Optimization control method of cementation filling system for longwall tunnel by tunnel

Abstract

The present invention discloses an optimization control method for a longwall lane-by-lane cementing filling system, which relates to the technical field of data processing and automatic control. The method comprises: step 1: sampling each component particle in the filling slurry to obtain the physical parameters of each level of particles; calculating the inhibition factor in the hydration reaction; calculating the particle characteristic coefficient of the filling slurry according to the physical parameters and the inhibition factor; step 2: calculating the hydration degree based on the particle characteristic coefficient; establishing a rheological strength coupling model according to the particle characteristic coefficient and the hydration degree; predicting the pipeline transmission pressure loss to obtain the predicted pressure loss; calculating the filling strength according to the pressure loss and the hydration degree using a preset strength development model; step 3: calculating the optimal ratio vector of the filling slurry according to step 1 and step 2; calculating the optimal control parameter vector according to step 1 and step 2. The present invention realizes the intelligent management of the filling process.

Description

Technical Field

[0001] The invention relates to the technical field of data processing and automatic control, and in particular to an optimization control method for a long wall lane-by-lane cementing filling system. Background Art

[0002] In order to cope with the problems of stratum collapse and surface subsidence caused by underground mine resource mining, mine filling technology has become a vital support and protection method in modern mining. By injecting filling slurry into the goaf, the filling system can inhibit rock movement and collapse to a certain extent and provide support for the mine. However, the existing filling technology still faces many technical challenges, especially in the control of filling effects in long-distance, high-intensity and complex mine environments. When facing complex mine environments, traditional filling systems often fail to accurately control the fluidity, strength development and hydration reaction rate of the slurry, resulting in unsatisfactory curing effects of the filling body, which in turn affects the support quality and long-term stability of the mine.

[0003] In the prior art, the filling system is generally operated by a simple material ratio and fixed conveying control, but this method is difficult to adapt to the changes in the complex mine environment. For example, factors such as the hydration reaction rate, pressure loss, particle distribution and rheology of the slurry will change with time and space, and fixed ratios and single control modes are often difficult to effectively cope with these dynamic changes, resulting in unstable filling effects. Studies have shown that the hydration reaction of the slurry during the filling process will significantly affect the viscosity and fluidity of the slurry. The hydration reaction will accelerate the coagulation of the fluid in the initial stage, resulting in increased viscosity; in the later stage of the reaction, the generated hydration products will further increase the curing strength. However, the existing filling technology still has deficiencies in the control of the hydration reaction, resulting in uneven strength growth of the filling body, and even in some cases, the problem of clogging the pipeline due to excessive hydration. In addition, the fluidity and pressure loss of the slurry during the filling process are also one of the main problems of the prior art. Under long-distance transportation conditions, the flow resistance of the slurry in the pipeline will continue to increase, especially in the complex flow path deep in the mine, the pressure loss of the slurry may cause incomplete filling or obstructed flow. Traditional filling technology often relies on a single pumping system, which is difficult to maintain a stable flow effect when facing long-distance transportation and complex mine terrain. Especially when there are multi-level particles and slurries of different concentrations in the mine, the system cannot flexibly adjust the pumping pressure and flow rate to cope with the dynamic changes of flow resistance. Therefore, the existing filling system often has poor filling effect due to improper flow control under long-distance and multi-resistance transportation conditions. Another important issue is the insufficient optimization of slurry ratio. Traditional filling systems usually rely on empirical ratios, and it is difficult to make scientific and reasonable slurry ratios according to the actual situation of the mine. Factors such as particle distribution, chemical composition and water-binder ratio in the slurry have an important influence on the rheological properties and curing effect of the slurry, but the existing filling technology lacks comprehensive optimization of these factors. Especially in the case of high-strength support requirements, the particle characteristics, concentration, viscosity and hydration reaction process of the slurry play a key role in the strength, uniformity and stability of the final filling body. The traditional ratio adjustment method cannot optimize the particle characteristic coefficient and hydration reaction rate in real time, which often leads to unsatisfactory mechanical properties of the filling body and makes it difficult to meet the long-term support requirements of the mine. Summary of the invention

[0004] The purpose of the present invention is to provide an optimization control method for a long wall lane-by-lane cementing filling system, thereby realizing intelligent management of the filling process.

[0005] In order to solve the above technical problems, the present invention provides an optimization control method for a longwall lane-by-lane cementing filling system, the method comprising:

[0006] Step 1: sampling the particles of each component in the filling slurry to obtain the physical parameters of each level of particles; calculating the inhibition factor in the hydration reaction according to the molar mass of the oxide in each level of particles in the filling slurry; calculating the particle characteristic coefficient of the filling slurry according to the physical parameters and the inhibition factor;

[0007] Step 2: Based on the particle characteristic coefficient, a preset hydration reaction kinetic model is used to calculate the hydration degree; based on the particle characteristic coefficient and the hydration degree, a rheological strength coupling model is established, and the rheological strength coupling model outputs the apparent viscosity; based on the particle characteristic coefficient and the apparent viscosity, the pipeline transportation pressure loss is predicted to obtain the predicted pressure loss; based on the pressure loss and the hydration degree, the preset strength development model is used to calculate the filling strength;

[0008] Step 3: According to step 1 and step 2, calculate the optimal ratio vector of the filling slurry; according to step 1 and step 2, calculate the optimal control parameter vector; the optimal control parameter vector includes: optimal pump speed, optimal filling pressure, optimal filling flow rate and optimal concentration control coefficient; based on the optimal control parameters, perform feedback control; based on the optimal ratio vector, adjust the ratio parameters of the filling slurry.

[0009] Furthermore, in step 1, each component particle in the filling slurry is sampled, and the physical parameters of each level of particles obtained include: particle diameter, specific surface area, particle density and volume fraction; oxides include: silicon dioxide, calcium hydroxide, aluminum oxide and water.

[0010] Furthermore, the particle characteristic coefficient of the filling slurry is calculated using the following formula: :

[0011] ;

[0012] in, is the total number of particles; For the Grade particle size; is the maximum particle size; For the Specific surface area of ​​graded particles; For the Grade particle density; For the The volume fraction of graded particles; For the The molar mass of silicon dioxide in the graded particles; For the The molar mass of alumina in the graded particles; For the The molar mass of calcium hydroxide in the graded particles; For the The molar mass of water in the graded particles; For inhibitory factors.

[0013] Furthermore, in step 2, the preset hydration reaction kinetic model is expressed using the following formula:

[0014] ;

[0015] in, The reaction time is Degree of hydration at ; is the hydration reaction rate constant; is the reaction temperature; is the reference temperature, with a value of 298 Kelvin; is the apparent activation energy; is the gas constant; Indicates water-to-binder ratio; The optimal water-to-binder ratio.

[0016] Furthermore, in step 2, the rheological strength coupling model established according to the particle characteristic coefficient and the hydration degree is expressed using the following formula:

[0017] ;

[0018] in, The reaction time is The apparent viscosity at ; is the preset initial apparent viscosity; is the preset reference particle characteristic coefficient; is the preset hydration influence coefficient; is the shear rate; is the preset reference shear rate; is the rheological index; is the preset hydration rate influence coefficient.

