Judgment feedback method for selective ore grinding dissociation effect of stirring mill and computer system

Abstract

The invention discloses a judgment feedback method for the selective ore grinding dissociation effect of a stirring mill and a computer system.The judgment feedback method comprises the following steps that S1, a comprehensive evaluation mathematical model Mb of the selective ore grinding dissociation effect is constructed; s2, determining a threshold value T of the mathematical model Mb according to historical field production data or tests; when the Mb value calculated in real time is lower than a threshold value T, the system judges that the current selective ore grinding dissociation effect is poor, and a regulation and control instruction is automatically triggered; and when the Mb value calculated in real time is continuously higher than the threshold value T and is kept stable, the system judges that the current ore grinding effect is good and excessive energy is input, and an energy-saving regulation and control instruction is triggered. According to the method, the comprehensive evaluation value of the ore grinding effect is calculated on line by establishing a comprehensive mathematical model formula Mb, a threshold triggering feedback mechanism is set, key operation parameters of the mill are automatically adjusted, and closed-loop control of evaluation-feedback-regulation is formed.

Description

Technical Field

[0001] This invention belongs to the field of mineral processing, and in particular relates to a method and computer system for evaluating the grinding effect of a stirred mill. Background Technology

[0002] In mineral processing, grinding is a crucial process for liberating valuable minerals from gangue minerals, and it is also the most energy-intensive step in the entire plant. Selective grinding aims to optimize grinding energy input, preferentially fracturing minerals at the interface to achieve efficient liberation while minimizing over-grinding, which is essential for energy conservation and consumption reduction. However, current evaluation of grinding effectiveness mainly relies on two methods: one is offline, long-cycle direct liberation degree detection (such as MLA analysis), which is costly and cannot be used for real-time control; the other is relying solely on the single indirect indicator of "grinding fineness (such as -0.075mm content)," which cannot comprehensively reflect the suitability of the grinding product for subsequent flotation or magnetic separation.

[0003] To achieve intelligent optimization of the grinding process, an indirect characterization model that comprehensively reflects energy efficiency and selectivity and can be calculated in real time must be constructed. Current technologies lack a mathematical model that dynamically links mill operating parameters (such as power, rotational speed, ball feed rate, and grinding concentration) with final beneficiation indicators (such as concentrate yield and grade), and have not yet developed a closed-loop method for automatic feedback control of the mill based on model output. This results in operational adjustments relying on manual experience, exhibiting strong lag, and making it difficult to ensure that grinding remains in a highly efficient, low-consumption selective grinding state.

[0004] Therefore, there is an urgent need in mineral processing sites for a method that can indirectly, quickly, and effectively characterize grinding effects, especially for evaluating the selective grinding performance of mills. Developing a judgment feedback method based on a multi-parameter mathematical model to evaluate the selective grinding and liberation effect of stirred mills in real time and automatically adjust operating parameters accordingly is of great significance for achieving intelligent, refined, and green production in mineral processing. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the shortcomings and defects mentioned in the background art above, and to provide a method and computer system for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill. This method establishes a comprehensive mathematical model formula Mb to calculate the comprehensive evaluation value of the grinding effect online, and sets up a threshold-triggered feedback mechanism to automatically adjust key operating parameters of the mill, forming a closed-loop control of "evaluation-feedback-control".

[0006] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill includes the following steps: S1: Construct a mathematical model Mb for the comprehensive evaluation of selective grinding and dissociation effects. The general expression of the mathematical model Mb is as follows: Mb = K × [f (grinding and beneficiation index) / f (energy consumption parameter)] × w; Wherein, K is a comprehensive adjustment coefficient related to ore properties and beneficiation process, w is the slurry concentration in the mill; f (grinding and beneficiation index) is positively correlated with one or more of the grinding particle size enhancement value and beneficiation index; f (energy consumption parameter) is positively correlated with one or more of the mill energy consumption parameters; S2: Determine the threshold T of the mathematical model Mb based on historical field production data or experiments; when the real-time calculated Mb value is lower than the threshold T, the system determines that the current selective grinding dissociation effect is poor, automatically triggers the control command, adjusts the mill energy consumption parameters, and makes the Mb value not lower than the threshold T; when the real-time calculated Mb value is consistently higher than the threshold T and remains stable, the system determines that the current grinding effect is good and there is excess energy input, triggers the energy-saving control command, adjusts the mill energy consumption parameters, and makes the Mb value decrease to the threshold T.

