Modeling the net present value in mining projects using the intelligent models and statistical multivariate regression

Document Type : Original Article

Author

Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj,

Abstract

Introduction
Net present value (NPV) index is one of the most important economic parameters in evaluating the mining investment projects. Considering the inherent uncertainty in effective parameters on the NPV in most of the mining projects, its precise estimation is a difficult process and application of the suitable methods is required. In this paper, fuzzy system, neural network and multivariate regression models are used for NPV determination in the Zarshuran gold mine project. In spite of the metal value, the effects of costs and discount rate on the NPV are also considered as an index for evaluation the Zarshuran gold mine project of Takab using the above models. One of the advantages of this research compared to the previous similar studies is the utilization of two new intelligent algorithms i.e., fuzzy system and neural network for modeling the NPV index with higher accuracy. These algorithms are efficient tools to solve the problems having ambiguity and uncertainty such as the mining investment projects and reduce the investment risks. Considering the management challenges in deciding on the economic justification of various projects and the existence of time and resource limitations, the use of these models helps clarify the economic future of a mineral investment project and make the final decision.
 
Materials and Methods
In this study, three new models including fuzzy system, neural network and statistical multivariate regression were used to determine the NPV and evaluate the effective parameters on this index in the Zarshuran gold mine investment project of Takab. The obtained results of the above models were compared with each other, with the real data, and with the similar previous studies. Gold price (main element), silver price (byproduct element) and discount rate were considered as input parameters for evaluating the NPV. Application of the fuzzy logic in NPV modeling, which is always accompanied by uncertainty, can lead to more realistic results. Also, the utilization of neural network can be effective due to its high capability in dealing with vague and noisy data that are somehow involved in the evaluation of mining projects.
Results and Discussion
Using the trial and error method, triangular and trapezoidal membership functions, Mamdani inference motor and center of gravity decentralization function were determined as the optimum parameters of the proposed fuzzy model. Also, a neural network with training function of Levenberg-Marquardt back-propagation type, transfer function of sigmoid logarithm kind, 3-5-10-1 structure and root mean square error of 0.0032 was found as an optimum network. Moreover, a statistical multivariate linear relation was proposed to predict the NPV. According to the performance evaluation indices including determination coefficient, absolute error and relative error, results of the proposed models were compared with each other, with the actual data, and with the previous similar investigations. The above comparison proved that the performance of both fuzzy and neural models in predicting the NPV is acceptable and they have a much better agreement with real data in comparison with the statistical model. However, the performance of fuzzy model is somewhat better than the proposed neural network models in this research and previous studies and its results are more consistent with the actual data. Finally, the sensitivity analysis of the fuzzy model results (due to its greater accuracy) are conducted using the cosine amplitude method (CAM). Accordingly, it was confirmed that the gold price has the highest effect and the discount rate has the least effect on the NPV index.
 
Conclusion
The present study proved the successful application of two new intelligent-based models including fuzzy logic and neural network algorithms to predict the NPV in the Zarshuran gold mine investment project. Also, a linear multivariate regression relation proposed to forecast the NPV index with a relative acceptable performance. Evaluation the results of proposed models showed that the fuzzy model performance was somewhat better than the proposed neural network models in this study and previous studies and both were much higher than the statistical model in predicting mining projects NPV. Based on the sensitivity analysis of the fuzzy model results (the most accurate proposed model), it was proved that the gold price and discount rate have the highest and lowest effects on the NPV, respectively. Considering the above results, it can be concluded that the proposed intelligent techniques in this research (especially the fuzzy model) can be used with good reliability in the evaluation of mining investment projects in order to cover their involved uncertainty and reduce the investment risk. 

Keywords

Main Subjects

References

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