Crushed material drying system and operation method thereof

Abstract

The invention relates to the technical field of control systems, and particularly discloses a crushed material drying system and an operation method thereof, and the crushed material drying system comprises a data acquisition module, a multi-source data fusion module, a dynamic shunting decision module, a cooperative control module and a tail gas treatment optimization module. The data acquisition module acquires particle size distribution, moisture content, stacking density and dust concentration data of materials in real time through a laser scattering type particle size analyzer and a microwave moisture sensor, and transmits the data to the multi-source data fusion module through a Modbus protocol; according to the operation method, through an abnormal working condition grading alarm mechanism, a genetic algorithm optimization fuzzy rule base and multi-module data linkage, the problems of local overheating and low energy efficiency caused by parameter mismatch in traditional control are solved, and global optimization of drying efficiency, energy consumption and operation stability is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of control systems, and particularly to a broken material drying system and an operation method thereof. Background Art

[0002] A broken material drying system is a continuous thermal drying device for processing loose materials after crushing, mainly composed of a crusher, a belt conveyor, a rotary kiln and a heat source device. Its core principle is that after the material is initially crushed, it is evenly fed into the rotary kiln by the belt conveyor. During the inclined rotation of the kiln body, the material contacts the high-temperature hot air reversely or in the same direction, and the moisture is removed through conduction, convection and radiation heat transfer methods. The system design needs to coordinate the conveying speed, the rotary kiln rotation speed and the hot air temperature, so that the material forms a dispersed thin layer during the conveying and tumbling process, reducing local accumulation and prolonging the heat exchange time. After the hot air generated by the heat source device is optimized by the distributor, the influence of the air flow dead angle on the drying uniformity can be reduced. At the same time, by adjusting the inclination angle of the kiln body and the internal material lifting plate structure, the material throwing effect is enhanced, and the thermal energy utilization rate is improved. For materials with different particle sizes, the system can dynamically adjust the operation parameters based on the real-time monitoring data, balance the drying efficiency and energy consumption, and achieve the stable drying goal.

[0003] In the prior art, when the system copes with the difference in particle size distribution after material crushing, a fixed or manual adjustment operation parameter control mode is mostly adopted, lacking a dynamic collaborative control mechanism based on the real-time working conditions. Due to the volatility of the particle size, moisture content and bulk density of the crushed material, the coupling relationship between the core parameters such as the rotary kiln rotation speed, the hot air temperature and the conveyor belt speed is complex, and it is difficult for the traditional control method to accurately match the non-linear relationship between multiple variables. Especially when the particle size distribution of the material changes suddenly, the fixed threshold parameters are likely to cause the mismatch of the contact time between the hot air and the material, resulting in local overheating areas or undried lumps, and at the same time causing fluctuations in the driving load of the rotary kiln and a decrease in the energy efficiency of the heat source device. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a broken material drying system and an operation method thereof, which are used to solve the technical problems of low heat exchange efficiency, high energy consumption and unstable operation of the drying system caused by the lack of adaptive collaborative control of multi-variable parameters in the prior art.

[0005] To solve the above technical problems, the specific technical solutions of the present invention are as follows: In the first aspect, a broken material drying system provided by the present invention includes: A data acquisition module, deployed at the outlet of the crushing equipment and the belt conveyor section, for real-time collecting data on the particle size distribution, moisture content, bulk density of the material and the dust concentration at the front end of the dust removal system; The multi-source data fusion module, connected to the data acquisition module, is used to perform time series alignment and noise rejection on the particle size distribution, moisture content, and bulk density data, and construct a dynamic material characteristic matrix; The dynamic shunt decision module, connected to the multi-source data fusion module, is used to generate a shunt instruction based on the dynamic material characteristic matrix. When the median particle size in the dynamic material characteristic matrix is greater than or equal to a preset threshold and the moisture content is higher than a set value, a rotary kiln path control signal is triggered; otherwise, a flash dryer path switching signal is generated; The cooperative control module, connected to the dynamic shunt decision module, is used to receive the shunt instruction and synchronously adjust the inclination angle of the rotary kiln, the air flow velocity of the flash dryer, and the conveyor belt speed using the fuzzy PID algorithm to dynamically match the hot air temperature with the material conveying volume; The tail gas treatment optimization module, connected to the data acquisition module and the cooperative control module, is used to dynamically adjust the dust cleaning frequency according to the dust concentration data collected by the data acquisition module, and recover the waste heat to the hot air circulation system of the cooperative control module.

[0006] Further, in the crushing material drying system of the present invention, the multi-source data fusion module further includes a genetic algorithm optimization unit, which is used to iteratively optimize the weight distribution of the dynamic material characteristic matrix through the genetic algorithm, and output the optimized weight parameters to the fuzzy PID algorithm to improve the adaptive accuracy of the fuzzy PID control parameters; The dynamic shunt decision module is linked with the cooperative control module. When the standard deviation of the particle size distribution detected by the characteristic matrix output by the genetic algorithm optimization unit exceeds the set threshold, the shunt threshold of the shunt decision module and the equipment operation parameters of the cooperative control module are synchronously corrected.

