Modeling Drying Process Parameters for Petroleum Drilling Sludge with ANN and ANFIS

Abstract

Petroleum drilling sludge (PDS) is one of the most significant waste products generated during drilling activities worldwide. The disposal of this waste must be carried out using the most cost-effective methods available. The objective of this manuscript is to mathematically model the parameters of drying processes experimentally applied to PDS. For this purpose, this study employed two different artificial intelligence techniques: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs). These methods were used to predict the parameters. In the calculations, the inputs included petroleum drilling mud with varying quantities (50 g, 100 g, and 150 g) and drying times, using a 120 W microwave drying power. The results indicated that the coefficient of determination (R2) and the root mean square error (RMSE) obtained during the test phase for ANFIS were 0.999965 and 0.005425, respectively, while for ANN, the R2 and RMSE were 0.999973 and 0.004774, respectively. Analysis of the evaluation results revealed that both methods provided predictions for moisture content that were closer to experimental values compared to drying rate values.