Cooperative control method for preventing spontaneous combustion and consolidation of high-sulfur ore

Abstract

The invention relates to the technical field of mining, in particular to a cooperative control method for preventing spontaneous combustion and consolidation of high-sulfur ore. Comprising the steps that mine sampling is carried out, spontaneous combustion tendency analysis is carried out, and mineral components, sulfur content, oxidation weight gain, self-heating and spontaneous combustion characteristics of ores are determined; according to the ore oxidation quality increase rate, the self-heating point and the spontaneous combustion point, an RS-cloud model is adopted to construct an ore spontaneous combustion tendency evaluation model, and evaluation is carried out; carrying out an ore consolidation determination experiment by adopting a weight increment method, analyzing the influence characteristics of the sulfur content and the water content on ore consolidation in a simulated stope temperature environment, and predicting the ore consolidation time by adopting a GA-BP neural network; the relationship between the spontaneous combustion tendency and the consolidation characteristic of the ore is established, and a quantitative control method is formulated from three aspects of watering, stockpiling time and an inhibitor. According to the design, the spontaneous combustion risk of the sulfide ore can be effectively reduced, meanwhile, stope ore consolidation is prevented, the stope stoping process optimization of the high-sulfur underground mine is guided, and the mining efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mining, in particular to a coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ores. Background Art

[0002] Sulfide ores refer to metallic minerals with a high sulfur content, with iron sulfide comprising the majority. According to relevant statistics, over one-third of mines in my country with safety hazards are pyrite mines. Because pyrite contains readily oxidizable components such as sulfur, it is susceptible to oxidation during both mining and transportation due to external factors such as oxygen and humidity. Ore oxidation in high-sulfur iron mines can lead to spontaneous combustion. Spontaneous combustion fires during the mining of high-sulfur deposits are a major hazard in non-coal mines, causing not only significant economic losses but also a range of safety and environmental issues. Furthermore, one-tenth of pyrite mines experience ore consolidation. Once this occurs, ore consolidation negatively impacts mining and transportation. Once consolidated, ore loading becomes impossible and may even require secondary blasting operations, severely impacting mine safety and efficiency.

[0003] Due to the serious hazards of spontaneous combustion in pyrite, its mechanisms, prevention measures, and other aspects have been extensively studied both domestically and internationally, resulting in a wealth of relevant research literature. However, despite the widespread existence of pyrite consolidation, research on this topic remains limited. Furthermore, the analysis and control measures for spontaneous combustion and consolidation prevention in sulfide ores are separate and independent, lacking a coordinated control method for both spontaneous combustion and consolidation in high-sulfur iron ores. Therefore, we propose a coordinated control method for both spontaneous combustion and consolidation in high-sulfur iron ores. Summary of the Invention

[0004] The object of the present invention is to provide a coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ores, so as to solve the problems raised in the above background technology.

[0005] To solve the above technical problems, the present invention provides a method for collaboratively controlling the prevention of spontaneous combustion and consolidation of high-sulfur ores, comprising the following steps: S100. Conduct mine sampling and spontaneous combustion tendency analysis to determine the ore mineral composition and sulfur content, oxidation weight gain, self-heating and spontaneous combustion characteristics; S200, based on the ore oxidation mass increase rate, autoheat point and autoignition point, the RS-cloud model is used to construct an ore spontaneous combustion tendency evaluation model and conduct an evaluation; S300, using the weight gain method to conduct ore consolidation measurement experiments, analyzing the effects of sulfur and water content on ore consolidation under simulated stope temperature environment, and using GA-BP neural network to predict ore consolidation time; S400, establish the relationship between the spontaneous combustion tendency of the ore and the consolidation characteristics, and develop a quantitative control method from the aspects of watering, storage time, and inhibitor.