[0007] Further, in the crushing material drying system of the present invention, the cooperative control module further includes an abnormal condition processing unit. When it is detected that the current of the conveyor belt motor exceeds the rated value or the temperature of the rotary kiln exceeds the safety threshold, a hierarchical alarm signal is generated and fed back to the fuzzy PID algorithm to dynamically reduce the hot air flow rate and the equipment rotation speed; The tail gas treatment optimization module receives the hierarchical alarm signal of the abnormal condition processing unit, increases the dust cleaning frequency of the bag filter according to the alarm level, and starts the negative pressure fan of the standby dust removal unit.

[0008] Further, in the crushing material drying system of the present invention, the dynamic shunt decision module is internally provided with an electric slide valve driven by a double cylinder, and the switching action of the slide valve is synchronized with the belt conveying speed through the feedforward control signal generated by the cooperative control module; The heat pipe heat exchanger of the tail gas treatment optimization module inputs the recovered waste heat into the proportional-integral valve of the hot air circulation system of the collaborative control module, and adjusts the waste heat supplement amount through the proportional-integral valve.

[0009] Further, in the broken material drying system of the present invention, the laser scattering particle size analyzer and the microwave moisture sensor of the data acquisition module transmit the acquired data to the PLC controller of the multi-source data fusion module in real time through the Modbus protocol; The genetic algorithm optimization unit generates an initial population based on historical operating condition data, iteratively optimizes the membership function boundary values in the fuzzy rule base of the fuzzy PID algorithm, and inputs the optimized parameters into the collaborative control module.

[0010] In a second aspect, a method for operating a broken material drying system provided by the present invention is applied to the broken material drying system as described above, and includes: real-time collecting data on the particle size distribution, moisture content, bulk density of the material, and the dust concentration at the front end of the dust removal system through multi-source sensors; Using a genetic algorithm to perform fusion processing on the particle size distribution, moisture content, and bulk density data to construct a dynamic material characteristic matrix; Generating a diversion instruction based on the dynamic material characteristic matrix. When the median particle size in the dynamic material characteristic matrix is greater than or equal to a preset threshold and the moisture content is higher than a set value, triggering a rotary kiln path control instruction, otherwise generating a flash dryer path switching signal; Receiving the diversion instruction through a fuzzy PID algorithm, and synchronously adjusting the inclination angle of the rotary kiln, the air flow velocity of the flash dryer, and the conveyor belt speed to dynamically match the hot air temperature and the material conveying amount; Dynamically adjusting the dust cleaning frequency of the bag filter according to the dust concentration data, and recovering the waste heat through a heat pipe heat exchanger to the proportional-integral valve of the hot air circulation system controlled by the fuzzy PID algorithm.

[0011] Further, in the method for operating a broken material drying system of the present invention, the genetic algorithm optimization includes: using the weight assignment of the dynamic material characteristic matrix as chromosome coding, evaluating the control error output by the fuzzy PID algorithm through a fitness function, and iteratively optimizing and outputting the optimal weight combination to the fuzzy PID algorithm; The fuzzy PID algorithm dynamically adjusts the collaborative relationship between the rotation speed of the rotary kiln drive motor and the rotation speed of the dispersion disc variable frequency motor of the flash dryer according to the optimal weight combination.

[0012] Further, in the method for operating a broken material drying system of the present invention, when it is detected that the current of the conveyor belt motor exceeds 15% of the rated value, triggering the reset of the initial population of the genetic algorithm optimization unit, regenerating the population and optimizing the fuzzy rule base of the fuzzy PID algorithm; In the waste heat recovery process, the proportional-integral valve of the hot air circulation system adjusts the amount of supplementary waste heat according to the real-time moisture content data collected by the data acquisition module.

[0013] Further, in the method for drying crushed materials of the present invention, after the dynamic diversion instruction is generated, a feedforward control signal is sent to the electric slide valve through the cooperative control module, so that the switching action of the slide valve is synchronized with the conveying speed of the belt conveyor. The signal for adjusting the dust cleaning frequency of the dust removal system is fed back to the genetic algorithm optimization unit as an input parameter for the energy consumption constraint condition in the fitness function.

[0014] Further, in the method for drying crushed materials of the present invention, the multi-source data fusion processing adopts a weighted Kalman filtering algorithm, and the genetic algorithm optimization unit optimizes the noise covariance matrix of the weighted Kalman filtering algorithm to improve the timing alignment accuracy of the dynamic material feature matrix; the output variables of the fuzzy PID algorithm include the coupling parameters of the hot air temperature gradient of the rotary kiln and the air flow velocity of the flash dryer, and the genetic algorithm optimization unit iteratively optimizes the boundary values of the membership functions in the fuzzy rule base of the fuzzy PID algorithm and inputs the optimized parameters into the cooperative control module.

