A filling method of mine cementitious materials based on volume ratio

Abstract

The present invention relates to the field of mine filling technology, and in particular to a method for filling mine cementitious materials based on volume ratio. The content includes: for different filling strengths, adaptive multivariable proportioning optimization algorithm is used to adaptively generate filling formulas and obtain a reference formula set; when the mine filling is running, process parameters are obtained in real time, and the volume ratio of cementitious materials and tailings slurry in the finished slurry is calculated in combination with the reference formula set; based on the volume ratio of cementitious materials and tailings slurry, the real-time addition amount of cementitious materials is calculated; delay compensation and multi-dimensional optimization prediction control algorithm are introduced to optimize the real-time addition amount of cementitious materials and obtain a corrected addition amount of cementitious materials; based on the corrected addition amount of cementitious materials, the filling of mine cementitious materials is realized. The technical problem of inaccurate control of the addition amount of cementitious materials and poor adaptability during mine filling is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of mine filling, and in particular to a filling method of mine cementitious materials based on volume ratio. Background Art

[0002] Backfill mining is the primary method for ensuring safe deep mining. The backfill process involves concentrating tailings from the concentrator to a suitable concentration for transport. Then, a cementing material is added and transported to the underground void, where it is allowed to solidify before safe mining can begin. As development deepens, the backfill distance gradually increases. Fine tailings and full tailings slurries are preferred for backfilling, as they are less prone to settling and easier to transport.

[0003] If the amount of filling cementitious material added is not accurate, the filling body will not solidify, which will lead to serious safety accidents; the traditional way of adding cementitious material is to add it according to the mass ratio of tailings dry sand to cementitious material, but when the tailings slurry concentration is too low, adding cementitious material according to a fixed mass ratio will reduce the filling strength. When the tailings slurry concentration is too high, the addition of cementitious material will increase, resulting in waste.

[0004] Therefore, the traditional method of adding the above-mentioned cementitious material has technical problems of inaccurate control of the amount of cementitious material added and poor self-adaptation. Summary of the Invention

[0005] The present invention provides a filling method of a mine cementitious material based on a volume ratio, so as to solve the technical problems of inaccurate control of the addition amount of the cementitious material and poor self-adaptation during the mine filling process.

[0006] The present invention provides a method for filling a mine cementitious material based on a volume ratio, which specifically includes the following technical solutions:

[0007] A filling method of a mine cementitious material based on volume ratio comprises the following steps:

[0008] S1. Adaptively generate filling recipes for different filling intensities using an adaptive multivariable ratio optimization algorithm and obtain a reference recipe set;

[0009] S2. During mine filling operation, process parameters are acquired in real time, and the volume ratio of cementitious material and tailings slurry in the finished slurry is calculated in combination with a reference formula set; based on the volume ratio of cementitious material and tailings slurry, the real-time addition amount of cementitious material is calculated; delay compensation and multi-dimensional optimization and predictive control algorithms are introduced to optimize the real-time addition amount of cementitious material to obtain a corrected addition amount of cementitious material; based on the corrected addition amount of cementitious material, the filling of the mine cementitious material is realized.

[0010] Preferably, the S1 specifically includes:

[0011] In the implementation of the adaptive multivariable mix optimization algorithm, influencing factors are extracted from historical filling tests, including tailings density, underflow concentration, cementitious material type factor, slurry fluidity coefficient and filling strength.

[0012] Preferably, the S1 specifically includes:

[0013] In the implementation of the adaptive multivariable ratio optimization algorithm, the influencing factors are taken as variables, and a multivariable nonlinear process response function with physical structure constraints is introduced. The nonlinear relationship between the variables is constructed by combining exponential operation, logarithmic operation and cubic root operation.

[0014] Preferably, the S1 specifically includes:

[0015] In the implementation process of the adaptive multivariable ratio optimization algorithm, a variable physical sensitive weight mechanism is introduced. The weight of each variable is calculated through a weight function based on the Gaussian kernel. The weights of all variables are summed and multiplied with the multivariable nonlinear process response function. The output of the multivariable nonlinear process response function is adjusted to obtain the weighted response function value.

