Construction method of mineral flotation process industrial knowledge graph based on multi-level cross-process association

Abstract

The application discloses a kind of mineral flotation industry knowledge graph construction methods based on multi-level cross-process association.The flotation process is systematically expressed by combining ontology modeling and tree structure.The method regards the flotation process as a dynamic tree, the root node is used to represent the feed parameters, the trunk and branch nodes correspond to the main process steps and their sub-processes respectively, and the leaf nodes represent process parameters and performance index feedback. The application introduces ontology in the knowledge graph, dynamically models the process data, process association and input-output relationship, and uses ontology semantics to realize the unified description and integrated expression of multi-source heterogeneous data. Through this method, the intuitive modeling problem of complex association of multiple parameters in the flotation process is effectively solved, providing a clear process expression and knowledge organization form, which lays a foundation for intelligent analysis and optimization of mineral flotation process.

Description

Technical Field

[0001] The present invention relates to the field of mineral flotation, and in particular to a method for constructing an industrial knowledge graph of a mineral flotation process based on multi-level cross-process association. Background Art

[0002] Mineral flotation is a widely used mineral processing technology for mineral separation and enrichment, involving multiple processes, parameters, and their dynamic relationships. Traditional process optimization methods are often based on empirical rules or single-process analysis, making it difficult to effectively model complex multi-level parameter relationships and inter-process feedback mechanisms. Furthermore, existing industrial knowledge graphs typically use static network structures, which cannot adapt to the dynamic changes in input-output relationships and upstream and downstream connections in flotation processes, making it difficult to quantify complex relationships. Therefore, improvements are needed. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes a method for constructing an industrial knowledge graph of the mineral flotation process based on multi-level cross-process associations.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process associations includes the following steps:

[0006] Step 1: Collect process data in the flotation process;

[0007] Step 2: Construct a flotation tree model and define the input-output relationship of the flotation tree model based on process data;

[0008] Step 3: Use ontology modeling to semantically describe the concepts and relationships in the flotation tree model to form an ontology modeled flotation tree model

[0009] Step 4: Use the flotation tree model of the ontology modeling to build a knowledge graph through the graph database Neo4j, and use visualization tools to display the graph.

[0010] As a further improvement, in step one, the process data includes raw material parameters, particle size distribution, moisture content, equipment status, environmental variables and reagent dosage.

[0011] In a further improvement, the raw material parameters include the type of ore minerals; the equipment status includes the stirring tank speed and the bubble generator gas flow; the environmental variables include the slurry temperature and pH value; and the reagent dosage includes the collector dosage and the foaming agent dosage.

[0012] As a further improvement, the process data is normalized and pre-processed, specifically by removing abnormal values ​​and duplicate values ​​in the ore flotation process and then converting the data in different formats in the ore flotation process into a unified structured format.

[0013] For further improvement, the specific steps of step 2 are as follows:

[0014] Step S21: The flotation tree model hierarchy is defined as follows: root node: mineral feed parameters, including mineral type, particle size distribution, and moisture content; trunk node: flotation process steps, including fast roughing, roughing, cleaning, and scavenging; branch node: sub-process parameters, including bubble distribution, pulp stability, and pulp flow rate; leaf node: final output production indicators, including concentrate grade and concentrate recovery rate;

[0015] Step S22: Define the input-output relationship of the flotation tree model:

[0016] Set up the flotation tree model for nutrient delivery: Set the final concentrate concentration of the target mineral to C 精矿 , the transfer process takes into account the efficiency factor of each step and the possible loss factor, and the formula is expressed as:

[0017]

[0018] Where: C 精矿 is the final concentrate concentration, C 原矿 is the original ore concentration, E 步骤i is the flotation efficiency factor of the i-th step, L 步骤i is the loss factor of step i, k j is the influence coefficient of slurry pH value on the efficiency of each process step, pH j is the pH value of step j, pH opt,j is the optimal pH value of the jth step; n is the total number of flotation steps, and m is the actual number of steps in the flotation process.