[0015] Advantages of the present invention: The present invention uses a laser scattering particle size analyzer and a microwave moisture sensor to collect the particle size distribution and moisture content data of the material in real time, and realizes the efficient transmission and synchronization of multi-source data through the Modbus protocol, solving the problem of control lag caused by data isolation in the traditional system; uses the genetic algorithm to iteratively optimize the historical working condition data, dynamically adjusts the boundary values of the membership functions of the fuzzy PID algorithm, and improves the self-adaptability of the control parameters to the fluctuations of the material particle size and moisture content; the dynamic diversion decision module generates a diversion instruction based on the real-time feature matrix, drives the double-cylinder slide valve to cooperate with the feedforward control signal, makes the path switching strictly synchronized with the conveying speed, and reduces the risk of material accumulation; the cooperative control module synchronously adjusts the inclination angle of the rotary kiln, the air flow velocity of the flash dryer and the conveyor belt speed through the fuzzy PID algorithm to realize the dynamic matching of the hot air temperature and the material conveying amount; the tail gas treatment module dynamically adjusts the dust cleaning frequency according to the dust concentration, recovers the waste heat to the hot air circulation system through a heat pipe heat exchanger, and realizes the closed-loop control of the supplementary waste heat amount in combination with the proportional-integral valve, reducing the dependence on external heat sources; the linkage mechanism between the abnormal working condition processing unit and the genetic algorithm optimization unit further enhances the stability and recovery ability of the system under complex working conditions. The synergistic effect of the above technical means effectively balances the drying efficiency, energy consumption and operation stability, and solves the problems of local overheating, undried materials and low energy efficiency caused by the mismatch of multi-variable parameters in the prior art. Description of the Drawings

[0016] To more clearly illustrate the technical solution of the present invention, the following will briefly introduce the drawings required in the embodiments. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the drawings.

[0017] Figure 1 It is a flowchart of the energy management measurement and control method for the integration of electric vehicles and renewable energy provided by the embodiments of the present invention. Specific embodiments

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in combination with the specific embodiments and corresponding drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. The following will detail the technical solutions provided by the embodiments of the present invention in combination with the drawings.

[0019] To better understand the objectives of the present invention, the following will further describe the present invention in detail.

[0020] In a first aspect, a crushed material drying system provided by the present invention includes: A data acquisition module, deployed at the outlet of the crushing equipment and the belt conveyor section, for real-time acquisition of data on the particle size distribution, moisture content, bulk density of the material, and the dust concentration at the front end of the dust removal system; A multi-source data fusion module, connected to the data acquisition module, for performing time series alignment and noise elimination on the particle size distribution, moisture content, and bulk density data, and constructing a dynamic material characteristic matrix; A dynamic shunt decision module, connected to the multi-source data fusion module, for generating a shunt instruction based on the dynamic material characteristic matrix. When the median particle size in the dynamic material characteristic matrix is greater than or equal to a preset threshold and the moisture content is higher than a set value, a rotary kiln path control signal is triggered; otherwise, a flash dryer path switching signal is generated; A collaborative control module, connected to the dynamic shunt decision module, for receiving the shunt instruction and synchronously adjusting the inclination angle of the rotary kiln, the air flow velocity of the flash dryer, and the conveyor belt speed using a fuzzy PID algorithm to dynamically match the hot air temperature with the material conveying volume; An exhaust gas treatment optimization module, connected to the data acquisition module and the collaborative control module, for dynamically adjusting the dust cleaning frequency according to the dust concentration data collected by the data acquisition module, and recovering the waste heat to the hot air circulation system of the collaborative control module.

[0021] The data acquisition module collects the particle size distribution and moisture content data of the crushed materials in real time through a laser scattering particle size analyzer and a microwave moisture sensor. At the same time, a pressure sensor is used to measure the bulk density, and a dust concentration sensor is deployed at the front end of the dust removal system. The multi-source data is transmitted to the PLC controller in real time through the Modbus protocol to form the initial data input. After receiving the above data, the multi-source data fusion module uses the weighted Kalman filter algorithm to align and remove noise from the time-series data, eliminating the phase shift caused by the difference in the sensor sampling frequencies; further optimizing the noise covariance matrix through the genetic algorithm, dynamically adjusting the data weight distribution, and constructing a feature matrix reflecting the real-time state of the materials to provide high-precision input for subsequent decision-making.

[0022] After receiving the feature matrix, the dynamic shunt decision module generates a shunt instruction based on the preset median particle size threshold and moisture content threshold: when the median particle size exceeds the critical value and the moisture content is higher than the set range, a rotary kiln path control signal is triggered to drive the double-cylinder electric slide valve to complete the path switching within 1 second; otherwise, a flash dryer switching signal is generated, and a feedforward control signal is sent through the collaborative control module to make the action of the slide valve strictly synchronized with the belt conveying speed, avoiding material retention during the switching process. The collaborative control module uses the fuzzy PID algorithm to synchronously adjust the inclination angle of the rotary kiln drive motor, the air flow speed of the flash dryer dispersion disk variable-frequency motor, and the rotation speed of the conveyor belt frequency converter based on the boundary values of the membership function optimized by the genetic algorithm, so that the hot air temperature gradient and the material conveying volume form a dynamic match; the abnormal condition processing unit monitors the motor current and the rotary kiln temperature in real time. When the current exceeds the limit or the temperature exceeds the standard, a hierarchical alarm is triggered and fed back to the fuzzy PID algorithm to dynamically reduce the hot air flow rate and the equipment rotation speed to prevent the system from overloading.

