Por: Vladimir Flores Castillo, ingeniero de Productividad y Costos Mina; Christian Villalobos Nuñez, ingeniero de Productividad Junior y Ruddy Poemape Gonzales, gerente de Operaciones, unidad minera La Arena.

Abstract

Haulage fleet management with constraint logics based on advanced trend analytics is a highly effective strategy to optimize production yields and save fuel in a mining operation. This report presents the results and findings of the implementation of this methodology at the La Arena mining unit.

First, an exhaustive analysis of the operation was carried out, considering that there are 27 CAT 777F/G trucks for material hauling, three Bucyrus RH90C hydraulic excavators and a CAT 992K front end loader for material hauling. Detailed operational data, including equipment performance and fuel consumption, were collected to establish a baseline and assess current performance.

Subsequently, advanced trend analytics techniques were applied to identify patterns and trends in the operational data. This allowed the development of predictive models that were the basis for the implementation of constraint logics in the management of the hauling fleet.

The constraint logics focused on efficient truck allocation, proper material loading and efficient cycle times. Rules and constraints were established based on predictive models and industry best practices.

The implementation of constraint logics had a significant impact on operational performance. A 1.3% increase in hauling efficiency was observed, which translated into increased production. In addition, a 0.2 gal/h reduction in fuel consumption of the CAT 777F/G trucks was achieved, resulting in savings of US$0.03/t in unit cost. 

The results obtained proved that the haulage fleet management methodology with constraint logics based on advanced trend analytics is highly effective in optimizing production yields and generating fuel savings. Successful implementation of this methodology requires robust data collection and analysis, the development of constraint logics tailored to the specific mining operation, training of personnel and a continuous focus on improving and adjusting operational decisions.

Analysis of the data collected also revealed promising trends for future improvements. Patterns in productive yields were identified and an additional 8% increase in hauling efficiency was projected through continued application of the constraint logics. These results demonstrate the potential of haulage fleet management with constraint logics based on advanced trend analytics as an effective strategy to improve productivity and generate fuel savings in the mining industry.

In summary, the implementation of the proposed methodology has shown promising results in the optimization of the hauling process. Significant improvement in hauling efficiency and fuel savings have been achieved. It is recommended to continue evaluating and adjusting the constraint logics to achieve higher operating performance and fuel savings in the medium mining operation.