Integrated scale inhibition and filtration intelligent linkage control method and system for cooling spray of coal mining machine

Abstract

The application discloses a kind of coal winning machine cooling spray integration scale inhibition filtering intelligent linkage control method and system, belong to mining machinery intelligent control technical field.The method includes: real-time acquisition cooling circuit and the multi-source water quality parameter of spray circuit;The multi-source water quality parameter is input into the scale tendency prediction model of physical mechanism and data driving fusion, and outputs scale risk index;According to the scale risk index and spray end flow joint trigger hierarchical scale inhibition strategy;While based on filter unit degradation model carries out residual life prediction, and establishes heat-flow coupling constraint between cooling and spray circuit.The application integrates cooling circuit and spray circuit, and significantly prolongs scale period and nozzle clogging interval by intelligent hierarchical scale inhibition control, reduces unplanned shutdown.

Description

Technical Field

[0001] This invention relates to the field of intelligent mining technology, and in particular to an intelligent linkage control method and system for integrated cooling spray scale inhibition and filtration of coal mining machines. Background Technology

[0002] When a coal mining machine operates underground, the cutting section generates a large amount of heat and dust, requiring a cooling system to cool the cutting motor and cutting teeth, and a spray system to suppress dust and cool the cutting teeth. Traditional coal mining machines have independent cooling and spray systems, each equipped with its own water source and treatment equipment, which presents the following problems: First, the two systems independently occupy limited downhole space, resulting in complex piping and a large amount of maintenance. Secondly, the water quality treatment standards are inconsistent when the cooling circuit and the spray circuit use water separately. The cooling circuit is sensitive to scaling, and the spray circuit is sensitive to clogging. However, both face serious scaling and clogging problems due to the high hardness of the water (the total hardness of the well water source is usually 300 to 800 mg / L). Third, the current maintenance of the cooling system and spray system adopts a regular inspection mode—regularly cleaning the radiator, regularly replacing the filter element, and regularly manually adding scale inhibitor. It cannot be dynamically adjusted according to changes in water quality. In actual use, the cooling efficiency often decreases or the spray becomes clogged before it is discovered and dealt with, which affects the coal mining efficiency.

[0003] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides an intelligent linkage control method and system for integrated cooling spray scale inhibition and filtration of coal mining machines, thereby effectively solving the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a smart linkage control method for integrated cooling spray scale inhibition and filtration in coal mining machines, comprising the following steps: Real-time collection of multi-source water quality parameters at each water quality monitoring node, including at least the total hardness, temperature, pH value, conductivity and suspended solids concentration of the influent, as well as the outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit; The multi-source water quality parameters are input into the scaling trend prediction model, which outputs the scaling risk index of the cooling circuit and the spray circuit under the current operating conditions within a future preset time window. The scaling trend prediction model is a hybrid model based on the fusion of physical mechanism and data-driven approach. The physical mechanism part establishes the scaling thermodynamic balance equation based on the Langerile saturation index, while the data-driven part uses historical water quality-scaling corresponding data to train a gradient boosting decision tree to correct the residuals of the physical mechanism model. A graded scale inhibition strategy is jointly triggered based on the scaling risk index and the flow rate at the end of the spray circuit.

[0006] Furthermore, the graded scale inhibition strategy includes: When the scaling risk index is lower than the first threshold and the flow rate at the end of the spray circuit is not lower than 90% of the rated flow rate, the current operating state is maintained; When the scaling risk index is between the first threshold and the second threshold, or when the flow rate at the end of the spray circuit drops to between 70% and 90% of the rated flow rate, a first-level scale inhibition measure is implemented, including simultaneously injecting scale inhibitor into the cooling circuit and the spray circuit and initiating the backwashing procedure of the filter unit. When the scaling risk index is not lower than the second threshold or the flow rate at the end of the spray circuit is lower than 70% of the rated flow rate, secondary scale inhibition measures are implemented, including bypassing the outlet water portion of the cooling circuit to the spray circuit on the basis of the primary scale inhibition measures to utilize the temperature rise of the cooling circuit to destroy the conditions for scale crystal formation, while increasing the concentration of scale inhibitor injection and switching to the standby filter unit.

