Complex mineral source decomposition particle size analysis method

Abstract

The invention relates to the field of ore source decomposition granularity analysis, and provides a complex ore source decomposition granularity analysis method which comprises the following steps: step 1, establishing granularity data models under different ore sources; 2, analyzing the particle size change condition of the system; 3, researching a system equipment process for removing decomposed organic matters; fourthly, the classifier equipment is maintained and optimized to guarantee efficient operation; and 5, carrying out regular cleaning and upgrading optimization on the wide plate to ensure a decomposition cooling system. By establishing granularity data models under different ore sources, analyzing and summarizing the influence of ore on decomposition granularity, and timely adjusting process technical indexes according to the change of the ore sources, the stable transition of the decomposition granularity indexes is ensured, and the qualified rate of product granularity is improved; parameters such as decomposition temperature and time can be recalculated based on the model, and process indexes can be adjusted in real time.

Description

Technical Field

[0001] This invention relates to the field of mineral source decomposition particle size analysis, specifically a method for complex mineral source decomposition particle size analysis. Background Technology

[0002] In recent years, the domestic bauxite supply situation has become increasingly strained. Environmental inspections, mine rectification, and policy restrictions have made domestic bauxite mining difficult. The impact of mineral resources has severely restricted the continuous and stable operation of alumina production. Currently, production targets can only be achieved by purchasing foreign bauxite and using a blend of foreign and domestic ores. Due to the influence of mineral source type and system organic matter, the particle size of decomposition varies greatly, and the particle size refinement cycle is long. There are not enough means to control the particle size under complex mineral sources. The existing adjustment means rely solely on the control measures of the classifier, resulting in high system energy consumption, shortened equipment cycle, and low decomposition rate and low circulation efficiency during the particle size refinement period, which increases system energy consumption and seriously affects output and product quality. Therefore, it is necessary to carry out research on the control of decomposition particle size under complex mineral sources.

[0003] A search revealed that, according to publication number CN117994318B, this invention relates to the field of image processing, specifically to a particle size detection method and system for ore transport. The method includes: converting a captured ore image to grayscale to obtain a grayscale image; performing edge detection on the grayscale image using an improved Sobel operator to obtain the edge response values ​​of target pixels; obtaining the edge response values ​​of all pixels in the grayscale image; comparing the edge response values ​​of all pixels with a set threshold to distinguish between edge pixels and non-edge pixels to obtain an enhanced image; and detecting the ore particle size based on the enhanced image to obtain a predicted ore particle size. Due to the impact of domestic ore supply, many alumina plants in China use a blend of domestic and imported ores to ensure the continuity of their production systems. The complexity of the ore sources leads to long particle size refinement cycles, large fluctuations in indicators, and unstable product quality indicators. Currently, particle size indicators are adjusted solely through classifier control measures, and there is a lack of experience in particle size control under complex ore sources. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for analyzing the particle size distribution of complex mineral resources, which solves the problems of long particle size refinement cycles, large fluctuations in indicators, and unstable product quality indicators.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for particle size analysis of complex mineral resources, comprising the following steps:

[0006] Step 1: Establish particle size data models for different mineral sources;

[0007] Step 2: Analyze the changes in system granularity;

[0008] Step 3: Study the system equipment and processes for decomposing and removing organic matter;

[0009] Step 4: Perform maintenance and optimization on the grading machine to ensure efficient operation;

[0010] Step 5: Conduct regular cleaning and upgrades of the wide plates to ensure the decomposition and cooling system.

[0011] Preferably, the establishment of particle size data models under different mineral sources specifically involves using an optical microscope to analyze the changing trends of decomposition particle size under different ore types and usage ratios, and establishing a comprehensive particle size data model.

[0012] Preferably, when establishing the particle size data model, it is necessary to collect corresponding data on mineral source type, mineral source proportion, decomposition time, decomposition temperature, and decomposition particle size, and use multiple regression analysis to construct a mathematical model. The model expression is as follows:

[0013] y = a0 + a1x1 + a2x2 + a3x3 + a4x4 + ε

[0014] Where y is the decomposition particle size, x1 is the mineral source type parameter, x2 is the mineral source proportion, x3 is the decomposition time, x4 is the decomposition temperature, a0, a1, a2, a3, and a4 are regression coefficients, and ε is the error term.

