Mine cementing material filling method based on volume ratio

Abstract

The invention relates to the technical field of mine filling, in particular to a filling method of a mine cementing material based on a volume ratio. The method comprises the following steps: for different filling strengths, adaptively generating a filling formula through an adaptive multivariable ratio optimization algorithm, and obtaining a reference formula set; during mine filling operation, process parameters are obtained in real time, and in combination with the reference formula set, the volume ratio of the cementing material to the tailing slurry in the finished product slurry is calculated; calculating the real-time addition amount of the cementing material based on the volume ratio of the cementing material to the tailing slurry; a delay compensation and multi-dimensional optimization predictive control algorithm is introduced, the real-time addition amount of the cementing material is optimized, and the corrected addition amount of the cementing material is obtained; and based on the corrected cementing material addition amount, filling of the mine cementing material is achieved. The technical problems that in the mine filling process, control over the adding amount of the cementing material is not accurate, and self-adaption is poor are solved.

Description

Technical Field

[0001] The 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 main mining method to ensure safe deep mining. The backfilling process is to concentrate the tailings produced by the ore dressing plant to a concentration that is convenient for transportation, then add cementitious materials, transport them to the empty area underground, and wait for solidification before the next step of safe mining. With the deepening of development, the backfilling distance is gradually extended, and fine tailings and full tailings slurry, which are not easy to settle and easy to transport, are preferred for backfilling.

[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 ratio of the mass of tailings dry sand to the mass of cementitious material, but when the concentration of tailings slurry is too low, adding cementitious material according to a fixed mass ratio will reduce the filling strength. When the concentration of tailings slurry is too high, the addition of cementitious material will increase, resulting in waste.

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

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

[0006] A filling method of a mine cementitious material based on volume ratio of the present invention 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. For different filling intensities, adaptive multivariable ratio optimization algorithm is used to adaptively generate filling formulas and obtain a reference formula set;

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

[0010] Preferably, the S1 specifically includes:

[0011] In the process of implementing 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 of the adaptive multivariable ratio optimization algorithm, a variable physical sensitive weight mechanism is introduced. The weight of each variable is calculated through a Gaussian kernel-based weight function. 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 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 cementitious material and tailings slurry and the density of cementitious material, the real-time addition amount of 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, combined with the flow rates 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 adopting the adaptive multivariable ratio optimization algorithm, the amount of cementitious material added can be accurately calculated according to the influencing factors such as tailings density, underflow concentration, type of cementitious material, slurry fluidity, etc. in different mining environments. The adaptive multivariable ratio optimization algorithm starts from the filling test data, and through nonlinear response control and process parameter back-calculation, it achieves the optimal match between filling strength and cementitious material, thereby minimizing the amount of cementitious material used while ensuring that the filling strength meets the design standard.

[0030] 2. The delay compensation and multi-dimensional optimization predictive control algorithm were introduced to predict the material delivery delay time according to the pipeline fluid mechanics model, and the real-time addition amount of the cementitious material was adjusted through the feedforward compensation strategy to avoid the control error caused by the delay; through the optimal combination of multi-dimensional influencing factors, such as tailings flow, underflow concentration, cementitious material type and slurry fluidity, the addition amount of the cementitious material was dynamically adjusted to further optimize the filling quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The present invention is a flow chart of a method for filling a mine cementitious material based on volume ratio. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 a filling method of mining cementitious materials based on volume ratio provided by the present invention is described in detail below with reference to the accompanying drawings.

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

[0036] S1. For different filling intensities, adaptive multivariable ratio optimization algorithm is used to adaptively generate filling formulas and obtain a reference formula set;

[0037] In order to meet the diverse filling strength requirements in a complex mining environment and achieve precise control of the amount of cementitious material 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 the core, drives the process parameter reverse deduction with the strength target, and intelligently generates the filling formula based on the 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; among them, tailings density reflects the accumulation state of solid phase, underflow concentration is used to describe the solid-liquid ratio of slurry, cementitious material type factor is used to quantify the influence of different material systems on filling strength, slurry fluidity coefficient determines the running stability of slurry in the conveying pipeline, and filling strength is based on the filling mining design of each mine; the optimization goal to be achieved is to determine the mass of cementitious material to be added to unit cubic meter of slurry, so as to meet the design requirements of filling strength while minimizing material cost;

[0039] In order to achieve the above objectives, 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, It 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 cement is set to 0.5, slag is set to 0.8, etc.; is the current amount of cementitious material added; exponential operation Nonlinear stretching capability for enhancing filling strength to multivariable nonlinear process response functions; 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 elaborated 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 "optimal material" 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 technical means well known 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 value ; 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 a plurality of reference formula sets with strong adaptability and transparent parameters. The filling formula includes data such as filling strength, underflow concentration, amount of cementitious material added, volume ratio, etc. It is a formula dynamically generated and driven by process response, rather than selected by human experience.

