System and method for controlling a complex atmosphere for gasification of waste circuit boards
By collecting characteristic data of waste circuit board shredded materials and applying machine learning models, precise control of the reaction stage in the gasification process of waste circuit boards was achieved, solving the problems of energy waste and harmful substance emissions in traditional processes, and improving processing efficiency and environmental friendliness.
Patent Information
- Application Number
- CN202511474565.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-15
- Publication Date
- 2026-05-19
- Estimated Expiration
- 2045-10-15
AI Technical Summary
Traditional waste circuit board gasification processes are difficult to control precisely during pyrolysis, combustion, and other reaction stages, resulting in energy waste, harmful emissions, and low processing efficiency.
By collecting characteristic data of waste circuit board shredded materials, a machine learning model is established to monitor changes in gas concentration in real time and dynamically adjust temperature and atmosphere control parameters to achieve precise conversion between pyrolysis, primary combustion and secondary combustion stages.
It improves heat treatment efficiency, reduces energy waste, reduces the generation of harmful substances, ensures process stability and environmental friendliness, and shortens the commissioning cycle.
Smart Images
Figure CN121254713B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of waste circuit board gasification technology, and more specifically, to a composite atmosphere control system and method for waste circuit board gasification treatment. Background Technology
[0002] With the rapid development and widespread use of electronic products, the amount of waste circuit boards generated has increased dramatically. These waste circuit boards typically contain complex and diverse components, such as metals, glass fibers, and organic materials. After pretreatment such as dismantling, drying, and crushing, the particle size distribution and component ratios of these components vary significantly, posing a serious challenge to subsequent thermal treatment processes. Traditional high-temperature incineration or gasification processes generally rely on empirical parameters or preset timing to control the transitions between reaction stages. This approach often ignores the fluctuations in particle size distribution and component ratios, making it difficult to achieve precise control over reaction stages such as pyrolysis and combustion. This can lead to incomplete or excessive reactions, resulting in a series of problems such as energy waste, low processing efficiency, and the emission of harmful substances.
[0003] In view of this, the present invention proposes an atmosphere control system and method for the gasification treatment of waste circuit boards to solve the above problems. Summary of the Invention
[0004] To overcome the aforementioned deficiencies of the prior art and achieve the above objectives, the present invention provides the following technical solution: a method for controlling the composite atmosphere in the gasification treatment of waste circuit boards, comprising:
[0005] Collect characteristic data from each batch of crushed material and extract the characteristics of each batch of crushed material. One experimental sample;
[0006] The heat treatment process is divided into a pyrolysis stage, a primary combustion stage, and a secondary combustion stage; the critical values for pyrolysis, primary combustion, and secondary combustion are defined as critical values. Each experimental sample underwent a heat treatment process.
[0007] During the pyrolysis stage, critical pyrolysis parameters are collected.
[0008] During the initial combustion stage, critical initiation indicators are collected.
[0009] During the secondary combustion stage, critical secondary combustion indicators are collected.
[0010] A critical prediction model is trained based on feature data and critical values, which is used to predict critical values using feature data.
[0011] Based on real-time feature data, a critical prediction model is used to predict critical values; the reaction stages are then transitioned according to the critical values; and temperature and atmosphere control parameters are adjusted at each reaction stage.
[0012] Furthermore, the characteristic data includes average particle size and composition ratio; the composition ratio is the mass ratio of the metal part, glass fiber part and organic component part in the waste circuit board.
[0013] Furthermore, the methods for extracting experimental samples include:
[0014] The mass of the crushed material processed in a single run by the high-temperature incineration equipment is taken as the mass of a single sample; for each batch of crushed material, the mass of the crushed material is extracted based on the mass of the single sample. One sample was labeled as an experimental sample.