[0006] As a further improvement of the technical solution, in step S100, the method for analyzing the spontaneous combustion tendency of the sulfide ore sample is as follows: Carrying out ore sampling at representative stope in different sections of underground mine, and carrying out mineral composition detection and analysis of the sulfide ore sample to obtain the percentage of sulfur content of the sample, and obtain the oxidation weight gain characteristics, oxidation self-heating characteristics and ore heat accumulation and temperature rise characteristics of the sample; The oxidation weight gain characteristics are obtained by the following method: The sample is crushed and ground, a certain weight of the sample is placed in a glass dish, the sample is oxidized under constant temperature and humidity conditions, the ore sample is turned regularly to ensure uniform and complete oxidation, and the weight is measured at regular time every day until the sample mass no longer increases obviously, and the change rule of the oxidation weight gain of the ore sample with time is obtained; The oxidation self-heating characteristics are obtained by the following method: The ore sample is subjected to gradient heating in a high temperature test box, and the temperature of the ore sample and the environmental temperature data are monitored by using thermocouple and temperature acquisition module, and the size of the intersection point value of the edge region temperature and the center temperature of the ore sample is compared to determine whether the ore sample is easy to self-heat and the self-heating point; The ore heat accumulation and temperature rise characteristics are obtained by the following method: The ore sample placed in the reactor is placed in a high temperature test box, and the temperature of the ore sample and the environmental temperature are measured by using thermocouple; the environmental temperature at which the two cross is recorded, which is approximately defined as the spontaneous combustion point of the ore sample, and the ore heat accumulation and temperature rise characteristics are obtained.

[0007] As a further improvement of the technical solution, in step S100, when carrying out ore sampling at representative stope in different sections of underground mine, the particle size of the ore sample is 80 mesh, and the sample weight for oxidation self-heating characteristics and ore heat accumulation and temperature rise characteristics is 100g.

[0008] As a further improvement of the technical solution, in step S200, the ore spontaneous combustion tendency evaluation based on the RS-cloud model is as follows: S201, selection of evaluation index: selecting the oxidation mass increase rate, self-heating point and spontaneous combustion point of the ore sample as the evaluation index; S202, determination of evaluation index weight based on rough set: using deviation standardization method to carry out dimensionless processing on the original data of the three indexes, and the data processing formula is as follows: ; ; wherein, denotes the original data of the th ore sample under the th evaluation index; wherein, corresponds to different ore samples, corresponds to different evaluation indexes (i.e. the oxidation mass increase rate, the self-heating point, and the spontaneous combustion point); denotes the maximum value in the original data of all ore samples under the th evaluation index; denotes the minimum value in the original data of all ore samples under the th evaluation index; denotes the data of the th ore sample after dimensionless processing under the th evaluation index; wherein, the formula (1) is used for dimensionless processing of the smaller-the-better index (the oxidation mass increase rate of the ore sample), and the formula (2) is used for dimensionless processing of the larger-the-better index (the self-heating point and the spontaneous combustion point); Further, the rough set method (RS) is selected to calculate the weight of each evaluation index; S203, cloud model and standardization: cloud droplets are generated by using a cloud generator according to the numerical characteristics (expectation , entropy , and hyper-entropy ) of the cloud, and a comprehensive discrimination (three indexes) cloud model of the spontaneous combustion tendency of sulfide ore is established; S204, model verification: combining with the actual examples of spontaneous combustion of sulfide ore in underground mines, the comprehensive evaluation of the spontaneous combustion tendency of the ore is carried out, and the evaluation results are compared with the Bayes discrimination results and the actual situation to verify the feasibility of the model; S205, evaluation of the spontaneous combustion tendency of the ore sample: the RS-standardized cloud model is used to comprehensively evaluate the spontaneous combustion tendency grade of the ore sample, and the evaluation grade is obtained.

[0009] As a further improvement of the technical solution, in the step S202, the rough set method RS is selected to calculate the weight of each evaluation index, and the specific steps are as follows: Step 1: according to the evaluation index attribute, the data is discretized and a knowledge base is established , wherein is a domain, is a set of condition attribute sets, is a condition attribute corresponding to a certain factor; Step 2: delete objects with the same attribute value (eliminate duplicate rows) to make the decision table the simplest and reduce the calculation time; Step 3: calculate the domain the set of condition attributes on the equivalence class classification of ; Step4: Calculate , where is the cardinality of the set (the number of elements); Step5: Calculate the domain of each condition attribute on and importance of , where is the cardinality of the set, refers to the condition attribute currently calculating the importance of (i.e. one of the evaluation indexes such as the oxidation mass increase rate, self-heating point or spontaneous ignition point), denotes the importance of attribute , used to measure the distinguishing ability and contribution of the attribute in the ore spontaneous combustion tendency evaluation index system; Step6: For each condition attribute , calculate the classification of the set of condition attributes on after , and then calculate ; Step7: Calculate the importance of , and the importance of each attribute and objective weight is normalized data after normalization. As a further improvement of the technical solution, in step S203, the process of cloud model and its standardization includes:

[0010] The numerical characteristics of the cloud model are calculated using the following formula: ; Where, represents expectation, represents entropy, represents hyper-entropy; and are the maximum and minimum boundary values corresponding to the risk level standard, respectively; is a constant, i.e. hyper-entropy, mainly affecting the dispersion degree of cloud droplets when the cloud model is generated, and has: .