[0015] Preferably, the particle size change analysis system is based on an established particle size data model, which monitors changes in ore source in real time, analyzes the impact of ore on decomposition particle size, and adjusts process technology indicators in a timely manner.

[0016] Preferably, the system equipment process for studying the decomposition and removal of organic matter includes selecting a suitable organic matter removal device, solidifying operating conditions, and reducing the organic matter content. The operating conditions include controlling the temperature at 80-100℃, the reaction time at 2-4h, and the reagent addition concentration at 0.5-1.5g / L.

[0017] Preferably, the maintenance and optimization of the classifier equipment includes optimizing and upgrading spare parts such as the classifier's feed and discharge densities and the sand settling nozzle. The feed and discharge density control range is: feed density 1.5-2.0 g / cm³. 3 The output density is 1.2-1.6 g / cm³. 3 .

[0018] Preferably, when the undersand nozzle spare part is optimized and upgraded, the diameter of the undersand nozzle is adjusted according to the processing capacity of the classifier, and the adjustment formula is:

[0019] d = 0.01Q 0.5

[0020] Where d is the diameter of the sand settling nozzle (mm) and Q is the classifier's processing capacity (t / h).

[0021] Preferably, the regular cleaning and upgrading of the wide plate includes regularly performing alkaline washing and acid washing on the wide plate. The alkaline washing uses a 5% to 10% NaOH solution, the temperature is controlled at 60-80℃, and the washing time is 1-2 hours. The acid washing uses a 3% to 5% HCl solution, the temperature is controlled at 40-60℃, and the washing time is 0.5-1 hour.

[0022] Preferably, when ensuring the decomposition cooling regime, the decomposition temperature gradient is controlled as follows: the temperature of the first tank is 60-70℃, the temperature of the last tank is 30-40℃, and the cooling rate is controlled at 1-2℃ / h.

[0023] Preferably, the analytical method described above achieves the control of particle size distribution of -45μm in the flat plate ≤10% and the particle size distribution of -45μm in the calcined product ≤15%.

[0024] Preferably, the method further includes the step of adjusting the process technical indicators according to the changes in the ore source. The adjustment is based on the established particle size data model. When the change in the type or proportion of the ore source exceeds 10%, the process parameters such as decomposition temperature and time are recalculated and adjusted.

[0025] This invention provides a method for particle size analysis of complex mineral deposits. It has the following beneficial effects:

[0026] 1. This invention establishes particle size data models for different ore sources, analyzes and summarizes the impact of ore on decomposition particle size, and adjusts process technical indicators in a timely manner according to changes in ore source. This ensures a smooth transition of decomposition particle size indicators and improves the product particle size qualification rate. When the type or proportion of ore source changes by more than 10%, parameters such as decomposition temperature and time can be recalculated based on the model, and process indicators can be adjusted in real time. This dynamic control mechanism solves the problems of long particle size refinement cycles and large indicator fluctuations caused by complex ore sources in traditional methods.

[0027] 2. This invention reduces the interference of organic matter on the decomposition process and improves equipment operating efficiency by solidifying the organic matter removal process parameters and optimizing the feed and discharge densities of the classifier and the diameter of the sand discharge nozzle. Simultaneously, the regular alkaline and acid washing processes on the wide plates ensure a stable decomposition temperature gradient, avoiding increased energy consumption caused by equipment scaling. Attached Figure Description

[0028] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see the appendix Figure 1 This invention provides a method for particle size analysis of complex mineral deposits, comprising the following steps:

[0031] Step 1: Establish particle size data models for different mineral sources;

[0032] By using an optical microscope, we will analyze the changing trends of decomposition particle size under different ore types and usage proportions, and establish a comprehensive particle size data model. This requires collecting corresponding data on ore type, ore proportion, decomposition time, decomposition temperature, and decomposition particle size. A mathematical model will be constructed using multiple regression analysis, and the model expression is as follows:

[0033] y = a0 + a1x1 + a2x2 + a3x3 + a4x4 + ε

[0034] Where y is the decomposition particle size, x1 is the mineral source type parameter, x2 is the mineral source proportion, x3 is the decomposition time, x4 is the decomposition temperature, a0, a1, a2, a3, and a4 are regression coefficients, and ε is the error term.

