CN120819504A - Hydraulic pump control optimization method and system for rare earth magnetic material extraction - Google Patents
Hydraulic pump control optimization method and system for rare earth magnetic material extractionInfo
- Publication number
- CN120819504A CN120819504A CN202511315826.1A CN202511315826A CN120819504A CN 120819504 A CN120819504 A CN 120819504A CN 202511315826 A CN202511315826 A CN 202511315826A CN 120819504 A CN120819504 A CN 120819504A
- Authority
- CN
- China
- Prior art keywords
- hydraulic
- pump control
- dynamic
- pressure
- output
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B49/00—Control, e.g. of pump delivery, or pump pressure of, or safety measures for, machines, pumps, or pumping installations, not otherwise provided for, or of interest apart from, groups F04B1/00 - F04B47/00
- F04B49/06—Control using electricity
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a hydraulic pump control optimization method and a system for rare earth magnetic material extraction, and relates to the technical field of hydraulic pump control, wherein the method comprises the steps of positioning a plurality of process stages with hydraulic pump service in a rare earth magnetic material extraction system, and directionally associating a plurality of hydraulic association index groups; the method comprises the steps of carrying out local control strategy fitting according to a plurality of hydraulic association index groups, outputting a plurality of initial pump control strategies, outputting a plurality of optimized pump control strategies through cross-stage coupling conflict arbitration, carrying out time sequence cooperative control on the hydraulic pump after receiving and adopting the plurality of optimized pump control strategies, and carrying out closed-loop dynamic update on control parameters of a plurality of service hydraulic pumps in a rare earth magnetic material extraction system. The invention solves the technical problems of low extraction stability and efficiency caused by insufficient control precision of the hydraulic pump in the rare earth magnetic material extraction process in the prior art, achieves the technical effects of realizing accurate control of the hydraulic pump in the rare earth magnetic material extraction process and improving the stability and efficiency of the extraction process.
Description
Technical Field
The invention relates to the technical field of hydraulic pump control, in particular to a hydraulic pump control optimization method and system for rare earth magnetic material extraction.
Background
In the rare earth magnetic material extraction process, a hydraulic pump is used as key equipment of a hydrometallurgical production line and plays an important role in power supply and material conveying, and the operation state of the hydraulic pump directly influences the extraction efficiency and the product quality. The traditional hydraulic pump control mode mostly adopts single parameter adjustment or centralized control, is difficult to adapt to the differentiated requirements of different process stages, and is easy to cause problems of cross-stage parameter interference, response lag, excessive energy consumption and the like, so that the stability of the extraction process is insufficient, the resources are wasted and the purity of the product is fluctuated.
The prior art has the technical problems of low extraction stability and efficiency caused by insufficient control precision of a hydraulic pump in the rare earth magnetic material extraction process.
Disclosure of Invention
The application provides a hydraulic pump control optimization method and a hydraulic pump control optimization system for rare earth magnetic material extraction, which are used for solving the technical problems of low extraction stability and efficiency caused by insufficient hydraulic pump control precision in the rare earth magnetic material extraction process in the prior art.
In view of the above, the present application provides a hydraulic pump control optimization method and system for rare earth magnetic material extraction.
In a first aspect of the application, a hydraulic pump control optimization method for rare earth magnetic material extraction is provided, the method comprising:
The method comprises the steps of positioning a plurality of process stages with hydraulic pump service in a rare earth magnetic material extraction system, directionally associating a plurality of hydraulic association index groups according to the hydraulic pump function attribute of the process stages, collecting a plurality of real-time process information in the process stages in real time by a plurality of edge computing nodes which are independently deployed in the process stages according to the hydraulic association index groups to carry out local control strategy fitting, outputting a plurality of initial pumping strategies, receiving the initial pumping strategies uploaded by the edge computing nodes by a cooperative control center, then arbitrating through cross-stage coupling conflict, outputting a plurality of optimized pumping strategies, receiving and adopting the optimized pumping strategies issued by the cooperative control center by the edge computing nodes, carrying out time sequence cooperative control on the service hydraulic pumps in the process stages, and carrying out closed-loop dynamic updating on control parameters of the service hydraulic pumps in the rare earth magnetic material extraction system by the edge computing nodes according to the dynamic disturbance range of the hydraulic association index groups.
In a second aspect of the application, there is provided a hydraulic pump control optimization system for rare earth magnetic material extraction, the system comprising:
The system comprises an index group association module, a pump control strategy output module, a closed-loop dynamic updating module, a pump control strategy optimization module and a closed-loop dynamic updating module, wherein the index group association module is used for locating a plurality of process stages with hydraulic pump service in a rare earth magnetic material extraction system, and directionally associating a plurality of hydraulic association index groups according to the hydraulic pump function attribute of the plurality of process stages, the pump control strategy output module is used for collecting a plurality of real-time process information in the plurality of process stages according to the plurality of hydraulic association index groups for carrying out local control strategy fitting according to a plurality of hydraulic association index groups, outputting a plurality of initial pump control strategies, the pump control strategy optimization module is used for receiving the plurality of initial pump control strategies uploaded by the plurality of edge computing nodes by a cooperative control center and then outputting a plurality of optimized pump control strategies through cross-stage coupling conflict arbitration, and the cooperative control module is used for receiving and adopting the plurality of optimized pump control strategies issued by the cooperative control center for carrying out time sequence cooperative control on a plurality of service hydraulic pumps in the plurality of process stages, and the closed-loop dynamic updating module is used for carrying out closed-loop dynamic control on the hydraulic pump control parameters in the rare earth magnetic material extraction system according to the dynamic disturbance ranges of the plurality of the hydraulic association index groups.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The method comprises the steps of positioning a plurality of process stages with hydraulic pump service in a rare earth magnetic material extraction system, directionally associating a plurality of hydraulic association index groups, collecting a plurality of real-time process information in the process stages in real time by a plurality of edge computing nodes which are independently deployed in the process stages according to the hydraulic association index groups to perform local control strategy fitting, outputting a plurality of initial pumping strategies, outputting a plurality of optimized pumping strategies after receiving the initial pumping strategies uploaded by the edge computing nodes by a cooperative control center, executing time sequence cooperative control on a plurality of service hydraulic pumps in the process stages after receiving and adopting the optimized pumping strategies issued by the cooperative control center, and executing closed-loop dynamic update on control parameters of the service hydraulic pumps in the rare earth magnetic material extraction system according to dynamic disturbance ranges of the hydraulic association index groups. The method achieves the technical effects of realizing the accurate control of the hydraulic pump in the extraction of the rare earth magnetic material and improving the stability and efficiency of the extraction process.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a hydraulic pump control optimization method for rare earth magnetic material extraction according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a hydraulic pump control optimization system for rare earth magnetic material extraction according to an embodiment of the present application.