[0011] As a further improvement of the technical solution, in step S205, the risk level standard of sulfide ore spontaneous combustion tendency is divided into four levels: Level I: Extremely dangerous, extremely easy to self-ignite; Level II: High risk, easy to self-ignite; ​Grade III: general risk, easy to self-heat; Grade IV: small risk, not easy to self-ignite.

[0012] As a further improvement of the technical solution, in step S300, the method for predicting the ore consolidation time by using GA-BP neural network is as follows: The weight gain method is used to carry out ore consolidation determination experiments under the influence of sulfur content and water content, and the consolidation characteristics of the sample are obtained according to the change rule of the sample weight gain rate with time; The experimental data of the influence of water content on pyrite consolidation are analyzed, the relationship between the consolidation degree of various ore samples and the initial water content is fitted, and the optimal functional relationship between water content and pyrite consolidation effect is obtained: ; Among them, The weight gain rate of the ore sample is in percentage (%), which is used to reflect the degree of mass change of the ore during the consolidation process, and is an important index for measuring the consolidation characteristics of the ore; The water content of the ore sample is in percentage (%), that is, the content of water in the ore, which is one of the key factors affecting the consolidation characteristics of the ore; 、 、 The constant parameters are fitted by analyzing the experimental data of the influence of water content on pyrite consolidation, fitting the relationship between the consolidation degree of various ore samples and the initial water content, and are used to quantify the functional relationship between water content and weight gain rate; Using the function, the theoretical maximum weight gain rate of the ore sample at different times and the most likely to cause consolidation water content can be calculated and obtained; A GA-BP neural network prediction model of ore consolidation time is established, the sulfur content, water content and time are used as the input values of the neural network, and the pyrite weight gain rate is used as the output value, the BP neural network optimized by genetic algorithm is used to predict the pyrite consolidation, and an intelligent prediction method of ore consolidation time is obtained.

[0013] As a further improvement of the technical solution, in step S400, the relationship between the spontaneous combustion tendency and the consolidation characteristics of the ore is established, and a quantitative control method is developed from three aspects of watering, storage time and inhibitor, including the following: The relationship between the spontaneous combustion tendency and the consolidation characteristics mainly considers the anti-consolidation control for the ore without spontaneous combustion tendency, and carries out the collaborative control of anti-spontaneous combustion and consolidation for the ore with spontaneous combustion tendency; For the ore without spontaneous combustion tendency, only anti-consolidation control is considered, and the control measures are mainly to control the ore storage time, and secondarily to spray the anti-inhibitor (anti-consolidation agent); The ore with spontaneous combustion tendency carries out the collaborative control of spontaneous combustion prevention and consolidation, and the control measures are mainly to control the ore stacking time, and the stacking time is obtained by using a GA-BP neural network prediction model to prevent the ore from spontaneous combustion. The ore with spontaneous combustion tendency carries out the collaborative control of spontaneous combustion prevention and consolidation, and the control measures are mainly to control the ore stacking time, and the stacking time is obtained by using a GA-BP neural network prediction model to prevent the ore from spontaneous combustion.

[0014] As a further improvement of the technical solution, the type selection and concentration ratio of the anti-consolidation agent should be determined by a consolidation determination experiment.

