prediction of recovery of gold thiosulfate on activated carbon using artificial neural networks

Prediction of recovery of gold thiosulfate on activated carbon using artificial neural networks

Since a high toxicity of cyanide which use as a reagent in the gold processing plant, thiosulfate has been recognized as a environmental friendly reagent for leaching of gold from ore. After gold leaching process it's important for recovery of gold from solution using adsorption or extraction methods, One of these methods is activated carbon.The loading of gold from industrial thiosulfate solut...

Prediction of Gas Lift Parameters Using Artificial Neural Networks

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

Prediction of Kinematic Viscosity of Petroleum Fractions Using Artificial Neural Networks

In this work, artificial neural network (ANN) was utilized to develop a new model for the prediction of the kinematic viscosity of petroleum fractions. This model was generated as a function of temperature (T), normal boiling point temperature (Tb), and specific gravity (S). In order to develop the new model, different architectures of feed-forward type were examined. Finally, the optimum struc...

Prediction of the deformation modulus of rock masses using Artificial Neural Networks and Regression methods

Static deformation modulus is recognized as one of the most important parameters governing the behavior of rock masses. Predictive models for the mechanical properties of rock masses have been used in rock engineering because direct measurement of the properties is difficult due to time and cost constraints. In this method the deformation modulus is estimated indirectly from classification syst...

The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks

In this work, artificial neural network (ANN) has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocar...