Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network Models for Predicting Time-Dependent Moisture Levels in Hazelnut Shells (Corylus avellana L.) and Prina (Oleae europaeae L.)

Abstract

Nowadays, in parallel with the rapid increase in industrialization and human population, a significant increase in all types of waste, especially domestic, industrial, and agricultural waste, can be observed. In this study, microwave drying, one of the disposal methods for agricultural waste, such as prina and hazelnut shell, was performed. To reduce the time, energy, and cost spent on drying processes, two recently prominent machine learning prediction methods (Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)) were applied. In this study, our aim is to model the disposal of waste using artificial intelligence techniques, especially considering the importance of environmental pollution in today’s context. Microwave power values of 120, 350, and 460 W were used for 100 g of hazelnut shell, and 90 W, 360 W, and 600 W were used for 7 mm thickness of prina. Both ANN and ANFIS approaches were applied to a dataset obtained from the calculation of moisture content and drying rate values. It was observed that the ANFIS and ANN models were applicable for predicting moisture levels, but not applicable for predicting drying rates. When the coefficient of determination (R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values for moisture level are examined both for ANN and ANFIS models’ predictions, it is seen that the R2 value is between 0.981340 and 0.999999, the RMSE value is between 0.000012 and 0.015010 and the MAPE value is between 0.034268 and 23.833481.