[0016] Preferably, the S1 specifically includes:

[0017] In the implementation process of the adaptive multivariable matching optimization algorithm, a fuzzy residual calibration mechanism is introduced. Based on the weighted response function value and the expected value, the error is calculated, and the error is nonlinearly corrected through a bidirectional adjustment function to obtain the error correction amount. Based on the error correction amount, the expected value is error-corrected to obtain the final response value after error correction.

[0018] Preferably, the S1 specifically includes:

[0019] Based on the final response value after error correction, the multivariable nonlinear process response function is reversely analyzed to obtain the optimal amount of cementitious material added and generate a filling formula; the filling formulas generated under different filling strengths are combined into a reference formula set.

[0020] Preferably, the S2 specifically includes:

[0021] Based on the volume ratio of the cementitious material and the tailings slurry and the density of the cementitious material, the real-time addition amount of the cementitious material is calculated.

[0022] Preferably, the S2 specifically includes:

[0023] In the process of implementing the delay compensation and multi-dimensional optimization predictive control algorithm, the predicted delay time of material transportation is obtained based on the pipeline fluid mechanics model. Based on the predicted delay time of material transportation and combined with the flow rate of tailings and water in the process parameters, the predicted delay volume is calculated.

[0024] Preferably, the S2 specifically includes:

[0025] Based on the predicted delayed volume, a feedforward compensation control strategy is applied, a control cycle is introduced, and the real-time addition amount of the cementitious material is adjusted to obtain the compensated addition amount of the cementitious material.

[0026] Preferably, the S2 specifically includes:

[0027] By introducing multi-dimensional influencing factors and performing weighted combination, the amount of cementitious material added after compensation is optimized and adjusted to obtain the corrected amount of cementitious material added.

[0028] The beneficial effects of the technical solution of the present invention are:

[0029] 1. By employing an adaptive multivariable mix optimization algorithm, the amount of cementitious material to be added can be accurately calculated based on factors such as tailings density, underflow concentration, cementitious material type, and slurry fluidity in different mining environments. Starting from filling test data, the adaptive multivariable mix optimization algorithm achieves the optimal match between filling strength and cementitious material through nonlinear response control and process parameter back-calculation, thereby minimizing the amount of cementitious material used while ensuring that the filling strength meets the design standard.

[0030] 2. A delay compensation and multi-dimensional optimization predictive control algorithm was introduced to predict the material delivery delay time based on the pipeline fluid mechanics model, and to adjust the real-time addition amount of cementitious material through a feedforward compensation strategy to avoid control errors caused by delays. By optimizing the combination of multi-dimensional influencing factors, such as tailings flow rate, underflow concentration, cementitious material type and slurry fluidity, the addition amount of cementitious material is dynamically adjusted to further optimize the filling quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of a method for filling mining cementitious materials based on volume ratio according to the present invention. DETAILED DESCRIPTION

[0032] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0034] The specific scheme of the method for filling mining cementitious materials based on volume ratio provided by the present invention is described in detail below with reference to the accompanying drawings.

[0035] Refer to the attached Figure 1 , which shows a flow chart of a method for filling a mine cementitious material based on a volume ratio provided by one embodiment of the present invention, the method comprising the following steps:

[0036] S1. Adaptively generate filling recipes for different filling intensities using an adaptive multivariable ratio optimization algorithm and obtain a reference recipe set;

[0037] In order to meet the diverse filling strength requirements in complex mining environments and achieve precise control of the amount of cementitious materials added, an adaptive multivariable proportioning optimization algorithm is introduced to adaptively generate filling formulas. The adaptive multivariable proportioning optimization algorithm takes the physical variable response mechanism as its core, drives the process parameter reverse deduction with the strength target, and intelligently generates filling formulas based on filling test data through multivariable analysis and nonlinear response control, providing adaptive decision support for mine filling. The adaptive multivariable proportioning optimization algorithm covers four stages: variable modeling, process weight control, fuzzy residual calibration, and proportion reverse solution. The specific implementation process is as follows:

[0038] Multiple influencing factors were extracted from historical filling tests, including tailings density, underflow concentration, cementitious material type factor, slurry fluidity coefficient, and filling strength. Tailings density reflects the accumulation state of solid phases, underflow concentration describes the solid-liquid ratio of the slurry, cementitious material type factor quantifies the impact of different material systems on filling strength, slurry fluidity coefficient determines the operational stability of the slurry in the conveying pipeline, and filling strength is based on the filling mining design of each mine. The optimization goal is to determine the mass of cementitious material that should be added per cubic meter of slurry to meet the design requirements for filling strength while minimizing material costs.