Reference numerals illustrate an index set association module 10, a pumping strategy output module 20, a pumping strategy optimization module 30, a cooperative control module 40, and a closed loop dynamic update module 50.
Detailed Description
The application provides a hydraulic pump control optimization method and a hydraulic pump control optimization system for rare earth magnetic material extraction, which are used for solving the technical problems of low extraction stability and efficiency caused by insufficient hydraulic pump control precision in the rare earth magnetic material extraction process in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In a first embodiment, as shown in fig. 1, the present application provides a hydraulic pump control optimization method for rare earth magnetic material extraction, the method comprising:
and step 100, positioning a plurality of process stages with hydraulic pump service in the rare earth magnetic material extraction system, and directionally associating a plurality of hydraulic association index groups according to the hydraulic pump functional attributes of the process stages.
The system comprises a plurality of process stages, such as an ore pulp conveying stage, a filter pressing separation stage, a solvent extraction stage, a back extraction washing stage and the like, wherein the stages which are provided with a hydraulic pump for providing process auxiliary services are positioned, and then according to the functional attributes of the hydraulic pump in the process stages, such as the fact that the hydraulic pump in the ore pulp conveying stage bears material conveying power supply, the filter pressing separation stage is responsible for pressure regulation and control so as to realize solid-liquid separation, the solvent extraction stage needs to accurately control flow so as to ensure extraction efficiency, the back extraction washing stage needs to stabilize pressure so as to ensure washing effect, and the like, hydraulic association index groups matched with the functional attributes are respectively oriented and associated, so that the hydraulic association index groups of each process stage can accurately reflect the core operation parameters and process requirements of the hydraulic pump in the stage.
Step 200, a plurality of edge computing nodes which are independently deployed in the plurality of process stages collect a plurality of pieces of real-time process information in real time in the plurality of process stages according to the plurality of hydraulic association index groups to perform local control strategy fitting, and a plurality of initial pump control strategies are output.
Specifically, in a plurality of process stages such as an ore pulp conveying stage, a filter pressing separation stage, a solvent extraction stage, a back extraction washing stage and the like, corresponding edge computing nodes are independently deployed in each stage, each edge computing node is used for constructing a pump control correction model framework comprising a static deviation layer, a dynamic characteristic layer and a fusion output layer according to a hydraulic association index group associated with the stage through a pre-laid special sensing topology, such as a special topology formed by local binding of uploading authority of a sensor network of a certain process stage, real-time process information comprising physical quantity layer information, chemical quantity layer information and time sequence layer information is acquired in real time in the process stage, hydraulic process auxiliary deviation recognition is carried out on the basis of the information, each dimensional data is obtained by analyzing the information, static deviation is calculated by comparing with preset multi-layer process set values, confidence weighting is carried out by combining dynamic trend characteristic analysis and sensing precision attributes, a hydraulic auxiliary deviation vector is output, a pump control correction model framework comprising the static deviation layer, the dynamic characteristic layer and the fusion output layer is constructed according to the deviation vector, the deviation and the dynamic characteristic is respectively processed and cross-coupled through each layer, the local control strategy fitting is finished, and the corresponding initial pump control strategy of each stage is finally output.
And step 300, after receiving the plurality of initial pump control strategies uploaded by the plurality of edge computing nodes, the cooperative control center outputs a plurality of optimized pump control strategies through cross-stage coupling conflict arbitration.
The method comprises the steps of receiving a plurality of initial pumping strategies uploaded by edge computing nodes which are independently deployed in a plurality of process stages such as an ore pulp conveying stage, a filter pressing separation stage, a solvent extraction stage, a back extraction washing stage and the like by a cooperative control center, firstly carrying out adjacent strategy conflict detection on the initial pumping strategies, identifying and outputting a material balance conflict set, wherein the material balance conflict set comprises material accumulation or shortage caused by mismatching of flow of each stage, the energy consumption accumulation conflict set comprises an excessive energy consumption and a process interference conflict set caused by simultaneous high-pressure operation of a plurality of stages, the extraction effect deviation caused by mutual influence of pressure fluctuation among the stages is included, then carrying out dynamic flow calibration according to process priority on the material balance conflict set to output a flow calibration strategy, carrying out time-sharing peak-staggering pressurizing time sequence planning on the basis of the energy consumption accumulation conflict set to output a time sequence avoidance strategy, carrying out neutral instruction grafting on the process interference conflict set to output a chemical isolation strategy, and finally carrying out cross mutual exclusion on the flow calibration strategy, the time sequence avoidance strategy and the chemical isolation strategy, eliminating and repeating among strategies, and finally outputting a plurality of optimized pumping strategies which are adapted to each process stage.
And step 400, after the edge computing nodes receive and adopt the optimized pump control strategies issued by the cooperative control center, implementing time sequence cooperative control on the service hydraulic pumps in the process stages.
Specifically, edge computing nodes which are independently deployed in a plurality of process stages such as an ore pulp conveying stage, a filter pressing separation stage, a solvent extraction stage, a back extraction washing stage and the like respectively receive optimized pump control strategies which are issued by a cooperative control center and are adapted to the respective stages, then, based on the strategies, time sequence cooperative control is implemented on service hydraulic pumps of the stages, and the operation time sequences of the hydraulic pumps of the different process stages are coordinated by integrating reliable pressure control instructions, fatigue self-adaptive flow modulation, dead time compression parameters and the like in the optimized pump control strategies, for example, after material conveying is completed in the ore pulp conveying stage, the hydraulic pumps of the filter pressing separation stage start to pressurize according to the preset time sequences, and the hydraulic pumps of the solvent extraction stage and the back extraction washing stage also act sequentially or cooperatively according to time sequence planning, so that the operation rhythm of the hydraulic pumps of the stages is ensured to match with the process connection requirements, cross-stage interference is reduced, and the overall extraction efficiency is improved.
And S500, the plurality of edge computing nodes execute closed-loop dynamic update on control parameters of a plurality of service hydraulic pumps in the rare earth magnetic material extraction system according to the dynamic disturbance ranges of the plurality of hydraulic association index groups.
The method comprises the steps of carrying out closed-loop dynamic updating on control parameters of each service hydraulic pump in a rare earth magnetic material extraction system, such as pressure, flow and response delay compensation quantity, based on the disturbance range and in combination with the control requirements of hydraulic pumps of each stage, carrying out closed-loop dynamic updating on the control parameters of each service hydraulic pump in the rare earth magnetic material extraction system, such as pressure, flow and response delay compensation quantity, and the like, and enabling the pump control parameters to be always adapted to real-time process information through continuous feedback adjustment, so that stable operation of the hydraulic pump when indexes fluctuate is ensured, high-efficiency cooperation of each process stage is maintained, and continuity and reliability of the rare earth magnetic material extraction process are further ensured.
