Method for calculating processing capacity of intelligent ore sorting system based on particle size analysis

Abstract

The application discloses a kind of based on particle size analysis calculation ore intelligent sorting system processing capacity method. Through the statistics of the number of ore particles when the maximum processing capacity of intelligent sorting machine, the quantitative particle size-processing capacity relationship model is constructed, and the processing capacity when the same equipment processes different particle size ores is calculated. The application establishes the quantitative relationship model of ore particle size and intelligent sorting machine processing capacity, breaks through the limitation of traditional empirical formula sorting, realizes the accurate prediction and optimization of processing capacity. The method takes the number of particles as the core control parameter, converts the equipment processing capacity from the quality dimension to the quantity dimension, effectively eliminates the processing capacity fluctuation caused by the density difference of different particle size ores, and provides a scientific basis for the process parameter optimization of intelligent sorting machine.

Description

Technical Field

[0001] This invention relates to a method for calculating the processing capacity of an intelligent ore sorting system, belonging to the field of intelligent mineral processing sorting technology. Background Technology

[0002] In the field of mineral processing, as resource development continues, the grade of valuable elements in ores is constantly decreasing, and the cost of mineral processing is constantly increasing. Traditional ore sorting methods are no longer sufficient to meet the demand for efficient and low-cost mineral processing.

[0003] As a current technology, mineral processing is typically carried out using intelligent ore sorting systems. The amount of ore fed into these systems significantly impacts their processing capacity; for example, reducing the proportion of finer ore increases the system's throughput. However, intelligent ore sorting systems only provide maximum throughput data for all ore sizes. Achieving a higher maximum throughput through quantitative selection of ore sizes, and obtaining the specific value of that higher maximum throughput, remains a persistent technical challenge in the industry. Therefore, the inability to accurately predict and optimize processing capacity presents the following difficulties for mining operations:

[0004] First, production planning lacks a scientific basis. When preparing annual and quarterly production plans, mining companies are unable to accurately assess the actual processing capacity of intelligent sorting systems under different particle size ratios, resulting in a serious disconnect between planned output and actual capacity. This often leads to extreme situations where equipment is idle or operating under overload, causing resource waste or increased equipment wear and tear.

[0005] Secondly, there is a lack of direction in equipment selection and configuration. When purchasing intelligent sorting equipment, newly built or expanded mines design the equipment based solely on the maximum processing capacity parameters for all particle sizes provided by the manufacturer, without considering the actual particle size distribution characteristics of the ore being processed. This results in a significant gap between the actual processing capacity and the design expectations after the equipment is put into operation, greatly reducing the return on investment.

[0006] Third, process optimization is hampered. When mineral processing technicians try to increase the system's throughput by adjusting the crushing and screening process to change the feed particle size, they lack a quantitative particle size-throughput relationship model and can only rely on experience to explore. This results in long experimental cycles, high costs, and difficulty in finding the optimal particle size control range.

[0007] Fourth, economic benefits are difficult to calculate accurately. Due to the inability to accurately predict the system's processing capacity under different particle size conditions, mining companies tend to be highly subjective in selecting processing capacity parameters when conducting technical and economic analyses. This reduces the credibility of cost calculations, profit forecasts, and return on investment analyses, thus affecting the quality of business decisions.

[0008] To address the aforementioned technical challenges, there is an urgent need in this field to establish a method that can quantitatively describe the relationship between ore particle size and the processing capacity of intelligent sorting systems, providing reliable technical support for optimizing mine production.

[0009] Therefore, it is particularly important to develop a system and method that can improve the processing capacity of intelligent ore sorting machines based on particle-level optimization. Summary of the Invention

[0010] The technical problem to be solved by the present invention is to provide a method for calculating the throughput of an intelligent ore sorting system based on particle size analysis. The method uses a quantitative particle size-throughput relationship model to accurately calculate the throughput of the intelligent ore sorting system, which can be used to guide mine sorting production.

[0011] The technical solution of the present invention is as follows:

[0012] A method for calculating the processing capacity of an intelligent ore sorting system based on particle size analysis is proposed. When particle sizes j and above are input, the calculation formula for the intelligent sorting machine's processing capacity is as follows:

[0013] ;

[0014] In the formula, Y j Representative input The processing capacity of the intelligent sorting machine per unit time for particle sizes of 1 and above;

[0015] i represents the different grain level numbers, ranging from 1 to 6;

[0016] R i This represents the number of particles corresponding to different i values ​​at different particle levels;

[0017] K i This represents the mass of a single ore particle in the i-th particle size.

[0018] Preferably, K i The calculation formula is as shown in equation (3), R i The calculation formula is as shown in equation (5);

[0019] Equation (3);

[0020] In equation (3), ρ represents the specific gravity of the ore;

[0021] D ave(i) Represents the average particle size of ores of different particle sizes;

[0022] Equation (5);

[0023] In equation (5), Q i The mass corresponding to different i values ​​of the particle size is obtained from equation (4).