[0039] To achieve the above goals, a multivariable nonlinear process response function with physical structure constraints is introduced in the variable modeling stage to construct a nonlinear relationship between variables (i.e., influencing factors). The multivariable nonlinear process response function is defined as follows:

[0040]

[0041] in, is the output value of the multivariable nonlinear process response function (i.e., the optimization objective function); is the filling strength, which is determined according to specific needs; is the tailings density, which is determined according to the specific material; is the underflow concentration, which is determined according to specific needs; is the slurry fluidity coefficient (i.e., Marshall funnel test seconds); It is the cementitious material type factor, which is used to reflect the influence of cementitious material type on filling strength. It is determined according to the specific material properties, such as 0.5 for cement and 0.8 for slag. is the current amount of cementitious material added; exponential operation It is used to enhance the nonlinear stretching capability of filling strength to multivariable nonlinear process response function; is the logarithmic processing of the sum of tailings density and underflow concentration, reflecting the coupling relationship between density and cementation state; the cubic root operation is used to perform nonlinear shrinkage processing on the slurry fluidity coefficient to prevent high addition redundancy caused by poor slurry fluidity; The structure is used to prevent the denominator from approaching zero; the final square term The amount of cementitious material added is standardized, and the nonlinear growth effect of the amount of cementitious material added on the final effect is emphasized. The above variables are all used after unifying the dimensions to avoid the problem of dimension mismatch; the dimension unification methods such as dimensionless normalization method, unit conversion method, and engineering experience index processing method are all technical means well known to those skilled in the art and will not be described in detail here. The above multivariable nonlinear process response function combines and nonlinearly processes the variables, so that the multivariable nonlinear process response function tends to achieve the dual goals of "material optimization" under the premise of "sufficient strength";

[0042] After the multivariable nonlinear process response function is constructed, the variable physical sensitivity weight mechanism is introduced to deal with the problem of uneven actual impact of each variable on the multivariable nonlinear process response function under different mining conditions; the weight of each variable is calculated through the Gaussian kernel-based weight function, and the weights of all variables are summed and multiplied with the above multivariable nonlinear process response function to adjust the output of the multivariable nonlinear process response function to obtain the weighted response function value The Gaussian kernel-based weight function is a well-known technical method to those skilled in the art and will not be described in detail here.

[0043] Furthermore, a fuzzy residual calibration mechanism is introduced to compensate for the ratio error caused by the actual filling deviation. Based on the weighted response function value and the expected value, the error is calculated. , It represents the expected value, which is calculated based on the expected variable value set according to the expert experience method; the error is corrected nonlinearly through the bidirectional adjustment function to obtain the error correction amount ; Based on the error correction amount, the expected value is error corrected to obtain the final response value after error correction; the bidirectional adjustment function is a technical means well known to those skilled in the art and will not be described in detail here;

[0044] Furthermore, based on the final response value after error correction, the above multivariable nonlinear process response function is reversely analyzed to obtain the optimal amount of cementitious material added. , the specific formula is as follows:

[0045]

[0046] in, It indicates the final response value after error correction based on the filling strength; the optimal amount of cementitious material added is calculated by reverse calculation using the final response value after error correction. ;

[0047] Based on the optimal amount of cementitious material added, a filling formula is generated; the filling formulas generated under different filling strengths are combined to form multiple reference formula sets with strong adaptability and transparent parameters. The filling formula includes data such as filling strength, underflow concentration, cementitious material addition amount, volume ratio, etc., and is dynamically generated and driven by process response, rather than selected by human experience.

