CN-121978911-A - Control parameter optimization method and system for ultra-high nickel ternary anode material mixing equipment

CN121978911ACN 121978911 ACN121978911 ACN 121978911ACN-121978911-A

Abstract

The invention discloses a control parameter optimization method and a control parameter optimization system for ultra-high nickel ternary anode material mixing equipment, wherein the control parameter optimization method comprises the following steps: and arranging multi-mode sensors on the stirring blade and the inner wall of the equipment to acquire temperature, humidity and vibro-acoustic data, constructing a basic field and a motion field in a finite element model, and realizing space-time joint estimation of three key distributions in the mixing process to obtain a mixing state field reflecting actual mixing behaviors. Based on the mixing state field, the equipment space is divided into a strong flow area, a medium flow area and a weak flow area, and the mixing quality risk is output through a pre-training Bayesian network by combining the flowability index, the volume ratio and the stirring load characteristic of each distribution. And then, a particle swarm algorithm with an agent training mechanism is adopted, the influence rule of the control parameters on the three distributions is learned by utilizing a history control record, a control parameter sequence of a future control window is generated, and a sequence with the maximum quality risk reduction value is selected as a current optimal control strategy, so that the rolling optimization of the mixing equipment is realized.

Inventors

LUO DAJUN
ZHANG XUELIANG
HUANG FANG
ZHANG SONG
WU XINGYI
ZHANG PENGPENG
YAO SHENGYU
GUO XINGWANG

Assignees

贵州理工学院

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. The control parameter optimization method of the ultra-high nickel ternary anode material mixing equipment is characterized by comprising the following steps of: In the mixing process, multi-source data of the mixture in the equipment are acquired through arranging multi-mode sensors on the stirring blades and the inner wall of the equipment; Carrying out joint estimation on the mixing states in the space according to the multi-source data to obtain a mixing state field representing the spatial distribution characteristic of the mixing cavity; After comprehensively evaluating the quality risk of the mixing state field, taking the maximum quality risk reduction value as an optimization target, and carrying out prediction optimization on control parameters of equipment to obtain a control parameter sequence with the quality risk reduction; And the control parameter sequence is acted on the predictive optimization process of the control parameters, and rolling update is carried out on the mixing equipment based on the optimized control parameters.
2. The method for optimizing control parameters of the ultra-high nickel ternary cathode material mixing equipment according to claim 1, wherein the multi-source data comprises temperature data acquired by temperature sensors arranged on stirring paddles and the inner wall of the equipment; acquiring humidity data of the water content change of the mixed materials through a stirring blade and a microwave water content sensor arranged on the inner wall of the equipment; Acoustic data for determining flow conditions, cavity resonance changes, and particle agglomeration scale collected by vibration and acoustic sensors disposed on the stirring blade and the inner wall of the apparatus.
3. The method for optimizing control parameters of the ultra-high nickel ternary cathode material mixing equipment is characterized in that the joint estimation comprises classifying based on the transformation of the position of a sensor in space in time sequence, wherein the construction of a basic field is carried out by utilizing multi-source data acquired by the sensor on the inner wall of the equipment, and the construction of a sport field is carried out by utilizing the multi-source data acquired by the sensor in a stirring blade; Carrying out state fusion on the basic field and the motion field to obtain the mixing state field; Taking the inner space of a mixing cavity as a target area, and carrying out finite element dispersion on the space according to the preset grid size and equipment geometric characteristics to obtain a mixing finite element model consisting of a plurality of finite element units; establishing a corresponding relation between the installation positions of various sensors arranged on the inner wall of the equipment and the finite element units, and assimilating the multi-source data into the corresponding finite element units according to the corresponding relation; For each sensing data type, carrying out spatial interpolation and smoothing on the observed values in each finite element unit according to the physical relation between adjacent finite element units, so that the distribution of the sensing data of the same type in the whole finite element model is changed from discrete values into continuously-changed distribution; Aggregating the temperature distribution, the humidity distribution and the vibro-acoustic characteristic distribution after the smoothing treatment to obtain a basic field integrating the three distributions; meanwhile, in the basic field, the confidence of each distribution in each finite element is calculated respectively, and the confidence of the ith finite element is expressed as ; Wherein, the Representing the confidence of the ith finite element under the temperature distribution; Representing the confidence of the ith finite element under the humidity distribution; Representing the confidence of the ith finite element under the distribution of the vibro-acoustic characteristics; Index is set Then ; Wherein, the Representing the confidence of the ith finite element under the distribution of the index; Represents the finite element set where the index x corresponds to the sensor, r represents Element index of (a); Representation of The center of mass of the finite element with the closest center distance from the finite element i is the center of mass distance from the finite element i; The weight of index r is represented as a numerical value At the position of In the above, normalized results; according to the sensor on the stirring blade, building an integration result of temperature distribution, humidity distribution and vibration acoustic characteristic distribution at each moment and the confidence level of each finite element on each distribution by a construction method of the basic field, and taking the integration result as the sports field; and in time sequence, the base field and the motion field are respectively ordered to obtain field sequences of the two fields.
