A jig debugging and optimization method based on response surface analysis method

Abstract

The present invention discloses a jig debugging and optimization method based on response surface analysis method, belonging to the metallurgical technology field. It determines whether the adjustment value meets the jig operation standard by detecting the bed looseness and the liquid surface bubble performance, and determines that the key variables affecting the enrichment ratio and recovery rate are the cover valve frequency, the intake period duration, the expansion period duration, the exhaust period duration, and the rest period duration. The test plan is designed through a full factor experiment, and the optimal condition combination is obtained by fitting. It greatly reduces the debugging time, improves the production efficiency, and optimizes the process indexes.

Description

Technical Field

[0001] The present invention belongs to the field of metallurgical technology, and particularly relates to a jig debugging and optimization method based on response surface analysis method. Background Art

[0002] When mining polymetallic mines, a large amount of low-grade ores and ore-containing waste rocks are often produced. To improve the economic benefits of mining enterprises, a jig is needed to enrich the low-grade ores, and the enriched ores are then sent to a concentrator for beneficiation. In the actual use of the jig, the enrichment ratio and recovery rate are the most important technological indicators. There are many relevant adjustable variables for the jig. During the debugging process, some adjustable variables can be limited according to the situation of the ore raw materials. According to different ore raw materials, the jig parameters need to be modified to meet the requirements of the technological indicators of the enrichment ratio and recovery rate. The existing jig uses a debugging method of successive manual experiments to adjust the jig parameters, which cannot determine the key variables of the jig, and the number of experiments is extremely large, affecting the production efficiency. Summary of the Invention

[0003] Aiming at the technical problems in the existing jig debugging method that the key variables cannot be determined and the number of experiments is large, the present invention determines whether the adjustment value meets the jig operation standard by detecting the bed looseness, and determines that the key variables affecting the enrichment ratio and recovery rate are the cover valve frequency, intake period duration, expansion period duration, exhaust period duration, and rest period duration. The optimal condition combination is obtained by fitting through the full factorial experimental design of the experimental scheme.

[0004] To achieve the above object, the present invention is realized through the following technical solutions: A jig debugging and optimization method based on response surface analysis method includes the following steps:

[0005] Step 1: Set the gate valve and feed frequency of the jig according to the feed rate and feed particle size;

[0006] Step 2: Adjust the water supply pressure, water supply volume, intake pressure, exhaust pressure, and blower frequency, and determine whether the adjustment value meets the jig operation standard by detecting the bed looseness;

[0007] Step 3: Select the cover valve frequency, intake period duration, expansion period duration, exhaust period duration, and rest period duration as input variables, and the enrichment ratio and recovery rate as response variables, and formulate a full factorial experimental design scheme to set the factor levels and the number of experiments;

[0008] Step 4: Conduct experiments according to the full factorial design experimental scheme, record the experimental data, and fit the recovery rate and enrichment ratio multiple quadratic equations according to the experimental data;

[0009] Step 5: Establish a response surface model based on the recovery rate and enrichment ratio multiple quadratic equations, and determine the optimal condition combination through the response surface model.

[0010] Further, by using a wooden round bar to detect the looseness of the bed layer, when the material on the jigging bed layer is inserted with the wooden stick and shows a loose state along with the pulsation of water flow and air flow, and there are obvious lifting and sucking phenomena along with the pulsation of water flow, and at the same time, 5-10 mm bubbles uniformly appear on the water surface, it is determined that the adjusted value meets the operation standard of the jig.

[0011] Further, in step 3, the setting range of the cover valve frequency F is 60 - 80 times / min, the setting range of the intake period duration a is 5 - 15%, the setting range of the expansion period duration b is 30 - 50%, the setting range of the rest period duration d is 5 - 15%, and the setting range of the exhaust period duration c is c = 1 - (a + b + d).

[0012] Further, in step 3, the factor levels are as follows: the cover valve frequency F is 60 times / min, 70 times / min, 80 times / min; the intake period duration a is 5%, 10%, 15%; the expansion period duration b is 30%, 40%, 50%; the rest period duration is 5%, 10%, 15%.

[0013] Further, in step 5, the optimal condition combination is a cover valve frequency of 70 times / min, an intake period duration of 10%, an expansion period duration of 43%, an exhaust period duration of 34%, and a rest period of 13%.

