Designing and utilizing expert systems for evaluating mining productivity

In this research, a rule-based expert system, with a graphical user interface, has been designed and implemented to measure, evaluate and provide a policy of improving mining productivity corresponding to the productivity management cycle. The expert system includes a set of rules related to how to calculate and analyze the productivity gap, and the inference engine is based on the analysis of the leading rules. Major policies are being improved based on quantitative and qualitative indicators. In order to evaluate the effective variables and to determine the relative importance of these policies, a fuzzy inference system based on expert opinions has also been implemented. To evaluate system performance, data from a gold mining complex was used for a period of eight years, and various indicators were developed in technical, financial, human resources and markets. For undesirable growth indicators, seven improvement policies were presented, with the highest and lowest share being related to the policy of "improving the production process" and "human resource management." Based on the evaluation of 40 qualitative components, 21 cases were in a rapid recovery situation. Also, after implementing the fuzzy inference system, the policy of "reducing energy consumption" with the importance of 56% was the most important.