4. The method for optimizing control parameters of the ultra-high nickel ternary cathode material mixing equipment according to claim 3, wherein the state fusion comprises the steps of constructing an integration result of temperature distribution, humidity distribution and vibration acoustic characteristic distribution at each moment as the mixing state field according to the stirring blade and the sensor on the inner wall of the equipment through the construction method of the basic field; In a time sequence, the fields are divided into three areas by the similarity between the basic field and the mixing state field and between the motion field and the mixing state field respectively at the current time, wherein the similarity between the motion field and the mixing state field is greater than or equal to a maximum area with a preset value of 1 and is used as a strong flow area with the motion field as a main part, the similarity between the motion field and the mixing state field is greater than or equal to a maximum area with a preset value of 2 and is used as a weak flow area with the basic field as a main part, and the middle flow area is outside the two areas; if the finite element exists between the strong flow area and the weak flow area under any distribution and the judgment of the preset value 1 and the preset value 2 is respectively met in the two areas, respectively acquiring the confidence coefficients in the two areas of the current finite element under the current distribution, and selecting the area with high confidence coefficient for attribution; The method comprises the steps of determining a preset value 1, wherein each region comprises a plurality of subareas, the subareas are continuous, the similarity of each subarea meets the judgment of the preset value 1, the size of the space occupied by each subarea is larger than the preset minimum region constraint, no intersection exists between each subarea, and the union of the three areas is a complete equipment space.
5. The method for optimizing control parameters of the ultra-high nickel ternary cathode material mixing equipment according to claim 4, wherein the comprehensive evaluation of the quality risk comprises the steps of calculating a strong flow area, a medium flow area and a weak flow area according to temperature distribution, humidity distribution and vibration acoustic characteristic distribution respectively, and calculating a mobility index and a volume ratio under each distribution; And inputting the fluidity index and the volume ratio of each region under each distribution and the torque data of the stirring blade at the current moment into a pre-trained Bayesian network model, and outputting the quality risk of the mixing process by the Bayesian network for representing the quality risk level of the ultra-high nickel ternary cathode material under the current mixing state.
6. The method for optimizing control parameters of the ultra-high nickel ternary cathode material mixing equipment according to claim 5, wherein the predictive optimization comprises the steps of dividing a control process into K control windows according to time, keeping control parameters in each control window consistent, setting the current remaining time windows as K, training the intelligent body parameters of a particle swarm algorithm in each time window by using a control parameter sequence before the current time window, generating K control parameters for the current remaining time windows by using the trained particle swarm algorithm, and forming a predicted control parameter sequence; And obtaining the current optimal control parameter sequence by selecting the sequence with the most quality risk reduction.
7. The method for optimizing control parameters of an ultra-high nickel ternary cathode material mixing device according to claim 6, wherein in the particle swarm algorithm, an individual particle represents a candidate control parameter sequence, the position of the particle represents a specific value of the control parameter sequence corresponding to the particle, and the speed represents the adjustment direction and the adjustment amplitude of the control parameter sequence in the next generation; Training the intelligent agent parameters of the particle swarm algorithm in each time window comprises the steps of utilizing an executed control parameter sequence and corresponding temperature distribution change, humidity distribution change and vibration acoustic characteristic distribution change in the previous control window to construct a history control record; according to the influence relation of different control parameters on three distribution change trends in the historical control record, training neural network parameters of an intelligent agent, so that the intelligent agent can generate a change result of the three distributions according to the three distributions and the control parameters of each time window; and obtaining the final quality risks of the three distributions under the predicted control parameter sequence through the accumulation of k time windows.
8. The control parameter optimization system of the ultra-high nickel ternary anode material mixing equipment adopting the method as claimed in any one of claims 1-7 is characterized in that an acquisition unit acquires multi-source data of the mixing in the equipment by arranging multi-mode sensors on stirring paddles and the inner wall of the equipment in the mixing process; the computing unit is used for carrying out joint estimation on the mixing state in the space according to the multi-source data to obtain a mixing state field representing the spatial distribution characteristic of the mixing cavity; the analysis unit is used for carrying out comprehensive evaluation on the quality risk on the mixing state field, taking the maximum quality risk reduction value as an optimization target, and carrying out prediction optimization on control parameters of equipment to obtain a control parameter sequence with reduced quality risk; And the updating unit is used for acting the control parameter sequence on the predictive optimization process of the control parameters and carrying out rolling updating on the mixing equipment based on the optimized control parameters.
9. Computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method according to any of claims 1-7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of the claims 1-7.