[0019] Furthermore, in step 2, the following formula is used to predict the pipeline transportation pressure loss based on the particle characteristic coefficient and the apparent viscosity to obtain the predicted pressure loss:

[0020] ;

[0021] in, To predict pressure loss; is the average flow velocity; is the length of the pipeline; is the pipe diameter; is the volume concentration; is the maximum allowable volume concentration.

[0022] Furthermore, in step 2, the filling strength is calculated according to the pressure loss and the degree of hydration using the preset strength development model using the following formula:

[0023] ;

[0024] in, The reaction time is Filling strength at 10000 ℃; is the maximum filling strength; is the maximum allowable pressure loss; is the maximum allowable apparent viscosity.

[0025] Furthermore, in step 3, the optimal ratio vector of the filling slurry is calculated using the following formula: :

[0026] ;

[0027] in, represents the matching vector; is the objective function, and the matching vector that maximizes the objective function value is obtained through iterative solution, which is taken as the optimal matching vector; The preset optimal filling strength; is the preset optimal apparent viscosity; is the preset optimal hydration degree; the ratio vector The elements include: the total number of particles; the particle size of each level of particles; the specific surface area of ​​each level of particles; the density of each level of particles; the volume fraction of each level of particles; the molar mass of silicon dioxide in each level of particles; the molar mass of aluminum oxide in each level of particles; the molar mass of calcium hydroxide in each level of particles; the molar mass of water in each level of particles; and the water-to-binder ratio.

[0028] Furthermore, in step 3, the optimal control parameter vector is calculated using the following formula: :

[0029] ;

[0030] in, The reaction time is The optimal parameter control vector when ; is the gravitational constant; is the optimal pump speed; is the optimal filling pressure; is the optimal filling flow rate; is the optimal concentration control coefficient; is the pump impeller diameter; when performing feedback control, use Multiply by the real-time filling slurry concentration to adjust the filling slurry concentration.

[0031] The optimization control method of the long wall lane-by-lane cementing filling system of the present invention has the following beneficial effects:

[0032] First, precise control of the rheology, viscosity and strength development of the slurry during transportation is achieved. This multi-parameter coordinated control method can fully adapt to different mine environments and working conditions, allowing the slurry to maintain stable fluidity and final solidification strength during long-distance transportation and complex flow environments. Compared with the single or fixed parameter control method in traditional filling systems, the intelligent control method of the present invention effectively improves the uniformity and solidification effect of the filling slurry and reduces the flow instability during transportation.

[0033] Secondly, the present invention uses the optimal control parameter vector to achieve all-round dynamic optimization of the filling process. By calculating and adjusting these key control parameters in real time, the system can automatically adjust when the hydration reaction of the slurry is accelerated or slowed down, and the flow resistance is increased or decreased, to ensure that the flow state of the slurry is always in the optimal state. Especially under long-distance transportation and high-viscosity flow conditions, traditional filling systems are often difficult to adjust flow and pressure in real time, and are prone to problems such as blockage or uneven flow rate. The present invention effectively reduces the pressure loss in long-distance transportation through the coordinated regulation of pump speed, pressure and concentration, allowing the slurry to pass smoothly through complex mine pipelines, thereby improving the continuity and consistency of filling.

[0034] In terms of slurry ratio optimization, the present invention realizes the precise configuration of parameters such as particle size, density, specific surface area and water-binder ratio of each level of particles in the slurry through the optimal ratio vector. In traditional filling technology, the ratio of slurry is usually based on experience, which is difficult to adapt to the dynamic changes of mine conditions, resulting in uneven filling effect or insufficient strength. In the present invention, the optimization calculation of the ratio vector is based on the actual particle characteristics and hydration reaction rate of the slurry, which can automatically optimize the particle ratio under different mine environments to ensure that the slurry achieves the ideal strength development during the curing process. Through scientific and reasonable ratio control, the system effectively improves the density and strength of the filling body, so that the filling body has higher mechanical stability and long-term support performance in the tunnel support. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0036] Figure 1 A schematic diagram of a method flow chart of an optimization control method for a longwall lane-by-lane cementing filling system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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 creative work are within the scope of protection of the present invention.

[0038] Example 1, reference Figure 1 : An optimization control method for a longwall lane-by-lane cementing filling system, the method comprising:

[0039] Step 1: sampling the particles of each component in the filling slurry to obtain the physical parameters of each level of particles; calculating the inhibition factor in the hydration reaction according to the molar mass of the oxide in each level of particles in the filling slurry; calculating the particle characteristic coefficient of the filling slurry according to the physical parameters and the inhibition factor;

[0040] First, in the filling operation, in order to ensure that the rheological properties and hydration reaction rate of the slurry reach the optimal state, we need to accurately collect the physical properties of particles of different levels, such as particle size, density, specific surface area, etc. Since the characteristics of different particles directly affect the hydration reaction process and the flow characteristics of the slurry, the present invention comprehensively characterizes the particle characteristics by constructing a particle characteristic coefficient, which can more effectively reflect the specific role of each component in the filling process. The particle characteristic coefficient is dimensionless in the present invention. It combines the physical properties and chemical component characteristics of the particles and can accurately quantify the contribution of different particles in the hydration reaction. This characteristic coefficient not only makes the subsequent model more adaptable and accurate, but also provides a quantitative indicator for optimization control. In addition, in order to further accurately characterize the hydration reaction characteristics of the filling slurry, the present invention introduces an inhibition factor, which is calculated based on the molar mass ratio of the oxide components in the slurry to reflect the influence of different chemical components in the hydration process. During the filling process, the ratio of chemical components such as alumina and silica will directly affect the rate of the hydration reaction. These components will form specific reaction conditions chemically and affect the generation rate of hydrates. Through the calculation of the inhibition factor, the present invention can more accurately control the hydration process through the chemical characteristics in addition to the physical characteristics of the particles. For example, when the concentrations of alumina and silica are high, the inhibition factor will reflect the inhibitory effect on the hydration reaction rate in the calculation, so that the subsequent model can automatically adjust the parameters to make the filling process more stable and avoid affecting the filling quality due to the hydration reaction being too fast or too slow. After completing the measurement of the physical parameters of the particles and the inhibition factor, the present invention further comprehensively calculates the particle characteristic coefficient of the slurry based on these data. The characteristic coefficient not only includes the physical information such as the particle size, specific surface area, density, etc. of each component particle, but also integrates the influence of the hydration reaction inhibition factor. In this way, the particle characteristic coefficient can more comprehensively quantify the overall characteristics of the filling slurry under different filling conditions, so that the system can still achieve accurate optimization control in a complex mine environment. The introduction of the characteristic coefficient is not only a simple summary of the physical parameters of the particles, but also a comprehensive consideration of the complex interactions of the particles in the hydration process. Through comprehensive analysis of these interactive factors, the present invention can effectively predict key characteristics of the slurry, such as fluidity and curing speed, thereby providing reliable data support for subsequent hydration reaction models and rheological analysis. In the complex filling environment of longwall tunnels, this control method based on particle characteristics and hydration dynamics ensures that the slurry can always maintain an efficient and stable state during the filling process, greatly improving the intelligent level of the filling operation.