[0024] Equation (4);

[0025] In equation (4), P represents the maximum processing capacity per unit time for the entire particle size determined by the intelligent sorting machine sorting experiment; γ i This represents the mass percentage of the i-th particle size ore.

[0026] More preferably, D ave(i) It is obtained from equation (2);

[0027] Equation (2);

[0028] In equation (2), D max(i) This represents the maximum particle size in the i-th particle size class;

[0029] D min(i) This represents the smallest particle size in the i-th particle size class.

[0030] Preferably, by performing particle size sieving on the intelligent sorting ore raw material, different particle size mass M is obtained. i And the yield γ of different particle sizes was calculated according to equation (1). i ;

[0031] Equation (1);

[0032] In equation (1), M i This represents the mass of the i-th particle size ore.

[0033] The beneficial effects of this invention are as follows:

[0034] First, this invention breaks through the limitations of traditional empirical sorting by establishing a quantitative relationship model between ore particle size and the processing capacity of intelligent sorting machines, achieving accurate prediction and optimization of processing capacity. This method uses particle number as the core control parameter, transforming the equipment's processing capacity from a quality dimension to a quantity dimension, effectively eliminating fluctuations in processing capacity caused by density differences in ores of different particle sizes, and providing a scientific basis for optimizing the process parameters of intelligent sorting machines.

[0035] Secondly, the particle size optimization strategy proposed in this invention has significant engineering adaptability. By dividing a wide-sized ore into multiple narrow-sized intervals through screening, and combining the coupled calculation of the mass yield of each particle size with the mass of a single particle, the particle size combination that maximizes the number of particles passing through per unit time can be accurately identified.

[0036] This invention, through precise mathematical calculations, obtains the input-output correspondence between quantitatively selected ore particle sizes and a set higher maximum processing capacity, enabling accurate calculation of the specific value of this higher maximum processing capacity. It has the following significance in guiding mine production:

[0037] (1) Provide accurate data support for production planning. Mining enterprises can use the method of this invention to calculate the system processing capacity under different particle size distribution conditions in advance, based on the characteristics of ore particle size distribution in the mining area, and formulate scientific and reasonable production operation plans to effectively avoid idle equipment capacity or overload operation and achieve optimal allocation of production resources.

[0038] (2) Guiding equipment selection and system design. In the construction or expansion of mines, designers can reverse the calculation of the required specifications and quantity of intelligent sorting equipment based on the target throughput and ore particle size composition, or optimize the crushing and screening process to obtain a suitable feed particle size, thus ensuring the technical and economic rationality of the design scheme from the source.

[0039] (3) Optimize process flow and operating parameters. Mineral processing technicians can use the particle size-processing capacity relationship model established by this invention to quickly determine the optimal particle size control range for increasing the system's processing capacity, clarify the adjustment direction and target of the crushing and screening process, significantly shorten the process optimization test cycle, and reduce technology development costs.

[0040] (4) Improve the reliability of technical and economic analysis. Based on the processing volume prediction data obtained by the method of this invention, mining enterprises can establish more accurate cost calculation and benefit evaluation models, provide quantitative basis for investment decisions, production operations and performance evaluation, and increase the scientific nature of business management decisions.

[0041] (5) Promote the refined development of intelligent sorting technology. This invention moves ore sorting from extensive experience-based operation to quantitative and precise control, laying a methodological foundation for the automation and intelligent upgrading of intelligent sorting systems, and helping to realize digital management and intelligent control of the mineral processing production process.

[0042] Third, the method of this invention possesses excellent scalability and compatibility. Parameters such as the number of screening particle sizes and the width of the particle size intervals involved in the calculation process can be flexibly adjusted according to the ore properties and equipment specifications, making it applicable to various intelligent sorting technology platforms such as X-ray sorting, photoelectric sorting, and laser-induced breakdown spectroscopy sorting. Furthermore, this method can be seamlessly integrated with existing crushing and screening processes without requiring structural modifications to the main equipment, resulting in low investment costs and a short implementation cycle. It has significant practical value for promoting intelligent transformation and cost reduction / efficiency improvement in mining enterprises. Detailed Implementation

[0043] The present invention will be further described below with reference to embodiments.

[0044] An embodiment of the method for calculating the throughput of an intelligent ore sorting system based on particle size analysis according to the present invention is carried out according to the following steps:

[0045] S1. Determine the maximum processing capacity P per unit time across all particle sizes through sorting experiments using an intelligent sorting machine;

[0046] In this embodiment, a smart sorting machine was used to conduct a sorting experiment on the full particle size (10-60mm) of the ore that meets the requirements for smart sorting, and the maximum hourly processing capacity of the full particle size was determined to be P=20t.

[0047] S2. By performing particle size analysis on the intelligent sorting ore raw materials, different particle size mass M is obtained. i And the yield γ of different particle sizes was calculated according to equation (1). i ;

[0048] Equation (1).