[0007] Furthermore, the cooling circuit and the spray circuit are connected by a three-way proportional regulating valve to distribute water volume. The water outlet of the cooling circuit is heated by 10°C to 25°C after heat exchange. The spray circuit is used for dust suppression and cooling of the cutting teeth of the coal mining machine. The cooling circuit and the spray circuit are further provided with a pre-filtration unit, a fine filtration unit and a scale inhibitor injection unit, wherein the pre-filtration unit and the fine filtration unit are respectively located at the inlet end of the cooling circuit and the spray circuit.

[0008] Furthermore, the formula for calculating the Langerile saturation index in the physical mechanism section is as follows: ; in , The total dissolved solids coefficient, For temperature coefficient, This refers to the calcium hardness coefficient. The total alkalinity coefficient; the residual correction is based on historical data. The deviation between the calculated values ​​and the actual observed fouling values ​​is used as the training label for the gradient boosting decision tree.

[0009] Furthermore, in the primary scale inhibition measure: The scale inhibitor concentration is 5 to 15 mg / L. The backwashing procedure for the filter unit is as follows: first, pulsate flush with 0.3 MPa air pressure for 10 seconds, then backwash with clean water at 1.2 times the rated flow rate for 15 seconds. Repeat three cycles to complete the backwashing.

[0010] Furthermore, in the secondary scale inhibition measures: The bypass ratio of the cooling circuit outlet water portion to the spray circuit is 20% to 40% of the total flow rate of the spray circuit. The outlet water temperature of the cooling circuit is 10°C to 20°C higher than the inlet water temperature of the spray circuit, thereby accelerating the dissolution of calcium carbonate scale crystals by utilizing the temperature rise. The concentration of the scale inhibitor is increased to 20 to 40 mg / L.

[0011] Furthermore, it also includes health status assessment and predictive maintenance steps for the filter unit: Collect the inlet and outlet water pressure difference and cumulative water flow of each filter unit. Using the inlet and outlet water pressure difference and the cumulative water flow as input, output the remaining effective filtration capacity and predicted replacement time of each filter unit through a pre-trained remaining life prediction model. When the remaining effective filtration capacity is lower than the preset capacity threshold, a replacement warning is generated and the system automatically switches to the backup filtration unit.

[0012] Furthermore, the remaining lifetime prediction model is a degradation model based on the Wiener process. It uses historical pressure difference-water flow degradation curves as training data, estimates the degradation rate parameter through maximum likelihood, and uses the time when the pressure difference limit is first reached as the predicted remaining lifetime. The confidence interval for predicting replacement time is generated by Monte Carlo simulation based on the standard deviation of the degradation rate.

[0013] Furthermore, a thermal-fluid coupling constraint is formed between the outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit: When the outlet water temperature of the cooling circuit exceeds 60°C, it is determined that the cooling efficiency has decreased, triggering a command to increase the flow rate of the spray circuit to utilize low-temperature spray water for auxiliary cooling. When the flow rate at the end of the spray circuit is less than 50% of the rated flow rate, it is determined that the spray is severely blocked, triggering an automatic power reduction command for the coal mining machine until the spray returns to normal.

[0014] This invention also includes an integrated intelligent linkage control system for cooling spray and scale inhibition filtration in coal mining machines, using the method described above. The system includes: The water quality monitoring unit is used to collect multi-source water quality parameters from each water quality monitoring node in real time. The multi-source water quality parameters include at least the total hardness of the influent, temperature, pH value, conductivity and suspended solids concentration, as well as the outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit. The scaling trend prediction unit is used to input the multi-source water quality parameters into the scaling trend prediction model and output the scaling risk index of the cooling circuit and the spray circuit in the future within a preset time window under the current operating conditions. The scaling trend prediction model is a hybrid model based on the fusion of physical mechanism and data-driven approach. The physical mechanism part establishes the scaling thermodynamic balance equation based on the Langerile saturation index, and the data-driven part uses historical water quality-scaling corresponding data to train a gradient boosting decision tree to correct the residuals of the physical mechanism model. The control unit is used to jointly trigger a graded scale inhibition strategy based on the scaling risk index and the flow rate at the end of the spray circuit.