[0023] The tail gas treatment optimization module dynamically adjusts the pulse cleaning frequency of the bag filter according to the dust concentration data, and increases the cleaning cycle to maintain the filtration efficiency when the concentration increases; the recovered waste heat is input into the proportional-integral valve of the hot air circulation system through a heat pipe heat exchanger, and the waste heat supplement ratio is adjusted in combination with the real-time moisture content data to form a thermal closed-loop management. The cleaning frequency adjustment signal is synchronously fed back to the genetic algorithm optimization unit as the energy consumption constraint condition of the fitness function to optimize the self-adaptability of the control parameters. Through the closed-loop interaction of the data flow and the control signal, the above modules realize the global collaborative optimization of multi-variable parameters, and maintain the balance between the drying efficiency and the energy consumption under the fluctuation of the material particle size.

[0024] Specifically, in the crushed material drying system of the present invention, the multi-source data fusion module further includes a genetic algorithm optimization unit for iteratively optimizing the weight distribution of the dynamic material feature matrix through the genetic algorithm, and outputting the optimized weight parameters to the fuzzy PID algorithm to improve the adaptive accuracy of the fuzzy PID control parameters; The dynamic shunt decision-making module is linked with the collaborative control module. When the standard deviation of the particle size distribution detected by the feature matrix output by the genetic algorithm optimization unit exceeds the set threshold, the shunt threshold of the shunt decision-making module and the equipment operation parameters of the collaborative control module are synchronously corrected.

[0025] The genetic algorithm optimization unit in the multi-source data fusion module realizes weight optimization through the following process: extracting typical sample data from the historical working condition database to generate an initial population, and the chromosome coding corresponds to the weight distribution of the particle size, moisture content, and density parameters in the feature matrix; the fitness function takes the weighted sum of squares of the hot air temperature deviation and the material conveying volume error output by the fuzzy PID algorithm as the evaluation index, and iteratively optimizes the weight combination through crossover and mutation operations, and finally outputs the optimal weight parameters to the rule base of the fuzzy PID algorithm. After receiving the optimized feature matrix, the dynamic shunt decision-making module calculates the standard deviation of the particle size distribution in real time. When the standard deviation exceeds the set threshold, it triggers a parameter correction mechanism: the shunt threshold is dynamically increased according to the standard deviation increment by a preset proportional coefficient. At the same time, the collaborative control module adjusts the speed compensation amount of the rotary kiln drive motor according to the membership function in the fuzzy rule base based on the moisture content change rate in the feature matrix, realizing the real-time matching of equipment operation parameters and material state.

[0026] The data interaction between the genetic algorithm optimization unit and the fuzzy PID algorithm is realized through the following path: the optimal weight parameters are transmitted to the input interface of the fuzzy PID algorithm through the Modbus-TCP protocol of the PLC controller, and the membership function boundary values of the particle size weight factor in the rule base are dynamically updated; the fuzzy PID algorithm generates three groups of control signals, namely the rotary kiln inclination adjustment amount, the flash dryer air flow speed correction amount, and the conveyor belt speed compensation amount, and synchronously executes them through the frequency converter and the servo driver. The shunt threshold correction logic of the dynamic shunt decision-making module is further associated with the abnormal working condition processing unit: when the standard deviation of the particle size distribution continues to exceed the limit, the grading alarm system triggers a secondary warning, forcibly reduces the belt conveyor speed and increases the flash dryer air flow speed, and eliminates the risk of material accumulation through feedforward control, forming an adaptive protection mechanism with multi-module linkage.

[0027] The technical effects of the above optimization and linkage mechanism are reflected as follows: the genetic algorithm optimization unit improves the response sensitivity of the fuzzy PID algorithm to the change of material state by dynamically adjusting the weight distribution of the feature matrix; the threshold correction mechanism based on standard deviation detection of the dynamic shunt decision-making module solves the problem of control lag caused by sudden particle size change in the traditional fixed threshold mode; the multi-parameter synchronous adjustment strategy of the collaborative control module enables the hot air temperature gradient and the material conveying volume to maintain dynamic balance under complex working conditions, reducing the occurrence probability of local overheating or undried phenomena. The data flow and control signals between each module form a closed-loop feedback, realizing the global parameter adaptive collaborative control of the crushed material drying system.

[0028] Specifically, in the crushed material drying system of the present invention, the collaborative control module also includes an abnormal condition processing unit, which generates a graded alarm signal and feeds it back to the fuzzy PID algorithm to dynamically reduce the hot air flow and equipment speed when it detects that the conveyor belt motor current exceeds the rated value or the rotary kiln temperature exceeds the safety threshold; The exhaust gas treatment optimization module receives the graded alarm signal of the abnormal operating condition processing unit, increases the cleaning frequency of the bag filter according to the alarm level, and starts the negative pressure fan of the standby dust removal unit.