[0049] In equation (1), i represents different particle size numbers, ranging from 1 to 6;

[0050] M i This represents the mass of the i-th particle size ore;

[0051] γ i This represents the mass percentage of the i-th particle size ore.

[0052] In this embodiment, 60 kg of intelligent sorting ore raw material was subjected to particle size sieve analysis. The specific grading and calculation results are shown in Table 1.

[0053] Table 1

[0054]

[0055] S3. Calculate the average particle size D of different particle sizes of ore according to formula (2). ave(i) ;

[0056] Equation (2).

[0057] In equation (2), D max(i) D represents the maximum particle size in the i-th particle size class. min(i) D represents the smallest particle size in the i-th particle size class. ave(i) This represents the average particle size of the i-th particle size class.

[0058] The calculation results of this embodiment are shown in Table 2.

[0059] Table 2

[0060]

[0061] S4. Using formula (3) and based on the ore specific gravity (ρ), calculate the mass K of a single ore particle of different particle sizes. i ;

[0062] Equation (3).

[0063] In equation (3), ρ represents the specific gravity of the ore, and K i This represents the mass of a single ore particle in the i-th particle size.

[0064] The values ​​of ρ and the calculation results in this embodiment are shown in Table 3.

[0065] Table 3

[0066]

[0067] S5. Calculate the mass Q corresponding to different i values ​​of particle size using equation (4). i ;

[0068] Equation (4).

[0069] And use equation (5) to calculate the number of particles R corresponding to different i values. i .

[0070] Equation (5).

[0071] The calculation results of this embodiment are shown in Table 4.

[0072] Table 4

[0073]

[0074] S6. When the j-th and above particle sizes are input, the calculation formula for the processing capacity of the intelligent sorting machine is shown in equation (6).

[0076] Equation (6).

[0077] In equation (6), This represents the total number of particles that the equipment can process. It is the processing per unit time at full granularity runtime. The number of particles of size 2 and above. For full-granularity runtime, the number of processes processed per unit time Total mass of particles of different sizes For full-granularity runtime processing per unit time The total mass of particles of size 1 and above, This represents the amount of data processed per unit time during full-granularity runtime. The average mass of a single particle at or above the particle size, Y j Represents the total number of particles that the equipment can process per unit time. Investment The total mass corresponding to particle sizes of 1 and above.

[0078] Substituting the data from the previous steps, the calculation results are as follows:

[0079] When j=1 (full particle size), the hourly processing capacity of the intelligent ore sorting machine is 20T.

[0080] When j=2, the hourly processing capacity of the intelligent ore sorting machine is 31.36T.

[0081] When j=3, the hourly processing capacity of the intelligent ore sorting machine is 70.85T.

[0082] When j=4, the hourly processing capacity of the intelligent ore sorting machine is 169.84T.

[0083] When j=5, the hourly processing capacity of the intelligent ore sorting machine is 284.61T.

[0084] When j=6 (single-particle size), the hourly processing capacity of the intelligent ore sorting machine is 456.75T.

Claims

1. A method for calculating the throughput of an intelligent ore sorting system based on particle size analysis, characterized in that: When inputting particles of the jth grade and above, the processing capacity of the intelligent sorting machine is calculated using the following formula: ； In the formula, Y j Representative input The processing capacity of the intelligent sorting machine per unit time for particle sizes of 1 and above; i represents the different grain level numbers, ranging from 1 to 6; R i This represents the number of particles corresponding to different i-values ​​at different particle levels; K i This represents the mass of a single ore particle in the i-th particle size.

2. The method for calculating the processing capacity of an intelligent ore sorting system based on particle size analysis as described in claim 1, characterized in that: K i The calculation formula is as shown in equation (3), R i The calculation formula is as shown in equation (5); Equation (3); In equation (3), ρ represents the specific gravity of the ore; D ave(i) Represents the average particle size of ores of different particle sizes; Equation (5); In equation (5), Q i The mass corresponding to different i values ​​of the particle size is obtained from equation (4). Equation (4); In equation (4), P represents the maximum processing capacity per unit time for the entire particle size range determined by the intelligent sorting machine sorting experiment; γ i This represents the mass percentage of the i-th particle size ore.

3. The method for calculating the processing capacity of an intelligent ore sorting system based on particle size analysis as described in claim 2, characterized in that: D ave(i) It is obtained from equation (2); Equation (2); In equation (2), D max(i) This represents the maximum particle size in the i-th particle size class; D min(i) This represents the smallest particle size in the i-th particle size class.

4. The method for calculating the processing capacity of an intelligent ore sorting system based on particle size analysis as described in claim 2 or 3, characterized in that: By performing particle size analysis on intelligent sorting ore raw materials, different particle size mass M is obtained. i And the yield γ of different particle sizes was calculated according to equation (1). i ; Equation (1); In equation (1), M i This represents the mass of the i-th particle size ore.

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