In one possible implementation, step S100 further includes:
The process stages comprise an ore pulp conveying stage, a filter pressing separation stage, a solvent extraction stage and a back extraction washing stage.
Specifically, the plurality of process stages specifically include a pulp conveying stage, a filter pressing separation stage, a solvent extraction stage, and a stripping washing stage. The method comprises a storage device, a pressure filtration separation stage, a solvent extraction stage, a stripping washing stage and a stripping agent and a washing liquid, wherein the rare earth ore pulp is mainly conveyed to a subsequent treatment link from the storage device through a hydraulic pump, the stability of conveying flow and pressure is required to be ensured to avoid ore pulp precipitation or pipeline blockage, the hydraulic pump provides pressure to enable ore pulp to pass through a filter medium to realize solid-liquid separation in the pressure filtration separation stage, the pressure and the retention time are required to be accurately controlled to improve the separation efficiency, the hydraulic pump is a key link of rare earth separation and purification and is responsible for conveying aqueous phase ore pulp and organic phase solvent to an extraction device in proportion, the flow control accuracy of the extraction balance and separation effect is directly influenced, and the stripping washing stage is required to be used for conveying stripping agent and washing liquid through the hydraulic pump to elute and purify target rare earth elements in a loaded organic phase, and flow and pressure parameters are required to be coordinated to ensure stripping rate and product purity.
In one possible implementation, step S300 further includes:
And step S310, performing adjacent strategy conflict detection on the plurality of initial pumping strategies, and outputting a material balance conflict set, an energy consumption accumulation conflict set and a process interference conflict set.
And step 320, performing process priority dynamic flow calibration based on the material balance conflict set, and outputting a flow calibration strategy.
And step S330, carrying out time-sharing peak-shifting pressurization time sequence planning based on the energy consumption accumulation conflict set, and outputting a time sequence avoidance strategy.
And S340, performing transition buffer area and instruction splicing aiming at the process interference conflict set, and outputting a chemical isolation strategy.
And step 350, carrying out cross mutual exclusion digestion on the flow calibration strategy, the time sequence avoidance strategy and the chemical isolation strategy, and outputting the plurality of optimized pump control strategies.
Specifically, the cooperative control center performs comparison analysis on parameters of adjacent process stages in a plurality of initial pump control strategies by constructing a conflict detection matrix, extracts flow set values and material transmission rates in each strategy aiming at material balance conflicts, calculates input and output difference values of the adjacent stages, and includes a material balance conflict set when the difference values exceed a preset balance threshold, calculates pressurizing power and duration of each strategy aiming at energy consumption accumulation conflicts, overlaps adjacent stage energy consumption data, includes an energy consumption accumulation conflict set if total energy consumption exceeds a stage energy consumption upper limit, analyzes a pressure fluctuation range and a chemical reagent transmission rhythm of the adjacent strategies aiming at process interference conflicts, and finally outputs three conflict sets.
Based on the problem of material accumulation or shortage caused by flow mismatch of each adjacent process stage in the material balance conflict set, firstly, determining the process priority according to the core effect of each process stage in the rare earth magnetic material extraction flow, for example, the priority of a solvent extraction stage which directly influences the extraction purity is higher than that of an ore pulp conveying stage, then dynamically calibrating flow parameters related to the conflict set according to the priority, adaptively adjusting the flow of a hydraulic pump in a low-priority stage by calculating the material demand difference value of the adjacent stages so as to match the material processing capacity of a high-priority stage, dynamically correcting the flow deviation by combining data such as material concentration, transmission rate and the like in real-time process information, and finally outputting a flow calibration strategy for ensuring the material input and output balance of each stage.
Based on the problem that energy consumption superposition exceeds standard caused by simultaneous high-pressure operation in a plurality of adjacent process stages in an energy consumption accumulation conflict set, parameters such as hydraulic pump pressurizing power, pressurizing duration, operation period and the like corresponding to each initial pump control strategy in the conflict set are analyzed firstly, then time-sharing peak-staggering pressurizing time sequence planning is carried out by combining pressurizing necessity and energy consumption sensitivity degree of each process stage, high-pressure operation periods of different process stages are staggered by adjusting the execution time of high-energy pressurizing operation, for example, high-pressure conveying of a pulp conveying stage is arranged in a low-pressure maintaining period of a filter pressing separation stage, so that energy consumption accumulation is avoided, meanwhile, pressurizing time sequence interval thresholds are set according to process requirements of each stage, process consistency is not influenced by peak staggering arrangement, and finally a time sequence avoidance strategy for coordinating pressurizing time sequences of each stage is output so as to reduce overall energy consumption.
Aiming at the mutual interference problem caused by pressure fluctuation, flow mutation and the like in adjacent process stages in process interference conflict sets, firstly identifies process connection nodes involved in the conflict, sets transition buffer areas at the nodes, clearly determines parameters such as pressure range, flow fluctuation threshold, duration and the like of the buffer areas, then inserts neutralization instructions into relevant initial pumping strategies based on the buffer area parameters, inserts pressure buffer adjustment instructions to smooth the pressure fluctuation when the ore pulp conveying stage transits to a filter pressing separation stage, inserts flow gradual change instructions at the connection position of a solvent extraction stage and a stripping washing stage to reduce reagent conveying impact, isolates the process interference of the adjacent stages through the insertion of the neutralization instructions, avoids adverse effects of the parameter fluctuation on chemical reaction conditions, separation effects and the like of the subsequent stages, and finally outputs a chemical isolation strategy capable of eliminating the process interference between the stages.
The method comprises the steps of carrying out cross mutual exclusion digestion on a flow calibration strategy, a time sequence avoidance strategy and a chemical isolation strategy, firstly comparing three types of strategies one by one in terms of parameter setting, time sequence arrangement, process connection requirements and the like, identifying the contradiction points, for example, the flow calibration strategy requires a certain period to improve the flow, the time sequence avoidance strategy limits the pressurizing power in the period to cause the flow to be unable to be improved, or the transition buffer time of the chemical isolation strategy conflicts with the dynamic regulation period of the flow calibration, carrying out priority balance and parameter adjustment by combining the core requirements of each process stage of rare earth magnetic material extraction according to the contradiction points, eliminating mutual exclusion among the strategies, ensuring that the three types of strategies can be mutually adapted and have no conflict when in cooperation, and finally integrating the digested strategies to form a plurality of optimized pump control strategies which are suitable for each process stage of ore pulp conveying, filter pressing separation, solvent extraction, back extraction washing and the like, and outputting.