Description

Control parameter optimization method and system for ultra-high nickel ternary anode material mixing equipment Technical Field The invention relates to the technical field of control optimization, in particular to a control parameter optimization method and system for ultra-high nickel ternary positive electrode material mixing equipment. Background The ultra-high nickel ternary cathode material has high energy density, high compaction density and excellent electrochemical performance, and has higher requirements on uniformity of a mixing process, water content control and agglomeration inhibition. In the traditional process, the mixing process usually depends on a stirring mode with fixed rotating speed or fixed power, lacks real-time sensing capability on the internal state of a mixing cavity, is difficult to accurately grasp key parameters such as temperature distribution, humidity distribution, material flow state and the like, and causes phenomena such as local overheating, local water content deviation, formation of an agglomeration zone and the like to frequently occur. In the prior art, although partial sensors are introduced to monitor the process, only local point location data can be obtained generally, the integral characterization of the dynamic behavior in the mixing cavity cannot be formed, and the mixing state cannot be accurately estimated through the combination of spatial characteristics and time sequence characteristics. In addition, the ultra-high nickel material is extremely sensitive to moisture, and the moisture content abnormality can lead to risks such as oxidization, lattice structure damage and the like, so that the humidity change must be accurately regulated and controlled in the mixing process. Meanwhile, the motion of the stirring blade can cause complicated local flow field disturbance, so that the traditional single-point feedback is difficult to effectively control the actual mixing state. For optimization of the mixing process, most of the existing methods are based on empirical parameter adjustment or simple model prediction methods, effective correlation between mixing parameters and quality risks cannot be established, and an intelligent decision mechanism capable of dynamically generating control parameter sequences is lacking. Along with the development of intelligent control and material mixing equipment structure upgrading, the real-time sensing, quality risk assessment and self-adaptive control optimization of the material mixing process are realized by combining a multi-mode sensing technology, a dynamic field construction technology and an intelligent optimization algorithm, so that the method has become a key requirement for improving the quality of the ultra-high nickel ternary material. Disclosure of Invention The present invention has been made in view of the above-described problems. Therefore, the invention solves the technical problems that the existing mixing process lacks the space sensing and risk assessment capability for temperature, humidity and flow state, realizes a predictable, optimizable and rollably updated mixing control parameter generation method, and improves mixing uniformity and quality stability. In order to solve the technical problems, the invention provides the following technical scheme that the control parameter optimization method of the ultra-high nickel ternary anode material mixing equipment comprises the following steps: In the mixing process, multi-source data of the mixture in the equipment are acquired through arranging multi-mode sensors on the stirring blades and the inner wall of the equipment; Carrying out joint estimation on the mixing states in the space according to the multi-source data to obtain a mixing state field representing the spatial distribution characteristic of the mixing cavity; After comprehensively evaluating the quality risk of the mixing state field, taking the maximum quality risk reduction value as an optimization target, and carrying out prediction optimization on control parameters of equipment to obtain a control parameter sequence with the quality risk reduction; And the control parameter sequence is acted on the predictive optimization process of the control parameters, and rolling update is carried out on the mixing equipment based on the optimized control parameters. As a preferable scheme of the ultra-high nickel ternary cathode material mixing equipment control parameter optimization method, the multi-source data comprises temperature data acquired through temperature sensors arranged on stirring paddles and the inner wall of the equipment; acquiring humidity data of the water content change of the mixed materials through a stirring blade and a microwave water content sensor arranged on the inner wall of the equipment; Acoustic data for determining flow conditions, cavity resonance changes, and particle agglomeration scale collected by vibration and acoustic sensors disposed on the stirrin