[0015] Compared with the prior art, the present application has the following beneficial effects: 1. In the collaborative control method of high-sulfur ore spontaneous combustion prevention and consolidation, before mining, the sulfide ore is sampled, the spontaneous combustion tendency of the sample is analyzed, the ore mineral composition and sulfur content, oxidation weight gain, self-heating and spontaneous combustion characteristics are determined, the oxidation mass increase rate, self-heating point and spontaneous combustion point of the ore sample are selected as evaluation indexes, the evaluation index weight is determined by rough set, a comprehensive discriminant cloud model of sulfide ore spontaneous combustion tendency is established, the comprehensive evaluation of the spontaneous combustion tendency of the ore is carried out by combining the typical underground mine sulfide ore spontaneous combustion example, the evaluation results are compared with the Bayes method discriminant results and the actual situation to test the feasibility of the model, the RS-standardized cloud model is used to comprehensively evaluate the spontaneous combustion tendency grade of the ore sample, and the evaluation grade is obtained, the weight gain method is used to obtain the consolidation characteristics of the sample, the optimal function relationship between the water content and the pyrite consolidation effect is obtained by fitting, the GA-BP neural network prediction model of the ore consolidation time is established, the consolidation of the pyrite is predicted, the relationship between the spontaneous combustion tendency and the consolidation characteristics of the ore is established, and the quantitative control method is developed from the three aspects of watering, stacking time and inhibitor, if the ore does not have spontaneous combustion tendency, only the anti-consolidation control is considered, the stacking time is obtained by using the GA-BP neural network prediction model, if the ore has spontaneous combustion tendency, the collaborative control of spontaneous combustion prevention and consolidation should be carried out, and the anti-inhibitor (anti-consolidation agent) should be sprayed when the stacking time exceeds the ore consolidation time. 2. The collaborative control method of high-sulfur ore spontaneous combustion prevention and consolidation can effectively reduce the spontaneous combustion risk of sulfide ore and prevent the consolidation of the ore in the stope, which is helpful to guide the optimization of the stope mining process in the high-sulfur underground mine and improve the mining efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The present application is an exemplary method flowchart. DETAILED DESCRIPTION

[0017] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described in order to make the technical solutions in the embodiments of the present application apparent to those skilled in the art. Obviously, the described embodiments are only a part rather than all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0018] Embodiment 1 As shown in the embodiment, the embodiment provides a synergistic control method for preventing spontaneous combustion and consolidation of high-sulfur ore, comprising the following steps: Figure 1 S100, carrying out mine sampling and analyzing spontaneous combustion tendency to determine ore mineral composition, sulfur content, oxidation weight gain, self-heating and spontaneous combustion characteristics; In the embodiment, the method for analyzing the spontaneous combustion tendency of sulfide ore samples is as follows: In the mine, ore sampling is carried out in different representative stope sections underground, and mineral composition detection and analysis of sulfide ore samples are carried out to obtain the percentage of sulfur content in the samples, and the oxidation weight gain characteristics, oxidation self-heating characteristics and ore heat accumulation and temperature rise characteristics of the samples are obtained; The oxidation weight gain characteristics are obtained by the following method: The sample is crushed and ground, a certain weight of the sample is placed in a glass dish, the sample is oxidized under constant temperature and humidity conditions, the ore sample is turned regularly to ensure uniform and complete oxidation, and the weight is measured at regular time every day until the sample quality no longer increases obviously, and the change rule of the oxidation weight gain of the sample with time is obtained; The oxidation self-heating characteristics are obtained by the following method: The oxidation self-heating characteristics are obtained by the following method: The oxidation self-heating characteristics are obtained by the following method: The ore heat accumulation and temperature rise characteristics are obtained by the following method: The ore heat accumulation and temperature rise characteristics are obtained by the following method:

[0019] When ore sampling is carried out in different representative stope sections underground, the particle size of the ore sample is 80 mesh, and the sample weight for oxidation self-heating characteristics and ore heat accumulation and temperature rise characteristics is 100g.

[0020] In the above steps, the sulfur content percentage of the samples was obtained by sampling from representative stopes in different middle sections of the mine. The oxidation weight gain, self-heating and spontaneous combustion characteristics of the mine samples were determined by combining constant temperature oxidation weight gain experiments, autothermal point determination experiments, and autoignition point determination experiments, so as to construct an RS-cloud model for evaluating the spontaneous combustion tendency of ores that is highly consistent with typical examples and improve the accuracy of spontaneous combustion tendency prediction.