In one possible implementation, step S200 further includes:
Step S210, carrying out uploading authority local binding of the pre-distributed first sensor network in the first process stage according to the first hydraulic association index set to obtain a first special sensing topology.
Step S220, the first edge computing node receives the first real-time process information uploaded by the first dedicated sensor topology.
And step S230, carrying out hydraulic process auxiliary deviation recognition based on the first real-time process information, and outputting a first hydraulic auxiliary deviation vector.
And step 240, carrying out pump control parameter correction modeling according to the first hydraulic auxiliary deviation vector, and outputting a first initial pump control strategy.
Specifically, for the first process stage, according to a first hydraulic association index group corresponding to the stage, indexes related to the operation of the hydraulic pump, such as pressure, flow, chemical reagent concentration and the like, are covered, uploading authority local binding is performed on a first sensor network which is pre-arranged, the data uploading range of the sensor network is limited to be within the local range of the process stage, the information collected by the sensor is ensured to be only directionally transmitted to an edge computing node responsible for the stage, cross-stage data interference is avoided, and finally a first special sensing topology special for the first process stage is formed.
The first real-time process information uploaded by the first special sensing topology is received through a preset communication protocol by the first edge computing node deployed in the first process stage, wherein the information comprises physical quantity layer information such as pressure and flow data of a hydraulic pump in the stage, chemical quantity layer information such as concentration and pH value of an extraction liquid and time sequence layer information such as change time sequence of each parameter, and original data support is provided for follow-up local control strategy fitting.
When hydraulic process auxiliary deviation recognition is carried out based on first real-time process information, the information is analyzed, first physical quantity layer information, first chemical quantity layer information and first time sequence layer information are extracted from the information, predefined first process stage multilayer process set values are called, mapping comparison is carried out on the extracted layer information and corresponding set values respectively, first pressure dimension deviation, first chemical dimension deviation and first time sequence dimension deviation are obtained through calculation of difference values, meanwhile dynamic trend feature analysis is carried out on the first physical quantity layer information, a first dynamic trend feature set is output, finally, confidence weighting processing is carried out on the first dynamic trend feature set, the first pressure dimension deviation, the first chemical dimension deviation and the first time sequence dimension deviation by combining sensing precision attributes of a first special sensing topology, and a first hydraulic auxiliary deviation vector is comprehensively generated and output.
When pump control parameter correction modeling is carried out according to a first hydraulic auxiliary deviation vector, a pump control correction model framework comprising a static deviation layer, a dynamic characteristic layer and a fusion output layer is firstly constructed, wherein the static deviation layer and the dynamic characteristic layer are in parallel connection, the output ends of the static deviation layer and the dynamic characteristic layer are connected with the fusion output layer, parallel correction compensation is carried out on the first pressure dimension deviation, the first chemical dimension deviation and the first time sequence dimension deviation in the static deviation layer, a first basic pressure correction term, a first flow correction term and a first response delay compensation quantity are respectively output, multi-mode compensation modeling is carried out on a first dynamic trend characteristic group in the dynamic characteristic layer, a first dynamic compensation instruction set comprising a first pressure impact pre-compensation pulse, a first PID integral adjustment quantity, a first reverse damping control item and a first mechanical fatigue inhibition coefficient is output, cross coupling is carried out on the first dynamic compensation instruction set and the first basic pressure correction term, the first flow correction term and the first response delay compensation quantity in the fusion output layer, and a first initial pump control strategy is output through confidence weighting.
In one possible implementation, step S230 further includes:
And step S231, analyzing the first real-time process information to obtain first physical quantity layer information, first chemical quantity layer information and first time sequence layer information.
Step S232, predefining a multi-layer process set point for the first process stage.
And S233, mapping and comparing the first physical quantity layer information, the first chemical quantity layer information and the first time sequence layer information by adopting the multi-layer process set value, performing static deviation calculation, and outputting a first pressure dimension deviation, a first chemical dimension deviation and a first time sequence dimension deviation.
And step S234, carrying out dynamic trend feature analysis on the first physical quantity layer information and outputting a first dynamic trend feature group.
And S235, carrying out confidence weighting on the first dynamic trend feature group, the first pressure dimension deviation, the first chemical dimension deviation and the first time sequence dimension deviation according to the sensing precision attribute of the first special sensing topology, and outputting the first hydraulic auxiliary deviation vector.
Specifically, the first real-time process information is analyzed, first physical quantity layer information reflecting the operation physical parameters of the hydraulic pump, such as pressure, flow rate, flow velocity and the like, first chemical quantity layer information reflecting the chemical characteristics of an extraction system, such as solution concentration, pH value, ion content and the like, and first time sequence layer information recording the change of each parameter along with time, such as a fluctuation sequence of the pressure along with time, a time node of the flow change and the like, are separated from the collected original data.
According to the process requirements and the hydraulic pump operation standard of the first process stage, predefining a multi-layer process set value which covers physical quantity standards such as a pressure threshold value, a flow range and the like corresponding to the first physical quantity layer information, chemical quantity standards such as a solution concentration interval, a pH value allowable range and the like corresponding to the first chemical quantity layer information, parameter change period, response time limitation and the like corresponding to the time sequence layer information.
The method comprises the steps of adopting a predefined multi-layer process set value, respectively carrying out mapping comparison with first physical quantity layer information, first chemical quantity layer information and first time sequence layer information which are obtained through analysis, correspondingly comparing the physical quantity layer set value with actual pressure and flow data in the first physical quantity layer information one by one, calculating the difference value between an actual value and the set value to obtain first pressure dimension deviation, carrying out matching comparison on the chemical quantity layer set value and data such as actual concentration, pH value and the like in the first chemical quantity layer information, calculating and outputting the first chemical dimension deviation through the deviation amount, comparing the time sequence layer set value with actual time sequence data in the first time sequence layer information, calculating to obtain first time sequence dimension deviation according to time difference or period deviation, and finally outputting the three types of static deviations.
When the dynamic trend feature analysis is carried out on the first physical quantity layer information, firstly, rolling time sequence segmentation is carried out on the information based on a preset time window to obtain a continuous data block sequence, then, stream pressure derivative feature item extraction is carried out on the continuous data block sequence to obtain a first dynamic derivative feature set, then, overrun fluctuation state quantization is carried out on the basis of the continuous data block, a first overrun fluctuation continuous duty ratio and a first pressure oscillation frequency spectrum intensity are output, the continuous data block is traversed to obtain a first pressurization cycle accumulation frequency which is associated with a first stream flow trend direction mark, and finally, the first dynamic derivative feature set, the first overrun fluctuation continuous duty ratio, the first pressure oscillation frequency spectrum intensity, the first pressurization cycle accumulation frequency and the first stream flow trend direction mark are packaged to output a first dynamic trend feature set.