[0048] S2. During mine filling operation, process parameters are acquired in real time, and the volume ratio of cementitious material and tailings slurry in the finished slurry is calculated in combination with a reference formula set; based on the volume ratio of cementitious material and tailings slurry, the real-time addition amount of cementitious material is calculated; delay compensation and multi-dimensional optimization and predictive control algorithms are introduced to optimize the real-time addition amount of cementitious material to obtain a corrected addition amount of cementitious material; based on the corrected addition amount of cementitious material, the filling of the mine cementitious material is realized.

[0049] During mine filling operations, data acquisition equipment (e.g., sensors) is used to obtain real-time process parameters (e.g., tailings and water flow rates, and cementitious material addition rate). This information is combined with the recommended cementitious material addition rate obtained by the mine site operator from a recipe, and the volume ratio of cementitious material to tailings slurry (tailings + water) in the actual finished slurry is calculated using an empirical calculation method. The recipe is selected by the mine site operator based on site requirements and a reference recipe set.

[0050] Calculate the real-time addition amount of cementitious material based on the volume ratio of cementitious material and tailings slurry : ;in, It is the flow rate of mortar and concentrated water, obtained by sensor equipment and summed calculation; is the volume ratio of cementitious materials and tailings slurry; The density of the cementitious material (such as cement) is determined according to the selected cementitious material;

[0051] Furthermore, to avoid material delivery delays caused by the length and complexity of mine filling pipelines, a delay compensation and multi-dimensional optimization predictive control algorithm is introduced to optimize the real-time addition amount of cementitious material and obtain a corrected cementitious material addition amount. The specific implementation process of the delay compensation and multi-dimensional optimization predictive control algorithm is as follows:

[0052] First, based on the pipeline fluid mechanics model, the fluid flow rate is calculated according to the length of the mine filling pipeline and the pipeline transportation flow rate. The predicted delay time of material transportation is obtained through the relationship between the fluid flow rate and the length of the mine filling pipeline. Furthermore, based on the predicted delay time of material transportation and the total flow of tailings and water, the predicted delay volume is calculated. Based on the predicted delayed volume, a feedforward compensation control strategy is applied to adjust the real-time addition amount of the gelling material to obtain the compensated addition amount of the gelling material. The feedforward compensation control strategy is to eliminate the error caused by the delivery delay by predictively adjusting the addition amount of the gelling material. The formula is as follows:

[0053]

[0054] in, is the amount of cementitious material added after compensation; is the control period, which is set based on the same unit as the predicted delay time of material delivery and is based on expert experience. The above formula adjusts the amount of cementitious material added to compensate for errors caused by delays during delivery. For example, if the predicted time delay for the slurry to arrive at the discharge point is too late, the amount of cementitious material added is increased in advance.

[0055] After the feedforward compensation strategy is implemented, in order to ensure that the amount of cementitious material added always meets the actual demand under various conditions, multi-dimensional influencing factors are introduced. The compensated amount of cementitious material added is optimized and adjusted through a dynamic optimization control algorithm to obtain a corrected amount of cementitious material added. The multi-dimensional influencing factors may include tailings flow rate, underflow concentration, cementitious material type factor, slurry fluidity coefficient, etc. The dynamic optimization control algorithm includes a weighted combination of the multi-dimensional influencing factors. The specific implementation formula is as follows:

[0056]

[0057] in, is the corrected amount of cementitious material added; It is The weight coefficient of the influencing factor indicates the The contribution of each influencing factor to the amount of cementitious material added is calibrated based on historical experience, and the value range is ; Indicates the Influencing factors; It is The influencing factor function is derived from empirical relationships established through actual mine operation data or through physical modeling. For example, the slurry fluidity coefficient may affect the fluidity of the cementitious material, and the tailings concentration may affect the demand for cementitious material. It is the total number of influencing factors and is set according to the specific scenario.

[0058] Finally, the filling of mine cementitious materials is achieved according to the revised cementitious material addition amount.

[0059] In summary, a filling method of mine cementitious materials based on volume ratio is completed.

