Model development for prediction of autogenous mill power consumption in Sangan iron ore processing plant

The variables including ore hardness based on the SAG power index (SPI), particle size of mill product (P80), trunnion pressure of the mill free head (p) and working time period of mill liner (H) were considered as variables for development of an adequate model for the prediction of autogenous (AG) mill power consumption in Sangan iron ore processing plant. The one-parameter models (SPI as variable) showed no adequate precision for the prediction of Sangan AG mill power consumption. Two-parameter models (SPI and P80 as variables), proposed by Starkey and Dobby, showed no adequate precision for the Sangan AG mill power consumption. Nonetheless, by exerting an adjustment factor in the model (0.604513 which obtained by what-if analysis using Solver Add-Ins program), the model precision increased significantly (an error of 7.11%). Finally, a four-parameter model in which the Sangan AG mill power consumption is predicated as a function of SPI, P80, p, and H was developed. Hence, initially the relationship between the mill power consumption and each of the variables was obtained and then the four-parameter model was developed by summation of these four equations and applying a similar coefficient of 0.25 for all of them. This model was modified through finding the best coefficients by what-if analysis using solver Add-Ins program through minimizing the ARE error function. The error function for the training and testing data sets was determined to be 2.93% and 2.39%, respectively

Application of adaptive neuro-fuzzy inference system for prediction of dissolved oxygen concentration in the gold cyanide leaching process

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 (0-40oC), salinity based on conductivity (0-59000 µS/cm), and atmospheric pressure (600-795 mmHg). The data set was randomly divided into two parts, training and testing sets. 80% of the data points (80% = 11556 datasets) were utilized for training the model and the remainder data points (20% =2889 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

Estimation of coal proximate analysis factors and calorific value by multivariable regression method and adaptive neuro-fuzzy inference system (ANFIS)

The proximate analysis is the most common form of coal evaluation and it reveals the quality of a coal sample. It examines four factors including the moisture, ash, volatile matter (VM), and fixed carbon (FC) within the coal sample. Every factor is determined through a distinct experimental procedure under ASTM specified conditions. These determinations are time consuming and require a significant amount of laboratory equipment. The calorific value is one of the most important properties of a solid fuel and its experimental determination requires special instrumentation and highly trained analyst to operate it. This paper develops mathematical and ANFIS models for estimation of two factors of proximate analysis based on the other two factors. Furthermore, the estimation of calorific value of coal samples based on proximate analysis factors is performed using multivariable regression, the Minitab 16 software package, and the ANFIS, Matlab software package. The results indicate that ANFIS is a more powerful tool for estimation of proximate analysis factors and calorific value than multivariable regression method. The following equation estimates the calorific value of coal samples with high precision: Calorific value (btu/lb)= 12204 - 170 Moisture + 46.8 FC - 127 As