Self-adaptive control system for solid waste treatment

Abstract

The invention discloses a self-adaptive control system for solid waste treatment, and relates to the technical field of solid waste treatment. The system comprises a three-parameter detection module used for obtaining the density, the average particle size and the water content of a material in real time; the fuzzy adaptive controller is used for calculating a target wind speed according to the density, the average particle size and the water content; the servo execution module is used for adjusting the real-time air speed of the sorting cavity according to the target air speed; and the closed-loop correction module is used for finely adjusting the output parameters of the fuzzy adaptive controller according to the sorting effect. The automation and intelligence level of the sorting process is effectively improved, the adaptability to material component fluctuation is enhanced, manual intervention is reduced, the stable and reliable sorting effect is guaranteed, and the construction waste wind power sorting technology is promoted to develop towards the efficient, intelligent and accurate direction.

Description

Technical Field

[0001] The present invention relates to the technical field of solid waste treatment, and specifically to an adaptive control system for solid waste treatment. Background Art

[0002] In the wind separation link of construction waste resource treatment, the efficient separation of light and heavy materials depends on precise wind speed control. However, traditional wind separation technologies use fixed wind speed parameters and are difficult to adapt to the dynamic changes in the composition of construction waste. Differences in material density, particle size, and moisture content will significantly affect the aerodynamic characteristics, resulting in frequent "over-separation" or "under-separation" phenomena during the separation process. Existing control methods rely on manual experience to adjust the fan frequency, with obvious control delays, and are unable to establish a real-time mapping relationship between material characteristics and wind speed, leading to unstable separation efficiency and high manual intervention costs.

[0003] The core defect of the existing technology lies in the lack of adaptive adjustment ability to changes in material characteristics, and the failure to achieve dynamic matching of "real-time material characteristics - optimal wind speed". There is an urgent need for an adaptive control system that can detect key material parameters in real time and automatically adjust control strategies to solve the problem of separation accuracy under multi-factor coupling. Summary of the Invention

[0004] The purpose of the present invention is to provide an adaptive control system for solid waste treatment to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An adaptive control system for solid waste treatment, comprising:

[0006] A three-parameter detection module for real-time acquisition of the density, average particle size, and moisture content of the material;

[0007] A fuzzy adaptive controller for calculating the target wind speed according to the density, average particle size, and moisture content;

[0008] A servo execution module for adjusting the real-time wind speed of the separation cavity according to the target wind speed;

[0009] A closed-loop correction module for fine-tuning the output parameters of the fuzzy adaptive controller according to the separation effect.

[0010] Preferably, the three-parameter detection module includes:

[0011] A particle size detection unit that uses a laser diameter gauge to scan the particle size distribution of the material in real time and calculates the average particle size through a weighted average algorithm

[0012]

[0013] where di is the detected value of each particle size, m i is the mass percentage of the material corresponding to the particle size;

[0014] The moisture content detection unit directly detects the moisture content H of the material by using a capacitive humidity sensor;

[0015] The density detection unit calculates the average density of the material by using the pressure difference method through the pressure sensor array arranged upstream of the sorting cavity

[0016]

[0017] In the formula, k is the calibration coefficient, ΔP is the detected value of the pressure difference, and v0 is the initial calibration wind speed of the system.

[0018] Preferably, the obtaining method of the calibration coefficient k of the density detection unit includes:

[0019] Put the standard material with a known density, measure the pressure difference ΔP at this time, and according to the formula Calculate, where ρ is the density of the standard material.

[0020] Preferably, the three-parameter detection module further includes a data processing unit, which is used to perform a 5-second moving average filtering process on the density data detected by the pressure sensor, and control the density detection error within 5%.