[0029] The abnormal condition processing unit of the collaborative control module implements fault response through the following mechanisms: deploying Hall current sensors in the conveyor belt drive motor circuit to monitor the current value in real time, installing infrared temperature sensors on the surface of the rotary kiln to collect temperature data, and when the current exceeds 15% of the rated value or the temperature exceeds the safety threshold, the PLC controller triggers the logic for generating graded alarm signals; the first-level alarm corresponds to a short-term over-limit of current or temperature, and the fuzzy PID algorithm dynamically reduces the inverter output frequency of the hot air circulation fan after receiving the alarm signal, and gradually reduces the speed of the rotary kiln drive motor according to the preset slope; the second-level alarm corresponds to a continuous over-limit condition, immediately cutting off part of the hot air supply and linking to reduce the conveyor belt speed to the safety threshold. After receiving the graded alarm signal, the exhaust gas treatment optimization module executes the response through the following path: the first-level alarm triggers the pulse cleaning cycle of the bag filter to be shortened to 50% of the original cycle, and the second-level alarm synchronously starts the negative pressure fan of the standby dust removal unit to improve the dust capture efficiency; at the same time, the proportional integral valve of the waste heat recovery system adjusts the opening according to the alarm level, maintains the waste heat replenishment amount during the first-level alarm, and closes the waste heat input during the second-level alarm to prevent the hot air temperature from getting out of control.

[0030] The data interaction between the abnormal condition processing unit and the fuzzy PID algorithm is realized in the following ways: the graded alarm signal is transmitted to the rule correction interface of the fuzzy PID algorithm via the RS485 communication protocol, triggering the dynamic adjustment of the boundary value of the membership function; when the rotary kiln temperature is detected to be continuously exceeded, the fuzzy rule base automatically increases the weight coefficient of the temperature deviation, and preferentially reduces the hot air temperature setting value to quickly suppress the temperature rise. The cleaning frequency adjustment signal of the exhaust gas treatment optimization module is further fed back to the genetic algorithm optimization unit, and participates in the fitness function calculation as an energy consumption constraint parameter. The optimized weight parameter is transmitted back to the fuzzy PID algorithm through the Modbus-TCP protocol to form a closed-loop iteration mechanism for the control parameters.

[0031] The logical association of the above technical process is reflected in that the abnormal condition processing unit realizes the fault gradient response through a hierarchical alarm mechanism, avoiding the full-load shutdown of the system due to the over-limit of a single parameter; the fuzzy PID algorithm dynamically adjusts the control rules based on the alarm signal, and maximally maintains the drying continuity on the premise of ensuring safety; the ash cleaning and waste heat recovery linkage strategy of the tail gas treatment module effectively balances the dust removal efficiency and the thermal energy stability; the genetic algorithm optimization unit continuously optimizes the weight distribution of the fuzzy rule base by receiving the feedback parameter of the ash cleaning frequency, improving the parameter self-adaptability of the system under abnormal conditions. The signal linkage and data closed-loop among the modules realize the stable operation and rapid recovery ability of the crushed material drying system under complex conditions.

[0032] Specifically, in the crushed material drying system of the present invention, the dynamic shunt decision module is internally provided with an electric slide valve driven by a double cylinder, and the switching action of the slide valve is synchronized with the belt conveying speed through the feedforward control signal generated by the cooperative control module; The heat pipe heat exchanger of the tail gas treatment optimization module inputs the recovered waste heat into the proportional-integral valve of the hot air circulation system of the cooperative control module, and adjusts the waste heat supplement amount through the proportional-integral valve.

[0033] The electric slide valve driven by a double cylinder in the dynamic shunt decision module realizes action synchronization through the following process: the feedforward control signal is generated by the cooperative control module based on the real-time speed data of the belt conveyor, and is output to the cylinder solenoid valve group of the slide valve through the servo driver, driving the slide valve to complete the opening or closing action within a preset time; the displacement sensor of the slide valve real-time feeds back the valve position state to the cooperative control module, forming a closed-loop position control, ensuring that the switching action is strictly matched with the material conveying rhythm, and avoiding material accumulation or interruption caused by path switching. The heat pipe heat exchanger of the tail gas treatment optimization module realizes waste heat recovery in the following way: the high-temperature tail gas exchanges heat with the cold-side fresh air through the tube side of the heat exchanger, and the recovered thermal energy is input into the air duct of the hot air circulation system after adjusting the flow rate through the proportional-integral valve; the opening of the proportional-integral valve is dynamically adjusted by the cooperative control module according to the real-time hot air temperature deviation. When the detected hot air temperature is lower than the set value, the opening is increased to supplement the waste heat, and vice versa, the opening is decreased to maintain the thermal energy balance.