[0021] S200, based on the ore oxidation mass increase rate, autoheat point and autoignition point, the RS-cloud model is used to construct an ore spontaneous combustion tendency evaluation model and conduct an evaluation; In this embodiment, the steps for evaluating the spontaneous combustion tendency of ore based on the RS-cloud model are as follows: S201. Selection of evaluation indicators: Selecting three factors, namely, oxidation mass increase rate, autoheat point, and autoignition point of the ore sample, as evaluation indicators; S202. Determine the weights of evaluation indicators based on rough sets: Use the deviation normalization method to perform dimensionless processing on the original data of the three indicators. The data processing formula is as follows: ; ; in, Indicates the The ore samples were The original data under the evaluation indicators; Corresponding to different ore samples, Corresponding to different evaluation indicators (i.e. oxidation mass increase rate, autoheating point, autoignition point); Indicates in The maximum value of the original data of all ore samples under each evaluation index; Indicates in The minimum value of all the original data of ore samples under the evaluation index; Indicates the The ore samples were The data after dimensionless processing under the evaluation index; The smaller the better the index (oxidation mass increase rate of the ore sample), the dimensionless treatment is carried out using formula (1), and the larger the better the index (autothermal point and autoignition point) is carried out using formula (2). Furthermore, the rough set method (RS) is selected to calculate the weight of each evaluation index; the specific steps are: Step 1: Discretize the data and build a knowledge base based on the evaluation index attributes ,in is the domain, Is a set of conditional attributes, is a conditional attribute corresponding to a certain factor; Step 2: Delete objects with the same attribute values ​​(eliminate duplicate rows) to make the decision table simpler and reduce calculation time; Step 3: Calculation Domain Conditional attribute collection on Equivalence class classification ; Step 4: Calculation ,in is the cardinality of the set (the number of elements); Step 5: Calculation Domain Each conditional attribute Classification and The importance of ,in is the cardinality of the set, Refers to the conditional attribute of the current calculation importance (i.e. one of the evaluation indicators, such as oxidation mass increase rate, autoheat point or autoignition point), Representation attributes The importance of is used to measure the distinguishing ability and contribution of this attribute in the evaluation index system of ore spontaneous combustion tendency; Step 6: For each conditional attribute ,calculate The subsequent domain Conditional attribute collection on Classification , then calculate ; Step 7: Calculation Importance, normalized importance of each attribute and objective weight is the normalized data; S203. Cloud model and its standardization: The digital characteristics of the cloud model are calculated using the following formula: ; in, Express expectations, represents entropy, represents super entropy; and They are the maximum and minimum boundary values ​​of the corresponding hazard level standards; is a constant, which represents the super entropy, and mainly affects the dispersion of cloud droplets when the cloud model is generated, and: ; Use cloud generator to calculate the cloud's digital characteristics (expected ,entropy , Super Entropy ) to generate cloud droplets, to establish a comprehensive discrimination model (three indexes) for the spontaneous combustion tendency of sulfide ores; S204, model verification: combining a typical underground mine sulfide ore spontaneous combustion example, the comprehensive evaluation of the spontaneous combustion tendency of the ore, and the evaluation results are compared with the Bayes method discrimination results and the actual situation to test the feasibility of the model; S205, ore sample spontaneous combustion tendency evaluation: using the RS-standardized cloud model to comprehensively evaluate the spontaneous combustion tendency grade of the ore sample, and obtain the evaluation grade; Among them, the risk grade standard of the spontaneous combustion tendency of sulfide ore is divided into four levels: Class I: extremely dangerous, extremely easy to spontaneously combust; Class II: high risk, easy to spontaneously combust; Class III: general risk, prone to self-heating; Class IV: low risk, not prone to spontaneous combustion.

[0022] In the above steps, the oxidation mass increase rate, self-heating point and spontaneous combustion point of the ore are selected as the evaluation indexes, the evaluation index weight is determined by rough set, the comprehensive discrimination cloud model of the spontaneous combustion tendency of sulfide ore is established, the spontaneous combustion tendency of the ore is comprehensively evaluated in combination with the project, the evaluation results are compared with the Bayes method discrimination results and the actual situation to test the feasibility of the model; the RS-standardized cloud model is used to comprehensively evaluate the spontaneous combustion tendency grade of the ore sample, and the evaluation grade is obtained. This method overcomes the limitation of the traditional normal cloud model due to the data dimension, and can accurately describe the spontaneous combustion concept of sulfide ore.