[0015] The beneficial effects of this invention are as follows: By integrating the cooling circuit and the spray circuit and employing a tiered scale inhibition strategy, unified water quality management and predictive automatic triggering of scale inhibition measures are achieved, reducing unplanned downtime caused by scaling and clogging. Furthermore, a scale trend prediction model that combines physical mechanisms with data-driven approaches enables precise quantitative assessment of scaling risk. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the system structure in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the system structure in Embodiment 2 of the present invention; Figure 4 This is a flowchart of the graded scale inhibition control process in Embodiment 2 of the present invention; Figure 5 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Example 1:

[0019] like Figure 1 The following is a method for intelligent linkage control of integrated cooling spray scale inhibition and filtration in coal mining machines, comprising the following steps: Real-time collection of multi-source water quality parameters at each water quality monitoring node. The multi-source water quality parameters include at least the total hardness, temperature, pH value, conductivity and suspended solids concentration of the influent, as well as the outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit. The scaling trend prediction model inputs multi-source water quality parameters and outputs the scaling risk index of the cooling circuit and spray circuit under the current operating conditions within a preset time window. The scaling trend prediction model is a hybrid model based on the fusion of physical mechanism and data-driven approach. The physical mechanism part establishes the scaling thermodynamic balance equation based on the Langerile saturation index, while the data-driven part uses historical water quality-scaling data to train a gradient boosting decision tree to correct the residuals of the physical mechanism model. A graded scale inhibition strategy is triggered by combining the scaling risk index and the flow rate at the end of the spray circuit.

[0020] By integrating the cooling and spray circuits and employing a tiered scale inhibition strategy, unified water quality management and predictive automatic triggering of scale inhibition measures were achieved, reducing unplanned downtime caused by scaling and clogging. A scale trend prediction model, combining physical mechanisms and data-driven approaches, enabled precise quantitative assessment of scaling risk.

[0021] In this embodiment, the graded scale inhibition strategy includes: When the scaling risk index is below the first threshold and the flow rate at the end of the spray circuit is not less than 90% of the rated flow rate, the current operating status shall be maintained. When the scaling risk index is between the first and second thresholds or the flow rate at the end of the spray circuit drops to between 70% and 90% of the rated flow rate, first-level scale inhibition measures are implemented, including injecting scale inhibitor into the cooling circuit and the spray circuit simultaneously and starting the backwashing procedure of the filter unit. When the scaling risk index is not lower than the second threshold or the flow rate at the end of the spray circuit is lower than 70% of the rated flow rate, secondary scale inhibition measures are implemented. This includes bypassing the outlet water of the cooling circuit to the spray circuit on the basis of the primary scale inhibition measures to use the temperature rise of the cooling circuit to destroy the conditions for scale crystal formation, while increasing the concentration of scale inhibitor injection and switching to the standby filter unit.

[0022] The cooling circuit and the spray circuit are distributed by a three-way proportional regulating valve. The water outlet of the cooling circuit is heated by 10°C to 25°C after heat exchange. The spray circuit is used for dust suppression and cooling of the cutting teeth of the coal mining machine. The cooling circuit and spray circuit are also equipped with a pre-filtration unit, a fine filtration unit and a scale inhibitor injection unit. The pre-filtration unit and the fine filtration unit are respectively located at the inlet end of the cooling circuit and the spray circuit.

[0023] As a preferred embodiment of the above, the formula for calculating the Langerile saturation index in the physical mechanism section is: ; in , The total dissolved solids coefficient, For temperature coefficient, This refers to the calcium hardness coefficient. Total alkalinity coefficient; residual correction based on historical data. The deviation between the calculated values ​​and the actual observed fouling values ​​is used as the training label for the gradient boosting decision tree.

[0024] In this embodiment, the primary scale inhibition measure includes: The scale inhibitor concentration is 5 to 15 mg / L. The backwashing procedure for the filter unit is as follows: first, pulsate flush with 0.3 MPa air pressure for 10 seconds, then backwash with clean water at 1.2 times the rated flow rate for 15 seconds. Repeat three cycles to complete the backwashing.