[0034] The logical relationship of the above technical process is reflected as follows: the feedforward control signal triggers the action of the slide valve in advance by collecting the belt conveying speed data in real time and predicting the change trend of the material flow rate, solving the lag problem existing in the traditional feedback control; the closed-loop position control of the slide valve further eliminates the mechanical response error and ensures the accuracy of the diversion path switching. The linkage mechanism between the heat pipe heat exchanger and the proportional-integral valve forms a closed-loop control of the hot air temperature through the dynamic adjustment of the waste heat recovery amount, reducing the energy consumption of the external heat source; the adjustment signal of the waste heat supplement amount is synchronously fed back to the fuzzy PID algorithm as the basis for the adaptive correction of the hot air temperature control parameters, realizing the coordinated optimization of the heat energy utilization efficiency and the drying stability. The control signals and data interactions among the various modules construct a global closed-loop system from material diversion to heat energy management, maintaining the dynamic balance between the drying efficiency and the energy consumption under complex working conditions.

[0035] Specifically, in the crushed material drying system of the present invention, the laser scattering type particle size analyzer and the microwave moisture sensor of the data acquisition module transmit the acquired data to the PLC controller of the multi-source data fusion module in real time through the Modbus protocol; The genetic algorithm optimization unit generates an initial population based on the historical working condition data, iteratively optimizes the membership function boundary values in the fuzzy rule base of the fuzzy PID algorithm, and inputs the optimized parameters into the cooperative control module.

[0036] In the data acquisition module, the laser scattering type particle size analyzer measures the particle size distribution curve of the crushed material in real time through the multi-wavelength scattering principle, and the microwave moisture sensor detects the moisture content of the material based on the change of the dielectric constant. The two types of sensors package and transmit the acquired data to the PLC controller of the multi-source data fusion module through the Modbus-RTU protocol; the PLC controller performs timestamp marking and cache management on the data, eliminating the timing misalignment problem caused by the difference in the sampling frequencies of the sensors. The genetic algorithm optimization unit realizes parameter optimization through the following process: extracting the particle size, moisture content, and equipment operation parameters from the historical working condition database to form a training sample set, and generating an initial population including the weight distribution scheme and the membership function boundary values; the fitness function takes the integral of the hot air temperature deviation and the variance of the conveyor belt speed fluctuation output by the fuzzy PID algorithm as the evaluation indexes, and iteratively optimizes through the tournament selection strategy and the multi-point crossover mutation operation, and finally outputs the optimal membership function boundary value parameters to the fuzzy rule base of the cooperative control module.

[0037] The logical relationship of the above technical process is reflected as follows: The multi-modal data fusion of laser scattering and microwave sensors provides high-precision input for the genetic algorithm, eliminating the interference of single-sensor errors on the feature matrix; The genetic algorithm optimizes the process driven by historical data, dynamically adjusts the membership relationship between the granularity weight and temperature deviation in the fuzzy PID rule base, and improves the adaptability of the control algorithm to the change of material state; The optimized parameters are written into the rule correction interface of the fuzzy PID algorithm in real time through the PLC controller, so that the inclination adjustment amount of the rotary kiln, the air flow speed compensation amount of the flash dryer, and the output of the conveyor belt frequency converter form a coordinated match. The three links of data acquisition, algorithm optimization and control execution form a closed-loop feedback, solving the problem of response lag caused by parameter solidification in traditional control methods, and maintaining the balance between drying efficiency and energy consumption under the condition of fluctuating material particle size and moisture content.

[0038] In a second aspect, a method for drying and operating crushed materials provided by the present invention is applied to the crushed material drying system as described above, and includes: Step S101, collecting data on the particle size distribution, moisture content, bulk density of the material, and the dust concentration at the front end of the dust removal system in real time through multi-source sensors; Step S102, using a genetic algorithm to fuse and process the data on particle size distribution, moisture content, and bulk density to construct a dynamic material feature matrix; Step S103, generating a diversion instruction based on the dynamic material feature matrix. When the median particle size in the dynamic material feature matrix is greater than or equal to a preset threshold and the moisture content is higher than a set value, a rotary kiln path control instruction is triggered; otherwise, a flash dryer path switching signal is generated; Step S104, receiving the diversion instruction through a fuzzy PID algorithm, and synchronously adjusting the inclination angle of the rotary kiln, the air flow speed of the flash dryer, and the conveyor belt speed to dynamically match the hot air temperature and the material conveying volume; Step S105, dynamically adjusting the dust cleaning frequency of the bag filter according to the dust concentration data, and recovering the waste heat to the proportional-integral valve of the hot air circulation system controlled by the fuzzy PID algorithm through a heat pipe heat exchanger.

[0039] Specifically, for the method for drying and operating crushed materials of the present invention, the genetic algorithm optimization includes: taking the weight distribution of the dynamic material feature matrix as chromosome coding, evaluating the control error output by the fuzzy PID algorithm through a fitness function, and iteratively optimizing to output the optimal weight combination to the fuzzy PID algorithm; The fuzzy PID algorithm dynamically adjusts the coordination relationship between the rotation speed of the driving motor of the rotary kiln and the rotation speed of the variable-frequency motor of the dispersion disk of the flash dryer according to the optimal weight combination.

