Prediction of the capacitance of the corn drying process parameter using adaptive- neuro-fuzzy intelligent technique with experimental validation

Abstract

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, horizontal ellipsis , s12) such as inlet air temperature T1 with +/- 0.5 degrees C sensitivity, relative humidity RH1 with +/- 0.1 RH, inlet temperatures T2 and Tw2 with the +/- 0.5 degrees C, dryer chamber temperature Ti (i = 3, horizontal ellipsis ,7) with the +/- 0.5 degrees C, outlet temperatures T8 and Tw8 with the +/- 0.5 degrees 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.