[0021] Preferably, the fuzzy adaptive controller includes:

[0022] Input variable module, defining the fuzzy domain of the material density ρ as [0.2, 1.2] g / cm 3 , the average particle size d avg The fuzzy domain of is [5, 100] mm, and the fuzzy domain of the moisture content H is [5%, 30%];

[0023] Fuzzy rule base module, using Mamdani fuzzy inference, setting triangular membership functions to divide the input variables into intervals, and the membership functions include:

[0024] Density ρ: low interval (0.2 - 0.5 g / cm 3 ), medium interval (0.5 - 1.0 g / cm 3 ), high interval (1.0 - 1.2 g / cm 3 );

[0025] Average particle size d avg : small interval (5 - 20 mm), medium interval (20 - 50 mm), large interval (50 - 100 mm);

[0026] Water content H: dry range (5% - 15%), medium range (15% - 25%), wet range (25% - 30%);

[0027] The fuzzy rule base module contains a total of 27 control rules of 3×3×3, establishing the mapping relationship between the input variables and the target wind speed v set of.

[0028] Preferably, the fuzzy adaptive controller further includes a dynamic weight adjustment module, which is used to introduce a water content correction factor α = 1 + 0.01H to perform real-time compensation on the target wind speed v output by the fuzzy rule base, and obtain the final target wind speed v set = v final ·α, where H is the percentage value of the water content detection value. set

[0029] Preferably, the servo execution module includes:

[0030] A servo fan, which uses a permanent magnet synchronous motor to drive a centrifugal fan and can perform wind speed adjustment;

[0031] A deflector, which is arranged in the sorting cavity.

[0032] Preferably, the closed-loop correction module includes:

[0033] A sorting effect detection unit, which sets an infrared opposed photoelectric sensor array downstream of the sorting cavity, and calculates the entrainment rate of light materials by counting the number of penetrations of heavy particles

[0034] A parameter fine-tuning unit. When the entrainment rate E > 5%, based on the target wind speed v corresponding to the current working condition, the target wind speed is increased or decreased in steps of 0.5 m / s, and after at most 3 adjustments, an artificial calibration prompt is triggered. set

[0035] Preferably, the adjustment direction of the parameter fine-tuning unit is determined according to the water content working condition of the material: when in the wet working condition, that is, the water content H > 25%, the target wind speed is increased, and when in the dry working condition, that is, the water content H < 15%, the target wind speed is decreased.

[0036] Preferably, the fuzzy adaptive controller is implemented by an industrial computer or a PLC program, and its working steps are as follows:

[0037] 50 cm before the material enters the separator, the three-parameter detection module synchronously collects the density ρ, average particle size d avg , water content α;

[0038] ​​The fuzzy adaptive controller matches the fuzzy rule base according to the membership degrees of three parameters, and obtains the initial target wind speed v through defuzzification by the centroid method set and then calculates the final target wind speed v through the moisture content correction factor α final ;

[0039] The servo execution module adjusts the wind speed in the sorting cavity to v within 1 second final and ensures the wind speed uniformity through the guide vane;

[0040] The entrainment rate E is detected every 10 seconds. If E>5% for two consecutive times, parameter fine-tuning is started; otherwise, the current control parameters are maintained.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] The three-parameter detection module accurately obtains the material characteristics, and the data processing unit ensures the data reliability; the fuzzy adaptive controller constructs a rule base based on multiple parameters and achieves precise control through dynamic weight adjustment; the servo execution module ensures stable adjustment of the wind speed, and the closed-loop correction module timely fine-tunes the parameters according to the sorting effect;

[0043] The full-process adaptive management from material characteristic detection to precise wind speed regulation and then to sorting effect optimization is realized, solving the problems of low sorting efficiency and strong manual dependence under traditional fixed wind speed control, effectively improving the automation and intelligence levels of the construction waste wind sorting process, enhancing the adaptability of the system to material composition fluctuations, and ensuring the stable and reliable sorting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic structural diagram of an adaptive control system for solid waste treatment provided by an embodiment of the present invention;

[0045] Figure 2 is a working step diagram of a fuzzy adaptive controller in an adaptive control system for solid waste treatment provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Please refer to Figure 1 , the present invention provides an adaptive control system for solid waste treatment, and the system is applied to the wind sorting link of solid waste, including:

[0048] A three-parameter detection module 11 for obtaining the density, average particle size, and moisture content of the material in real time;

[0049] A fuzzy adaptive controller 12 for calculating the target wind speed according to the density, average particle size, and moisture content;

[0050] A servo execution module 13 for adjusting the real-time wind speed of the sorting cavity according to the target wind speed;

[0051] A closed-loop correction module 14 for fine-tuning the output parameters of the fuzzy adaptive controller 12 according to the sorting effect.