[0041] Step 2: Based on the particle characteristic coefficient, a preset hydration reaction kinetic model is used to calculate the hydration degree; based on the particle characteristic coefficient and the hydration degree, a rheological strength coupling model is established, and the rheological strength coupling model outputs the apparent viscosity; based on the particle characteristic coefficient and the apparent viscosity, the pipeline transportation pressure loss is predicted to obtain the predicted pressure loss; based on the pressure loss and the hydration degree, the preset strength development model is used to calculate the filling strength;

[0042] Hydration reaction is the key process for the filling body to form adhesion, which can play a decisive role in material solidification and strength growth. During the hydration reaction, the filling material gradually forms hydration products, thereby realizing the bonding and solidification of the material particles. Therefore, accurately predicting the hydration reaction process through the hydration reaction kinetic model is the core link of system control. Based on the particle characteristic coefficient in step 1, the hydration reaction kinetic model can effectively calculate the hydration degree of the filling slurry. As an important parameter to describe the hydration degree of the material, the hydration degree can accurately reflect the real-time state of the reaction and the progress of material solidification. In the construction of the hydration reaction kinetic model, the system considers factors such as temperature, water-binder ratio and activation energy, which directly affect the hydration reaction rate and product formation process. Reaction temperature is an important condition affecting the hydration rate. Higher temperature can usually accelerate the reaction process. Therefore, temperature is introduced into the model, and the rate of hydration reaction is adjusted by the ratio between temperature and reference temperature, thereby accurately describing the hydration performance of the slurry under different temperature conditions. The water-binder ratio controls the proportion of water content in the slurry. The appropriate water-binder ratio can balance fluidity and solidification effect. A lower water-binder ratio will limit the reaction process and result in insufficient hydration products. A higher water-binder ratio can promote the reaction but will dilute the slurry and affect the strength after curing. Therefore, in the hydration model, the water-binder ratio is optimized to ensure that the reaction can proceed fully and the final filling body has sufficient strength. Activation energy is the initial energy condition of the reaction. Its size determines the starting conditions of the reaction. The model adds it as a factor other than temperature and water-binder ratio to more comprehensively control the rate of the hydration reaction.

[0043] After the hydration reaction kinetics model obtains the calculation results of the hydration degree, the system further uses the rheological strength coupling model to calculate the apparent viscosity of the filling slurry. Rheological properties have a direct impact on the transportation, flow and final molding quality of the filling slurry. As an important parameter of rheological properties, the apparent viscosity is one of the sources of resistance when the slurry flows in the pipeline. Therefore, the change in the viscosity of the slurry has an important impact on the operating state of the entire filling system. During the filling process, the viscosity of the slurry needs to be controlled within a reasonable range. Too high viscosity will increase the resistance of pipeline transportation and may even cause blockage, while too low viscosity will reduce flow stability, cause material stratification, and affect the filling quality. The rheological strength coupling model dynamically adjusts the apparent viscosity to ensure that the slurry maintains appropriate fluidity during transportation. The rheological strength coupling model accurately predicts the dynamic changes of slurry viscosity by combining the particle characteristic coefficient and the hydration degree. The particle characteristic coefficient reflects the physical properties of different particles in the slurry, including parameters such as particle size, density, and specific surface area. These characteristics determine the distribution of particles in the fluid and their influence on the fluid viscosity. The role of the particle characteristic coefficient in the rheological model is to summarize these physical properties into a comprehensive indicator to quantify the impact of particle characteristics on viscosity. In addition, the degree of hydration, as a dynamic parameter, can reflect in real time the changes in the rheological properties of the slurry caused by the hydration reaction. As the hydration reaction continues, hydration products are gradually generated, and the viscosity of the slurry usually increases. By introducing the degree of hydration into the rheological strength coupling model, the model can reasonably predict the change of viscosity over time, thereby avoiding the phenomenon of excessive or low viscosity caused by hydration reaction. By dynamically adjusting the viscosity, the system can ensure that the slurry always remains in an ideal state during the flow process, so as to smoothly complete the filling process.

[0044] After the calculation of the apparent viscosity is completed, the system further uses the particle characteristic coefficient and viscosity information to predict the pressure loss during pipeline transportation. Pressure loss is an important variable in the filling system, which directly affects the flow rate of the filling process and the propulsion efficiency of the slurry in the pipeline. During long-distance transportation, the slurry will be affected by the viscosity and flow rate when flowing in the pipeline, resulting in a gradual increase in the pressure loss in the pipeline. Excessive pressure loss may reduce the flow rate of the slurry, affect the filling efficiency, and may even cause pipeline blockage. Therefore, real-time monitoring and prediction of pressure loss during the filling process can effectively ensure the stable operation of the system. Through the particle characteristic coefficient and viscosity information, the model can predict the pressure change in the pipeline based on the basic principles of fluid mechanics, and combine the flow resistance and particle characteristics to calculate the resistance change that may be generated in the actual transportation of the slurry, thereby providing an estimate of the pressure loss. In actual operation, the pressure loss in the pipeline will be affected by multiple parameters such as the slurry flow rate, pipeline diameter, and viscosity. Therefore, the model will comprehensively consider these factors during calculation to obtain a more accurate pressure loss value. The introduction of particle characteristic coefficient enables the model to more accurately reflect the interaction between different particles in the slurry during transportation, avoiding pressure fluctuations caused by particle accumulation or stratification. In addition, the dynamic adjustment of viscosity also makes the prediction of pressure loss closer to the actual situation. When the hydration reaction causes the viscosity to change, the model can update the pressure loss value accordingly, ensuring that the system has good stability and reliability in long-distance transportation. Through the systematic application of the hydration reaction kinetic model, the rheological strength coupling model and the pressure loss prediction model, step 2 realizes the comprehensive control of various key parameters of the filling process. The hydration reaction kinetic model calculates the hydration degree, thereby accurately describing the solidification state of the filling material; the rheological strength coupling model calculates and adjusts the slurry viscosity in real time based on the hydration degree and particle characteristic coefficient to ensure the adaptability of rheology during the filling process; and the pressure loss prediction model monitors the pressure changes in the pipeline in real time based on viscosity and particle characteristics, thereby providing stable control of the filling system. Through the comprehensive consideration of multiple factors such as hydration reaction, rheological properties, and pressure loss, the system can stably realize the transportation and filling of filling materials in a complex mine environment, ensuring that the final filling effect meets the design requirements.

[0045] Step 3: According to step 1 and step 2, calculate the optimal ratio vector of the filling slurry; according to step 1 and step 2, calculate the optimal control parameter vector; the optimal control parameter vector includes: optimal pump speed, optimal filling pressure, optimal filling flow rate and optimal concentration control coefficient; based on the optimal control parameters, perform feedback control; based on the optimal ratio vector, adjust the ratio parameters of the filling slurry.

