Prediction of rock crushing from blasting using neural networks

Blasting is one of the key stages in the open-cast mining operations, and the main purpose of mine explosions is to predict the dimensions of the blasting rock that will impact subsequent operations such as loading, unloading, crushing and grinding and avoiding secondary costs. This is the field. Various parameters such as rock mass characteristics, explosive properties and geometrical properties of the explosion network are important in designing the explosion pattern and its results. To achieve optimal crushing and minimize the effects of blasting, rock throwing, etc. These factors first need to be determined and then the optimal explosion pattern is designed based on these factors. Over the past two decades there has been good progress in developing new methods in explosion pattern design and performance prediction. Accuracy in design, high speed and ease of use are also addressed in this study, crushing of Sarcheshmeh copper mine is predicted by the development of neural network model. Results showed that there is a close equation between actual (measured) data and predicted data with R= 0.995 and R2 = 0.990, respectively.

Keywords:

Blasting , Sarcheshmeh copper mine , neural network , equation.