[0025] Secondary scale inhibition measures: The bypass ratio of the cooling circuit outlet water section to the spray circuit is 20% to 40% of the total flow of the spray circuit. The outlet water temperature of the cooling circuit is 10°C to 20°C higher than the inlet water temperature of the spray circuit, so as to accelerate the dissolution of calcium carbonate scale crystals by utilizing the temperature rise. The concentration of scale inhibitor is increased to 20 to 40 mg / L.

[0026] As a preferred embodiment of the above, the method further includes a health status assessment and predictive maintenance step for the filter unit: Collect the inlet and outlet water pressure difference and cumulative water flow of each filter unit. Using the inlet and outlet water pressure difference and cumulative water flow as input, output the remaining effective filtration capacity and predicted replacement time of each filter unit through a pre-trained remaining life prediction model. When the remaining effective filtration capacity is lower than the preset capacity threshold, a replacement warning is generated and the system automatically switches to the backup filtration unit.

[0027] Among them, the remaining lifetime prediction model is a degradation model based on the Wiener process. It uses historical pressure difference-water flow degradation curves as training data, estimates the degradation rate parameter through maximum likelihood, and uses the time when the pressure difference limit is first reached as the predicted remaining lifetime. The confidence interval for predicting replacement time is generated by Monte Carlo simulation based on the standard deviation of the degradation rate.

[0028] As a preferred embodiment of the above, a thermal-fluid coupling constraint is formed between the cooling circuit outlet water temperature and the spray circuit terminal flow rate: When the outlet water temperature of the cooling circuit exceeds 60°C, it is determined that the cooling efficiency has decreased, triggering a command to increase the flow rate of the spray circuit to utilize low-temperature spray water for auxiliary cooling. When the flow rate at the end of the spray circuit is less than 50% of the rated flow rate, it is determined that the spray is severely blocked, triggering an automatic power reduction command for the coal mining machine until the spray returns to normal.

[0029] like Figure 2 As shown, this embodiment also includes an integrated intelligent linkage control system for cooling spray and scale inhibition filtration in coal mining machines. Using the method described above, the system includes: The water quality monitoring unit is used to collect multi-source water quality parameters from each water quality monitoring node in real time. The multi-source water quality parameters include at least the total hardness, temperature, pH value, conductivity and suspended solids concentration of the influent, as well as the outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit. The scaling trend prediction unit is used to input multi-source water quality parameters into the scaling trend prediction model and output the scaling risk index of the cooling circuit and the spray circuit in the future within a preset time window under the current operating conditions. The scaling trend prediction model is a hybrid model based on the fusion of physical mechanism and data-driven approach. The physical mechanism part establishes the scaling thermodynamic balance equation based on the Langerile saturation index, and the data-driven part uses historical water quality-scaling corresponding data to train a gradient boosting decision tree to correct the residuals of the physical mechanism model. The control unit is used to jointly trigger a graded scale inhibition strategy based on the scaling risk index and the flow rate at the end of the spray circuit. Example 2:

[0030] like Figure 3 As shown, this embodiment includes an integrated intelligent linkage control system for cooling spray and scale inhibition filtration in coal mining machines, specifically comprising: I. System Hardware Platform This embodiment describes the modification and deployment on an MG650 / 1710-WD coal mining machine in a certain mine. The system consists of an integrated water circuit unit—merging the inlets of the original independent cooling and spray systems, distributing water to the cooling circuit (rated flow rate 80L / min) and the spray circuit (rated flow rate 120L / min) via a three-way proportional regulating valve (3 / 4-inch electric ball valve); an outlet water temperature sensor is installed after the radiator in the cooling circuit, and a flow sensor (electromagnetic flow meter, accuracy ±0.5%) is installed at the end of the spray circuit. Water quality monitoring nodes are set at three locations: the main water inlet, the cooling circuit inlet, and the spray circuit inlet. Each node is equipped with a hardness sensor (calcium and magnesium ion selective electrode, range 0-1000mg / L), a temperature sensor (PT1000, accuracy ±0.1℃), a pH sensor (glass electrode, accuracy ±0.02), a conductivity sensor (four electrodes, range 0-5000μS / cm), and a turbidity sensor (optical scattering, range 0-1000NTU).