[0046] The calculation of the optimal ratio vector aims to determine the optimal combination of parameters such as the ratio of different particle components, water-binder ratio, and oxide concentration. This process requires precise matching of the physical and chemical properties of different particle components in order to achieve the best hydration reaction rate and curing effect in a complex mine environment. Particles of different particle sizes, specific surface areas, and volume fractions will affect the density, fluidity, and hydration reaction rate of the filling slurry. The appropriate ratio can not only ensure the flow stability of the slurry in the filling channel, but also form a uniform and dense solidified body in the filling area. Therefore, in the calculation of the optimal ratio vector, the system will accurately adjust the ratio of each component according to the requirements of the actual working conditions to meet the requirements of strength, fluidity, and hydration rate under different filling conditions. The optimization of the ratio vector also involves the adjustment of the water-binder ratio, which will directly affect the viscosity and hydration reaction activity of the slurry. A reasonable water-binder ratio can strike a balance between ensuring fluidity and curing speed. At the same time, the system will also calculate the optimal control parameter vector in step 3. The main components of the control parameter vector include pump speed, filling pressure, filling flow rate and concentration control coefficient, which together determine the transportation efficiency and stability of the slurry in the pipeline. Pump speed and filling pressure directly affect the flow rate and flow resistance of the slurry. Too high a flow rate may cause pressure fluctuations in the pipeline, while too low a flow rate may cause discontinuity of the filling body, thereby affecting the overall filling effect. Therefore, the system will dynamically adjust the pump speed and pressure according to the real-time viscosity and pressure changes to maintain the stable fluidity of the slurry during the filling process. At the same time, the flow rate and concentration control coefficient also play a key role in the optimization vector. The flow rate determines the overall speed of slurry transportation, while the concentration control coefficient is used to adjust the consistency of the slurry to ensure the flow requirements at different transportation stages. Too high a concentration may lead to insufficient fluidity and affect the transportation effect, while too low a concentration will affect the curing strength. Therefore, the reasonable setting of the concentration control coefficient is particularly important to ensure the adaptability of the slurry. After completing the calculation of the proportions and control parameters, the system will adjust the various parameters in the filling process in real time through the feedback control mechanism based on these optimal vectors. In actual operation, the mine filling environment is complex and may be affected by factors such as geological changes and temperature fluctuations, resulting in dynamic changes in the hydration reaction rate, viscosity and pressure loss. In order to cope with this change, the system is equipped with a feedback control system, which uses sensors to monitor parameters such as flow, viscosity, pressure and curing strength during the filling process in real time. Once it is detected that the actual values ​​of these parameters deviate from the expected range, the feedback control mechanism will adjust according to the optimal proportion and control vector. For example, if the sensor detects an abnormal increase in viscosity, the system will automatically reduce the pump speed or increase the flow rate to alleviate the pressure loss in the pipeline and ensure the normal transportation of the slurry.

[0047] Example 2: In step 1, the particles of each component in the filling slurry are sampled, and the physical parameters of each level of particles obtained include: particle diameter, specific surface area, particle density and volume fraction; oxides include: silicon dioxide, calcium hydroxide, aluminum oxide and water.

[0048] Specifically, particle diameter is an important indicator to describe the particle size distribution. Particles of different sizes have different effects on the fluidity and density of the slurry; smaller particles help to fill the gaps between larger particles, thereby improving the density and strength of the slurry. Specific surface area is one of the key factors affecting the activity of the hydration reaction. The larger the specific surface area, the larger the contact area between the particles and water, thereby increasing the hydration reaction rate. Particle density reflects the quality characteristics of the particles. Particles of different densities may form different sedimentation and distribution patterns in the slurry, which has an important impact on flow stability. The volume fraction characterizes the proportion of each level of particles in the overall slurry. The volume fraction of particles of different levels has a significant effect on the flow characteristics, density and hydration reaction process of the slurry. Through reasonable volume fraction configuration, the fluidity and curing effect of the slurry can be optimized. In addition to physical parameters, step 1 also samples and analyzes the chemical components in the filling slurry, especially the analysis of oxide components, including silica, calcium hydroxide, alumina and water. The concentration and distribution of these oxides directly affect the hydration reaction process of the slurry. Silica ( ) usually plays the role of filling the skeleton and can form a gel-like structure with other components during the hydration reaction, thereby increasing the overall strength of the filling. ) is one of the main reactants of the hydration reaction. The products produced during the hydration process help fill micropores and increase the density and strength of the filling body. ) can be used as an additive to adjust the reaction process, and its chemical properties enable it to inhibit or promote the progress of certain hydration reactions under certain conditions. The water content directly affects the fluidity of the slurry and the adequacy of the hydration reaction. The right amount of water can not only promote the hydration reaction, but also adjust the fluidity of the slurry to ensure that it can maintain good rheological properties during transportation and filling. By collecting and analyzing the above physical and chemical parameters, the system can further calculate the particle characteristic coefficient and the hydration reaction inhibition factor, thereby providing an accurate data basis for the subsequent hydration reaction kinetic model and rheological coupling model. These data comprehensively reflect the physical properties and chemical reaction activity of the slurry particles, enabling the system to scientifically control the slurry ratio and transportation parameters based on the actual material properties, ensuring that the cementation filling process achieves the best fluidity, density and curing effect in a complex mine environment.

[0049] Example 3: Calculate the particle characteristic coefficient of the filling slurry using the following formula: :

[0050] ;

[0051] in, is the total number of particles; For the Grade particle size; is the maximum particle size; For the Specific surface area of ​​graded particles; For the Grade particle density; For the The volume fraction of graded particles; For the The molar mass of silicon dioxide in the graded particles; For the The molar mass of alumina in the graded particles; For the The molar mass of calcium hydroxide in the graded particles; For the The molar mass of water in the graded particles; For inhibitory factors.

[0052] Specifically, the formula is first passed through It expresses the relative particle size of particles of different levels. This normalized square value of the particle size reflects the distribution characteristics of particles of each level in the overall slurry. Since particle size plays an important role in the rheology of the slurry, particles of different sizes will have a significant impact on the fluidity and density of the filling slurry. Larger particle sizes can provide the basic support structure of the filler, while smaller particles are filled between larger particles to improve the density and strength of the slurry. By squaring the particle size, the formula design of the present invention can effectively amplify the role of large particles in the support structure, so that the slurry can maintain an appropriate particle distribution during both the flow and curing stages. The second part of the formula, namely , taking into account the specific surface area of ​​each level of particles ,density and volume fraction Specific surface area directly affects the contact area between particles and water, and is therefore an important parameter for the activity of the hydration reaction. The larger the specific surface area, the faster the hydration reaction will proceed, which plays a key role in the solidification speed during the filling process. At the same time, particle density reflects the sedimentation characteristics of particles in the slurry. Particles with higher density are more likely to settle during the flow process, thus affecting the uniform distribution of the slurry. Volume fraction It is used to quantify the proportion of particles of each level in the slurry, which not only determines the overall rheological properties of the slurry, but also affects the uniformity of the hydration reaction. Through the combination of these parameters, the present invention can integrate the physical properties of different particles into the particle characteristic coefficient, thereby providing accurate physical parameter support for the hydration reaction model and rheological model. In order to further accurately quantify the influence of chemical components on the hydration reaction, the inhibition factor is introduced into the formula . This inhibition factor is based on the chemical properties of silica, calcium hydroxide, alumina and water and their interactions in the hydration reaction, and is used to describe the degree of influence of these chemical components on the reaction rate. In the hydration reaction, the presence of silica and alumina tends to change the reactivity of calcium hydroxide, and under certain conditions, it can inhibit or promote the reaction. The inhibition factor reflects this effect in the form of an exponential function, allowing the system to sensitively perceive the impact of small changes in the proportion of chemical components on the hydration reaction rate. The form of the exponential function ensures that the nonlinear relationship between the chemical components is effectively captured, thereby providing accurate reaction condition predictions for the subsequent hydration reaction kinetic model.

[0053] Example 4: In step 2, the preset hydration reaction kinetic model is expressed using the following formula:

[0054] ;

[0055] in, The reaction time is Degree of hydration at ; is the hydration reaction rate constant; is the reaction temperature; is the reference temperature, with a value of 298 Kelvin; is the apparent activation energy; is the gas constant; Indicates water-to-binder ratio; The optimal water-to-binder ratio.

