Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting

In the open-pit mines and civil projects, drilling and blasting is the most common method for rock fragmentation aims. This article proposes a new hybrid forecasting model based on firefly algorithm, as an algorithm optimizer, combined with the adaptive neuro-fuzzy inference system for estimating the fragmentation. In this regard, 72 datasets were collected from Shur river dam region, and the required parameters were measured. Using the different input parameters, six hybrid models were constructed. In these models, 58 and 14 data were used for training and testing, respectively. The proposed hybrid models were then evaluated in accordance with statistical criteria such as coefficient of determination and Nash and Sutcliffe. Based on obtained results, the proposed model with five input parameters, including burden, spacing, stemming, powder factor and maximum charge per delay can estimate rock fragmentation better than the linear multiple regression. The values of the coefficient of determination for the proposed hybrid model and linear multiple regression were 0.980 and 0.669, respectively, that demonstrate the hybrid forecasting model proposed in the present study can be introduced as a reliable method for estimating the fragmentation.