[0052] In an optional embodiment, the three-parameter detection module 11 includes:

[0053] A particle size detection unit that constructs a three-dimensional scanning matrix using a high-precision laser diameter gauge, performs real-time dynamic monitoring of the material through multi-beam cross-scanning technology, automatically collects particle size data of no less than 1000 detection points by the system, and calculates the average particle size in combination with the adaptive weighted average algorithm

[0054]

[0055] where d i is each particle size detection value, covering multi-dimensional data such as the major axis, minor axis, and equivalent diameter of the material particles, m i is the mass ratio of the material corresponding to the particle size, and this ratio is generated by synchronously matching the online weighing system with the particle size detection data;

[0056] A moisture content detection unit equipped with an industrial-grade capacitive humidity sensor, which uses the four-electrode method to eliminate the interference of edge effects and realizes the rapid and accurate detection of the moisture content H of the material. The sensor has an automatic temperature compensation function, and a Pt100 temperature sensor is built in to monitor the ambient temperature in real time, and the influence of temperature on the detection result is corrected through a compensation algorithm to ensure that the detection accuracy reaches ±0.5%;

[0057] A density detection unit arranges 6 groups of high-precision pressure sensors in an array upstream of the sorting cavity, constructs a differential pressure detection model using Bernoulli's principle, the system updates the calibration coefficient k in real time through a dynamic calibration program, optimizes the calculation model in combination with the Reynolds number correction algorithm, and finally calculates the average density of the material by the differential pressure method:

[0058]

[0059] where k is the calibration coefficient, ΔP is the differential pressure detection value obtained by the differential calculation of the pressure sensor array, v0 is the initial calibration wind speed of the system, and a hot-wire anemometer is used for multi-directional wind speed calibration to ensure the stability and reliability of the detection result.

[0060] In an alternative embodiment, the density detection unit further has a function of self-checking for faults, and monitors the output signals of the pressure sensor array in real time through a built-in diagnostic program. When it is detected that the deviation of any sensor data exceeds the threshold, the system automatically marks the faulty sensor and enables redundant sensors for data compensation to ensure the continuity and accuracy of density detection.

[0061] In an alternative embodiment, the method for obtaining the calibration coefficient k of the density detection unit includes:

[0062] Put a standard material with a known density, measure the pressure difference ΔP at this time, and according to the formula calculate, where ρ is the density of the standard material.

[0063] Specifically, the calibration coefficient k of the density detection unit is obtained through the following standardization process:

[0064] Preparation stage: Select at least three groups of standard materials with known densities ρ. The standard materials need to have stable physical and chemical properties, and their density values are calibrated by a nationally recognized metrological institution and a certificate is issued;

[0065] Measurement operation: Put the standard materials into the detection device in sequence. After the materials reach a stable flow rate v0 in the pipeline, record the measured values ΔP of the corresponding pressure difference sensors. Each standard material is measured three times and the average value is taken to reduce random errors;

[0066] Coefficient calculation: Calculate according to the calculation formula derived from Bernoulli's equation where v0 is a preset constant flow rate parameter, and finally take the weighted average of multiple groups of calculation results as the final calibration coefficient k;

[0067] Verification and correction: Use the newly obtained k value to detect another group of verification materials with known densities, compare the theoretical calculated value with the actual measured value, and repeat the above process for recalibration when the error exceeds ±2%.

[0068] In an alternative embodiment, the three-parameter detection module 11 further includes a data processing unit for performing a 5-second moving average filtering process on the density data detected by the pressure sensor to control the density detection error within 5%.

[0069] Specifically, this data processing unit takes 5 seconds as the time window, continuously collects and dynamically updates the real-time detection data of the pressure sensor, and effectively eliminates data fluctuations caused by factors such as environmental interference and sensor noise through arithmetic average calculation of the data within the window. After this processing, the density detection error can be accurately controlled within 5%, significantly improving the stability and reliability of the detection data, and ensuring that the density detection results can truly and accurately reflect the actual working conditions.