[0056] Specifically, the hydration Indicates reaction time The hydration degree of the slurry at this time reflects the process of the filling material gradually solidifying from a liquid slurry. As the hydration reaction proceeds, the hydration degree gradually increases until it approaches 1, indicating that the hydration reaction is close to completion. The items included in the model are Used to describe the change process of reaction rate, is the hydration reaction rate constant, reflecting the basic rate characteristics of the reaction; and the particle characteristic coefficient It combines the physical and chemical properties of slurry particles. The product of is the key regulating factor of the hydration reaction rate. A larger particle characteristic coefficient and rate constant can accelerate the hydration reaction. The exponential form of this part of the formula makes the hydration reaction grow rapidly in the early stage, and then gradually slow down until the reaction approaches saturation, showing typical hydration reaction kinetic characteristics. In order to further simulate the effect of ambient temperature on the hydration reaction, the temperature factor is introduced into the formula. ,in is the reaction temperature, is the reference temperature, which is 298K. Temperature plays a role in accelerating or slowing down the hydration reaction. Higher temperatures can provide more energy and accelerate the reaction, while lower temperatures will inhibit the reaction. The model of the present invention can automatically adjust the hydration rate according to temperature changes, thereby ensuring the stability and consistency of the hydration reaction under different temperature conditions. Another important temperature-related factor is the apparent activation energy and the gas constant , which together constitute the Arrhenius term in the formula, namely .activation energy Represents the minimum energy required for the reaction. A higher activation energy will make it difficult to start the reaction, thereby reducing the reaction rate. The gas constant As a proportional factor, it adjusts the relationship between temperature and activation energy. By introducing this term, the hydration reaction rate can be adjusted nonlinearly with temperature changes, which is more in line with the actual hydration reaction characteristics. This term will increase the hydration reaction rate at high temperatures and slow down the reaction rate at low temperatures, so that the hydration reaction efficiency of the slurry at different temperatures is reasonably controlled. The formula also includes the water-binder ratio and its optimal value The square ratio term, that is The water-binder ratio is one of the key factors affecting the fluidity and hydration reaction of the slurry. A too low water-binder ratio will lead to insufficient fluidity of the slurry, thus affecting the uniformity and adhesion of the filling; while a too high water-binder ratio will make the hydration reaction too diluted, ultimately affecting the strength of the filling body. , the model can adjust the water-binder ratio to the optimal range, ensuring that the fluidity and viscosity of the slurry are kept within a reasonable range while the hydration reaction is taking place.

[0057] Example 5: In step 2, the rheological strength coupling model established according to the particle characteristic coefficient and the hydration degree is expressed using the following formula:

[0058] ;

[0059] in, The reaction time is The apparent viscosity at ; is the preset initial apparent viscosity; is the preset reference particle characteristic coefficient; is the preset hydration influence coefficient; is the shear rate; is the preset reference shear rate; is the rheological index; is the preset hydration rate influence coefficient.

[0060] Specifically, the model uses the initial apparent viscosity As a benchmark, describe the viscosity characteristics of the slurry before the hydration reaction begins. Particle characteristic coefficient The particle characteristic coefficient combines the physical and chemical properties of different particles in the slurry, including particle size, specific surface area, density, etc., and determines the distribution characteristics and stability of the slurry during the flow process. It is used for normalization processing to eliminate the influence of different batches of slurry particle composition differences. The model can amplify the influence of the particle characteristic coefficient, making it more significantly reflect the regulating effect of particle characteristics on rheology in the viscosity change. This part makes the slurry with a larger particle characteristic coefficient show a higher apparent viscosity, which is suitable for improving the flow resistance of the slurry and maintaining uniformity. The term further considers the effect of hydration reaction. As time goes by, it describes the progress of the hydration reaction; and the preset hydration influence coefficient The hydration reaction controls the degree of viscosity enhancement. The hydration products generated by the hydration reaction will increase the internal structure of the slurry, resulting in a gradual increase in viscosity. By expressing it in exponential form, the model can reflect the significant increase in viscosity when the hydration reaction intensifies, while ensuring the nonlinear growth characteristics of viscosity with the hydration reaction. Hydration influence coefficient The setting of is crucial in the model, as it determines the sensitivity of the hydration reaction to viscosity changes, allowing the system to adapt to different filling conditions. In addition, the effect of shear rate is Expressed, among which is the current shear rate, is the reference shear rate, and the rheological index The influence of shear rate is controlled. In the filling system, the shear rate determines the flow state of the slurry in the pipeline. A higher shear rate can reduce the viscosity and enhance the fluidity of the slurry. This characteristic is called "shear thinning". By combining the shear rate with the rheological index, the model can describe the viscosity change of the slurry under different shear conditions, ensuring that the ideal rheological properties can be maintained at different conveying speeds. Finally, the model is The term introduces the effect of hydration rate on viscosity. Indicates the rate of change of hydration degree over time, reflecting the instantaneous rate of hydration reaction. Preset hydration rate influence coefficient The contribution of hydration rate to viscosity is controlled. When the hydration reaction rate is high, the generated hydration products will rapidly increase the viscosity of the slurry. Through this item, the model can sensitively respond to changes in the hydration rate, thereby timely adjusting the viscosity of the slurry when the reaction rate is fast, avoiding transportation difficulties caused by excessive viscosity growth at high hydration rates.

[0061] Example 6: In step 2, the following formula is used to predict the pipeline transportation pressure loss based on the particle characteristic coefficient and the apparent viscosity to obtain the predicted pressure loss:

[0062] ;

[0063] in, To predict pressure loss; is the average flow velocity; is the length of the pipeline; is the pipe diameter; is the volume concentration; is the maximum allowable volume concentration.

[0064] Specifically, in this formula, the apparent viscosity of the slurry is It is a core parameter that directly affects the magnitude of pressure loss. Apparent viscosity describes the viscosity characteristics of the slurry under the current hydration degree and shear conditions. It reflects the resistance required to overcome the internal friction when the slurry flows in the pipeline. As the hydration reaction proceeds, the apparent viscosity of the slurry may gradually increase, so the pressure loss will also increase. By accurately calculating the apparent viscosity , the system can dynamically predict the pressure loss in order to adjust the flow rate and delivery pressure at different hydration stages. is part of the common pressure loss formula based on pipe flow in fluid mechanics, where Indicates the length of the pipeline, Indicates the pipe diameter, is the average flow velocity. The larger the diameter The smaller it is, the greater the flow resistance of the slurry in the pipe, resulting in higher pressure loss. It directly affects the flow rate of the fluid through the pipeline per unit time. Although a higher flow rate helps to improve the transportation efficiency, it will also increase the resistance of the fluid. This part reflects the basic influence of pipeline length, diameter and flow rate on the slurry flow resistance. On this basis, the formula is further The particle characteristic coefficient Effect on pressure loss. Particle characteristic coefficient It combines the particle size, specific surface area, density and other parameters of the particles to reflect the effect of particle distribution on fluidity. A larger particle characteristic coefficient means that the physical properties of the particles in the slurry have a greater impact on the flow resistance, so The model amplifies the contribution of particle characteristics to pressure loss. This term allows the system to be more sensitive to changes in flow resistance when particle characteristics are significant. In addition, The term is used to express volume concentration Impact on pressure loss. Volume concentration refers to the proportion of solid particles in the slurry, which directly affects the density and flow resistance of the slurry. When the volume concentration is close to the maximum allowable volume concentration When the pressure loss increases significantly. This term adopts the form of an exponential function, which makes the pressure loss change more significant under high concentration conditions, ensuring that the system can quickly sense and adjust the flow state. Finally, The term introduces the effect of hydration rate on pressure loss. It indicates the time rate of change of hydration degree, which reflects the intensity and speed of hydration reaction. When the hydration rate is high, the generated hydration products will increase viscosity and resistance, thus leading to increased pressure loss. , the system can feed back the impact of changes in hydration rate on pressure into the pressure loss prediction, ensuring that accurate pressure loss values ​​can be obtained at different hydration reaction stages.