According to the sensing precision attribute of the first special sensing topology, including the precision grade, the data acquisition error range, the signal stability parameter and the like of each sensor in the topology, a first dynamic trend feature group, a first pressure dimension deviation, a first chemical dimension deviation and a confidence degree weight corresponding to each first time sequence dimension deviation are determined, for example, the features or the deviations corresponding to the data acquired by the sensors with higher precision and smaller error are given higher weights, then the features and the deviations are respectively multiplied with the weights, the calculation results are integrated, and finally the first hydraulic auxiliary deviation vector comprehensively reflecting each dimension deviation and the dynamic trend is output.
In one possible implementation, step S240 further includes:
Step S241, a pump control correction model framework is constructed, wherein the pump control correction model framework comprises a static deviation layer, a dynamic characteristic layer and a fusion output layer, the static deviation layer and the dynamic characteristic layer are connected in parallel, and an output end is connected with the fusion output layer.
And step S242, carrying out parallel correction compensation of the first pressure dimension deviation, the first chemical dimension deviation and the first time sequence dimension deviation on the static deviation layer, and outputting a first basic pressure correction term, a first flow correction term and a first response delay compensation quantity.
And step 243, performing multi-mode compensation modeling of the first dynamic trend feature group on the dynamic feature layer, and outputting a first dynamic compensation instruction set, wherein the first dynamic compensation instruction set comprises a first pressure impact pre-compensation pulse, a first PID integral adjustment quantity, a first reverse damping control item and a first mechanical fatigue suppression coefficient.
Step S244, executing the cross coupling of the first dynamic compensation instruction set, the first basic pressure correction term, the first flow correction term and the first response delay compensation quantity on the fusion output layer, and outputting the first initial pumping strategy.
Specifically, a pump control correction model architecture suitable for hydraulic pump control in the first process stage is constructed, which specifically comprises three core levels, namely a static deviation layer, a dynamic characteristic layer and a fusion output layer. The static deviation layer and the dynamic characteristic layer are arranged in parallel and serve as parallel input processing layers of the model, input static deviation information and dynamic trend characteristics can be processed independently, meanwhile, the output ends of the static deviation layer and the dynamic characteristic layer are connected with the fusion output layer, processing results of the two layers can be summarized to the fusion output layer to carry out comprehensive operation, a model structure of double-layer parallel processing and single-layer fusion output is formed, and architecture support is provided for accurate correction of follow-up pumping control parameters.
In the static deviation layer, a proportional-integral correction algorithm is adopted for the first pressure dimension deviation, a deviation value is multiplied by a preset pressure adjustment coefficient, integral compensation quantity is superimposed, a first basic pressure correction term is calculated, a chemical parameter-flow correlation mapping table is inquired for the first chemical dimension deviation, flow correction coefficients corresponding to different chemical deviations are prestored in the table, the deviation value is substituted into the table to be matched, a corresponding coefficient is obtained, the first flow correction term is output after the deviation value is multiplied by a reference flow, the first response delay compensation quantity is calculated based on a time sequence synchronization error formula of error value = actual response time-set response time, and three correction operations are synchronously executed through a parallel calculation module and output results.
The method comprises the steps of determining and outputting a first pressure impact pre-compensation pulse for counteracting pressure mutation in advance based on a pressure change rate in a first dynamic trend feature group by matching a preset pressure impact compensation pulse parameter library, then adjusting an integral time constant of a flow PID controller according to a preset rule according to a first overrun fluctuation continuous duty ratio, outputting a first PID integral adjustment quantity capable of inhibiting flow fluctuation, then inquiring a reverse damping control parameter library according to the first pressure oscillation frequency spectrum intensity, matching and outputting a first reverse damping control item capable of weakening pressure oscillation, and finally calculating and outputting a first mechanical fatigue inhibition coefficient for reducing equipment loss through a fatigue strength attenuation model by combining the first compression cycle accumulation times and a first flow trend direction mark, wherein the four instructions together form a first dynamic compensation instruction set.
In the fusion output layer, cross coupling operation is carried out on a first dynamic compensation instruction set, a first basic pressure correction item, a first flow correction item and a first response delay compensation quantity, an initial anti-disturbance pressure instruction is generated by linearly superposing the first basic pressure correction item and a first pressure impact pre-compensation pulse, then the initial anti-disturbance pressure instruction is injected into a first reverse damping control item to output a reliable pressure control instruction, product operation is carried out on the first flow correction item and a first mechanical fatigue suppression coefficient to obtain a reference fatigue self-adaptive flow, the response rate of the flow is dynamically modulated by a first PID integral adjustment quantity to output a modulated fatigue self-adaptive flow, the first response delay compensation quantity and the time sequence component of the first PID integral adjustment quantity are integrated to output a dead time compression parameter, finally confidence weighting treatment is carried out on the reliable pressure control instruction, the modulated fatigue self-adaptive flow and the dead time compression parameter, and the first initial pump control strategy is comprehensively output.
In one possible implementation, step S234 further includes:
step S2341, executing rolling time sequence segmentation on the first physical quantity layer information based on a preset time window to obtain a continuous data block sequence.
And S2342, extracting the stream pressure derivative characteristic item of the continuous data block sequence to obtain a first dynamic derivative characteristic set.
And S2343, quantifying the overrun fluctuation state based on the continuous data block, and outputting a first overrun fluctuation continuous duty ratio and a first pressure oscillation frequency spectrum intensity.
Step S2344, traversing the continuous data block to obtain a first number of accumulated pressurized cycles, wherein the first number of accumulated pressurized cycles is associated with a first flow trend direction marker.
And S2345, packaging the first dynamic derivative feature set, the first overrun fluctuation continuous duty ratio, the first pressure oscillation frequency spectrum intensity, the first pressurization cycle accumulation times and the first flow trend direction mark, and outputting the first dynamic trend feature set.
Specifically, the rolling time sequence segmentation is performed on the first physical quantity layer information based on a preset time window (such as 10 seconds/window), namely, after the data segment is cut according to the time window length from the initial moment, the window is slid backwards by a fixed step length (such as 5 seconds), the cutting is continued until all the information is covered, and finally, a continuous and partially overlapped continuous data block sequence is obtained, so that the time sequence segmentation of the physical quantity information is realized.