[0031] II. Scaling Trend Prediction Model The physical mechanism section establishes a scaling thermodynamic equilibrium equation based on the Langerile Saturation Index (LSI). LSI = pH - pHs, where pHs = (9.3 + A + B) - (C + D). Taking typical downhole water quality data (total hardness 450 mg / L, total alkalinity 350 mg / L, TDS 680 mg / L, temperature 25℃, pH 7.8) as an example, the calculated values ​​are A=0.68, B=1.82, C=2.65, D=2.54, pHs=6.61, and LSI=7.80-6.61=1.19>0, indicating a scaling tendency. The data-driven section trains a gradient boosting decision tree (300 trees, maximum depth 5) using historical water quality monitoring data and scaling records from 2019 to 2023 (a total of 1,872 data sets). The deviation between the calculated LSI value and the actual scaling level is used as the training label to learn the residual correction pattern. The hybrid model's output extends the scaling risk index from the traditional three levels (no / light / heavy) to continuous values ​​(0 to 1.0), achieving a prediction accuracy of 91.3% on the test set.

[0032] III. Implementation of the Graded Scale Inhibition Strategy like Figure 4 As shown, the first threshold T1 = 0.4 and the second threshold T2 = 0.7 were set. Under normal operating conditions, the total hardness of the influent was 420 mg / L, LSI = 1.15, and the scaling risk index output by the mixed model was 0.52 (between T1 and T2). The flow rate at the end of the spray circuit was 112 L / min (93.3% of the rated value), triggering the first-level scale inhibition measure: the metering pump simultaneously injected scale inhibitor (organophosphonate) into the cooling circuit and the spray circuit at a concentration of 10 mg / L; the filter unit executed a backwashing procedure (0.3 MPa air pressure pulsating flush for 10 seconds → 1.2 times flow rate clean water backwash for 15 seconds, repeated 3 times). After the backwashing was completed, the pressure difference between the filter inlet and outlet water decreased from 0.12 MPa to 0.04 MPa.

[0033] During a monitoring session on day 37, the total hardness of the influent rose to 580 mg / L, and the scaling risk index climbed to 0.82 (exceeding T2). Simultaneously, the spray terminal flow rate dropped to 78 L / min (65% of the rated value, below 70%), triggering secondary scale inhibition measures: In addition to the primary measures, 30% of the cooling circuit outlet water (temperature approximately 42°C) was bypassed to the spray circuit (the mixed spray water temperature was approximately 34°C, 12°C higher than the original water temperature), the scale inhibitor concentration was increased to 30 mg / L, and the system was switched to a standby filter unit. Two hours after the measures were implemented, the spray terminal flow rate recovered to 96 L / min (80% of the rated value).

[0034] IV. Prediction of Remaining Life of Filter Units Taking the fine filtration unit as an example, its inlet and outlet water pressure difference during normal operation is 0.03 to 0.04 MPa, with an upper limit set at 0.15 MPa. Degradation trajectory modeled using the Wiener process: ,in For degradation rate, This is standard Brownian motion. Parameters are estimated using the differential pressure-time data from the previous 6 months for this filter unit: , The predicted time to reach the 0.15 MPa upper limit is approximately (0.15-0.04) / 0.012 = 9.2 months, with a 95% confidence interval of 7.8 to 10.6 months. The model issued a replacement warning in the 8th month, allowing on-site personnel to prepare filter cartridges in advance and complete the replacement during the planned shutdown window in the 9th month, thus avoiding unplanned downtime.

[0035] V. Verification of Thermal-Fluid Coupling During a sustained heavy-load cutting operation, the cutting motor operated at over 95% of its rated power for approximately 40 minutes, causing the cooling circuit outlet water temperature to rise from 42℃ to 63℃ (exceeding the 60℃ threshold). The system automatically triggered a spray flow increase command, adjusting the cooling / spray distribution ratio of the three-way proportional control valve from 40:60 to 30:70, increasing the spray flow rate from 120L / min to 140L / min. After approximately 15 minutes, the cooling circuit outlet water temperature returned to 57℃. During this process, the dust suppression effect of the spray did not show a significant decrease (the dust concentration monitoring value changed from 8.2mg / m³ to 8.5mg / m³, within the allowable range). In another spray blockage event, the spray flow rate dropped to 52L / min (43% of the rated value, below the 50% threshold). The system automatically sent a load reduction command to the coal mining machine traction controller, automatically reducing the cutting power from 85% to 60% of the rated power, and automatically restoring it to the original power after 1.5 hours when the spray returned to normal.