[0065] Example 7: In step 2, the filling strength is calculated using the following formula based on the pressure loss and the degree of hydration using a preset strength development model:

[0066] ;

[0067] in, The reaction time is Filling strength at 10000 ℃; is the maximum filling strength; is the maximum allowable pressure loss; is the maximum allowable apparent viscosity.

[0068] Specifically, the first term of the model formula Indicates the degree of hydration Contribution to filling strength, among which It is the maximum filling strength of the filling material, indicating the theoretical strength limit of the material after it is fully hydrated and solidified. It is a variable that describes the hydration reaction process. As time goes by, the hydration degree gradually increases from the initial zero and eventually approaches 1, indicating that the material has completed solidification. As the hydration reaction deepens, the hydration products generated in the filling slurry gradually increase, and the slurry gradually forms a solid structure, which enhances the bonding strength of the material. In order to accurately reflect this process, the model uses the square relationship The contribution of hydration degree to strength in the later stages of the reaction is enhanced. Specifically, this quadratic relationship not only describes the nonlinear growth characteristics of hydration degree, but also enables accurate weighted prediction of strength when the reaction is close to complete solidification. This design is very important in practical applications because it can distinguish between the strength growth rates in the early and late stages of the hydration reaction, reflecting the accelerated improvement of overall strength in the later stages of the hydration reaction, making the model more consistent with the actual characteristics of slurry solidification. In addition to the influence of the hydration reaction, the model also The pressure loss is introduced The pressure loss describes the flow resistance of the slurry during pipeline transportation. The pressure loss in the pipeline will directly affect the flow rate and uniformity of the slurry, and a large pressure loss will usually have an adverse effect on the fluidity of the slurry, especially in long-distance transportation. Therefore, through this item, the model can describe the negative effect of pressure loss on filling strength, so that the system can appropriately reduce the strength prediction under high pressure loss conditions and avoid over-estimation of strength. Specifically, is the maximum allowable pressure loss, which is compared with the actual pressure loss The model can sensitively perceive the impact of pressure loss on the uniformity of slurry flow. When the actual pressure loss approaches or exceeds the allowable value, the exponential function will quickly reduce the strength prediction value, reflecting the inhibitory effect of flow resistance on the solidification effect. This exponential form enables the model to flexibly respond to different transportation resistance conditions. When the pipeline flow resistance is small, the filling strength maintains a high prediction value, while when the resistance is large, the prediction will be reduced to ensure the fluidity of the filling body in the pipeline and the final uniform solidification effect.

[0069] In addition, the particle characteristic coefficient The introduction of further enhances the adaptability of the model to slurry characteristics. The model can adaptively adjust the filling strength according to different particle compositions. Particle characteristic coefficient The particle size, specific surface area, density and other factors of the particles in the slurry are taken into consideration. These particle characteristics are crucial to the structural formation of the filling body during the hydration reaction. A larger particle characteristic coefficient often means that the slurry contains higher specific surface area and finer particles. Such particles can participate in the hydration reaction more quickly and fill the pores, thereby improving the overall density and strength. The model can accurately quantify the differences in strength development between different batches or sources of particles, making the system more adaptable in dealing with changes in particle characteristics. Apparent viscosity The effect of describes the relationship between fluidity and strength development. The apparent viscosity reflects the viscous resistance of the slurry during the hydration reaction. Too high viscosity may cause insufficient fluidity of the slurry, making the filling process uneven, thus affecting the overall strength of the filling body. The maximum allowable apparent viscosity in this formula item It is the basis for controlling viscosity, through the ratio The square root form of , which can sensitively adjust to changes in viscosity. When the apparent viscosity approaches the maximum allowable range, the strength prediction value will automatically decrease to reflect the potential risk of filling defects caused by excessive viscosity. Through this design, the model can dynamically control the balance between viscosity and strength development, so that the slurry can obtain a higher strength prediction value when the fluidity is good, and appropriately reduce the prediction when the fluidity is insufficient, to ensure the filling quality under high viscosity state.

[0070] ;

[0071] in, represents the matching vector; is the objective function, and the matching vector that maximizes the objective function value is obtained through iterative solution, which is taken as the optimal matching vector; The preset optimal filling strength; is the preset optimal apparent viscosity; is the preset optimal hydration degree; the ratio vector The elements include: the total number of particles; the particle size of each level of particles; the specific surface area of ​​each level of particles; the density of each level of particles; the volume fraction of each level of particles; the molar mass of silicon dioxide in each level of particles; the molar mass of aluminum oxide in each level of particles; the molar mass of calcium hydroxide in each level of particles; the molar mass of water in each level of particles; and the water-to-binder ratio.

[0072] Specifically, in the optimal matching vector In the calculation formula, firstly, Filling strength Normalization is performed to ensure that the intensity values ​​in the objective function are relative to the optimal intensity Standardization. Filling strength is the core indicator of filling body quality. Insufficient strength may lead to unstable tunnel support, while excessive strength may cause material waste. Therefore, by incorporating the ratio of actual strength to preset optimal strength into the objective function, the system can dynamically monitor and optimize the filling strength to ensure that the slurry maintains an ideal solidification effect during the flow process and does not affect fluidity due to over-solidification. The optimization of strength value occupies a core position in the objective function and is the key to achieving the optimal performance of filling slurry. Secondly, the formula The term is used to describe the effect of pressure loss on the filling process. Pressure loss It is the main source of resistance when the slurry is transported in the pipeline. A larger pressure loss means an increase in flow resistance, affecting the continuous flow of the slurry. Maximum allowable pressure loss It is used to normalize this value to maintain an appropriate balance of the effect of pressure loss on the objective function under different operating conditions. The pressure loss is in the form of an exponential function, which makes its effect on the objective function present a sensitive nonlinear characteristic. When the actual pressure loss increases close to or exceeds the allowable value, the exponential term will significantly reduce the value of the objective function, thereby avoiding adverse flow problems. This term allows the system to maintain high-intensity predictions when the flow resistance in the pipeline is small, and automatically adjust the slurry ratio when the resistance increases to optimize the flow state of the filling body.

[0073] Next, the formula The term takes into account the effect of apparent viscosity on slurry fluidity. Describes the viscosity of the slurry under the current hydration reaction state, while the optimal viscosity It is the target viscosity that ensures a balance between fluidity and solidification. By taking the reciprocal form, this term will significantly reduce the value of the objective function when the viscosity increases, to reflect the effect of increased flow resistance on the overall ratio. Too high viscosity may lead to reduced fluidity in the pipeline or even blockage. Therefore, in the objective function, the design of the viscosity term ensures that the slurry can maintain appropriate fluidity during different hydration reaction processes. Taking the reciprocal form can not only improve the objective function when the viscosity is close to the optimal value, but also make timely adjustments when the viscosity is too high, to avoid poor transportation of the slurry due to excessive viscosity. In addition, The term emphasizes the influence of the hydration reaction progress on the filling performance through the square relationship of the hydration degree. It is an important indicator to measure the progress of hydration reaction, which directly affects the curing effect and strength growth of the material. is the target value for achieving the best curing effect. Through the design of the square relationship, this term can significantly improve the value of the objective function when the hydration degree reaches the optimal value, ensuring that the system can be reasonably adjusted at different hydration stages. The square relationship of the hydration degree not only strengthens the contribution of the hydration reaction to the strength, but also reflects the acceleration of the strength improvement in the later stage of hydration, thereby achieving a balance between slurry curing and fluidity during the filling process. The last term of the formula is the particle characteristic coefficient The normalization process reflects the influence of the physical properties of particles on the objective function. The particle characteristic coefficient combines the particle size, specific surface area, density and other physical properties of the particles, which directly affects the rheological behavior and hydration reaction rate of the slurry during the filling process. The system can dynamically adjust the ratio according to different particle characteristics. A larger particle characteristic coefficient usually means a higher specific surface area and a smaller particle size, which helps to improve the hydration reaction rate and the density of the filling body, ensuring the final filling effect. In practical applications, the solution process of this formula is achieved through an iterative optimization algorithm. The system will adjust the ratio vector according to the current conditions. The various parameters are adjusted until the objective function reaches the maximum value, thereby determining the optimal ratio . Matching vector Including the total number of particles, particle size, specific surface area, density, volume fraction, and molar mass and water-binder ratio of components such as silica, alumina, calcium hydroxide and water. The precise adjustment of these parameters enables the system to optimize the fluidity, curing effect and strength of the filling body under various mine conditions, ensuring that the filling slurry maintains an efficient and uniform flow state during long-distance transportation, and ultimately forms a high-strength and stable support structure. Through the optimization calculation of this objective function, the system realizes the optimization of the proportion of filling slurry in a complex mine environment, ensuring that the expected strength and fluidity requirements can be achieved under different environments and different working conditions.

[0074] Example 9: In step 3, the optimal control parameter vector is calculated using the following formula: :

[0075] ;

[0076] in, The reaction time is The optimal parameter control vector when ; is the gravitational constant; is the optimal pump speed; is the optimal filling pressure; is the optimal filling flow rate; is the optimal concentration control coefficient; is the pump impeller diameter; when performing feedback control, use Multiply by the real-time filling slurry concentration to adjust the filling slurry concentration.

[0077] Specifically, the first term of the optimal control parameter vector Indicates the optimal filling flow , that is, the ideal flow rate of slurry in the pipeline under current conditions. Pressure loss It is the main source of resistance that the slurry needs to overcome when flowing in the pipeline, and determines the upper limit of the slurry flow rate. , Pipeline length and apparent viscosity The system can dynamically calculate the optimal flow rate under the current situation according to the change of flow resistance, ensuring that the slurry is evenly transported to the filling area in the pipeline. Specifically, this item combines the pressure-velocity relationship in fluid mechanics to control the pressure loss in the pipeline within a reasonable range, thereby avoiding excessive flow resistance caused by excessive flow velocity, or affecting the continuity and uniformity of the slurry due to too low flow velocity. The system can respond to the impact of hydration on fluidity through real-time adjustment, ensuring that the flow stability can be maintained even when the degree of hydration and viscosity gradually increase. Calculate the optimal pump speed The pump speed directly affects the slurry delivery efficiency and the pressure state in the pipeline, and is a key parameter to ensure the smooth flow of slurry. , Impeller diameter and particle characteristic coefficient To determine the ideal speed, in order to ensure the balance between flow and fluidity. A higher speed can speed up the slurry flow rate, but too high a speed may cause instability in the slurry flow and increase the pressure loss inside the system. Therefore, the speed control needs to find a proper balance between fluidity and system resistance. The system can adapt to the flow characteristics of slurries with different particle compositions by adjusting the ratio of particle characteristic coefficients. It reflects the physical properties of the particles (such as particle size, density, etc.) and can be used to adjust the pump speed to ensure that when the particle distribution and particle characteristics change, the system can dynamically adjust the speed to adapt to different flow resistances and rheological properties. This item helps to optimize the conveying intensity in actual operation, especially under high resistance flow conditions, it can effectively ensure the fluidity of the slurry. Indicates the optimal filling pressure , that is, the ideal pressure level for the slurry to maintain a stable flow in the pipeline. Indicates the pressure loss, is the density of the slurry, is the gravity constant, which combines the actual flow of the fluid under the action of gravity and pipeline resistance. By combining the pressure loss and fluid gravity factors, the system can calculate the appropriate filling pressure to ensure that the slurry is transported evenly and stably in the pipeline. At the same time, this item also adjusts the pressure through the influence of the hydration rate, that is, through the change rate of the hydration rate To dynamically adjust the pressure. When the hydration reaction rate increases, the increase in hydration products will increase the viscosity and reduce the fluidity, so it is necessary to increase the pressure appropriately to maintain the fluidity of the slurry. Through this item, the system can automatically adjust the filling pressure when the hydration rate is high, ensuring that the filling body can maintain uniform flow throughout the pipeline when the viscosity increases. This pressure control method can not only adapt to changes in the hydration reaction process, but also ensure flow uniformity and final filling effect under different flow resistance conditions. The last item is the concentration control coefficient , used to adjust the concentration in the slurry to adapt to different curing requirements and rheological properties. The concentration control coefficient is combined with the filling strength , particle characteristic coefficient and apparent viscosity The goal is to achieve a dynamic balance between the fluidity and curing properties of the slurry. This factor ensures that the strength of the slurry reaches an ideal level during the curing process. It is used to adjust the effect of concentration on slurry fluidity, while the apparent viscosity term It is used to prevent the fluidity from decreasing due to excessive concentration. The exponential form of the viscosity term ensures that when the viscosity approaches the upper limit, the concentration control coefficient will decrease rapidly, thereby avoiding flow problems caused by excessive concentration. Through this concentration adjustment, the system can reasonably manage the consistency and fluidity of the slurry, optimize the flow effect under different rheological environments, and ensure the stability of the slurry during transportation and solidification. Through the optimal control parameter vector Based on the above calculations, the system can dynamically feedback control the flow rate, pump speed, pressure and concentration during the filling process. This ensures stable flow state and ideal solidification strength under different hydration reaction processes, rheological states and particle composition conditions. The entire control process involves complex dynamic adjustments, which can adapt to various uncertainties in the mine environment, such as changes in pipeline resistance, fluctuations in particle characteristics and adjustments in hydration reaction rates, thereby providing a strong guarantee for the efficient transportation and strength development of the filling slurry. This control mechanism can significantly improve the stability and efficiency of the filling system in actual applications, help ensure that the slurry does not separate and unevenly fill during long-distance transportation, and effectively improve the support effect and safety of the mine tunnels.