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

[0049] During the mine filling operation, the process parameters (such as the flow rate of tailings and water, the amount of cementitious material added) in the mine filling process are obtained in real time through data acquisition equipment (such as sensors), and the volume ratio of cementitious material and tailings slurry (tailings + water) in the actual finished slurry is calculated by empirical calculation method in combination with the recommended amount of cementitious material added obtained from the formula by the mine site operator; the formula is selected by the mine site operator according to the site requirements and the reference formula 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 material and tailings slurry; The density of the cementitious material (such as cement) is determined according to the selected cementitious material;

[0051] Furthermore, in order to avoid the material transportation delay problem caused by the length and complexity of the mine filling pipeline, a delay compensation and multi-dimensional optimization prediction control algorithm is introduced to optimize the real-time addition amount of the cementitious material to obtain the corrected addition amount of the cementitious material; the specific implementation process of the delay compensation and multi-dimensional optimization prediction control algorithm is as follows:

[0052] First, based on the pipeline fluid mechanics model, the fluid velocity 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 velocity and the length of the mine filling pipeline. ; Further, based on the predicted delay time of material transportation, combined with 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 in the same unit as the predicted delay time of material transportation and is set according to the expert experience method. The above formula adjusts the amount of cementitious material added to compensate for the error caused by the delay in the transportation process. For example, if the predicted time lag of the slurry arriving at the discharge point is large, the amount of cementitious material added is increased in advance;

[0055] After the implementation of the feedforward compensation strategy, 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, and the amount of cementitious material added after compensation 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, underflow concentration, cementitious material type factor, slurry fluidity coefficient, etc. The dynamic optimization control algorithm includes a weighted combination of multi-dimensional influencing factors, and the specific implementation formula is as follows:

[0056]

[0057] in, is the amount of cementitious material added after correction; It is The weight coefficient of the influencing factor indicates 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 Influencing factors; It is The influencing factor function is derived from the empirical relationship established by the actual operation data of the mine 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 materials. 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 realized 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 of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some 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 referenced to each other, and 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 the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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 be included in the protection scope of the present invention.

Claims

1. A filling method of mining cementitious materials based on volume ratio, characterized in that: The following steps are involved: S1. For different filling intensities, adaptive multivariable ratio optimization algorithm is used to adaptively generate filling formulas and obtain a reference formula set; S2. During the mine filling operation, the process parameters are obtained in real time, and the volume ratio of the cementitious material and the tailings slurry in the finished slurry is calculated in combination with the reference formula set; based on the volume ratio of the cementitious material and the tailings slurry, the real-time addition amount of the cementitious material is calculated; the delay compensation and multi-dimensional optimization predictive control algorithm are introduced to optimize the real-time addition amount of the cementitious material and obtain the corrected addition amount of the cementitious material; based on the corrected addition amount of the cementitious material, the filling of the mine cementitious material is realized.

2. A method for filling mining cementitious materials based on volume ratio according to claim 1, characterized in that: The S1 specifically includes: In the process of implementing 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. A method for filling mining cementitious materials based on volume ratio according to claim 2, characterized in that: The S1 specifically includes: 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.

4. A method for filling mining cementitious materials based on volume ratio according to claim 3, characterized in that: The S1 specifically includes: In the implementation of the adaptive multivariable ratio optimization algorithm, a variable physical sensitive weight mechanism is introduced. The weight of each variable is calculated through a Gaussian kernel-based weight function. 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. A method for filling mining cementitious materials based on volume ratio according to claim 4, characterized in that: The 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 corrected to obtain the final response value after error correction.

6. A method for filling mining cementitious materials based on volume ratio according to claim 5, characterized in that: The S1 specifically includes: 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.

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

8. A method for filling mining cementitious materials based on volume ratio according to claim 7, characterized in that: The S2 specifically includes: 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, combined with the flow rates of tailings and water in the process parameters, the predicted delay volume is calculated.

9. A method for filling mining cementitious materials based on volume ratio according to claim 8, characterized in that: The S2 specifically includes: 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.

10. A method for filling mining cementitious materials based on volume ratio according to claim 9, characterized in that: The 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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