[0015] Furthermore, the methods for obtaining the pyrolysis critical index, the initial combustion critical index, and the secondary combustion critical index include:
[0016] The concentration of preset gas 1 is monitored online at a preset frequency; when the rate of change of the concentration of preset gas 1 is less than a preset threshold, the instantaneous concentration of preset gas 1 is used as the critical index for pyrolysis.
[0017] The concentration of preset gas 2 is monitored online at a preset frequency; when the rate of change of the concentration of preset gas 2 is less than a preset threshold, the instantaneous concentration of preset gas 2 is used as the critical index for initial combustion.
[0018] The concentration of the preset gas 3 is monitored online at a preset frequency; when the rate of change of the preset gas 3 concentration is less than the preset threshold, the instantaneous concentration of the preset gas 3 is used as the critical index for secondary combustion.
[0019] Furthermore, the methods for switching reaction stages include:
[0020] During the pyrolysis stage, the concentration of preset gas one is monitored online at a preset frequency. When the concentration of preset gas one equals the pyrolysis critical index, the primary combustion stage begins. During the primary combustion stage, the concentration of preset gas two is monitored online at a preset frequency. When the concentration of preset gas two equals the primary combustion critical index, the secondary combustion stage begins. During the secondary combustion stage, the concentration of preset gas three is monitored online at a preset frequency. When the concentration of preset gas three equals the secondary combustion critical index, the secondary combustion stage ends.
[0021] Furthermore, the preset gas includes carbon monoxide (CO), methane (CH4), and ethylene (C2H4).
[0022] Furthermore, the preset gas 2 includes carbon monoxide (CO) and oxygen (O2); if it is oxygen (O2), then the rate of change of the concentration of the preset gas 2 is replaced by the rate of change of the oxygen concentration difference between the inlet and outlet of the combustion chamber in the high-temperature incineration equipment.
[0023] Furthermore, the preset gas tri includes dioxin precursors; the dioxin precursors include 2,4,6-trichlorophenol (C6H3Cl3O) and chlorobenzene (C6H5Cl).
[0024] A composite atmosphere control system for waste circuit board gasification treatment, implementing the aforementioned composite atmosphere control method for waste circuit board gasification treatment, the system comprising:
[0025] The first data acquisition module is used to collect characteristic data of each batch of crushed material and extract the data from each batch of crushed material. One experimental sample;
[0026] The module defines the heat treatment process as three stages: pyrolysis, primary combustion, and secondary combustion. It defines the critical values for pyrolysis, primary combustion, and secondary combustion. Each experimental sample underwent a heat treatment process.
[0027] The second acquisition module acquires critical pyrolysis indicators during the pyrolysis stage;
[0028] The third acquisition module collects the critical indicators for initial combustion during the primary combustion stage;
[0029] The fourth data acquisition module collects critical parameters for secondary combustion during the secondary combustion stage.
[0030] The model training module trains a critical prediction model based on feature data and critical values, which is used to predict critical values using feature data.
[0031] The control module uses a critical prediction model to predict critical values based on real-time feature data; it then switches between reaction stages based on the critical values; and adjusts the temperature and atmosphere control parameters at each reaction stage.
[0032] The beneficial effects of the composite atmosphere control system and method for waste circuit board gasification treatment of the present invention are as follows:
[0033] This invention accurately collects characteristic data of pre-processed crushed waste circuit boards and combines it with online gas monitoring to achieve real-time capture of critical values at each reaction stage during the heat treatment process. By using machine learning models to train and predict the data, the critical states of key reaction stages such as pyrolysis, primary combustion, and secondary combustion are accurately determined. This effectively avoids the problems of insufficient or excessive reactions caused by fixed parameters in traditional processes, ensuring that the process remains stable and efficient when processing different batches of waste circuit boards.
[0034] Furthermore, this invention achieves precise stage transition control by dynamically adjusting process parameters such as temperature and oxygen supply. Based on real-time gas concentration monitoring and characteristic data prediction, this control strategy not only reduces energy waste and improves heat treatment efficiency, but also significantly inhibits the formation of harmful substances (such as dioxin precursors) during combustion, thus better meeting environmental protection requirements.