[0040] Specifically, the crushed material drying operation method of the present invention triggers the initial population reset of the genetic algorithm optimization unit, regenerates the population and optimizes the fuzzy rule base of the fuzzy PID algorithm when it is detected that the conveyor belt motor current exceeds 15% of the rated value; The waste heat recovery process adjusts the waste heat replenishment amount according to the real-time moisture content data collected by the data acquisition module through the proportional integral valve of the hot air circulation system.

[0041] Specifically, in the crushed material drying operation method of the present invention, after the dynamic diversion instruction is generated, a feedforward control signal is sent to the electric slide valve through the cooperative control module, so that the switching action of the slide valve is synchronized with the conveying speed of the belt conveyor; The dust cleaning frequency adjustment signal of the dust removal system is fed back to the genetic algorithm optimization unit as an input parameter of the energy consumption constraint condition in the fitness function.

[0042] Specifically, in the crushed material drying operation method described in the present invention, the multi-source data fusion processing adopts a weighted Kalman filter algorithm, the genetic algorithm optimization unit optimizes the noise covariance matrix of the weighted Kalman filter algorithm, and improves the timing alignment accuracy of the dynamic material feature matrix; the output variables of the fuzzy PID algorithm include the coupling parameters of the rotary kiln hot air temperature gradient and the flash dryer air flow velocity, the genetic algorithm optimization unit iteratively optimizes the membership function boundary values ​​in the fuzzy rule base of the fuzzy PID algorithm, and inputs the optimized parameters into the collaborative control module.

[0043] The present invention aims at solving the problems of low efficiency, high energy consumption and unstable operation of the drying system caused by the lack of multivariable parameter adaptive coordinated control in the prior art, and proposes the following technical solutions: The present invention deploys a particle size analyzer, a moisture sensor and a dust concentration sensor at the outlet of the crushing equipment and the conveyor section to collect the particle size distribution, moisture content, bulk density and dust data of the material in real time. A genetic algorithm is used to perform time series alignment and noise removal on multi-source data to construct a dynamic material feature matrix. The matrix optimizes the noise covariance through a weighted Kalman filter algorithm, improves the data fusion accuracy, and provides a high-confidence input basis for subsequent control.

[0044] The present invention is based on a dynamic material characteristic matrix. The system synchronously adjusts the rotary kiln inclination angle, the flash dryer air flow velocity and the conveyor belt speed through a fuzzy PID algorithm to achieve dynamic matching of the hot air temperature and the material delivery volume. When it is detected that the particle size distribution standard deviation exceeds the threshold, the genetic algorithm optimization unit corrects the diversion threshold of the diversion decision module in real time and adjusts the equipment operating parameters in a linked manner. In addition, the abnormal operating condition processing unit monitors the motor current and kiln body temperature, triggers a graded alarm and feeds back to the control algorithm, dynamically slows down or shuts down to ensure system stability.

[0045] The dust removal system of the present invention dynamically adjusts the dust cleaning frequency according to the dust concentration data, and recovers the waste heat to the hot air circulation system through a heat pipe heat exchanger. The amount of waste heat supplement is adjusted by a proportional-integral valve according to the real-time moisture content data to form a thermal closed loop. At the same time, the dust cleaning frequency signal is fed back to the genetic algorithm optimization unit as the energy consumption constraint condition of the fitness function to further optimize the control parameters. Through the cooperation of the above multi-modules, the system realizes the global optimal balance of heat exchange efficiency and energy consumption under the fluctuation of material particle size.

Claims

1. A crushed material drying system, characterized in that: include: The data acquisition module is deployed at the crushing equipment outlet and the belt conveyor section to collect real-time data on the particle size distribution, moisture content, bulk density and dust concentration at the front end of the dust removal system; A multi-source data fusion module, connected to the data acquisition module, is used to perform time series alignment and noise elimination on the particle size distribution, moisture content and bulk density data to construct a dynamic material feature matrix; A dynamic diversion decision module, connected to the multi-source data fusion module, is used to generate a diversion instruction based on the dynamic material feature matrix, and when the median particle size in the dynamic material feature matrix is ​​greater than or equal to a preset threshold and the moisture content is higher than a set value, a rotary kiln path control signal is triggered, otherwise a flash dryer path switching signal is generated; A collaborative control module is connected to the dynamic diversion decision module, and is used to receive the diversion instruction and use a fuzzy PID algorithm to synchronously adjust the rotary kiln inclination angle, the flash dryer air flow velocity and the conveyor belt speed to dynamically match the hot air temperature with the material conveying amount; The exhaust gas treatment optimization module is connected to the data acquisition module and the collaborative control module, and is used to dynamically adjust the cleaning frequency according to the dust concentration data collected by the data acquisition module, and recover the waste heat to the hot air circulation system of the collaborative control module.

