Proposta de um método para monitoramento e controle da perda de carga em minerodutos com base em variáveis operacionais do processo : um estudo de caso

Abstract

The transportation of ore through slurry pipelines has become a viable option for mining companies due to the large distances involved in the stages of the production process. Compared to other modes of transport, like railroads and highways, slurry pipeline transport has significant advantages, such as: low operating costs, continuous supply, safety, and low environmental impact. Brazil stands out as one of the leading users of this type of transportation, withstanding approximately 36.7% of the total length of slurry pipelines in the world and capacity to transport up to 75 million tons of ore annually. Samarco Mineração S.A. is a pioneer in ore transportation via slurry pipelines in Brazil, operating three pipelines, each approximately 400 km long. Due to the high demand for ore transportation from mines in Germano, the company uses pipeline 2 at its maximum capacity, registering a utilization rate of 99.7% in 2022. It is essential to understand all the variables that affect the pumping capacity of the pipeline. Over the course of a slurry pipeline’s operation, there is a tendency for material to accumulate at its internal ends, resulting in a reduction in the pipeline’s diameter, an increase of energy loss and a decrease of flow efficiency. Therefore, it is crucial to monitor and control energy loss to operate slurry pipelines more efficiently. Nowadays, energy loss prediction models are widely used in slurry pipeline design and construction. However, there are limitations when considering important operational variables. This study proposes the development of an energy loss prediction model using linear regression, based on data from the operation of an existing slurry pipeline. In this study, a model is proposed for monitoring and controlling energy loss. To achieve this, Samarco's Pipeline 2 was selected as a case study, where operational data were collected every 2 hours over a period of 7 months. Through correlation analysis, it was determined that the flow rate is the operational variable that most impacts energy loss. Thus, by establishing a linear regression equation based on the flow rate, it became possible to compare the proposed model with the actual energy loss. As the curves generated by both equations diverged, it was hypothesized that material deposition occurred in the line. Subsequently, corrective cleanings were performed with the passage of PIGs. The effectiveness of the cleaning was evidenced by the increase in flow rate by 11.98 m³/h immediately after the PIG passage, resulting in a punctual production gain of 13,299 tms. Additionally, critical pumping velocities were analyzed in relation to the variation in pulp concentration and its impact on energy loss, thereby allowing the determination of parameters for the preventive control of energy loss.