[0023] S300, using the weight gain method to carry out ore consolidation determination experiment, analyzing the influence of sulfur content and water content on ore consolidation under simulated stope temperature environment, and using GA-BP neural network to predict ore consolidation time; In this embodiment, the method for predicting ore consolidation time by GA-BP neural network is as follows: Using the weight gain method, ore consolidation determination experiments under the influence of sulfur content and water content are carried out, and the consolidation characteristics of the sample are obtained according to the change rule of the sample weight gain rate with time; The experimental data of the influence of water content on pyrite consolidation are analyzed, the relationship between the consolidation degree of various ore samples and the initial water content is fitted, and the optimal functional relationship between water content and pyrite consolidation effect is obtained: ; Among them, The weight gain rate of the ore sample is expressed by percentage (%), which is used to reflect the degree of mass change of the ore during the consolidation process, and is an important index for measuring the consolidation characteristics of the ore; The moisture content of the ore sample is a percentage (%), i.e., the content of water in the ore, which is one of the key factors affecting the consolidation characteristics of the ore; 、 、 is a constant parameter obtained by fitting, and the specific value is determined by analyzing the experimental data of the influence of moisture content on pyrite consolidation and fitting the relationship between the consolidation degree of various ore samples and the initial moisture content, which is used to quantify the functional relationship between the moisture content and the weight gain rate; Using the function, the theoretical maximum weight gain rate of the ore sample at different times and the most likely to cause consolidation moisture content can be calculated; A GA-BP neural network prediction model for ore consolidation time is established, the sulfur content, moisture content, and time are used as the input values of the neural network, and the pyrite weight gain rate is used as the output value. The genetic algorithm optimized BP neural network is used to predict the pyrite consolidation, and an intelligent prediction method for ore consolidation time is obtained.

[0024] In the above steps, the weight gain method is used to carry out ore consolidation determination experiments under the influence of sulfur content and moisture content, and the consolidation characteristics of the sample are obtained. The relationship between the consolidation degree of various ore samples and the initial moisture content is fitted, and the optimal functional relationship between the moisture content and the pyrite consolidation effect is obtained. The sulfur content, moisture content, and time are used as the input values of the neural network, and the pyrite weight gain rate is used as the output value. A GA-BP neural network prediction model for ore consolidation time is established, and an intelligent prediction method for ore consolidation time is obtained. This method optimizes the selection of the topology structure of the BP neural network, reduces the training workload and time, and improves the analysis efficiency under the premise of ensuring the analysis effect.

[0025] S400, establish the relationship between the spontaneous combustion tendency and the consolidation characteristics, and develop a quantitative control method from the aspects of watering, storage time, and inhibitor; In this embodiment, the relationship between the spontaneous combustion tendency and the consolidation characteristics is established, and a quantitative control method is developed from the aspects of watering, storage time, and inhibitor, including the following: The relationship between the spontaneous combustion tendency and the consolidation characteristics mainly considers the anti-consolidation control for ore without spontaneous combustion tendency, and carries out the collaborative control of anti-spontaneous combustion and consolidation for ore with spontaneous combustion tendency; For ore without spontaneous combustion tendency, only anti-consolidation control is considered, and the control measures are mainly to control the ore storage time, and secondarily to spray the anti-inhibitor (anti-consolidation agent); Among them, the type selection and concentration ratio of the anti-consolidation agent should be determined by the consolidation determination experiment; Specifically, the anti-consolidation agent ratio for sulfide ore is determined by the ore consolidation experiment, and it is recommended to spray the anti-consolidation agent with a mass ratio of HEC and NaHCO3 of 1:2 and a solution solute mass fraction of 2%; The ore with spontaneous combustion tendency carries out the collaborative control of spontaneous combustion prevention and consolidation, the control measures are mainly to control the ore stacking time, the stacking time is obtained by using a GA-BP neural network prediction model, so as to prevent the ore from spontaneous combustion; The ore with spontaneous combustion tendency carries out the collaborative control of spontaneous combustion prevention and consolidation, and when the stacking time exceeds the ore consolidation occurrence time, the anti-inhibitor (anti-consolidation agent) should also be sprayed.

[0026] In the above steps, the relationship between the ore spontaneous combustion tendency and the consolidation characteristics is established, and the quantitative control method is formulated from the aspects of watering, stacking time and inhibitor. If the ore does not have spontaneous combustion tendency, the anti-consolidation control can be considered, and the stacking time is obtained by using a GA-BP neural network prediction model. If the ore has spontaneous combustion tendency, the collaborative control of spontaneous combustion prevention and consolidation should be carried out, and when the stacking time exceeds the ore consolidation occurrence time, the anti-inhibitor (anti-consolidation agent) should also be sprayed.

[0027] Those skilled in the art can understand that the process of implementing all or part of the steps of the above embodiments can be completed by hardware, or by program to instruct related hardware.