[0070] In an alternative embodiment, the fuzzy adaptive controller 12 includes:

[0071] An input variable module that defines the fuzzy universe of discourse for the material density ρ as [0.2, 1.2] g / cm 3 , which covers the common density range from lightweight materials to relatively heavy materials; the fuzzy universe of discourse for the average particle size d avg is [5, 100] mm, adapting to the particle size characteristics from fine particles to larger lumps of materials; the fuzzy universe of discourse for the moisture content H is [5%, 30%], covering the material humidity states from dry, moderate to wet. The setting of each universe of discourse is based on the common distribution of material characteristics in actual engineering to ensure the universality and effectiveness of the controller input variables;

[0072] A fuzzy rule base module that uses Mamdani fuzzy inference and sets triangular membership functions to divide the input variables into intervals. The membership functions include:

[0073] Density ρ:

[0074] Low interval (0.2 - 0.5 g / cm 3 ), applicable to materials with relatively small densities such as foam and lightweight plastics;

[0075] Medium interval (0.5 - 1.0 g / cm 3 ), covering most conventional solid particle materials;

[0076] High interval (1.0 - 1.2 g / cm 3 ), corresponding to heavier materials such as metal scraps and high - density ores;

[0077] Average particle size d avg :

[0078] Small interval (5 - 20 mm), applicable to fine sand, small - particle feed, etc.;

[0079] Medium interval (20 - 50 mm), commonly found in crushed construction aggregates and medium - sized parts;

[0080] Large interval (50 - 100 mm), applicable to large - block ores and larger industrial raw materials;

[0081] Moisture content H:

[0082] Dry interval (5% - 15%), corresponding to air - dried or oven - dried materials;

[0083] Medium interval (15% - 25%), representing conventional materials with a certain humidity in the natural environment;

[0084] Humid range (25% - 30%), suitable for materials that have just been cleaned or stored in a high-humidity environment;

[0085] The fuzzy rule base module, based on the internal relationship between material characteristics and target wind speed, summarizes through historical experimental data and engineering experience, and contains a total of 27 control rules of 3×3×3. These rules establish the precise mapping relationship between the input variables (material density, average particle size, moisture content) and the target wind speed v set to ensure that the system can output a reasonable target wind speed under different material conditions and achieve efficient and stable operation.

[0086] In an alternative embodiment, the fuzzy adaptive controller 12 further includes a dynamic weight adjustment module. Based on the significant influence of moisture content on the target wind speed in actual engineering, a moisture content correction factor α is introduced. The calculation formula of α is α = 1 + 0.01H, where H represents the moisture content detection value (presented as a percentage value) collected in real time by a high-precision sensor and processed by signal processing. This correction factor can perform real-time compensation on the target wind speed v set output by the fuzzy rule base;

[0087] The specific compensation mechanism is: multiply the correction factor α by the initial target wind speed v set , that is, v final = v set ·α, thus obtaining the final target wind speed v final considering the influence of moisture content, effectively improving the control accuracy and stability of the system under different humidity conditions.

[0088] In an alternative embodiment, the servo execution module 13 includes:

[0089] A servo fan, which uses a permanent magnet synchronous motor to drive a centrifugal fan and can perform wind speed adjustment;

[0090] A deflector, which is arranged in the sorting cavity.

[0091] Specifically, the servo fan uses a high-performance permanent magnet synchronous motor as the driving core, is equipped with an efficient centrifugal fan structure, constructs a closed-loop control system, and through an advanced vector control algorithm, realizes precise regulation of the fan speed. Its speed control accuracy can reach ±0.5%, and it can complete wind speed adjustment within a very short response time of ≤1 second, ensuring the timeliness and stability of wind speed output during the sorting process;

[0092] The deflector is composed of multiple groups of streamlined deflectors, which are precisely arranged at key positions in the sorting cavity. Through the unique curved surface design and array layout of the deflectors, combined with CFD (Computational Fluid Dynamics) simulation optimization, it effectively guides the uniform distribution of air flow, ensuring that the deviation of the wind speed uniformity across the cross-section of the cavity is controlled within ≤ ±0.3 m / s, providing a stable air flow environment for accurate sorting of materials.