[0014] The beneficial effects of the present invention: By detecting the looseness of the bed layer and the performance of the liquid surface bubbles to determine whether the adjusted value meets the operation standard of the jig, it is determined that the key variables affecting the enrichment ratio and recovery rate are the cover valve frequency, the intake period duration, the expansion period duration, the exhaust period duration, and the rest period duration. Through the full factor experimental design of the experimental scheme, the optimal condition combination is obtained by fitting. Greatly reduce the commissioning time, improve production efficiency, and optimize the process indicators. Description of the Drawings

[0015] Figure 1 It is the contour line model diagram of the recovery rate of this embodiment;

[0016] Figure 2 It is the response surface model diagram of the recovery rate of this embodiment;

[0017] Figure 3 It is the contour line model diagram of the enrichment ratio of this embodiment;

[0018] Figure 4 It is the response surface model diagram of the enrichment ratio of this embodiment. Detailed Embodiment

[0019] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the drawings for the convenience of those skilled in the art to understand.

[0020] Step 1: Set the feeding frequency of the jigger gate valve according to the ore feeding amount and the ore feeding particle size.

[0021] In a specific embodiment, calculate the ore feeding amount according to the actual production demand, and set the position of the jigger gate valve and the frequency of the vibrating feeder according to the ore feeding amount. In this embodiment, according to the production demand, the ore feeding amount is 110 tons per hour, the lowest position of the gate valve is set, and the ore feeding frequency of the vibrating feeder is 38 Hz.

[0022] Step 2: Adjust the water supply pressure, water supply volume, intake pressure, exhaust pressure, and blower frequency, and determine whether the adjusted value meets the jigger operation standard through the detection of the bed looseness.

[0023] In a specific embodiment, detect the bed looseness by using a wooden round rod. When the inserted wooden rod shows that the materials on the jigger bed are in a loose state with the pulsation of water flow and air flow, and there are obvious lifting and sucking phenomena with the pulsation of water flow, and at the same time, 5-10 mm bubbles uniformly appear on the water surface, it is determined that the adjusted value meets the jigger operation standard. In this embodiment, the parameters that meet the jigger operation standard are as follows: the adjustment range of the water supply pressure is 0.95 bar to 1.05 bar; the adjustment range of the water supply volume is 900 m 3 / h to 1100 m 3 / h; the adjustment range of the intake pressure is 0.4 bar to 0.49 bar; the adjustment range of the exhaust pressure is 150 bar to 180 mbar; the adjustment range of the blower frequency is 40 Hz to 50 Hz.

[0024] Step 3: Select the cover valve frequency, intake period duration, expansion period duration, exhaust period duration, and rest period duration as input variables, and the enrichment ratio and recovery rate as response variables, and formulate a full factor experimental design plan to set the factor levels and the number of experiments.

[0025] In a specific embodiment, the set range of the cover valve frequency F is 60-80 times / min, the set range of the intake period duration a is 5-15%, the set range of the expansion period duration b is 30-50%, the set range of the rest period duration d is 5-15%, and the set range of the exhaust period duration c is c = 1-(a + b + d).

[0026] Adopt the BOX-Behnken experimental design with 3 repeated center points, divide the factor range into 3 levels, and a total of 27 groups of conditional test groups are designed. In this embodiment, the cover valve frequency F is 60 times / min, 70 times / min, 80 times / min; the intake period duration a is 5%, 10%, 15%; the expansion period duration b is 30%, 40%, 50%; the rest period duration is 5%, 10%, 15%.

[0027] Step 4: Conduct experiments according to the full factorial design experimental plan, record the experimental data, and obtain the multiple quadratic equations for recovery rate and enrichment ratio by fitting the experimental data.

[0028] In a specific embodiment, the full factorial design experimental plan and experimental results are shown in the following table.

[0029]

[0030]

[0031] Table 1 Full factorial experimental design plan and experimental results

[0032] The multiple quadratic equations for recovery rate and enrichment ratio obtained by fitting the experimental data are as follows:

[0033] Recovery rate:

[0034] H = -520.23958 + 10.901×F + 5.32267×a + 6.46725×b + 15.3265×d + 0.1537×Fa + 0.04025Fb - 0.10315Fd - 0.0718ab + 0.081ad - 0.00665bd - 0.095071F 2 -0.68113a 2 -0.1079b 2 -0.46653d 2

[0035] G = -15.4515 + 0.46052×F + 0.047033×a + 0.083617×b - 0.026133×d + 0.00635×Fa + 0.0013Fb - 0.0013Fd - 0.003ab + 0.0049ad + 0.00525bd - 0.00407F 2 -0.019843a 2 -0.00234833b 2 -0.0061433d 2

[0036] In the formula, H: recovery rate, G: enrichment ratio, F: cover plate valve frequency, a: intake period, b: expansion period, d: rest period.