[0007] In the above-mentioned method for judging and feeding back the selective grinding and dissociation effect of a stirred mill, preferably, the beneficiation index includes concentrate yield. Concentrate grade enhancement value and concentrate recovery rate Concentrate grade enhancement value This represents the difference between the concentrate grade and the feed grade.

[0008] In the above-mentioned method for judging and feeding back the selective grinding and dissociation effect of stirred mills, the preferred method is f (grinding and beneficiation index) = a* ×b* ×c* ×d* ,in, Let be the grinding particle size enhancement value, and a, b, c, and d be the weight indices of each parameter, where a+b+c+d=1, a is 0.15-0.25, b is 0.3-0.45, c is 0.1-0.2, and d is 0.2-0.3.

[0009] In the above-mentioned method for judging and feeding back the selective grinding and dissociation effect of the stirred mill, preferably, the mill energy consumption parameters include the mill operating power P, the ball quantity Q, and the mill speed V.

[0010] In the above-mentioned method for judging the selective grinding and dissociation effect of the stirred mill, preferably, f (energy consumption parameter) = e*P×f*Q×g*V, where e, f, and g are the weight indices of each parameter, and e+f+g=1, e is 0.1-0.3, f is 0.2-0.5, and g is 0.4-0.6.

[0011] In the above-mentioned method for judging and feeding back the selective grinding and dissociation effect of the stirred mill, preferably, the mill energy consumption parameter is adjusted so that the Mb value is not lower than the threshold T, and the adjustment is carried out according to the following priority: First priority: Increase the mill speed V first; if the Mb value is still lower than the threshold T after the mill speed V is increased to the preset upper limit, then activate the second priority control. Second priority: Increase the amount of balls added (Q) or increase the slurry concentration (w) inside the mill.

[0012] In the above-mentioned method for judging and feeding back the selective grinding and dissociation effect of the stirred mill, preferably, the mill energy consumption parameter is adjusted to reduce the Mb value to a threshold T, and the adjustment is carried out according to the following priority: First priority: reduce the mill speed V; if the mill speed V is reduced to the preset lower limit and the Mb value is still higher than the threshold T, then the second priority control is activated. Second priority: Reduce the amount of balls added (Q) or reduce the slurry concentration (w) in the mill.

[0013] In the above-mentioned method for judging and feeding back the selective grinding and dissociation effect of the stirred mill, preferably, the grinding particle size increase value is determined by an online particle size analyzer, and the mineral processing index is obtained by a grade analyzer or periodic sampling and testing.

[0014] As a general technical concept, the present invention provides a computer system including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0015] Compared with the prior art, the advantages of the present invention are as follows: The present invention provides a method for judging and feeding back the selective grinding and dissociation effect of a stirred mill, which is intelligent and real-time: a quantifiable mathematical model Mb is established, replacing the traditional method that relies on human experience and offline analysis, and realizing online and rapid judgment of grinding effect.

[0016] The method for judging and feedback the selective grinding and dissociation effect of the stirred mill of the present invention has strong comprehensiveness: the mathematical model Mb simultaneously couples "energy consumption input" and "dissociation output", realizing a direct economic evaluation of the effect of "selective grinding" and guiding the operation towards the goal of "minimum energy consumption and optimal dissociation".