[0035] Step 2: Analyze the particle size variation of the system. Based on the established particle size data model, monitor the changes in ore source in real time, analyze the impact of ore on decomposition particle size, and adjust the process technical indicators in a timely manner. Through analysis methods, achieve the control of the following indicators: -45μm particle size ≤10% for flat plate and -45μm particle size ≤15% for roasted product.

[0036] Step 3: Study the system equipment and process for decomposing and removing organic matter, including selecting a suitable organic matter removal device, solidifying operating conditions, and reducing the organic matter content. Operating conditions include temperature control at 80-100℃, reaction time of 2-4 hours, and reagent concentration of 0.5-1.5 g / L.

[0037] Step 4: Maintain and optimize the classifier equipment to ensure efficient operation. This includes optimizing and upgrading spare parts such as the classifier's feed and discharge densities and the sand settling nozzles. The feed and discharge density control range is: feed density 1.5-2.0 g / cm³. 3 The output density is 1.2-1.6 g / cm³. 3 When maintaining and optimizing the classifier equipment, and upgrading the sand settling nozzle spare parts, the diameter of the sand settling nozzle should be adjusted according to the classifier's processing capacity. The adjustment formula is as follows:

[0038] d = 0.01Q 0.5

[0039] Where d is the diameter of the sand settling nozzle (mm) and Q is the classifier's processing capacity (t / h).

[0040] Step 5: Conduct regular cleaning and optimization of the wide plates to ensure the decomposition cooling regime. This includes regular alkaline and acid washing of the wide plates. Alkaline washing uses a 5%–10% NaOH solution, with the temperature controlled at 60–80℃, and the washing time is 1–2 hours. Acid washing uses a 3%–5% HCl solution, with the temperature controlled at 40–60℃, and the washing time is 0.5–1 hour. To ensure the decomposition cooling regime is maintained, the decomposition temperature gradient is controlled as follows: first tank temperature 60–70℃, last tank temperature 30–40℃, and the cooling rate is controlled at 1–2℃ / h.

[0041] Adjust the process technical indicators according to changes in ore source. The adjustment is based on the established particle size data model. When the type or proportion of ore source changes by more than 10%, the process parameters such as decomposition temperature and time are recalculated and adjusted.

[0042] Example 1:

[0043] I. Establishing particle size data models for different bauxite sources

[0044] 1. Equipment and Analysis

[0045] Equipped with an optical microscope, the particle size variation trend of a mixture of high-silica bauxite (SiO2 content 12%) and high-iron bauxite (Fe2O3 content 18%) in a ratio of 40%:60% was analyzed.

[0046] 2. Data Acquisition and Modeling

[0047] Data collection parameters:

[0048] Mineral source type: High silica bauxite (parameter 1), high iron bauxite (parameter 2);

[0049] Mineral source proportion: High silicon 40% → 55% (change of 15%);

[0050] Decomposition time: 5.5 hours;

[0051] Decomposition temperature: 62℃;

[0052] Decomposition particle size: 8.7% of the products from the flat plate were 45μm in size.

[0053] Mathematical Model: The model is constructed using multiple regression analysis, and the expression is as follows:

[0054] y=28.5+1.2x1-0.8x2+0.5x3+0.3x4

[0055] II. Analysis of System Granularity Changes

[0056] Real-time monitoring and scheduling: When the proportion of high silica bauxite increases from 40% to 55%, the decomposition temperature is adjusted to 68℃ and the decomposition time is adjusted to 6.2h based on model calculation. The actual measured particle size of -45μm in the flat plate is 9.2%, and the particle size of -45μm in the roasted product is 14.3%, both of which meet the indicators of ≤10% and ≤15%.