[0019] Further improvements, efficiency factor E 步骤i and loss factor L 步骤i , modeled by the following dynamic formula:

[0020]

[0021] L 步骤i =α i ×C 步骤i,输入

[0022] Where: C 步骤i,输入 and C 步骤i,输出 are the input and output mineral concentrations of step i, α i is the loss coefficient of step i, reflecting the mineral loss caused by operating conditions during the flotation process; the operating conditions include the amount of reagent used and the pulp concentration.

[0023] Further improvements are made to the dynamic processing of the scanning step:

[0024]

[0025] Where: C 原矿 is the concentration of target mineral in the original ore, C 尾矿 is the concentration of target minerals in tailings, β pH and β 温度 are the coefficients of the influence of pulp pH value and temperature on recovery rate, pH opt and T opt are the optimal pH value and temperature, respectively; pH ​​and T are the current pH value and temperature of the slurry, respectively.

[0026] For further improvement, the specific steps of step three are as follows:

[0027] Step S31: defining the basic structure of the ontology model: concept classes and relationship classes; the concept classes include lead minerals, zinc minerals, and flotation cells; the relationship classes include the effects of collectors on lead recovery and pH value on pulp stability;

[0028] The amount of collector added is X 捕收剂 and recovery rate R 回收 The relationship between is modeled by the following formula:

[0029]

[0030] Among them, R 基准 is the baseline recovery, i.e., the recovery without collector, a and b are experimental fitting parameters, representing the nonlinear effect of collector on the recovery;

[0031] The relationship between pH value and slurry stability is expressed by the following formula:

[0032] L 稳定性 =k pH ·(pH-pH opt ) 2

[0033] Among them, k pH is the stability response factor, pH opt For the optimal pH value.

[0034] As a further improvement, the step 4 includes the following steps:

[0035] Step S41: knowledge graph construction, importing the flotation tree model of ontology modeling into Neo4j in RDF / XML format, and establishing a bidirectional dynamic relationship between nodes and edges. The nodes include mineral types, reagents and flotation cells; the edges include process parameter association and efficiency transmission.

[0036] Step S42: Knowledge graph visualization: The graph dynamically displays the transfer process of the mineral flotation process from raw materials to final products; visualizes the relationship between collector dosage and lead concentrate grade; and dynamically displays the impact of the pulp pH value in each process tank on the target mineral recovery rate.

[0037] The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process associations as described in claim 1, wherein the mineral flotation includes lead-zinc ore flotation.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention uses a multi-level cross-process associated knowledge graph construction method to abstract the mineral flotation process flow into a hierarchical tree structure, and combines ontology modeling technology to express the processes, parameters and their relationships semantically, overcoming the deficiency of traditional static networks in adapting to the dynamic changes of flotation processes. By introducing a unified integration and dynamic modeling mechanism for multi-source data, it effectively solves the problem of multi-variable and multi-level parameter associations in complex processes that are difficult to quantify, while achieving accurate expression of logical associations and dynamic feedback of upstream and downstream processes. The present invention can not only comprehensively and systematically characterize the input-output relationship of the flotation process, but also support the discovery of hidden influencing factors through semantic reasoning and association mining, providing a scientific basis for the optimal design and intelligent control of the flotation process, and improving the efficiency and accuracy of flotation process modeling and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention is further described with reference to the accompanying drawings, but the contents in the drawings do not constitute any limitation to the present invention.

[0041] Figure 1 This is a system structure diagram of the present invention, showing a schematic diagram of the overall process from data collection to knowledge graph generation and optimization.

[0042] Figure 2 This is a schematic diagram of the tree model of the present invention, which represents a hierarchical structure diagram of the flotation process.

[0043] Figure 3 This is a diagram of the bidirectional transmission mechanism of the present invention, simulating the dynamic flow diagram of "transmitting feedback downward and transmitting demand upward". DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0045] Example 1

[0046] See Figure 1The present invention provides a method for constructing an industrial knowledge graph of a mineral flotation process based on multi-level cross-process association, which comprises at least the following steps:

[0047] Step S1: Collect and integrate multi-source data in the flotation process to provide data support for subsequent modeling.