[0017] The present invention provides a method for judging and feeding back the selective grinding and dissociation effect of a stirred mill, which enables closed-loop control: it innovatively links model judgment with the feedback and control of operating parameters, sets clear threshold and priority control logic, and enables the mill to adaptively adjust according to changes in ore properties or operating conditions, thereby stabilizing production indicators.

[0018] The method for judging and feedback the selective grinding and dissociation effect of the stirred mill of the present invention has a clear energy-saving and consumption-reducing orientation: the feedback mechanism is specially set with a reverse energy-saving control path of "reducing speed and reducing balls", which actively seeks the most economical operating point under the minimum separation requirements, effectively avoiding energy waste.

[0019] The method for judging and feedback the selective grinding and dissociation effect of the stirred mill of the present invention is highly practical: most of the required parameters can be obtained through existing industrial sensors, the model calculation is simple, it is easy to integrate into the existing process control system, and the implementation cost is low. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a system flowchart of the method for judging and feeding back the selective grinding and dissociation effect of a stirred mill according to the present invention. Detailed Implementation

[0022] To facilitate understanding of the present invention, the present invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of protection of the present invention is not limited to the following specific embodiments.

[0023] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of the invention.

[0024] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.

[0025] Example: like Figure 1 As shown, a method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill includes the following steps: S1: Constructing a mathematical model for comprehensive evaluation of selective grinding dissociation effect. Mb: Under the process conditions of stable mill current, low ball loading, and relatively constant rotational speed, the mathematical model Mb comprehensively considers the grinding energy efficiency factor and the grinding product selectivity factor, and its general expression is: Mb = K × [f (grinding and beneficiation index) / f (energy consumption parameter)] × w; Where K is a comprehensive adjustment coefficient related to ore properties and beneficiation process, and w is the slurry concentration in the grinding system, in wt%; f (grinding and beneficiation index) and grinding particle size enhancement value (e.g., a lift of -0.074mm or -0.045mm) is positively correlated with mineral processing indicators; these indicators include concentrate yield. (%), Concentrate Grade Enhancement Value (%) and recovery rate (%), Concentrate grade enhancement value This represents the difference between the concentrate grade and the feed grade. f (grinding and beneficiation index) = a* ×b* ×c* ×d* ,in, The value represents the improvement in grinding particle size. a, b, c, and d are the weight indices of each parameter, and a + b + c + d = 1. a is 0.15-0.25, b is 0.3-0.45, c is 0.1-0.2, and d is 0.2-0.3. The larger the value, the better the selectivity of the ground product. The weight indices are adjusted according to the mineral properties. Mill energy consumption parameters include mill operating power P (in kW), ball quantity Q (mass percentage of mineral content, %), and mill speed V (in r / min). f (energy consumption parameter) = e*P×f*Q×g*V, where e, f, and g are the weight indices of each parameter, and e+f+g=1, e is 0.1-0.3, f is 0.2-0.5, and g is 0.4-0.6. Each weight index is adjusted according to the mineral properties.

[0026] S2. Determine the threshold T of the mathematical model Mb based on historical production data or experiments, for example, corresponding to 80% of the theoretical optimal value. Establish the following feedback mechanism: 1) When the real-time calculated or predicted Mb value is lower than the threshold T, the system determines that the current selective grinding and dissociation effect is poor and automatically triggers a control command. The priority order of the control is as follows: First priority: Increase the mill speed V to increase grinding intensity and improve the fineness of mineral liberation; Second priority: If the Mb value is still lower than the threshold T after the mill speed is increased to the preset upper limit, then the ball addition amount Q or the slurry concentration w is increased in turn to further optimize the grinding environment.

[0027] 2) When the real-time calculated or predicted Mb value is consistently higher than the threshold T and remains stable, the system determines that the current grinding effect is good and there may be excess energy input, triggering an energy-saving control command: under the premise of ensuring that the Mb value is not lower than the threshold T, try to reduce the mill speed V, reduce the ball addition Q, or increase the system water addition to reduce the slurry concentration w in order to seek the lowest energy consumption operating point.