[0035] Pre-built machine learning models enable the process to adapt and adjust, providing real-time feedback on equipment debugging and process optimization information, thereby shortening the debugging cycle and improving overall operational safety and stability.
[0036] In summary, this invention organically combines traditional experience-based control with advanced data-driven technology, achieving intelligent and precise control of each stage of heat treatment. This not only improves energy utilization and reduces production costs, but also significantly reduces harmful gas emissions, laying a solid foundation for the efficient, environmentally friendly, and economical treatment of waste circuit boards. Attached Figure Description
[0037] Figure 1 This is a schematic diagram of the composite atmosphere control system module for the gasification treatment of waste circuit boards according to the present invention.
[0038] Figure 2 This is a schematic diagram of the high-temperature incineration equipment of the present invention;
[0039] Figure 3 This is a flowchart of the composite atmosphere control method for the gasification treatment of waste circuit boards according to the present invention. Detailed Implementation
[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0041] Example 1
[0042] See Figure 1 The composite atmosphere control system for waste circuit board gasification treatment described in this embodiment includes: a first acquisition module, a setting module, a second acquisition module, a third acquisition module, a fourth acquisition module, a model training module, and a control module.
[0043] The thermal treatment process of waste circuit board incineration includes four typical reaction stages: pyrolysis, primary combustion, and secondary combustion.
[0044] This system is used for the identification and dynamic control of reaction stages in the heat treatment process, that is, to accurately identify the current stage, determine when to enter the next stage, and make corresponding adjustments to the control parameters.
[0045] A pretreatment process precedes the heat treatment. This pretreatment includes dismantling and drying the waste circuit boards, followed by batch-by-batch mechanical crushing, cutting, pulverizing, and turning. Each batch yields crushed material with a uniform particle size and composition. Notably, each batch of crushed material exhibits different average particle size, metal content, glass fiber content, and organic component content.
[0046] Following the heat treatment process is a post-treatment process for the flue gas and residue. The post-treatment process includes flue gas quenching, heat recovery, flue gas purification, and metal recovery.
[0047] The preprocessing and postprocessing processes are existing technologies and will not be described in detail here.
[0048] The first data acquisition module is used to collect characteristic data of each batch of crushed material and extract the data from each batch of crushed material. One experimental sample. Characteristic data include average particle size and composition ratio. The composition ratio is the mass ratio of metal, glass fiber, and organic components in the waste circuit board.
[0049] The methods for acquiring characteristic data include: for each batch of crushed material, extracting a sample of a predetermined mass, and determining the average particle size of the crushed material using methods such as sieving or image analysis; determining the composition ratio of the crushed material using methods such as physical sorting, X-ray fluorescence spectrometry (XRF), inductively coupled plasma mass spectrometry (ICP-MS), and thermogravimetric analysis (TGA); the average particle size and composition ratio constitute the characteristic data. Various methods for acquiring the average particle size and composition ratio are existing technologies and will not be elaborated upon here.
[0050] To accommodate changes in the type and composition of circuit boards, the residence time for each reaction stage during heat treatment should be adjusted accordingly. If the residence time and control parameters for each reaction stage remain fixed, it will not be able to adapt to changes in the type and composition of waste circuit boards, resulting in inefficiency and energy waste. For example, for a batch of waste circuit boards, the pyrolysis stage should ideally end in 8 minutes, but if it is actually held for 10 minutes, then 2 minutes of time and energy are wasted.
[0051] To determine the residence time for each reaction stage, this system first collects the characteristic data of the crushed material and the critical values between two adjacent reaction stages using real-time monitoring data for each stage. Then, using the characteristic data as input and the critical values as output, a machine learning model is trained to predict the critical values based on the characteristic data. The resulting machine learning model can generalize to different characteristic data. For the characteristic data of each batch of crushed material, the machine learning model determines the critical value. When the real-time monitoring data reaches the critical value, the reaction stage is switched and the control parameters are adjusted.