[0036] VI. Comparison of Results

[0037] This embodiment has the following beneficial effects: First, by integrating the cooling circuit and the spray circuit and employing a tiered scale inhibition strategy, unified water quality management and predictive automatic triggering of scale inhibition measures were achieved. In field tests of an MG650 / 1710-WD coal mining machine at a certain mine, the scaling cycle of the cooling system radiator was extended from the traditional 45 days to 120 days (an increase of 167%), the nozzle clogging interval of the spray system was extended from an average of 15 days to 50 days (an increase of 233%), and the number of unplanned shutdowns due to scaling and clogging was reduced from an average of 8.7 times per year to 1.2 times per year.

[0038] Second, a scaling trend prediction model that integrates physical mechanisms and data-driven approaches was developed, enabling accurate quantitative assessment of scaling risk. During six months of continuous operation, the hybrid model achieved a 91.3% accuracy rate in predicting scaling risk, compared to 76.8% for the pure physical mechanism model and 82.5% for the pure data-driven model.

[0039] Third, by linking the cooling water outlet to the spray circuit, the approximately 15°C temperature rise of the cooling water disrupts the conditions for scale formation, reducing the scaling rate of the spray circuit by about 35% in a secondary scale inhibition scenario. The heat-fluid coupling constraint mechanism, by increasing the spray flow rate to assist cooling when the cooling water temperature exceeds 60°C, reduces the peak temperature of the cutting motor by about 8°C. The filter unit's remaining life prediction model has a prediction error of within ±3 days for filter element replacement time, reducing filter element consumption by about 40% compared to periodic replacement.

[0040] Please see Figure 5 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0041] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0042] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0043] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0044] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0045] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0046] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0047] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0048] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0049] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0050] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for intelligent linkage control of integrated cooling spray scale inhibition and filtration in coal mining machines, characterized in that, Includes the following steps: Real-time collection of multi-source water quality parameters at each water quality monitoring node, including at least the total hardness, temperature, pH value, conductivity and suspended solids concentration of the influent, as well as the outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit; The multi-source water quality parameters are input into the scaling trend prediction model, which outputs the scaling risk index of the cooling circuit and the spray circuit under the current operating conditions within a future preset time window. The scaling trend prediction model is a hybrid model based on the fusion of physical mechanism and data-driven approach. The physical mechanism part establishes the scaling thermodynamic balance equation based on the Langerile saturation index, while the data-driven part uses historical water quality-scaling corresponding data to train a gradient boosting decision tree to correct the residuals of the physical mechanism model. A graded scale inhibition strategy is jointly triggered based on the scaling risk index and the flow rate at the end of the spray circuit.

2. The method according to claim 1, characterized in that, The graded scale inhibition strategy includes: When the scaling risk index is lower than the first threshold and the flow rate at the end of the spray circuit is not lower than 90% of the rated flow rate, the current operating state is maintained; When the scaling risk index is between the first threshold and the second threshold, or when the flow rate at the end of the spray circuit drops to between 70% and 90% of the rated flow rate, a first-level scale inhibition measure is implemented, including simultaneously injecting scale inhibitor into the cooling circuit and the spray circuit and initiating the backwashing procedure of the filter unit. When the scaling risk index is not lower than the second threshold or the flow rate at the end of the spray circuit is lower than 70% of the rated flow rate, secondary scale inhibition measures are implemented, including bypassing the outlet water portion of the cooling circuit to the spray circuit on the basis of the primary scale inhibition measures to utilize the temperature rise of the cooling circuit to destroy the conditions for scale crystal formation, while increasing the concentration of scale inhibitor injection and switching to the standby filter unit.