[0048] Step S2: Based on the bidirectional dynamic process characteristics of the flotation process, a tree model with hierarchical association relationships is constructed.

[0049] Step S3: Use ontology modeling to semantically describe the concepts and relationships in the flotation process, thereby achieving accurate construction of the knowledge graph.

[0050] Step S4: Build a knowledge graph using the graph database Neo4j and display the graph using visualization tools.

[0051] Specifically, in a lead flotation embodiment of the present invention, a method for constructing a multi-level, cross-process-related industrial knowledge graph for a mineral flotation process includes the following steps:

[0052] Step S1: The data of lead-zinc ore flotation process is the main source, including production data and relevant technical information collected at the mine site. Step S1 specifically includes the following steps:

[0053] Step S11: Collect key data during the flotation of lead-zinc ore. Raw material parameters: lead-zinc ore mineral type (sphalerite, galena, pyrite, etc.), particle size distribution (0.01-0.1mm, 0.1-1mm, etc.), moisture content (8%, 10%, etc.); equipment status: stirring tank speed (200rpm, 300rpm, etc.), bubble generator air flow (0.2m 3 / min, etc.); environmental variables: slurry temperature (25°C, etc.), pH value (7.5-9.0, etc.); reagent dosage: collector (ethylthiocarbamate, 50g / t, 100g / t, etc.), foaming agent (pine oil, 20g / t, 30g / t, etc.).

[0054] Step S12: Preprocess the collected data. Data cleaning: remove outliers and duplicate values ​​in the lead-zinc ore flotation process; data formatting: convert the data in different formats in the lead-zinc ore flotation process into a unified structured format.

[0055] Step S2: Combined with the lead-zinc ore flotation process, based on the bidirectional dynamic process construction concept of the flotation tree model, a hierarchical association model is designed. Step S2 specifically includes the following steps:

[0056] Step S21: Model hierarchy definition. Root node: Mineral feed parameters, such as mineral type, particle size distribution, and moisture content; Trunk node: Main flotation process steps, including fast roughing, roughing, cleaning, and scavenging; Branch node: Sub-process parameters, such as bubble distribution, slurry stability, and slurry flow rate; Leaf node: Final output production indicators, such as lead concentrate grade and zinc concentrate recovery rate.

[0057] Step S22: defining the input-output relationship of the flotation tree model.

[0058] Flotation tree model nutrient delivery, setting the final concentration of target minerals to C 精矿 The transfer process takes into account the efficiency factor and possible loss factor of each step, and this transfer process is regulated by multiple dynamic parameters. It takes into account the interaction between different steps in the flotation process and the influence of environmental factors (such as pH value, temperature, etc.). The formula can be expressed as:

[0059]

[0060] Where: C 精矿 is the final concentrate concentration, C 原矿 is the original ore concentration, E 步骤i is the flotation efficiency factor of the i-th step, L 步骤i is the loss factor of step i, k j is the influence coefficient of slurry pH value on the efficiency of each process step, pH j is the pH value of step j, pH opt,j is the optimal pH value for step j.

[0061] Furthermore, the efficiency factor E 步骤i and loss factor L 步骤i , which can be modeled by the following dynamic formula:

[0062]

[0063] L 步骤i =α i ×C 步骤i,输入

[0064] Where: C 步骤i,输入 and C 步骤i,输出 are the input and output mineral concentrations of step i, α i is the loss coefficient of the i-th step, reflecting the mineral loss caused by operating conditions (such as reagent usage, pulp concentration, etc.) during the flotation process.

[0065] The absorption mechanism of the flotation tree model is the scavenging recovery rate. In the scavenging step, the residual target minerals are recovered. The scavenging recovery rate R 扫选It reflects the degree of recovery of residual minerals in tailings. The formula is:

[0066]

[0067] Where: C 原矿 is the concentration of target mineral in the original ore, C 尾矿 is the concentration of target mineral in tailings.