[0060] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0061] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A filling method of mine cementitious materials based on volume ratio, characterized in that: The following steps are involved: S1. Adaptively generate filling formulas for different filling intensities through an adaptive multivariable ratio optimization algorithm; The filling formulas generated under different filling intensities are combined into a reference formula set; The adaptive multivariable ratio optimization algorithm constructs a nonlinear relationship between variables through a multivariable nonlinear process response function. It further introduces a variable physical sensitivity weight mechanism to calculate the weighted response function value. Based on the weighted response function value, a fuzzy residual calibration mechanism is used to obtain the final response value after error correction. Based on the final response value after error correction, the multivariable nonlinear process response function is reversely analyzed to obtain the optimal amount of cementitious material added and generate a filling formula. S2. During mine filling operations, process parameters are acquired in real time and, in combination with a reference recipe set, the volume ratio of the cementitious material to the tailings slurry in the finished product is calculated. Based on this volume ratio, the real-time addition amount of the cementitious material is calculated. Delay compensation and a multi-dimensional optimization and predictive control algorithm are introduced to optimize the real-time addition amount of the cementitious material, resulting in a revised addition amount of the cementitious material. Based on the revised addition amount of the cementitious material, the mine filling process is completed. In the implementation of the delay compensation and multi-dimensional optimization predictive control algorithm, the predicted delay time of material transportation is obtained based on the pipeline fluid dynamics model. Based on the predicted delay time of material transportation and the flow rate of tailings and water in the process parameters, the predicted delay volume is calculated. Based on the predicted delay volume, a feedforward compensation control strategy is applied, a control cycle is introduced, and the real-time addition amount of the cementitious material is adjusted to obtain the compensated cementitious material addition amount. The formula is as follows: ； in, is the amount of cementitious material added after compensation; is the real-time addition amount of cementitious material; is the predicted delay volume; is the control period.

2. The method for filling mining cementitious materials based on volume ratio according to claim 1, characterized in that: Said S1 specifically includes: In the implementation of the adaptive multivariable mix optimization algorithm, influencing factors are extracted from historical filling tests, including tailings density, underflow concentration, cementitious material type factor, slurry fluidity coefficient and filling strength.

3. The method for filling mining cementitious materials based on volume ratio according to claim 2, characterized in that: Said S1 specifically includes: In the implementation of the adaptive multivariable ratio optimization algorithm, the influencing factors are used as variables, and a multivariable nonlinear process response function with physical structure constraints is introduced. The nonlinear relationship between the variables is constructed by combining exponential operations, logarithmic operations, and cubic root operations. The multivariable nonlinear process response function is defined as follows: ； in, is the output value of the multivariable nonlinear process response function; is the filling strength; is the tailings density; is the underflow concentration; is the slurry fluidity coefficient; is the cementitious material type factor; is the current amount of cementitious material added.

4. The method for filling mining cementitious materials based on volume ratio according to claim 3, characterized in that: Said S1 specifically includes: In the implementation process of the adaptive multivariable ratio optimization algorithm, a variable physical sensitive weight mechanism is introduced. The weight of each variable is calculated through a weight function based on the Gaussian kernel. The weights of all variables are summed and multiplied with the multivariable nonlinear process response function. The output of the multivariable nonlinear process response function is adjusted to obtain the weighted response function value.

5. The method for filling mining cementitious materials based on volume ratio according to claim 4, characterized in that: Said S1 specifically includes: In the implementation process of the adaptive multivariable matching optimization algorithm, a fuzzy residual calibration mechanism is introduced. Based on the weighted response function value and the expected value, the error is calculated, and the error is nonlinearly corrected through a bidirectional adjustment function to obtain the error correction amount. Based on the error correction amount, the expected value is error-corrected to obtain the final response value after error correction.

6. The method for filling mining cementitious materials based on volume ratio according to claim 1, characterized in that: Said S2 specifically includes: Based on the volume ratio of the cementitious material and the tailings slurry and the density of the cementitious material, the real-time addition amount of the cementitious material is calculated.

7. The method for filling mining cementitious materials based on volume ratio according to claim 1, characterized in that: Said S2 specifically includes: By introducing multi-dimensional influencing factors and performing weighted combination, the amount of cementitious material added after compensation is optimized and adjusted to obtain the corrected amount of cementitious material added.

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