For a continuous data block sequence obtained by rolling time sequence segmentation of the first physical quantity layer information, calculating the first derivative and the second derivative of flow and pressure parameters in each data block one by one, extracting characteristic items such as the change rate of flow-pressure ratio and the partial derivative of pressure to flow, and the like, integrating and summarizing the characteristic items reflecting the dynamic change rate of the parameters to form a first dynamic derivative characteristic set capable of reflecting the dynamic response characteristic of the hydraulic system.
When the overrun fluctuation state quantification is carried out based on continuous data blocks, a normal fluctuation threshold range is set for physical quantity parameters such as pressure, flow and the like in each continuous data block, the threshold value is predefined based on the technological requirement of a first technological stage, each data block is traversed, the duration time of the physical quantity parameter exceeding the preset threshold value is counted, the ratio of the time to the total duration time of the data block is used as a single overrun duty ratio, the single overrun duty ratio of all the data blocks is averaged to obtain a first overrun fluctuation continuous duty ratio, meanwhile, the pressure fluctuation signal in each data block is subjected to spectrum analysis such as Fourier transformation, the energy intensity corresponding to main oscillation frequency in a spectrum is extracted, the analysis results of all the data blocks are synthesized, and the first pressure oscillation spectrum intensity capable of reflecting the intensity of the pressure fluctuation is output.
And simultaneously, when each pressurizing cycle is recorded, combining the changing direction of the flow parameter in the corresponding circulating process, if the flow increases along with the pressure rise, the flow is marked as the forward direction, and the flow is marked as the reverse direction along with the pressure rise, and the binding of the circulating times and the flow changing trend is realized for the first pressurizing cycle accumulating times in association with the corresponding first flow trend direction mark.
And carrying out structured packaging on the extracted first dynamic derivative feature set, the first overrun fluctuation continuous duty ratio, the first pressure oscillation frequency spectrum intensity, the first pressurization cycle accumulation times and the associated first flow trend direction marks, integrating the feature information into a complete feature set in a feature matrix and tag combination mode according to a preset data format, and finally outputting a first dynamic trend feature set capable of comprehensively reflecting the dynamic change trend of the first physical quantity layer information.
In one possible implementation, step S243 further includes:
And step S2431, outputting the first pressure impact pre-compensation pulse based on the pressure change rate matching of the first dynamic trend feature set.
And step S2432, adjusting the integral time constant of the flow PID controller based on the first overrun fluctuation continuous duty ratio, and outputting a first PID integral adjustment quantity.
And step S2433, outputting a first reverse damping control item according to the first pressure oscillation frequency spectrum intensity matching.
And step S2434, performing fatigue strength attenuation modeling according to the first pressurization cycle accumulation times and the first flow trend direction marks, and outputting the first mechanical fatigue inhibition coefficient.
Specifically, the rate of change of pressure contained in the first dynamic trend feature set is based on a first dynamic derivative feature set, which is matched to a pre-stored pressure shock pre-compensation pulse parameter library. The parameter library comprises parameters such as pulse amplitude, width, trigger time and the like corresponding to different pressure change rates, when the pressure change rate reaches or exceeds a set impact early warning threshold, the corresponding pulse parameters are selected according to a matching result, and a first pressure impact pre-compensation pulse is generated and output so as to intervene in the pressure output of the hydraulic pump in advance and counteract impending pressure impact.
And dynamically adjusting the integral time constant of the flow PID controller according to a preset adjustment rule based on the first overrun fluctuation continuous duty ratio, if the first overrun fluctuation continuous duty ratio is relatively high, indicating that the flow fluctuation is relatively frequent and severe, reducing the integral time constant at the moment to accelerate the response speed of the integral action and enhance the correction capability of flow deviation, and if the first overrun fluctuation continuous duty ratio is relatively low, indicating that the flow is relatively stable, increasing the integral time constant to avoid system oscillation caused by excessive integral, and finally outputting the adjusted integral time constant as the integral adjustment quantity of the first PID.
According to the first pressure oscillation frequency spectrum intensity, a preset reverse damping control item parameter library is queried, and parameters such as damping coefficients, suppression frequency bands, control force and the like corresponding to different frequency spectrum intensity ranges are stored in the parameter library. And according to the specific value of the current first pressure oscillation frequency spectrum intensity and the main oscillation frequency component, matching the optimal reverse damping parameter combination from the parameter library, and generating and outputting a first reverse damping control item for suppressing the pressure oscillation so as to weaken the amplitude and duration of the pressure oscillation.
According to the accumulated times of the first pressurizing cycles and the direction marks of the first flow trend, fatigue strength attenuation modeling is carried out, the accumulated times of the pressurizing cycles are used as core variables and substituted into a preset fatigue attenuation formula, wherein k is an attenuation coefficient, the attenuation coefficient k is corrected by combining the direction marks of the first flow trend, the k value is increased to accelerate attenuation correction when the flow is in a reverse trend, a compensation coefficient reflecting the mechanical fatigue state of a current hydraulic pump is calculated through the model, and finally the first mechanical fatigue suppression coefficient for reducing equipment loss is output.
In one possible implementation, step S244 further includes:
and step S2441, after an initial disturbance rejection pressure instruction is generated by linearly superposing the first basic pressure correction term and the first pressure impact pre-compensation pulse, the initial disturbance rejection pressure instruction is injected into the first reverse damping control term, and a reliable pressure control instruction is output.
And step S2442, performing product operation on the first flow correction term and the first mechanical fatigue suppression coefficient, generating a reference fatigue self-adaptive flow, dynamically modulating the response rate of the reference fatigue self-adaptive flow by using the first PID integral adjustment quantity, and outputting a modulated fatigue self-adaptive flow.
Step S2443 outputs a dead time compression parameter by integrating the first response delay compensation amount and the timing component of the first PID integration adjustment amount.
And step S2444, performing confidence weighting processing on the reliable pressure control instruction, the modulation fatigue self-adaptive flow and the dead time compression parameter, and outputting the first initial pumping strategy.
The method comprises the steps of firstly carrying out linear superposition processing on a first basic pressure correction item and a first pressure impact pre-compensation pulse, namely directly adding numerical values of the first basic pressure correction item and the first pressure impact pre-compensation pulse according to corresponding dimensions to generate an initial anti-disturbance pressure command capable of initially resisting pressure burst interference, then inputting the initial anti-disturbance pressure command into a first reverse damping control item, further weakening pressure fluctuation by means of the inhibition effect of reverse damping, and finally outputting a reliable pressure control command with higher stability.