[0037] Perform a significance analysis on the regression equation models of the recovery rate and enrichment ratio. The significance analysis of the model is mainly a detection method for testing the data fitting accuracy and reliability of the multiple quadratic equation fitted by the experimental data, and at the same time, the reliability of the prediction results of the regression equation under given conditions. In the significance analysis, the smaller the P-value, the higher the significance, and the closer the fitting function is to the actual data distribution law. The P-value of the significance analysis of the recovery rate model is <0.0001, showing significance, and the model is effective; the P-value of the significance analysis of the enrichment ratio model is <0.0064, showing significance, and the model is effective.

[0038] Step 5: Establish a response surface model based on the multiple quadratic equations of the recovery rate and enrichment ratio, and determine the optimal condition combination through the response surface model.

[0039] In a specific embodiment, use (Design-Expert 8.0.6 Trial) software to establish a recovery rate contour model, a recovery rate response surface model, an enrichment ratio contour model, and an enrichment ratio response surface model. In this factual example, the recovery rate contour model is as Figure 1 shown, and the recovery rate response surface model is as Figure 3 shown. The enrichment ratio contour model is as Figure 2 shown, and the enrichment ratio response surface model is as Figure 4 shown.

[0040] According to the model as Figures 1-4 described, the optimal condition combination corresponding to the optimal index selected in the current conditions of use is: the frequency of the cover valve is 70 times / min, the intake period duration is 10%, the expansion period duration is 43%, the exhaust period duration is 34%, and the rest period is 13%.

[0041] From the perspective of the final optimization effect, before optimization, the average time required for each jig adjustment was 40 hours. After optimization using this method, the reduction rate of the jig adjustment time was 70%, and each adjustment was shortened by 12 hours. The optimization effect of production efficiency is obvious. After actual testing, the average enrichment ratio per batch increased from an average of 1.5 times to 2.7 times, and the recovery rate increased from an average of 43% to 65%. The optimization effect of process quality is obvious.

[0042] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the invention and are not restrictive. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the protection scope of the present invention.

Claims

1. A jig debugging and optimization method based on response surface analysis method, characterized in that: It includes the following steps: Step 1: Set the jig feeding frequency according to the ore feeding amount and ore feeding particle size; Step 2: Adjust the water supply pressure, water supply volume, intake pressure, exhaust pressure, and blower frequency, and determine whether the adjusted value meets the jig operation standard through the detection of bed layer looseness; Step 3: Select the cover valve frequency, intake period duration, expansion period duration, exhaust period duration, and rest period duration as input variables, and the enrichment ratio and recovery rate as response variables, and formulate a full factorial experimental design plan to set the factor levels and the number of experiments; Step 4: Conduct experiments according to the full factorial design experimental plan, record the experimental data, and obtain the multiple quadratic equations of the recovery rate and enrichment ratio by fitting the experimental data; Step 5: Establish a response surface model based on the multiple quadratic equations of the recovery rate and enrichment ratio, and determine the optimal condition combination through the response surface model.

2. The jig debugging and optimization method based on response surface analysis method according to claim 1, characterized in that: In Step 2, the jig operation standard detection method is as follows: By using a wooden round rod to detect the bed layer looseness, when the materials on the jig bed layer are inserted with the wooden stick and show a loose state along with the water flow and air flow pulsation, and there is an obvious lifting and sucking phenomenon along with the water flow pulsation, and at the same time, 5 - 10 mm bubbles evenly appear on the water surface, it is determined that the adjusted value meets the jig operation standard.

3. The jig debugging and optimization method based on response surface analysis method according to claim 1, characterized in that: In Step 3, the setting range of the cover valve frequency F is 60 - 80 times / min, the setting range of the intake period duration a is 5 - 15%, the setting range of the expansion period duration b is 30 - 50%, the setting range of the rest period duration d is 5 - 15%, and the setting range of the exhaust period duration c is c = 1 - (a + b + d).

4. The jig debugging and optimization method based on response surface analysis method according to claim 1 or 3, characterized in that: In Step 3, the factor levels are as follows: The cover valve frequency F is 60 times / min, 70 times / min, 80 times / min; the intake period duration a is 5%, 10%, 15%; the expansion period duration b is 30%, 40%, 50%; the rest period duration is 5%, 10%, 15%.

5. The jig debugging and optimization method based on response surface analysis method according to claim 1, characterized in that: In Step 5, the optimal condition combination is a cover valve frequency of 70 times / min, an intake period duration of 10%, an expansion period duration of 43%, an exhaust period duration of 34%, and a rest period of 13%.

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