BR-102024018024-A2 - METHOD AND SYSTEM FOR OPTIMIZING THE REPLACEMENT OF GRINDING MEDIA IN WET BALL MILLS, AND MEDIUM READABLE BY INDUSTRIAL CONTROLLER

BR102024018024A2BR 102024018024 A2BR102024018024 A2BR 102024018024A2BR-102024018024-A2

Abstract

The present invention provides a method for optimizing the replacement of grinding media in mills comprising the steps of: determining, by means of a fuzzy controller (10), a required power value of at least one mill (M1, M2, Mn); estimating, by means of a means (20) for estimating mill power, a power of at least one mill (M1, M2, Mn) based on operational data of at least one mill (M1, M2, Mn); determining, by means of a means (30) for determining the replacement of grinding media, grinding media replacement parameters for at least one mill (M1, M2, Mn); and automating, by means of a means (40) for automating the replacement of grinding media, the replacement of grinding media based on the grinding media replacement parameters for at least one mill (M1, M2, Mn). The present invention also provides an industrial controller-readable means that includes instructions which, when executed by at least one processor, cause the at least one processor to perform steps of a method for optimizing the replacement of grinding media in mills. The present invention also provides a system for optimizing the replacement of grinding media in mills comprising a grinding media replacement device (50); at least one mill (M1, M2, Mn) attachable to the grinding media replacement device (50); and an industrial controller-readable means.

Inventors

DANIEL LUIZ DE SOUZA
MÁRIO SÉRGIO DOS SANTOS
CÁSSIO PASCOAL COSTA
LUCIANO PERDIGÃO COTA
MARCONE JAMILSON FREITAS SOUZA

Assignees

VALE S.A.
ASSOCIAÇÃO INSTITUTO TECNOLOGICO VALE - ITV
UNIVERSIDADE FEDERAL DE OURO PRETO ? UFOP

Dates

Publication Date: 20260317
Application Date: 20240902

Claims (14)