And then, dynamically modulating the response speed of the reference fatigue self-adaptive flow by applying a first PID integral regulating quantity, and adjusting the speed of flow change in real time according to the magnitude of the integral regulating quantity, so that the flow response is more in accordance with the process time sequence requirement, and finally outputting the modulated fatigue self-adaptive flow subjected to speed modulation.
And extracting time-related time sequence components in the first response delay compensation quantity, such as delay compensation duration, trigger time and the like, and time sequence components in the first PID integral adjustment quantity, such as integral action starting time, adjustment period and the like, integrating the two components on the same time axis through a time sequence alignment algorithm, and calculating to obtain comprehensive correction parameters capable of shortening a response dead zone of the system, namely dead zone time compression parameters and outputting the parameters.
According to the reliable pressure control instruction, the modulation fatigue self-adaptive flow and the importance degree and influence weight of dead time compression parameters in the rare earth magnetic material extraction process, corresponding confidence coefficient is respectively given to the three parameters, for example, the confidence coefficient of the reliable pressure control is set to be 0.4, the confidence coefficient of the modulation fatigue self-adaptive flow is set to be 0.3, the confidence coefficient of the dead time is set to be 0.3, then each parameter is multiplied by the respective confidence coefficient, and the multiplication results are summed and integrated to form a comprehensive control strategy parameter set, and finally, a first initial pump control strategy capable of guiding the operation of the hydraulic pump in the first process stage is output.
In the second embodiment, based on the same inventive concept as the hydraulic pump control optimization method for rare earth magnetic material extraction in the foregoing embodiment, as shown in fig. 2, the present application provides a hydraulic pump control optimization system for rare earth magnetic material extraction, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
The index set association module 10 is used for positioning a plurality of process stages with hydraulic pump service in the rare earth magnetic material extraction system, and directionally associating a plurality of hydraulic association index sets according to the hydraulic pump functional attributes of the process stages.
And the pump control strategy output module 20 is configured to collect a plurality of real-time process information in real time at the plurality of process stages according to the plurality of hydraulic association index sets at a plurality of edge computing nodes independently deployed at the plurality of process stages to perform local control strategy fitting, and output a plurality of initial pump control strategies.
And the pump control strategy optimization module 30 is configured to output a plurality of optimized pump control strategies through cross-stage coupling conflict arbitration after the cooperative control center receives the plurality of initial pump control strategies uploaded by the plurality of edge computing nodes.
And the cooperative control module 40 is configured to implement time sequence cooperative control on the plurality of service hydraulic pumps in the plurality of process stages after the plurality of edge computing nodes receive and adopt the plurality of optimized pump control strategies issued by the cooperative control center.
The closed-loop dynamic updating module 50 is configured to perform closed-loop dynamic updating on control parameters of a plurality of service hydraulic pumps in the rare earth magnetic material extraction system according to dynamic disturbance ranges of the plurality of hydraulic association index sets by the plurality of edge computing nodes.
Further, the system is further configured to implement the following functions:
The method comprises the steps of carrying out adjacent strategy conflict detection on a plurality of initial pump control strategies, outputting a material balance conflict set, an energy consumption accumulation conflict set and a process interference conflict set, carrying out process priority dynamic flow calibration on the basis of the material balance conflict set, outputting a flow calibration strategy, carrying out time-sharing peak-shifting pressurization time sequence planning on the basis of the energy consumption accumulation conflict set, outputting a time sequence avoidance strategy, carrying out transitional buffer region neutralization instruction grafting on the process interference conflict set, outputting a chemical isolation strategy, carrying out cross mutual exclusion resolution on the flow calibration strategy, the time sequence avoidance strategy and the chemical isolation strategy, and outputting the plurality of optimized pump control strategies.
Further, the system is further configured to implement the following functions:
The method comprises the steps of carrying out uploading authority local binding of a first sensor network pre-arranged at a first process stage according to a first hydraulic association index group to obtain a first special sensing topology, receiving first real-time process information uploaded by the first special sensing topology by a first edge computing node, carrying out hydraulic process auxiliary deviation recognition based on the first real-time process information to output a first hydraulic auxiliary deviation vector, carrying out pump control parameter correction modeling according to the first hydraulic auxiliary deviation vector, and outputting a first initial pump control strategy.
Further, the system is further configured to implement the following functions:
Analyzing the first real-time process information to obtain first physical quantity layer information, first chemical quantity layer information and first time sequence layer information, predefining a multi-layer process set value of the first process stage, carrying out static deviation calculation on the first physical quantity layer information, the first chemical quantity layer information and the first time sequence layer information by adopting the multi-layer process set value mapping comparison, outputting first pressure dimension deviation, first chemical dimension deviation and first time sequence dimension deviation, carrying out dynamic trend feature analysis on the first physical quantity layer information, outputting a first dynamic trend feature group, carrying out confidence weighting on the first dynamic trend feature group, the first pressure dimension deviation, the first chemical dimension deviation and the first time sequence dimension deviation according to the sensing precision attribute of the first special sensing topology, and outputting the first hydraulic auxiliary deviation vector.
Further, the system is further configured to implement the following functions:
The method comprises the steps of constructing a pump control correction model framework, wherein the pump control correction model framework comprises a static deviation layer, a dynamic characteristic layer and a fusion output layer, the static deviation layer and the dynamic characteristic layer are connected in parallel, the output end of the static deviation layer is connected with the fusion output layer, parallel correction compensation of the first pressure dimension deviation, the first chemical dimension deviation and the first time dimension deviation is carried out on the static deviation layer, a first basic pressure correction term, a first flow correction term and a first response delay compensation amount are output, multi-mode compensation modeling of the first dynamic trend characteristic group is carried out on the dynamic characteristic layer, a first dynamic compensation instruction set is output, the first dynamic compensation instruction set comprises a first pressure impact pre-compensation pulse, a first PID integral adjustment amount, a first reverse damping control term and a first mechanical fatigue inhibition coefficient, and cross coupling of the first dynamic compensation instruction set, the first basic pressure correction term, the first flow correction term and the first response delay compensation amount is carried out on the fusion output layer, and the first initial pump control strategy is output.
Further, the system is further configured to implement the following functions:
The method comprises the steps of carrying out rolling time sequence segmentation on first physical quantity layer information based on a preset time window to obtain a continuous data block sequence, carrying out stream pressure derivative characteristic item extraction on the continuous data block sequence to obtain a first dynamic derivative characteristic set, carrying out overrun fluctuation state quantization on the continuous data block, outputting a first overrun fluctuation continuous duty ratio and first pressure oscillation frequency spectrum intensity, traversing the continuous data block to obtain a first pressurization cycle accumulation frequency, wherein the first pressurization cycle accumulation frequency is related to a first flow trend direction mark, packaging the first dynamic derivative characteristic set, the first overrun fluctuation continuous duty ratio, the first pressure oscillation frequency spectrum intensity, the first pressurization cycle accumulation frequency and the first flow trend direction mark, and outputting the first dynamic trend characteristic set.