[0028] S3. Implement online judgment and feedback: Parameters such as mill operating power P, mill speed V, ball loading Q, and slurry concentration w are collected online using sensors; the changes in fineness before and after grinding are obtained using online particle size analyzers and grade analyzers or periodic sampling tests. Calculation of concentrate grade enhancement value based on feed and concentrate grades and calculate the yield. and recovery rate Input the above parameters into the mathematical model Mb for real-time or near-real-time calculation; compare the calculation results with the threshold T, and automatically generate operation parameter adjustment suggestions according to the logic of step S2 or directly send them to the mill control system for execution.

[0029] The computer system of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above method.

[0030] A specific implementation case is as follows: A certain iron ore beneficiation plant uses a vertical stirred mill for fine grinding. The method of this embodiment is applied for optimization control, and the specific steps are as follows: Model Establishment: Based on the characteristics of the iron ore magnetic separation process in this plant, its mathematical model is determined as: Mb=K×[(a* ×b* ×c* ×d* ) / (e*P×f*Q×g*V)]×w; where K is determined to be 0.085 through regression of historical data, and a is determined to be 0.2, b to be 0.4, c to be 0.15, d to be 0.25, e to be 0.2, f to be 0.3, g to be 0.5 according to the properties of iron ore, and the threshold T is set to 2.50 (determined through experiments).

[0031] Initial state: The mill operates at a power of P=550kW, ball feed rate Q=35%, and rotational speed V=22r / min. At this time, online calculations show a 25% increase in 0.074mm particle size and a high concentrate yield. =41%, grade improved by 24%, recovery rate =88%, slurry concentration is w=48%. The current Mb1 is calculated to be 0.085×[(0.2*41×0.4*24×0.15*88×0.25*25) / (0.2*550×0.3×35*0.5×22)]×48≈2.08.

[0032] Judgment and Feedback: The system detected that Mb1 (2.08) < threshold T (2.5), indicating insufficient dissociation effect. Triggering Level 1 Control: Automatically increasing the mill speed V from 22 r / min to 26 r / min.

[0033] Performance evaluation: After the speed increase stabilized, the new parameter measured was: P = 580kW. Increased to 45%, The purity is increased to 90%, the fineness is increased by 28%, and the grade is increased by 26%. The new Mb2 is calculated as 0.085 × [(0.2*45×0.4*26×0.15*90×0.25*28) / (0.2*580×0.3×35*0.5×26)] × 48 ≈ 2.28. Mb2 is still lower than the threshold T.

[0034] Secondary feedback: Because the rotation speed has reached the preset upper limit, the system triggers secondary control: automatically increasing the ball quantity Q from 35% to 38%.

[0035] Optimization achieved: After the ball loading amount stabilized, the measured parameter was: P = 590kW. =48%, =92%, fineness improved by 30%, grade improved by 28%. The calculated Mb3 is 0.085 × [(0.2*48×0.4*28×0.15*92×0.25*30) / (0.2*590×0.3×38*0.5×26)]×48≈2.59. The system continuously monitors this. If, due to the ore becoming more grindable, the Mb value remains consistently above the threshold T, it will trigger reverse energy-saving control, such as gradually reducing the mill speed V to find a new optimal balance point.

[0036] As demonstrated in this embodiment, this method can automatically identify deteriorating operating conditions and intervene according to predetermined priorities, effectively improving the adaptability and overall technical and economic indicators of the grinding system. In actual production, the threshold T can be adjusted in a timely manner according to actual current needs.