3. The method according to claim 2, characterized in that, The cooling circuit and the spray circuit are connected by a three-way proportional control valve to distribute water volume. The water outlet of the cooling circuit is heated by 10°C to 25°C after heat exchange. The spray circuit is used for dust suppression and cooling of the cutting teeth of the coal mining machine. The cooling circuit and the spray circuit are further provided with a pre-filtration unit, a fine filtration unit and a scale inhibitor injection unit, wherein the pre-filtration unit and the fine filtration unit are respectively located at the inlet end of the cooling circuit and the spray circuit.

4. The method according to claim 1, characterized in that, The formula for calculating the Langerier saturation index in the physical mechanism section is as follows: ； in , The total dissolved solids coefficient, For temperature coefficient, This refers to the calcium hardness coefficient. The total alkalinity coefficient; the residual correction is based on historical data. The deviation between the calculated values ​​and the actual observed fouling values ​​is used as the training label for the gradient boosting decision tree.

5. The method according to claim 2, characterized in that, In the primary scale inhibition measures: The scale inhibitor concentration is 5 to 15 mg / L. The backwashing procedure for the filter unit is as follows: first, pulsate flush with 0.3 MPa air pressure for 10 seconds, then backwash with clean water at 1.2 times the rated flow rate for 15 seconds. Repeat three cycles to complete the backwashing.

6. The method according to claim 2, characterized in that, In the aforementioned secondary scale inhibition measures: The bypass ratio of the cooling circuit outlet water portion to the spray circuit is 20% to 40% of the total flow rate of the spray circuit. The outlet water temperature of the cooling circuit is 10°C to 20°C higher than the inlet water temperature of the spray circuit, thereby accelerating the dissolution of calcium carbonate scale crystals by utilizing the temperature rise. The concentration of the scale inhibitor is increased to 20 to 40 mg / L.

7. The method according to claim 2, characterized in that, It also includes health assessment and predictive maintenance steps for the filter unit: Collect the inlet and outlet water pressure difference and cumulative water flow of each filter unit. Using the inlet and outlet water pressure difference and the cumulative water flow as input, output the remaining effective filtration capacity and predicted replacement time of each filter unit through a pre-trained remaining life prediction model. When the remaining effective filtration capacity is lower than the preset capacity threshold, a replacement warning is generated and the system automatically switches to the backup filtration unit.

8. The method according to claim 7, characterized in that, The remaining lifetime prediction model is a degradation model based on the Wiener process. It uses historical pressure difference-water flow degradation curves as training data, estimates the degradation rate parameter through maximum likelihood, and uses the time when the pressure difference limit is first reached as the predicted remaining lifetime. The confidence interval for predicting replacement time is generated by Monte Carlo simulation based on the standard deviation of the degradation rate.

9. The method according to claim 3, characterized in that, The outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit constitute a thermal-fluid coupling constraint. When the outlet water temperature of the cooling circuit exceeds 60°C, it is determined that the cooling efficiency has decreased, triggering a command to increase the flow rate of the spray circuit to utilize low-temperature spray water for auxiliary cooling. When the flow rate at the end of the spray circuit is less than 50% of the rated flow rate, it is determined that the spray is severely blocked, triggering an automatic power reduction command for the coal mining machine until the spray returns to normal.

10. A smart linkage control system for integrated cooling spray scale inhibition and filtration in a coal mining machine, characterized in that, Using the method of any one of claims 1 to 9, the system comprises: The water quality monitoring unit is used to collect multi-source water quality parameters from each water quality monitoring node in real time. The multi-source water quality parameters include at least the total hardness of the influent, temperature, pH value, conductivity and suspended solids concentration, as well as the outlet water temperature of the cooling circuit and the flow rate at the end of the spray circuit. The scaling trend prediction unit is used to input the multi-source water quality parameters into the scaling trend prediction model and output the scaling risk index of the cooling circuit and the spray circuit in the future within a preset time window under the current operating conditions. The scaling trend prediction model is a hybrid model based on the fusion of physical mechanism and data-driven approach. The physical mechanism part establishes the scaling thermodynamic balance equation based on the Langerile saturation index, and the data-driven part uses historical water quality-scaling corresponding data to train a gradient boosting decision tree to correct the residuals of the physical mechanism model. The control unit is used to jointly trigger a graded scale inhibition strategy based on the scaling risk index and the flow rate at the end of the spray circuit.

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