Further, the system is further configured to implement the following functions:
The first pressure impact precompensation pulse is output based on the pressure change rate matching of the first dynamic trend feature group, the integral time constant of the flow PID controller is adjusted based on the first overrun fluctuation continuous duty ratio, the first PID integral adjustment quantity is output, the first reverse damping control item is output according to the first pressure oscillation frequency spectrum intensity matching, fatigue intensity attenuation modeling is conducted according to the first pressurization cycle accumulation times and the first flow trend direction mark, and the first mechanical fatigue suppression coefficient is output.
Further, the system is further configured to implement the following functions:
The method comprises the steps of generating an initial disturbance rejection pressure instruction by linearly superposing a first basic pressure correction term and a first pressure impact pre-compensation pulse, injecting the initial disturbance rejection pressure instruction into a first reverse damping control term to output a reliable pressure control instruction, performing product operation on the first flow correction term and a first mechanical fatigue suppression coefficient to generate a reference fatigue self-adaptive flow, dynamically modulating the response rate of the reference fatigue self-adaptive flow by using a first PID integral regulating quantity to output a modulated fatigue self-adaptive flow, integrating the first response delay compensating quantity and the time sequence component of the first PID integral regulating quantity to output a dead time compression parameter, performing confidence weighting treatment on the reliable pressure control instruction, the modulated fatigue self-adaptive flow and the dead time compression parameter, and outputting the first initial pump control strategy.
Further, the system is further configured to implement the following functions:
The process stages comprise an ore pulp conveying stage, a filter pressing separation stage, a solvent extraction stage and a back extraction washing stage.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (9)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202511315826.1A CN120819504B (en) | 2025-09-16 | 2025-09-16 | Hydraulic pump control optimization method and system for rare earth magnetic material extraction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202511315826.1A CN120819504B (en) | 2025-09-16 | 2025-09-16 | Hydraulic pump control optimization method and system for rare earth magnetic material extraction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN120819504A true CN120819504A (en) | 2025-10-21 |
| CN120819504B CN120819504B (en) | 2025-11-18 |
Family
ID=97367829
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202511315826.1A Active CN120819504B (en) | 2025-09-16 | 2025-09-16 | Hydraulic pump control optimization method and system for rare earth magnetic material extraction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN120819504B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090112423A1 (en) * | 2007-10-29 | 2009-04-30 | Gm Global Technology Operations, Inc. | Method and apparatus to control operation of a hydraulic pump for an electro-mechanical transmission |
| US20120283882A1 (en) * | 2011-05-06 | 2012-11-08 | Hongliu Du | Method and apparatus for controlling multiple variable displacement hydraulic pumps |
| CN107590544A (en) * | 2017-09-11 | 2018-01-16 | 合肥工业大学 | The maintenance policy optimization method and system of a kind of hydraulic pump |
| CN120402342A (en) * | 2025-07-07 | 2025-08-01 | 河北绿鸿科技有限公司 | A dynamic optimization control method and system for a multi-modal pump group |
| CN120444230A (en) * | 2025-07-11 | 2025-08-08 | 中交天航环保工程有限公司 | Control device and method for relay pump |
-
2025
- 2025-09-16 CN CN202511315826.1A patent/CN120819504B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090112423A1 (en) * | 2007-10-29 | 2009-04-30 | Gm Global Technology Operations, Inc. | Method and apparatus to control operation of a hydraulic pump for an electro-mechanical transmission |
| US20120283882A1 (en) * | 2011-05-06 | 2012-11-08 | Hongliu Du | Method and apparatus for controlling multiple variable displacement hydraulic pumps |
| CN107590544A (en) * | 2017-09-11 | 2018-01-16 | 合肥工业大学 | The maintenance policy optimization method and system of a kind of hydraulic pump |
| CN120402342A (en) * | 2025-07-07 | 2025-08-01 | 河北绿鸿科技有限公司 | A dynamic optimization control method and system for a multi-modal pump group |
| CN120444230A (en) * | 2025-07-11 | 2025-08-08 | 中交天航环保工程有限公司 | Control device and method for relay pump |
Also Published As
| Publication number | Publication date |
|---|---|
| CN120819504B (en) | 2025-11-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5777872A (en) | Method and system for controlling a multiple input/output process with minimum latency | |
| Hägglund | A unified discussion on signal filtering in PID control | |
| US5541833A (en) | Multivariable feedforward adaptive controller | |
| CN109047683B (en) | Continuous casting billet intelligence basis weight scale on-line control system | |
| Aydin et al. | NMPC using Pontryagin’s Minimum Principle-Application to a two-phase semi-batch hydroformylation reactor under uncertainty | |
| CN120819504B (en) | Hydraulic pump control optimization method and system for rare earth magnetic material extraction | |
| CN120370838A (en) | Filling control method and system based on online parameter identification | |
| CN108549214A (en) | A kind of high-performance PID control method | |
| Shehu et al. | Applications of MPC and PI controls for liquid level control in coupled-tank systems | |
| Krishnamoorthy et al. | Robust extremum seeking control with application to gas lifted oil wells | |
| CN120335282A (en) | A method for tuning PID parameters using large language model | |
| Yavarian et al. | Adaptive Neuro Fuzzy Inference System PID Controller for AVR System Using SNR-PSO Optimization. | |
| US5182703A (en) | Method and apparatus of two-degrees-of-freedom time difference comparison compensator | |
| Liu et al. | Automatic clutch control using ADRC with continuous adaptive extended state observer | |
| JPH04266101A (en) | Estimation controller for multivariable model | |
| JPS60218105A (en) | Control device | |
| CN102606551B (en) | Remote hydraulic synchronous control method for multigroup proportioning pumps | |
| CN107191154B (en) | Wellhead back pressure regulating method and device | |
| Waschl et al. | A novel tuning approach for offset-free mpc | |
| CN121467682A (en) | Reconfigurable casting process module hierarchical control method | |
| WO1993004412A1 (en) | Multivariable adaptive feedforward controller | |
| CN120986202B (en) | High-speed magnetic levitation system control method and system based on edge calculation and transducer prediction | |
| Stebel et al. | Balance-based adaptive control of the second order systems | |
| Diep et al. | Performance indices evaluation of cascade controller tuning method based on soft oscillation index | |
| CN121172923A (en) | Self-adaptive frequency adjusting method and system for resonant wireless charging |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |