Ali Behnamfard - Faculty of Engineering, University of Birjand, Birjand, South Khorasan, Iran
Mohammad Rivaz - Faculty of Engineering, University of Birjand, Birjand, South Khorasan, Iran

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the dissolved oxygen concentration (DOC) as a function of the solution temperature (۰-۴۰oC), salinity based on conductivity (۰-۵۹۰۰۰ µS/cm), and atmospheric pressure (۶۰۰-۷۹۵ mmHg). The data set was randomly divided into two parts, training and testing sets. ۸۰% of the data points (۸۰% = ۱۱۵۵۶ datasets) were utilized for training the model and the remainder data points (۲۰% =۲۸۸۹ datasets) were utilized for its testing. Several indices of performance such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of correlation (R) were used for checking the accuracy of data modeling. ANFIS models for the prediction of DOC were constructed with various types of membership functions (MFs). The model with the generalized bell MF had the best performance among all of the given models. The results indicate that ANFIS is a powerful tool for the accurate prediction of DOC in the gold cyanidation tanks.

Dissolved oxygen concentration, Cyanidation process, Data modeling, Adaptive neuro-fuzzy inference system