1. Method for optimizing grinding media replacement in mills characterized in that it comprises the steps of: determining, using a fuzzy controller (10), a required power value for at least one mill (Mi, M2, Mn); estimating, using a means (20) for estimating mill power, a power for at least one mill (Mi, M2, Mn) based on operational data for at least one mill (Mi, M2, Mn); determining, using a means (30) for determining grinding media replacement, grinding media replacement parameters for at least one mill (Mi, M2, Mn); and automating, using a means (40) for automating grinding media replacement, the grinding media replacement based on the grinding media replacement parameters for at least one mill (Mi, M2, Mn).
2. Method according to claim 1, characterized in that the step of automating the replacement of grinding media further comprises controlling, through the means (40) for automating the replacement of grinding media, a grinding media replacement device (50).
3. Method according to claim 1 or 2, characterized in that the step of determining a required power value of at least one mill (Mi, M2, Mn) comprises implementing a power consumption prediction model of at least one mill (M1, M2, Mn).
4. Method according to claim 3, characterized in that the energy consumption prediction model of at least one mill (Mi, M2, Mn) comprises Equation (1): where: E is the energy consumed, on a pinion shaft, per unit mass fed into the circuit (kWh/t); Rf is the percentage of material retained, at the desired diameter, in the feed control loop; Rp is the percentage of material retained, at the desired diameter, in the product control loop; and K is a characteristic parameter of ore (t/kWh).
5. Method according to any one of claims 1 to 4, characterized in that the step of estimating the power of at least one mill (Mi, M2, Mn) comprises implementing a model to estimate the power of at least one mill (Mb M2, Mn) over time.
6. Method according to claim 5, characterized in that the model for estimating the power of at least one mill (Mi, M2, Mn) in the grinding stage over time comprises Equation (2): in which: is the power decay in kW in at least one mill j (Mi, M2, Mn) at time z; is the mass flow rate in t/h at at least one mill j (Mi, M2, Mn) at time z; ay is the feed rate in t/h at at least one mill j (Mi, M2, Mn) at time z; is a percentage of solids in at least one mill j (Mi, M2, Mn) at time z; wherein the model for estimating the power of at least one mill (Mi, M2, Mn) in the grinding stage over time additionally comprises Equation (3): in which: is the power increase, in kW, by replacing grinding media in at least one mill j (Mi, M2, Mn) at time z; xij is the ball load in kg in at least one mill (Mi, M2, Mn) at time i.
7. Method according to claim 5, characterized in that the model for estimating the power of at least one mill (Mi, M2, Mn) in the regrinding step over time comprises Equation (4): in which: = mass flow rate of feed into the hydrocyclone belonging to mill j (Mi, M2, Mn) at time z in t/h; wherein the method for estimating the power of at least one mill (Mb M2, Mn) in the regrinding stage over time additionally comprises Equation (5): wherein the model for estimating the power of at least one mill (Mb M2, Mn) over time additionally comprises Equation (6): in which: is the estimated power of mill j (Mi, M2, Mn) at time z in kW; is the average power in the last 15 minutes in mill j (Mi, M2, Mn) in kW.
8. Method according to any one of claims 1 to 6, characterized in that the step of estimating the power of at least one mill (Mi, M2, Mn) comprises implementing a model to establish a correlation between the power of at least one mill (Mb M2, Mn) and the degree of filling.
9. Method according to claim 7, characterized in that the model for establishing a correlation between the power of at least one mill (Mi, M2, Mn) and the degree of filling comprises Equation (7): where: Xij is the grinding media load in mill j (Mb, M2, Mn) at time z, in kg; Dj is the diameter of at least one mill j (Mi, M2, Mn), inside the lining, in meters; Lj is the length of at least one mill j (Mi, M2, Mn), in meters; Pj is the apparent density of the load in mill j (Mi, M2, Mn), in kg/m3; ey is the degree of filling of at least one mill j (Mi, M2, Mn) at time z; wherein the model to establish a correlation between the power of at least one mill (Mi, M2, Mn) and the degree of filling additionally comprises Equation (8), to estimate the power per ton of balls for mills with a diameter greater than 2.44 m: in which: is the power per ton of balls, in the pinion of mill j (Mi, M2, Mn) at time z in kW/t; Dj is the diameter of at least one mill j (Mi, M2, Mn), inside the lining, in meters; βij is the degree of filling of at least one mill j (Mi, M2, Mn) at time i in %; Vj is the fraction of the critical speed of mill j (Mi, M2, Mn) in %; Sj is the correction factor for the ball size of mill j (Mi, M2, Mn); wherein the model to establish a correlation between the power of at least one mill (Mi, M2, Mn) and the degree of filling additionally comprises Equation (9), for mills with a diameter greater than 3.3 m: where: Bj is the diameter of the largest ball used (topsize) in mill j (Mi, M2, Mn), in millimeters; Sj is a parameter that should only be considered when the diameter of the balls is less than 1/80 of the diameter of at least one mill j (Mi, M2, Mn); wherein the model to establish a correlation between the power of at least one mill (Mi, M2, Mn) and the degree of filling additionally includes Equation (10) to estimate the power in the pinion of at least one mill (Mi, M2, Mn): in which: is the power at the pinion of mill j (Mi, M2, Mn) at time z, in kW; wherein the model to establish a correlation between the power of at least one mill (Mb M2, Mn) and the degree of filling additionally comprises Equation (16) to estimate the mechanical power at the motor shaft, which is the ratio between the power at the pinion and the meshing efficiency: in which: is the mechanical power at the motor shaft of at least one mill j (Mi, M2, Mn) at time i, in kW; rj is the gearing efficiency; wherein the model for establishing a correlation between the power of at least one mill (Mi, M2, Mn) in the grinding stage and the degree of filling additionally comprises Equation (12): wherein the model for establishing a correlation between the power of at least one mill (Mi, M2, Mn) in the regrinding stage and the degree of filling additionally comprises Equation (13): wherein the model for establishing a correlation between the power of at least one mill (Mi, M2, Mn) and the degree of filling additionally comprises Equation (14): where: a = -1.7227; b = 183.76 for the mills in the grinding stage and 180.38 for the mills in the regrinding stage; wherein the model for establishing a correlation between the power of at least one mill (Mi, M2, Mn) and the degree of filling additionally comprises Equation (15) for estimating the weight of the load in at least one mill (Mi, M2, Mn): wherein the model for establishing a correlation between the power of at least one mill (Mi, M2, Mn) and the degree of filling additionally comprises Equation (16) for determining the net power of at least one mill (Mi, M2, Mn) with grinding media: in which: is the net power in mill j (Mi, M2, Mn) at time z; eJP is the difference between the net power and the mechanical power at the motor shaft of at least one mill (Mi, M2, Mn).
10. A method according to any one of claims 1 to 8, characterized in that the step of determining replacement parameters for grinding media for at least one mill (Mi, M2, Mn) comprises implementing a metaheuristic optimization model that includes an objective function to minimize a difference between the required power of at least one mill (Mi, M2, Mn) and the estimated power of at least one mill (Mi, M2, Mn).
11. Method according to claim 9, characterized in that the metaheuristic optimization model is of the local search type and includes an Enhanced ILS algorithm.
12. Method according to claim 9 or 10, characterized in that the step of determining grinding media replacement parameters for at least one mill (Mb M2, Mn) comprises defining a grinding media replacement sequence for at least one mill (Mi, M2, Mn) and defining a grinding media rate for at least one mill (Mi, M2, Mn).
13. A controller-readable means, characterized in that it includes instructions that, when executed by at least one processor, cause that at least one processor to perform steps of a method for optimizing the replacement of grinding media in mills as defined in any one of claims 1 to 11, comprising: determining, through a fuzzy controller (10), a required power value of at least one mill (Mi, M2, Mn); estimating, through a means (20) for estimating mill power, a power of at least one mill (Mi, M2, Mn) based on operational data of at least one mill (Mi, M2, Mn); determining, through a means (30) for determining the replacement of grinding media, grinding media replacement parameters for at least one mill (Mi, M2, Mn); and to automate, by means of a means (40) to automate the replacement of grinding media, the replacement of grinding media based on the grinding media replacement parameters for at least one mill (Mi, M2, Mn).
14. System for optimizing the replacement of grinding media in mills, characterized in that it comprises: a grinding media replacement device (50); at least one mill (Mi, M2, Mn) attachable to the grinding media replacement device (50); a means readable by an industrial controller, as defined in claim 12, which includes instructions that, when executed by at least one processor, cause the at least one processor to perform steps of a method for optimizing the replacement of grinding media in mills comprising: determining, through a fuzzy controller (10), a required power value of at least one mill (Mi, M2, Mn); estimating, through a means (20) for estimating mill power, a power of at least one mill (Mi, M2, Mn) based on operational data of at least one mill (Mi, M2, Mn); determining, through a means (30) for determining the replacement of grinding media, grinding media replacement parameters for at least one mill (Mb, M2, Mn); and automating, through a means (40) for automating the replacement of grinding media, the replacement of grinding media based on the grinding media replacement parameters for at least one mill (Mi, M2, Mn); wherein the means (40) for automating the replacement of grinding media is additionally configured to control the grinding media replacement device (50).