[0068] In addition, the scavenging recovery rate is also affected by environmental variables such as pulp concentration and pH value, which can be further dynamically processed through the following model:

[0069]

[0070] Where: β pH and β 温度 is the coefficient of the influence of pulp pH value and temperature on recovery rate, pH opt and T opt are the optimal pH value and temperature, pH and T are the current pH value and temperature of the slurry, respectively.

[0071] Step S3: Based on the semantic description of ontology modeling, combined with the flotation tree model and the dynamic mechanism of the tree, ontology modeling is used to realize the semantic description of the flotation process. Step S3 specifically includes the following steps:

[0072] Step S31: Define the basic structure of the flotation tree model ontology. Concept classes: for example, "lead minerals," "zinc minerals," "flotation cells," etc.; relationship classes: for example, "collector affects lead recovery rate," "pH value regulates pulp stability."

[0073] The relationship between collector and recovery rate, the amount of collector added X 捕收剂 and recovery rate R 回收 The relationship between is usually nonlinear and can be modeled by the following formula:

[0074]

[0075] Among them, R 基准 is the baseline recovery (the recovery without collector), a and b are the experimental fitting parameters, which represent the nonlinear effect of the collector on the recovery.

[0076] The relationship between pH value and slurry stability. The effect of pH value on slurry stability can be expressed by the following formula:

[0077] L 稳定性 =k pH ·(pH-pH opt ) 2

[0078] Among them, kpH is the response coefficient of stability, pH opt is the optimum pH value.

[0079] Step S32: instantiating the concept in the floating dendritic model body and corresponding to the dendritic model node. Based on the ontology model defined in step S31, the specific flotation process data is mapped into the ontology concept to generate an instance. For example, the "lead mineral" concept is instantiated as "galena" and "sphalerite", and the "flotation tank" is instantiated as "fast roughing tank", "cleaning tank", etc. The instance properties are defined using the Protege tool, for example: the properties of the galena instance: particle size range (0.01-0.1mm), content (4.5%). The properties of the flotation tank instance: processing capacity (500t / d), bubble generator flow (0.2m 3 / min). Then each node in the dendritic model is one-to-one corresponding to the concept instance in the ontology model, and the description logic (OWL DL) is used to define the associated rules for each mapping node, for example: if the bubble generator flow of a certain flotation tank exceeds 0.3m 3 / min, then add a "low stability" label to the ore pulp stability node; if the dosage of the collector increases by 10g / t, then dynamically update the estimated value of the lead concentrate recovery rate in the leaf node.

[0080] Step S4: knowledge graph construction and visualization. Step S4 specifically includes the following steps:

[0081] Step S41: knowledge graph construction, importing the ontology model into Neo4j through RDF / XML format to establish a bidirectional dynamic relationship between nodes and edges. Nodes: mineral types, reagents, flotation tanks, etc.; edges: process parameter association, efficiency transmission, etc.

[0082] Step S42: knowledge graph visualization. The graph dynamically displays the transmission process of the lead-zinc ore flotation process from raw materials to final products; the relationship between the dosage of the collector and the grade of the lead concentrate is visualized; the dynamic influence of the pulp pH value in each process tank on the recovery rate of the target mineral is visualized.

[0083] Through the above steps, the present application not only realizes the full-process modeling of the lead-zinc ore flotation process, but also introduces a bidirectional dynamic process transmission mechanism of the flotation dendritic model, making the knowledge graph more dynamic and relevant, and providing a theoretical basis and technical support for complex process optimization.

[0084] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not intended to limit the scope of protection of the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the essence and scope of the technical solutions of the present application.