[0093] In an optional embodiment, the closed-loop correction module 14 includes:

[0094] A sorting effect detection unit, which accurately deploys an infrared opposed photoelectric sensor array 1 m downstream of the sorting cavity. This array is composed of multiple groups of opposed photoelectric sensors arranged in a matrix, capable of real-time monitoring of the movement trajectories of particles in the material flow. By counting the number of times heavy particles penetrate the infrared light curtain and combining with the total number of light materials, according to the formula accurately calculate the entrainment rate of light materials, providing quantitative evaluation data on the sorting effect for the system;

[0095] A parameter fine-tuning unit sets the entrainment rate threshold at 5% as the trigger condition for automatic adjustment. When the entrainment rate E > 5%, based on the target wind speed v corresponding to the current working condition set as the benchmark, start the wind speed adaptive adjustment mechanism, increment or decrement the target wind speed in steps of 0.5 m / s, and trigger a manual calibration prompt after at most 3 adjustments;

[0096] Among them, if after 3 automatic adjustments, the entrainment rate still does not drop below the threshold, immediately trigger a manual calibration prompt, lock the current equipment operating parameters at the same time to prevent over-adjustment, and push a detailed calibration suggestion report containing the current working condition data and historical adjustment records to the operator.

[0097] In an optional embodiment, the adjustment direction of the parameter fine-tuning unit is determined according to the moisture content working condition of the material: increase the target wind speed when in a humid working condition, i.e., the moisture content H > 25%, and decrease the target wind speed when in a dry working condition, i.e., the moisture content H < 15%.

[0098] Specifically, the adjustment direction of the parameter fine-tuning unit includes:

[0099] Humid working condition (moisture content H > 25%): Start the positive compensation mechanism, increment the target wind speed at a gradient of 0.3 - 0.5 m / s per hour, and simultaneously start the operation of the auxiliary drying component to ensure rapid dehydration of the material in a high-humidity environment;

[0100] Dry working condition (moisture content H < 15%): Trigger the reverse adjustment program, decrement the target wind speed by an amplitude of 0.2 - 0.4 m / s per batch, and automatically turn off the heating device to prevent quality loss of the material due to over-drying;

[0101] Critical operating condition (water content 15% ≤ H ≤ 25%): Maintain the current wind speed parameters, start the real-time monitoring module, collect water content data at a frequency of 3 times per minute. When the detected values exceed the threshold range for 3 consecutive times, automatically switch to the corresponding operating condition adjustment mode.

[0102] In this embodiment, the material characteristics are accurately obtained through the three-parameter detection module 11, and the data processing unit ensures data reliability; the fuzzy adaptive controller 12 constructs a rule base based on multiple parameters and achieves precise control through dynamic weight adjustment; the servo execution module 13 ensures stable adjustment of the wind speed, and the closed-loop correction module 14 fine-tunes the parameters in a timely manner according to the separation effect;

[0103] It realizes the full-process adaptive management from material characteristic detection to precise wind speed regulation and then to separation effect optimization, solves the problems of low separation efficiency and strong dependence on manual operation under traditional fixed wind speed control, effectively improves the automation and intelligent level of the construction waste wind separation process, enhances the adaptability of the system to material composition fluctuations, and ensures the stable and reliable separation effect.

[0104] Based on the above embodiments, as Figure 2 shown, the present invention also provides the working steps of the fuzzy adaptive controller 12 in an adaptive control system for solid waste treatment. The working steps of the fuzzy adaptive controller 12 are implemented through an industrial computer or a PLC program, and the working steps include:

[0105] Step S1, 50 cm before the material enters the separator, the three-parameter detection module 11 synchronously collects the density ρ, average particle size d avg , water content α;

[0106] Specifically, step S1 is the multi-parameter real-time collection stage. In the preset trigger area 50 cm before the material enters the separator, the three-parameter detection module 11 starts the synchronous collection program. Among them, the density detection unit uses the γ-ray transmission method, which can achieve high-precision measurement of ±0.05 g / cm 3 ; the average particle size detection is completed by a laser diffraction particle size analyzer, and the detection range covers 0.1 mm to 100 mm; the water content detection uses a capacitive sensor, combined with a temperature compensation algorithm, to control the measurement error within ±1.5%;

[0107] Step S2, the fuzzy adaptive controller 12 matches the fuzzy rule base according to the membership degrees of the three parameters, obtains the initial target wind speed v set through defuzzification by the centroid method, and then calculates the final target wind speed v final through the water content correction factor α;