[0028] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above embodiments, the above embodiments and descriptions in the specification are only preferred examples of the present application, and are not intended to limit the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ores, characterized in that: The steps include: S100. Conduct mine sampling and spontaneous combustion tendency analysis to determine the ore mineral composition and sulfur content, oxidation weight gain, self-heating and spontaneous combustion characteristics; S200, based on the ore oxidation mass increase rate, autoheat point and autoignition point, the RS-cloud model is used to construct an ore spontaneous combustion tendency evaluation model and conduct an evaluation; S300, using the weight gain method to conduct ore consolidation measurement experiments, analyzing the effects of sulfur and water content on ore consolidation under simulated stope temperature environment, and using GA-BP neural network to predict ore consolidation time; S400. Establish the relationship between the spontaneous combustion tendency of ore and its consolidation characteristics, and develop a quantitative control method from three aspects: watering, storage time, and inhibitors.

2. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 1, characterized in that: In step S100, the method for analyzing the spontaneous combustion tendency of the sulfide ore sample is as follows: Ore sampling was carried out in representative stopes at different middle sections of the mine, and the mineral composition of the sulfide ore samples was tested and analyzed to obtain the percentage of sulfur content in the samples, as well as the oxidation weight gain characteristics, oxidation self-heating characteristics, and ore heat accumulation and temperature rise characteristics of the samples; The oxidative weight gain characteristics are obtained by: The sample is crushed and ground, and a certain weight of the sample is weighed and placed in a glass dish. The sample is oxidized under constant temperature and humidity conditions. The ore sample is regularly turned to ensure uniform and thorough oxidation. The weight of the sample is measured regularly every day until the mass of the sample no longer increases significantly, and the change pattern of the ore sample oxidation weight gain over time is obtained; The oxidation self-heating characteristics are obtained by: A high-temperature test chamber is used to gradually increase the temperature of the ore sample. The temperature rise data of the ore sample and the environment are monitored using thermocouples and temperature acquisition modules. The intersection value of the temperature of the edge area of ​​the ore sample and the temperature of the center of the ore sample is compared. Whether the ore sample is prone to self-heating and the self-hot spot is determined by whether the center temperature of the ore sample exceeds the edge temperature; The ore heat accumulation and temperature raising characteristics are obtained by the following methods: The ore sample placed in the reactor is placed in a high-temperature test chamber, and a thermocouple is used to simultaneously measure the ore sample temperature and the ambient temperature; the ambient temperature at the intersection of the two is recorded and approximately defined as the auto-ignition point of the ore sample to obtain the heat accumulation and temperature rise characteristics of the ore.

3. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 2, characterized in that: In the step S100, when ore sampling is carried out in representative stopes at different middle sections of the mine, the particle size of the ore sample is 80 mesh, and the weight of the oxidative self-heating characteristics and ore heat accumulation temperature rise characteristics samples is 100g.

4. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 1, characterized in that: In step S200, the steps for evaluating the spontaneous combustion tendency of the ore based on the RS-cloud model are as follows: S201. Selection of evaluation indicators: Selecting three factors, namely, oxidation mass increase rate, autoheat point, and autoignition point of the ore sample, as evaluation indicators; S202. Determine the weights of evaluation indicators based on rough sets: Use the deviation normalization method to perform dimensionless processing on the original data of the three indicators. The data processing formula is as follows: ； ； in, Indicates the The ore samples were The original data under the evaluation indicators; Indicates in The maximum value of the original data of all ore samples under each evaluation index; Indicates in The minimum value of all the original data of ore samples under the evaluation index; Indicates the The ore samples were The data after dimensionless processing under the evaluation index; The smaller the better the index, the dimensionless processing is carried out using formula (1), and the larger the better the index, the dimensionless processing is carried out using formula (2); The rough set method RS is selected to calculate the weight of each evaluation index; S203. Cloud model and its standardization: Use a cloud generator to generate cloud droplets based on the digital characteristics of the cloud, and establish a comprehensive cloud model for identifying the spontaneous combustion tendency of sulfide ores; S204, Model Verification: Combined with typical examples of spontaneous combustion of sulfide ores in underground mines, a comprehensive evaluation of the spontaneous combustion tendency of the ore is conducted. The evaluation results are compared with the Bayesian method judgment results and the actual situation to verify the feasibility of the model; S205. Evaluation of spontaneous combustion tendency of ore samples: Use the RS-standardized cloud model to comprehensively evaluate the spontaneous combustion tendency level of the ore samples to obtain an evaluation level.

5. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 4, characterized in that: In step S202, the rough set method RS is selected to calculate the weight of each evaluation index, and the specific steps are as follows: Step 1: Discretize the data and establish a knowledge base based on the evaluation index attributes ,in is the domain, Is a set of conditional attributes, is a conditional attribute corresponding to a certain factor; Step 2: Delete objects with the same attribute values ​​to make the decision table simplest and reduce calculation time; Step 3: Calculation Domain Conditional attribute collection on Equivalence class classification ; Step 4: Calculation ,in is the cardinality of the set; Step 5: Calculation Domain Each conditional attribute Classification and The importance of ,in is the cardinality of the set; Step 6: For each conditional attribute ,calculate The subsequent domain Conditional attribute collection on Classification , then calculate ; Step 7: Calculation The importance of each attribute and the objective weight are normalized data.

6. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 4, characterized in that: In step S203, the cloud model and its standardization process include: The digital characteristics of the cloud model are calculated using the following formula: ； in, Express expectations, represents entropy, represents super entropy; and They are the maximum and minimum boundary values ​​of the corresponding hazard level standards; is a constant, which represents the super entropy, and mainly affects the dispersion of cloud droplets when the cloud model is generated, and: 。 7. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 4, characterized in that: In step S205, the sulfide ore spontaneous combustion tendency hazard level standard is divided into four levels: Level Ⅰ: extremely dangerous, very likely to spontaneously combust; Level II: Highly dangerous, prone to spontaneous combustion; Level III: moderately dangerous, prone to self-heating; Level IV: Low risk, not prone to spontaneous combustion.

8. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 1, characterized in that: In step S300, the method for predicting ore consolidation time using the GA-BP neural network is as follows: The weight gain method was used to carry out the ore consolidation test under the influence of sulfur content and water content, and the consolidation characteristics of the samples were obtained according to the change of sample weight gain rate over time. By analyzing the experimental data on the effect of water content on pyrite consolidation and fitting the relationship between the consolidation degree and initial water content of various ore samples, the optimal functional relationship between water content and pyrite consolidation effect was obtained: ； in, is the weight gain rate of the ore sample, in percentage (%); is the moisture content of the ore sample, in percentage (%); 、 、 The constant parameter obtained by fitting is determined by analyzing the experimental data on the effect of water content on pyrite consolidation and fitting the relationship between the consolidation degree and initial water content of various ore samples. This function can be used to calculate the theoretical maximum weight gain rate of the ore sample at different times and the corresponding water content that is most likely to cause consolidation; A GA-BP neural network prediction model for ore consolidation time was established, with sulfur content, water content, and time as neural network input values, and pyrite weight gain rate as output value. The BP neural network optimized by genetic algorithm was used to predict the consolidation of pyrite, and an intelligent prediction method for ore consolidation time was obtained.

9. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 1, characterized in that: In step S400, a relationship between the spontaneous combustion tendency of the ore and the consolidation characteristics is established, and a quantitative control method is developed from three aspects: watering, storage time, and inhibitors, including the following: The relationship between spontaneous combustion tendency and consolidation characteristics is mainly that for ores without spontaneous combustion tendency, only anti-consolidation control is considered, while for ores with spontaneous combustion tendency, coordinated control of spontaneous combustion and consolidation is carried out; For ores that do not have a tendency to spontaneous combustion, only anti-consolidation control is considered. The control measures are mainly to control the ore storage time, supplemented by spraying anti-inhibitors / anti-consolidation agents; For ores with a tendency to spontaneous combustion, coordinated control of spontaneous combustion and consolidation is carried out. The control measure is mainly to control the storage time of the ore. The storage time is obtained using the GA-BP neural network prediction model to prevent the ore from spontaneous combustion. For ores with a tendency to self-ignite, coordinated control of self-ignition and consolidation should be carried out. When the storage time exceeds the time when ore consolidation occurs, anti-inhibitors / anti-consolidation agents should also be sprayed.

10. The coordinated control method for preventing spontaneous combustion and consolidation of high-sulfur ore according to claim 9, characterized in that: The type selection and ratio concentration of the anti-solidification agent should be determined by solidification measurement experiments.

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