Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm - Zhang, Hong; Nguyen, Hoang; Bui, Xuan-Nam; Nguyen-Thoi, Trung; Bui, Thu-Thuy; Nguyen, Nga; Vu, Diep-Anh; Mahesh, Vinyas; Moayedi, Hossein (Resources policy, 2020)
This study aims to propose a novel artificial intelligence model for forecasting the capital cost (CC) of open-pit mining projects with high accuracy. It is a unique combination of a deep neural network (DNN) and ant colony optimization (ACO) algorithm, abbreviated as ACO-DNN. In this model, MineAP (annual mine production), SR (stripping ratio), MillAP (annual production of the mill), RMG (reserve mean grade), and LOM (life of mine) were used to consider the CC of open-pit mining projects. A series of simple and complex artificial neural networks (ANN) was developed for forecasting CC of 74 copper mining projects herein. Subsequently, the ACO algorithm has been applied to optimize the developed ANN and DNN models to improve the accuracy of them. Finally, an optimal hybrid model was defined (i.e., ACO-DNN 5-25-20-18-15-1) with superior performance than other models (i.e., RMSE of 130.988, R2 of 0.991, MAE of 115.274, MAPE of 0.072, and VAF of 99.052). The findings of this study showed that the DNN models could predict the CC for open-pit mining projects with more accuracy than those of the simple ANN models. In particular, the ACO algorithm played an essential role in improving the accuracy of forecasting models. Also, MineAP, MillAP, SR, and LOM have been confirmed as critical parameters that affect the accuracy of the selected model in forecasting the CC of open-pit mining projects, especially MineAP. In conclusion, this study offers a useful tool to improve resource policies of mining projects, especially copper mining projects. •A novel ACO-DNN model was developed for estimating the CC of mining projects with high accuracy.•Ten ANN and DNN models were optimized by the ACO algorithm for estimating the CC of mining projects.•The performance of the ACO-ANN and ACO-DNN models was compared and evaluated.•ANOVA test was used to assess the strength and statistical significance of the developed models.•The sensitivity of input variables was analyzed for predicting the CC of mining projects.