect of air velocity on pressure drop and drying, to show the usability of equations commonly used in the literature. Drying gas temperature is kept constant at 103 °C and inlet gas velocity values are changed to find out velocity effects on pressure drop and drying behavior. Experimental data are modeled with a curve fitting method using MATLAB software. Lewis, Page, Henderson and Pabis, Logarithmic, Midilli and Wang and Singh models, which are known as thin-layer models in the literature are used. The effect of drying air velocity on the coefficients of the best thin layer drying model was determined. Results show the most suitable model is the Midilli model. The obtained results are given in comparison with the results in the literature and a model is proposed for use in control of industrial dryers. [ABSTRACT FROM AUTHOR]