[0057] III. Implementation of Organic Matter Removal Process

[0058] Specific operating parameters: temperature controlled at 92℃, reaction time 3.2h, reagent (polyaluminum chloride) concentration 1.2g / L. Treatment effect: bauxite organic matter content decreased from 1.5% to 0.4%.

[0059] IV. Classification Machine Equipment Maintenance and Optimization

[0060] 1. Control of feed and discharge density

[0061] Feed density: 1.75 g / cm³ 3 Output density: 1.38 g / cm³ 3 .

[0062] 2. Calculation of the diameter of the undersink nozzle

[0063] Classifier capacity: 480t / h; Calculation formula: d=0.01005×480=4.824 (unit mm), rounded to 5mm.

[0064] V. Conduct regular cleaning and upgrade / optimization of wide plates.

[0065] 1. Cleaning process parameters

[0066] Alkaline washing: Use 6% NaOH solution at 75℃ for 1 hour and 40 minutes; Acid washing: Use 3.5% HCl solution at 55℃ for 45 minutes.

[0067] 2. Decomposition of cooling system

[0068] Temperature gradient: first tank temperature 67℃, last tank temperature 36℃; cooling rate: 1.6℃ / h.

[0069] VI. Dynamic Adjustment of Process Parameters

[0070] When the proportion of high-speed bauxite decreases from 60% to 40% (a change of 20%), the process parameters are recalculated based on the model:

[0071] Decomposition temperature: 70℃; Decomposition time: 7h; After adjustment, the measured particle size of -45μm on the flat plate was 9.8%, and the particle size of -45μm on the roasted product was 14.7%, and the indicators were stable and met the standards.

[0072] Example 2:

[0073] I. Establishing particle size data models for different bauxite sources

[0074] 1. Equipment and Analysis

[0075] Equipped with an optical microscope, the particle size variation trend of a mixture of low-iron bauxite (5% Fe2O3 content) and monohydrate soft bauxite (65% Al2O3 content) in a ratio of 30%:70% was analyzed.

[0076] 2. Data Acquisition and Modeling

[0077] Data collection parameters:

[0078] Mineral source type: low-iron bauxite (parameter 1), monohydrate soft bauxite (parameter 2);

[0079] Mineral source proportion: Low iron bauxite 30% → 45% (change of 15%);

[0080] Decomposition time: 4.8 hours;

[0081] Decomposition temperature: 60℃;

[0082] Decomposition particle size: 9.3% of the products from the flat plate had a particle size of -45μm.

[0083] Mathematical Model: The model is constructed using multiple regression analysis, and the expression is as follows:

[0084] y=30+1.5x1-1.2x2+0.6x3+0.4x4

[0085] II. Analysis of System Granularity Changes

[0086] Real-time control example: When the proportion of low-iron bauxite increased from 30% to 45%, the decomposition temperature was adjusted to 65℃ and the decomposition time to 5.5h based on model calculations. The actual measured particle size of -45μm in the flat plate was 9.5%, and the particle size of -45μm in the roasted product was 14.2%, both of which met the index requirements.

[0087] III. Implementation of Organic Matter Removal Process

[0088] Specific parameters: temperature controlled at 88℃, reaction time 3 hours, reagent (polyacrylamide) concentration: 1.0 g / L. Treatment effect: the organic matter content of bauxite decreased from 1.2% to 0.3%.

[0089] IV. Classification Machine Equipment Maintenance and Optimization

[0090] 1. Control of feed and discharge density

[0091] Feed density: 1.6 g / cm³ 3Output density: 1.3 g / cm³ 3 .

[0092] 2. Calculation of the diameter of the undersink nozzle

[0093] Classifier capacity: 500t / h; Calculation formula: d=0.01005×500=5.025 (unit mm), rounded to 5mm.

[0094] V. Conduct regular cleaning and upgrade / optimization of wide plates.

[0095] 1. Cleaning process parameters

[0096] Alkaline washing: 8% NaOH solution, 70℃, wash for 1.5h; Acid washing: 4% HCl solution, 50℃, wash for 45min.