[0037] The feedback method for judging the selective grinding and dissociation effect of the stirred mill in this embodiment addresses the difficulties in directly detecting the degree of dissociation and the limitations of a single evaluation method for grinding effect. It constructs a comprehensive evaluation mathematical model Mb that integrates energy consumption parameters and separation indices. This model quantifies the process of achieving optimal grinding and separation effect (high energy consumption, low P, Q, V) with minimal energy consumption (low P, Q, V). , , , The invention employs a selective grinding target of "…". A model threshold T and a corresponding feedback mechanism are further defined: when the Mb value is below threshold T, the system automatically intensifies grinding according to the priority of "increasing speed first, then increasing ball concentration"; when the Mb value remains above threshold T, it automatically optimizes to energy-saving mode according to the priority of "decreasing speed first, then reducing ball concentration". This invention achieves closed-loop intelligent control from effect judgment to operational feedback, effectively improving the adaptability, stability, and economy of the grinding process.

Claims

1. A method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill, characterized in that, Includes the following steps: S1: Construct a mathematical model Mb for the comprehensive evaluation of selective grinding and dissociation effects. The general expression of the mathematical model Mb is as follows: Mb = K × [f (grinding and beneficiation index) / f (energy consumption parameter)] × w; Wherein, K is a comprehensive adjustment coefficient related to ore properties and beneficiation process, w is the slurry concentration in the mill; f (grinding and beneficiation index) is positively correlated with one or more of the grinding particle size enhancement value and beneficiation index; f (energy consumption parameter) is positively correlated with one or more of the mill energy consumption parameters; S2: Determine the threshold T of the mathematical model Mb based on historical field production data or experiments; when the real-time calculated Mb value is lower than the threshold T, the system determines that the current selective grinding dissociation effect is poor, automatically triggers the control command, adjusts the mill energy consumption parameters, and makes the Mb value not lower than the threshold T; when the real-time calculated Mb value is consistently higher than the threshold T and remains stable, the system determines that the current grinding effect is good and there is excess energy input, triggers the energy-saving control command, adjusts the mill energy consumption parameters, and makes the Mb value decrease to the threshold T.

2. The method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill according to claim 1, characterized in that, The mineral processing indicators include concentrate yield. Concentrate grade enhancement value and concentrate recovery rate Concentrate grade enhancement value This represents the difference between the concentrate grade and the feed grade.

3. The method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill according to claim 2, characterized in that, f (grinding and beneficiation index) = a* ×b* ×c* ×d* ,in, Let be the grinding particle size enhancement value, and a, b, c, and d be the weight indices of each parameter, where a+b+c+d=1, a is 0.15-0.25, b is 0.3-0.45, c is 0.1-0.2, and d is 0.2-0.

3.

4. The method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill according to claim 1, characterized in that, The mill energy consumption parameters include mill operating power P, ball loading amount Q, and mill rotation speed V.

5. The method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill according to claim 4, characterized in that, f (energy consumption parameter) = e*P×f*Q×g*V, where e, f, and g are the weighting indices of each parameter, and e+f+g=1, e is 0.1-0.3, f is 0.2-0.5, and g is 0.4-0.

6.

6. The method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill according to claim 1, characterized in that, Adjust the mill energy consumption parameters to ensure that the Mb value is not lower than the threshold T, according to the following priority: First priority: Increase the mill speed V first; if the Mb value is still lower than the threshold T after the mill speed V is increased to the preset upper limit, then activate the second priority control. Second priority: Increase the amount of balls added (Q) or increase the slurry concentration (w) inside the mill.

7. The method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill according to claim 1, characterized in that, Adjust the mill energy consumption parameters to reduce the Mb value to the threshold T, according to the following priority: First priority: reduce the mill speed V; if the mill speed V is reduced to the preset lower limit and the Mb value is still higher than the threshold T, then the second priority control is activated. Second priority: Reduce the amount of balls added (Q) or reduce the slurry concentration (w) in the mill.

8. The method for judging and providing feedback on the selective grinding and dissociation effect of a stirred mill according to claim 1, characterized in that, The grinding particle size enhancement value is determined by an online particle size analyzer, and the mineral processing index is obtained by a grade analyzer or periodic sampling and testing.

9. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of any of the methods described in claims 1-8.

Images