[0108] Specifically, step S2 is the fuzzy logic decision-making stage: the fuzzy adaptive controller 12 maps the measured values to the three-level fuzzy subsets of "low / medium / high" through the membership function based on the three parameters of the collected density ρ, average particle size d avg , and moisture content α. The built-in fuzzy rule base of the system contains 27 control rules optimized through experiments. Through the Mamdani inference algorithm, logical matching is completed, and the centroid method is used in the defuzzification process to calculate the initial target wind speed v set , and a moisture content correction factor α is introduced for secondary adjustment. This factor dynamically takes values according to the three-dimensional mapping table constructed based on the material humidity characteristics, and finally determines the target wind speed v that meets the sorting requirements final ;

[0109] In step S3, the servo execution module 13 adjusts the wind speed in the sorting cavity to v within 1 second final , and ensures the wind speed uniformity through the guide plate;

[0110] Specifically, step S3 is the dynamic response control stage: after receiving the control instruction, the servo execution module 13 completes the adjustment of the wind speed in the sorting cavity within 1 second through the closed-loop PID regulation system. Cooperating with the adjustable guide plate mechanism, which includes 12 groups of spoiler blades with independently adjustable angles, through the layout scheme optimized by CFD simulation, ensures that the wind speed uniformity in the cavity reaches more than 95%;

[0111] In step S4, the entrainment rate E is detected every 10 seconds. If E>5% for two consecutive times, parameter fine-tuning is started, otherwise the current control parameters are maintained;

[0112] Specifically, step S4 is the intelligent feedback optimization stage: the system detects the sorting entrainment rate E every 10 seconds, and the machine vision counting method is used to achieve a detection accuracy of ±0.3%. When E>5% is detected continuously twice, the parameter fine-tuning mechanism is triggered: first, a sub-micron particle detection channel with higher sensitivity is started, and correlation analysis is carried out in combination with the historical parameter database, and then the weight coefficients of the fuzzy rule base are optimized through the genetic algorithm, and finally the adaptive adjustment of the control parameters is realized; if the detection result meets the standard, the current control parameters are maintained and the operation continues.

[0113] In this embodiment, the material characteristics are accurately obtained through the three-parameter detection module 11, and the data processing unit ensures the data reliability; the fuzzy adaptive controller 12 constructs a rule base based on multiple parameters and achieves accurate control through dynamic weight adjustment; the servo execution module 13 ensures stable adjustment of the wind speed, and the closed-loop correction module 14 fine-tunes the parameters in a timely manner according to the sorting effect;

[0114] It realizes adaptive management of the entire process from material property detection to precise wind speed control to optimization of sorting effect, solving the problems of low sorting efficiency and strong dependence on manual labor under traditional fixed wind speed control, effectively improving the automation and intelligence level of the wind sorting process of construction waste, enhancing the system's adaptability to fluctuations in material composition, and ensuring stable and reliable sorting effects.

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed modules and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or equipment, which can be electrical, mechanical or other forms.

[0116] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0118] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0119] It should be noted that the above examples are only specific embodiments of the present invention, and the present invention is obviously not limited to the above examples, and there are many similar variations. All variations directly derived or associated from the contents disclosed by the technicians in this field should fall within the protection scope of the present invention.

[0120] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An adaptive control system for solid waste treatment, characterized in that, Including: A three-parameter detection module for obtaining the density, average particle size, and moisture content of the material in real time; A fuzzy adaptive controller for calculating the target wind speed according to the density, average particle size, and moisture content; A servo execution module for adjusting the real-time wind speed of the sorting cavity according to the target wind speed; A closed-loop correction module for fine-tuning the output parameters of the fuzzy adaptive controller according to the sorting effect.

2. The adaptive control system for solid waste treatment according to claim 1, characterized in that, The three-parameter detection module includes: A particle size detection unit that uses a laser diameter gauge to scan the particle size distribution of the material in real time and calculates the average particle size through a weighted average algorithm where d i is the detection value of each particle size, and m i is the mass percentage of the material corresponding to the particle size; A moisture content detection unit that directly detects the moisture content H of the material using a capacitive humidity sensor; A density detection unit that calculates the average density of the material using the differential pressure method through a pressure sensor array arranged upstream of the sorting cavity In the formula, k is the calibration coefficient, ΔP is the differential pressure detection value, and v0 is the initial calibration wind speed of the system.