[0078] The concentration control coefficient of the present invention realizes dynamic optimization of slurry concentration by regulating in association with apparent viscosity and strength. The slurry concentration directly affects its fluidity and strength. A fixed concentration is usually used in traditional filling systems, which makes it difficult to cope with the impact of changes in slurry viscosity and strength, resulting in fluctuations in filling effect. The present invention can automatically reduce the concentration when the concentration is too high and the fluidity decreases, and appropriately increase the concentration when the concentration is low but the curing strength is insufficient, through the optimized design of the concentration control coefficient, so as to maintain the balance between the fluidity and strength of the slurry at different stages and working conditions. Through this concentration adjustment mechanism, the system can effectively avoid the problems of excessive conveying resistance or insufficient curing strength caused by improper concentration control. The present invention also accurately predicts the strength development of the slurry at different hydration stages through the hydration reaction kinetic model and the rheology-strength coupling model. Hydration reaction is a key factor affecting the final strength and curing speed of the filling body. It is difficult for traditional systems to accurately control the hydration reaction, resulting in uneven development of the slurry strength. The hydration reaction kinetic model in the present invention can dynamically adjust the reaction speed and curing process according to the real-time calculated hydration degree, reaction temperature and particle characteristic coefficient, ensuring that the filling body maintains uniform strength growth during the curing process. In addition, the rheological-strength coupling model realizes the prediction and regulation of the strength change of the slurry during the flow process through the synergistic effect of the apparent viscosity and the hydration reaction, ensuring that the slurry can be effectively distributed and cured into a high-strength filling body during the transportation process under high rheological conditions. Finally, the present invention realizes the intelligent control of the filling system through real-time feedback control and dynamic optimization of parameters. In traditional filling technology, the slurry flow parameters often need to be manually monitored and adjusted, and cannot adapt to the complex changes in mine working conditions. The present invention uses a feedback control system to monitor the key parameters such as flow rate, viscosity, pressure and curing strength during the filling process through sensors in real time, and automatically adjusts the control parameters when deviations occur, so that the slurry can maintain a stable state throughout the entire transportation process and curing stage. This intelligent control mechanism not only improves the efficiency of the filling operation, but also reduces the need for manual intervention, significantly improving the overall reliability and work efficiency of the filling system, and is particularly suitable for the support needs of complex and high-strength mines.

[0079] The present invention is described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. An optimization control method for a longwall tunnel-by-tunnel cementing filling system, characterized in that: The method comprises: Step 1: sampling the particles of each component in the filling slurry to obtain the physical parameters of each level of particles; calculating the inhibition factor in the hydration reaction according to the molar mass of the oxide in each level of particles in the filling slurry; calculating the particle characteristic coefficient of the filling slurry according to the physical parameters and the inhibition factor; Step 2: Based on the particle characteristic coefficient, a preset hydration reaction kinetic model is used to calculate the hydration degree; based on the particle characteristic coefficient and the hydration degree, a rheological strength coupling model is established, and the rheological strength coupling model outputs the apparent viscosity; based on the particle characteristic coefficient and the apparent viscosity, the pipeline transportation pressure loss is predicted to obtain the predicted pressure loss; based on the pressure loss and the hydration degree, the preset strength development model is used to calculate the filling strength; Step 3: According to step 1 and step 2, calculate the optimal ratio vector of the filling slurry; according to step 1 and step 2, calculate the optimal control parameter vector; the optimal control parameter vector includes: optimal pump speed, optimal filling pressure, optimal filling flow rate and optimal concentration control coefficient; based on the optimal control parameters, perform feedback control; based on the optimal ratio vector, adjust the ratio parameters of the filling slurry; In step 3, the optimal control parameter vector is calculated using the following formula: : ； in, The reaction time is The optimal parameter control vector when ; is the gravitational constant; is the optimal pump speed; is the optimal filling pressure; is the optimal filling flow rate; is the optimal concentration control coefficient; is the pump impeller diameter; when performing feedback control, use Multiplying the concentration of the filling slurry in real time to adjust the concentration of the filling slurry; To predict pressure loss; is the length of the pipeline; is the pipe diameter; The reaction time is The apparent viscosity at ; is the average flow velocity; is the particle characteristic coefficient of the filling slurry; is the preset reference particle characteristic coefficient; The reaction time is Filling strength at 10000 ℃; The preset optimal filling strength; is the preset optimal apparent viscosity; The reaction time is Degree of hydration at ; is the preset hydration rate influence coefficient; is the density of the filling slurry.

2. The optimization control method for the longwall tunnel-by-tunnel cementing filling system according to claim 1, characterized in that: In step 1, each component particle in the filling slurry is sampled, and the physical parameters of each level of particles obtained include: particle diameter, specific surface area, particle density and volume fraction; oxides include: silicon dioxide, calcium hydroxide, aluminum oxide and water.

3. The optimization control method for the longwall tunnel-by-tunnel cementing filling system according to claim 2 is characterized in that: Use the following formula to calculate the particle characteristic coefficient of the filling slurry : ； in, is the total number of particles; For the Grade particle size; is the maximum particle size; For the Specific surface area of ​​graded particles; For the Grade particle density; For the The volume fraction of graded particles; For the The molar mass of silicon dioxide in the graded particles; For the The molar mass of alumina in the graded particles; For the The molar mass of calcium hydroxide in the graded particles; For the The molar mass of water in the graded particles; For inhibitory factors.

4. The optimization control method for the longwall tunnel-by-tunnel cementing filling system according to claim 3 is characterized in that: In step 2, the preset hydration reaction kinetic model is expressed using the following formula: ； in, is the hydration reaction rate constant; is the reaction temperature; is the reference temperature, with a value of 298 Kelvin; is the apparent activation energy; is the gas constant; Indicates water-to-binder ratio; The optimal water-to-binder ratio.

5. The optimization control method for the longwall tunnel-by-tunnel cementing filling system according to claim 4 is characterized in that: In step 2, the rheological strength coupling model established based on the particle characteristic coefficient and hydration degree is expressed using the following formula: ； in, is the preset initial apparent viscosity; is the preset hydration influence coefficient; is the shear rate; Preset reference shear rate; is the rheological index.

6. The optimization control method for the longwall tunnel-by-tunnel cementing filling system according to claim 5, characterized in that: In step 2, the following formula is used to predict the pipeline transportation pressure loss based on the particle characteristic coefficient and apparent viscosity to obtain the predicted pressure loss: ； in, is the volume concentration; is the maximum allowable volume concentration.

7. The optimization control method for the longwall tunnel-by-tunnel cementing filling system according to claim 6 is characterized in that: In step 2, the filling strength is calculated based on the pressure loss and hydration degree using the preset strength development model using the following formula: ； in, is the maximum filling strength; is the maximum allowable pressure loss; is the maximum allowable apparent viscosity.

8. The optimization control method for the longwall lane-by-lane cementing filling system according to claim 7 is characterized in that: In step 3, the optimal ratio vector of the filling slurry is calculated using the following formula: : ； in, represents the matching vector; is the objective function, and the matching vector that maximizes the objective function value is obtained through iterative solution, which is taken as the optimal matching vector; is the preset optimal hydration degree; the ratio vector The elements include: the total number of particles; the particle size of each level of particles; the specific surface area of ​​each level of particles; the density of each level of particles; the volume fraction of each level of particles; the molar mass of silicon dioxide in each level of particles; the molar mass of aluminum oxide in each level of particles; the molar mass of calcium hydroxide in each level of particles; the molar mass of water in each level of particles; and the water-to-binder ratio.

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