2. The crushed material drying system according to claim 1, characterized in that: The multi-source data fusion module further includes a genetic algorithm optimization unit, which is used to iteratively optimize the weight distribution of the dynamic material feature matrix through a genetic algorithm, and output the optimized weight parameters to the fuzzy PID algorithm to improve the adaptive accuracy of the fuzzy PID control parameters; The dynamic diversion decision module is linked with the collaborative control module. When the characteristic matrix output by the genetic algorithm optimization unit detects that the standard deviation of the particle size distribution exceeds a set threshold, the diversion threshold of the diversion decision module and the equipment operating parameters of the collaborative control module are synchronously corrected.

3. The crushed material drying system according to claim 2 is characterized in that: The collaborative control module also includes an abnormal condition processing unit, which generates a graded alarm signal and feeds it back to the fuzzy PID algorithm to dynamically reduce the hot air flow and equipment speed when it detects that the conveyor belt motor current exceeds the rated value or the rotary kiln temperature exceeds the safety threshold; The exhaust gas treatment optimization module receives the graded alarm signal of the abnormal operating condition processing unit, increases the cleaning frequency of the bag filter according to the alarm level, and starts the negative pressure fan of the standby dust removal unit.

4. The crushed material drying system according to claim 3 is characterized in that: The dynamic diversion decision module has a built-in electric slide valve driven by dual cylinders, and the switching action of the slide valve is synchronized with the belt conveying speed through the feedforward control signal generated by the collaborative control module; The heat pipe heat exchanger of the exhaust gas treatment optimization module inputs the recovered waste heat into the proportional integral valve of the hot air circulation system of the collaborative control module, and the waste heat supplement amount is adjusted by the proportional integral valve.

5. The crushed material drying system according to claim 4, characterized in that: The laser scattering particle size analyzer and microwave moisture sensor of the data acquisition module transmit the collected data to the PLC controller of the multi-source data fusion module in real time through the Modbus protocol; The genetic algorithm optimization unit generates an initial population based on historical operating condition data, iteratively optimizes the membership function boundary values ​​in the fuzzy rule base of the fuzzy PID algorithm, and inputs the optimized parameters into the collaborative control module.

6. A crushed material drying operation method, applied to the crushed material drying system according to any one of claims 1 to 5, characterized in that: include: The multi-source sensors can be used to collect the material's particle size distribution, moisture content, bulk density and dust concentration data at the front end of the dust removal system in real time; A genetic algorithm is used to fuse the particle size distribution, moisture content and bulk density data to construct a dynamic material feature matrix; Generate a diversion instruction based on the dynamic material feature matrix, and trigger a rotary kiln path control instruction when the median particle size in the dynamic material feature matrix is ​​greater than or equal to a preset threshold and the moisture content is higher than a set value, otherwise generate a flash dryer path switching signal; The diversion instruction is received through the fuzzy PID algorithm, and the rotary kiln inclination angle, the flash dryer air flow speed and the conveyor belt speed are synchronously adjusted to dynamically match the hot air temperature with the material conveying amount; The cleaning frequency of the bag filter is dynamically adjusted according to the dust concentration data, and the waste heat is recovered through the heat pipe heat exchanger to the proportional integral valve of the hot air circulation system controlled by the fuzzy PID algorithm.

7. The crushed material drying operation method according to claim 6, characterized in that: The genetic algorithm optimization includes: using the weight distribution of the dynamic material feature matrix as chromosome encoding, evaluating the control error output by the fuzzy PID algorithm through a fitness function, and outputting the optimal weight combination to the fuzzy PID algorithm after iterative optimization; The fuzzy PID algorithm dynamically adjusts the coordination relationship between the rotation speed of the rotary kiln driving motor and the rotation speed of the flash dryer dispersion disk variable frequency motor according to the optimal weight combination.

8. The crushed material drying operation method according to claim 7, characterized in that: When it is detected that the conveyor belt motor current exceeds 15% of the rated value, the initial population of the genetic algorithm optimization unit is reset, the population is regenerated, and the fuzzy rule base of the fuzzy PID algorithm is optimized; The waste heat recovery process adjusts the waste heat replenishment amount according to the real-time moisture content data collected by the data acquisition module through the proportional integral valve of the hot air circulation system.

9. The crushed material drying operation method according to claim 8, characterized in that: After the dynamic diversion instruction is generated, a feedforward control signal is sent to the electric slide valve through the cooperative control module, so that the switching action of the slide valve is synchronized with the conveying speed of the belt conveyor; The dust cleaning frequency adjustment signal of the dust removal system is fed back to the genetic algorithm optimization unit as an input parameter of the energy consumption constraint condition in the fitness function.

10. The crushed material drying operation method according to claim 9, characterized in that: The multi-source data fusion processing adopts a weighted Kalman filter algorithm, and the genetic algorithm optimization unit optimizes the noise covariance matrix of the weighted Kalman filter algorithm to improve the timing alignment accuracy of the dynamic material feature matrix; the output variables of the fuzzy PID algorithm include the coupling parameters of the hot air temperature gradient of the rotary kiln and the air flow velocity of the flash dryer, and the genetic algorithm optimization unit iteratively optimizes the membership function boundary values ​​in the fuzzy rule base of the fuzzy PID algorithm, and inputs the optimized parameters into the collaborative control module.

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