Description

TECHNICAL FIELD [0001] The present invention relates to the field of control systems specially adapted to comminution. Specifically, the present invention relates to a method and a system for optimizing the replacement of grinding media in wet ball mills. DESCRIPTION OF THE STATE OF THE ART [0002] Comminution operations are of crucial importance in mineral beneficiation processes and aim to achieve three main objectives, namely, adjusting the particle size of the material for subsequent stages, increasing the surface area of the particles and adapting the particle size for immediate commercialization purposes. [0003] The energy expended to fragment the material and the mechanical effort involved in the process have a significant impact on operating and capital costs in crushing and grinding circuits. Therefore, optimizing energy efficiency in comminution operations can positively impact revenue and overall energy demand. [0004] Due to the high costs and considerable energy consumption in grinding processes, there is a search for alternatives to reduce energy consumption per ton of ore fed. Autogenous (AG), semi-autogenous (SAG) and ball mills have been widely used for this purpose. [0005] Several operational parameters influence the performance of such mills, among them, the degree of filling, which is one of the most relevant. This parameter represents the volume of the load in relation to the total volume available in the mill. This parameter not only controls the mill's performance and energy consumption, but also determines the particle size of the final product. [0006] Thus, operating rotary mills within the ideal ranges of filling parameters is essential for optimizing their energy consumption, since these parameters establish the maximum production capacity. [0007] Methods and systems for optimizing mill operating parameters are known in the state of the art. Document WO2023242752 (Al) discloses a system and a method for real-time optimization in mining circuits, aiming to reduce downtime, increase operational accuracy, optimize efficiency, and minimize energy consumption and loss of grinding media. Using historical, real-time operational, and laboratory data, as well as specific models for grinding systems, the method taught in this document employs predictive models for feed, grinding media loads, drive, and material trajectory in the mill, in order to reduce variability in the size of the final product. Regarding grinding media replacement, wear prediction models, filling degree estimation, and replacement recommendation are applied, based on data such as ball trajectory, mill power, equipment dimensions, physical characteristics of the grinding media, and discharge system. However, the method in this document does not use predictive models to plan grinding media replacement in mills throughout a work shift. [0008] Document CN112588424 (B) discloses a control method for a ball mill pulverization system based on a cloud-based intelligent model. This document aims to offer a control method using artificial intelligence and fuzzy control for equipment automation. The method involves a local controller and an intelligent model with an output conversion algorithm. The controller analyzes the system output signal, adjusting the input. The system uses multiple outputs and a conversion algorithm to obtain the target output, including the powder output. The local controller adjustment quantity is the system input, while the state detection signal is the output, with input vectors such as hot air, recirculated air, and coal supply, and output vectors such as outlet temperature, negative input pressure, pressure difference, and grinding machine load. The powder output is determined by the aforementioned parameters using the output conversion algorithm. This document, however, does not use a fuzzy controller with laboratory data and specific energy consumption to generate the ideal power set point for the mills within a planning window. [0009] Document W02007110466 (Al) discloses a method, apparatus, and computer program by which the quantity of balls among the ore material contained in a mill is estimated as a percentage by volume of the total mill volume. Preferably, the invention relates to semi-autogenous mills (SAG). In this document, an expanded Kalman filter is used to estimate the ball load using process measurements and process models. The document combines the exact measurement of the mill fill level and some other measurement that depends on the fill level and the ball load. By feeding this information into the Kalman filter algorithm, an accurate estimate for the ball load can be calculated. However, this document does not specify the use of a fuzzy controller nor does it disclose a model for planning the replacement of grinding media in a work shift. [00010] Although the state of the art reveals some documents that deal with optimizing mill operation, none of them reveals a method that uses model