Claims

1. A method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association, characterized in that: The steps include: Step 1: Collect process data in the flotation process; Step 2: Construct a flotation tree model and define the input-output relationship of the flotation tree model based on process data; Step 3: Use ontology modeling to semantically describe the concepts and relationships in the flotation tree model to form an ontology modeled flotation tree model; Step 4: Use the flotation tree model of the ontology modeling to construct a knowledge graph through the graph database Neo4j, and use visualization tools to display the graph; The specific steps of step 2 are as follows: Step S21: The flotation tree model hierarchy is defined as follows: root node: mineral feed parameters, including mineral type, particle size distribution, and moisture content; trunk node: flotation process steps, including fast roughing, roughing, cleaning, and scavenging; branch node: sub-process parameters, including bubble distribution, pulp stability, and pulp flow rate; leaf node: final output production indicators, including concentrate grade and concentrate recovery rate; Step S22: Define the input-output relationship of the flotation tree model: Set up the flotation tree model for nutrient delivery: Set the final concentrate concentration of the target mineral to C 精矿 , the transfer process takes into account the efficiency factor of each step and the possible loss factor, and the formula is expressed as: Where: C 精矿 is the final concentrate concentration, C 原矿 is the original ore concentration, E 步骤i is the flotation efficiency factor of the i-th step, L 步骤i is the loss factor of step i, k j is the influence coefficient of slurry pH value on the efficiency of each process step, pH j is the pH value of step j, pH opt,j is the optimal pH value of the jth step; n is the total number of flotation steps, and m is the actual number of steps in the flotation process; The specific steps of step three are as follows: Step S31: defining the basic structure of the ontology model: concept classes and relationship classes; the concept classes include lead minerals, zinc minerals, and flotation cells; the relationship classes include the effects of collectors on lead recovery and pH value on pulp stability; The amount of collector added is X 捕收剂 and recovery rate R 回收 The relationship between is modeled by the following formula: Among them, R 基准 is the baseline recovery, i.e., the recovery without collector, a and b are experimental fitting parameters, representing the nonlinear effect of collector on the recovery; The relationship between pH value and slurry stability is expressed by the following formula: L 稳定性 =k pH ·(pH-pH opt ) 2 Among them, k pH is the stability response factor, pH opt For the optimal pH value.

2. The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association according to claim 1, characterized in that: In step 1, the process data includes raw material parameters, particle size distribution, moisture content, equipment status, environmental variables and reagent dosage.

3. The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association according to claim 2, characterized in that: The raw material parameters include the type of ore minerals; the equipment status includes the stirring tank rotation speed and the bubble generator gas flow rate; the environmental variables include the slurry temperature and pH value; and the reagent dosage includes the collector dosage and the foaming agent dosage.

4. The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association according to claim 1, characterized in that: The process data is normalized and pre-processed, and the specific method is as follows: outliers and duplicate values ​​in the ore flotation process are removed, and then the data in different formats in the ore flotation process are converted into a unified structured format.

5. The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association according to claim 1, characterized in that: Efficiency factor E 步骤i and loss factor L 步骤i , modeled by the following dynamic formula: L 步骤i =a i ×C 步骤i,输入 Where: C 步骤i,输入 and C 步骤i,输出 are the input and output mineral concentrations of step i, α i is the loss coefficient of step i, reflecting the mineral loss caused by operating conditions during the flotation process; the operating conditions include the amount of reagent used and the pulp concentration.

6. The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association according to claim 1, characterized in that: Dynamic processing of the scanning step: Where: C 原矿 is the concentration of target mineral in the original ore, C 尾矿 is the concentration of target minerals in tailings, β pH and β 温度 are the coefficients of the influence of pulp pH value and temperature on recovery rate, pH opt and T opt are the optimal pH value and temperature, respectively; pH ​​and T are the current pH value and temperature of the slurry, respectively.

7. The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association according to claim 1, characterized in that: The step 4 includes the following steps: Step S41: knowledge graph construction, importing the flotation tree model of ontology modeling into Neo4j in RDF / XML format, and establishing a bidirectional dynamic relationship between nodes and edges. The nodes include mineral types, reagents, and flotation cells; Edges include process parameter association and efficiency transfer; Step S42: Knowledge graph visualization: The graph dynamically displays the transfer process of the mineral flotation process from raw materials to final products; visualizes the relationship between collector dosage and lead concentrate grade; and dynamically displays the impact of the pulp pH value in each process tank on the target mineral recovery rate.

8. The method for constructing an industrial knowledge graph for a mineral flotation process based on multi-level cross-process association according to claim 1, characterized in that: The mineral flotation includes lead-zinc ore flotation.

Images