[0097] 2. Decomposition of cooling system

[0098] Temperature gradient: First tank temperature: 65℃; Last tank temperature: 35℃; Cooling rate: 1.5℃ / h.

[0099] VI. Dynamic Adjustment of Process Parameters

[0100] When the proportion of monohydrate bauxite decreases from 70% to 55% (a change of 15%), the process parameters are recalculated based on the model:

[0101] Decomposition temperature: 70℃; Decomposition time: 6h; After adjustment, the measured particle size of the flat plate (-45μm) was 9.8%, and the particle size of the roasted product (-45μm) was 14.5%, with the indicators being stable and meeting the standards.

[0102] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for particle size analysis of complex mineral resources, characterized in that, Includes the following steps: Step 1: Establish particle size data models for different mineral sources; Step 2: Analyze the changes in system granularity; Step 3: Study the system equipment and processes for decomposing and removing organic matter; Step 4: Perform maintenance and optimization on the grading machine to ensure efficient operation; Step 5: Conduct regular cleaning and upgrades of the wide plates to ensure the decomposition and cooling system.

2. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, The establishment of particle size data models under different mineral sources specifically involves using an optical microscope to analyze the changing trends of decomposition particle size under different ore types and usage proportions, and establishing a comprehensive particle size data model.

3. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, When establishing the granularity data model, it is necessary to collect corresponding data on mineral source type, mineral source proportion, decomposition time, decomposition temperature, and decomposition granularity. A mathematical model is then constructed using multiple regression analysis, and the model expression is as follows: y = a0 + a1x1 + a2x2 + a3x3 + a4x4 + ε Where y is the decomposition particle size, x1 is the mineral source type parameter, x2 is the mineral source proportion, x3 is the decomposition time, x4 is the decomposition temperature, a0, a1, a2, a3, and a4 are regression coefficients, and ε is the error term.

4. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, The analysis system for particle size variation is based on an established particle size data model, which monitors changes in ore source in real time, analyzes the impact of ore on decomposition particle size, and adjusts process technology indicators in a timely manner.

5. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, The research on the system equipment process for decomposing and removing organic matter includes selecting a suitable organic matter removal device, solidifying operating conditions, and reducing the organic matter content. The operating conditions include controlling the temperature at 80-100℃, the reaction time at 2-4h, and the reagent addition concentration at 0.5-1.5g / L.

6. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, The maintenance and optimization of the classifier equipment includes upgrading and optimizing spare parts such as the classifier's feed and discharge densities and the sand settling nozzles. The feed and discharge density control range is: feed density 1.5-2.0 g / cm³. 3 The output density is 1.2-1.6 g / cm³. 3 .

7. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, When optimizing and upgrading the sand settling nozzle spare parts, the diameter of the sand settling nozzle is adjusted according to the classifier's processing capacity. The adjustment formula is as follows: d=0.01Q 0.5 Where d is the diameter of the sand settling nozzle (mm) and Q is the classifier's processing capacity (t / h).

8. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, The aforementioned regular cleaning and upgrading of the wide plates includes regular alkaline and acid washing. The alkaline washing uses a 5%–10% NaOH solution, with the temperature controlled at 60–80°C and the washing time being 1–2 hours. The acid washing uses a 3%–5% HCl solution, with the temperature controlled at 40–60°C and the washing time being 0.5–1 hour.

9. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, When ensuring the decomposition cooling regime, the decomposition temperature gradient is controlled as follows: the temperature of the first tank is 60-70℃, the temperature of the last tank is 30-40℃, and the cooling rate is controlled at 1-2℃ / h. Alternatively, the analytical method described above can be used to achieve the following control parameters: -45μm particle size ≤10% for flat-plate samples and -45μm particle size ≤15% for calcined products.

10. The method for particle size analysis of complex mineral resources according to claim 1, characterized in that, It also includes the step of adjusting process technical indicators according to changes in ore source. The adjustment is based on the established particle size data model. When the type or proportion of ore source changes by more than 10%, the process parameters such as decomposition temperature and time are recalculated and adjusted.

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