3. The adaptive control system for solid waste treatment according to claim 2, wherein The method for obtaining the calibration coefficient k of the density detection unit includes: Inject a standard material with a known density, measure the pressure difference ΔP at this time, and calculate according to the formula where ρ is the density of the standard material.

4. The adaptive control system for solid waste treatment according to claim 2, wherein The three-parameter detection module further includes a data processing unit for performing a 5-second moving average filtering process on the density data detected by the pressure sensor to control the density detection error within 5%.

5. The adaptive control system for solid waste treatment according to claim 2, wherein, The fuzzy adaptive controller includes: Input variable module, defining the fuzzy universe of discourse for the material density ρ as [0.2, 1.2] g / cm 3 , the average particle size d avg with the fuzzy universe of discourse of [5, 100] mm and the fuzzy universe of discourse of the water content H as [5%, 30%]; A fuzzy rule base module that uses Mamdani fuzzy inference to set triangular membership functions to divide the input variable intervals. The membership functions include: Density ρ: low range (0.2 - 0.5 g / cm 3 ), medium range (0.5 - 1.0 g / cm 3 ), high range (1.0 - 1.2 g / cm 3 ); Average particle size d avg : Small interval (5 - 20 mm), medium interval (20 - 50 mm), large interval (50 - 100 mm); Moisture content H: dry interval (5% - 15%), medium interval (15% - 25%), wet interval (25% - 30%); The fuzzy rule base module contains a total of 27 control rules of 3×3×3, and establishes the mapping relationship between the input variables and the target wind speed v set .

6. The adaptive control system for solid waste treatment according to claim 5, wherein The fuzzy adaptive controller further includes a dynamic weight adjustment module, which is used to introduce a moisture content correction factor α = 1 + 0.01H to compensate the target wind speed v output by the fuzzy rule base in real time, and obtain the final target wind speed v set = v final ·α, where H is the percentage value of the moisture content detection value. set ​ 7. The adaptive control system for solid waste treatment according to claim 1, characterized in that The servo execution module includes: A servo fan that uses a permanent magnet synchronous motor to drive a centrifugal fan and can perform wind speed adjustment; A deflector that is arranged inside the sorting cavity.

8. The adaptive control system for solid waste treatment according to claim 1, wherein The closed-loop correction module includes: Sorting effect detection unit, an infrared opposed photoelectric sensor array is arranged downstream of the sorting cavity, and the entrainment rate of light materials is calculated by counting the penetration times of heavy particles Parameter fine-tuning unit, when the entrainment rate E > 5%, taking the target wind speed v corresponding to the current working condition set as a reference, incrementing or decrementing the target wind speed in steps of 0.5 m / s, and triggering a manual calibration prompt after at most 3 adjustments.

9. The adaptive control system for solid waste treatment according to claim 2, wherein The adjustment direction of the parameter fine-tuning unit is determined according to the moisture content condition of the material: when in the wet condition, i.e., the moisture content H > 25%, the target wind speed is increased, and when in the dry condition, i.e., the moisture content H < 15%, the target wind speed is decreased.

10. The adaptive control system for solid waste treatment according to any one of claims 1 to 9, characterized in that The fuzzy adaptive controller is implemented through an industrial computer or a PLC program, and its working steps are as follows: 50 cm before the material enters the separator, the three-parameter detection module simultaneously collects the density ρ, the average particle size d avg , and the moisture content α; The fuzzy adaptive controller matches the fuzzy rule base according to the membership degrees of three parameters, and obtains the initial target wind speed v by defuzzifying using the centroid method set , and then calculates the final target wind speed v through the moisture content correction factor α final ; The servo execution module adjusts the air velocity in the sorting cavity to v within 1 second final and ensures the air velocity uniformity through the flow deflector; Detect the entrainment rate E every 10 seconds. If E > 5% for two consecutive times, start parameter fine-tuning, otherwise maintain the current control parameters.

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