[0052] The module defines the heat treatment process as three stages: pyrolysis, primary combustion, and secondary combustion. It defines the critical values for pyrolysis, primary combustion, and secondary combustion. Each experimental sample underwent a heat treatment process.
[0053] The method for extracting experimental samples included: using the mass of the crushed material processed in a single run by the high-temperature incineration equipment as the mass of a single sample; and extracting the crushed material from each batch according to the mass of the single sample. One sample was labeled as an experimental sample.
[0054] If data acquisition for large-scale high-temperature incineration equipment is too resource-intensive, a miniaturized model corresponding to the high-temperature incineration equipment can be built. The method for building the miniaturized model includes scaling down the industrial high-temperature incineration equipment to a predetermined scale to obtain a miniaturized model.
[0055] The benefits of using miniaturized models include: significantly lower material and energy consumption compared to industrial-scale high-temperature incineration equipment; precise control of key parameters such as temperature, oxygen supply, and residence time; easier detailed study and debugging of each reaction stage in the heat treatment process, providing data support and theoretical basis for optimizing industrial incineration processes; and real-time data acquisition of the heat treatment process and reactant conversion using high-precision sensors and monitoring instruments. The application of miniaturized models reduces experimental costs and energy consumption while providing reliable data support and theoretical basis for large-scale industrial applications.
[0056] The second acquisition module collects critical pyrolysis indicators during the pyrolysis stage.
[0057] The method for obtaining the pyrolysis critical index includes: monitoring the concentration of a preset gas 1 online at a preset frequency; when the rate of change of the concentration of the preset gas 1 is less than a preset threshold, the instantaneous concentration of the preset gas 1 is taken as the pyrolysis critical index.
[0058] The preset gases include carbon monoxide (CO), methane (CH4), and ethylene (C2H4).
[0059] During the pyrolysis stage, the organic components in the crushed material are continuously vaporized, and the concentrations of gases such as carbon monoxide (CO), methane (CH4), and ethylene (C2H4) continuously increase. When the concentration of the preset gas 1 tends to stabilize, the pyrolysis stage can be ended.
[0060] The third acquisition module collects critical indicators for initial combustion during the primary combustion stage.
[0061] The method for collecting the initial combustion critical index includes: monitoring the concentration of preset gas 2 online at a preset frequency; when the rate of change of the concentration of preset gas 2 is less than a preset threshold, the instantaneous concentration of preset gas 2 is used as the initial combustion critical index.
[0062] Preset gas two includes carbon monoxide (CO) and oxygen (O2). If it is oxygen (O2), then the rate of change of the concentration of preset gas two is replaced with the rate of change of the oxygen concentration difference between the inlet and outlet of the combustion chamber in the high-temperature incineration equipment.
[0063] In the initial combustion stage, combustible gases such as carbon monoxide (CO) continue to burn, and their concentration decreases continuously. As the combustible gases approach complete combustion, the oxygen concentration difference between the inlet and outlet of the combustion chamber tends to be 0.
[0064] The fourth acquisition module collects critical indicators for secondary combustion during the secondary combustion stage.
[0065] The method for collecting the critical index of the secondary combustion includes: monitoring the concentration of a preset gas 3 online at a preset frequency; when the rate of change of the concentration of the preset gas 3 is less than a preset threshold, the instantaneous concentration of the preset gas 3 is used as the critical index of the secondary combustion.
[0066] The presupposed gaseous component includes dioxin precursors. Dioxin precursors include 2,4,6-trichlorophenol (C6H3Cl3O) and benzene chloride (C6H5Cl).
[0067] During the secondary combustion stage, dioxin precursors are decomposed, and the concentration of the pre-selected gas 3 continuously decreases. Following the secondary combustion stage, in the flue gas quenching stage, the flue gas is cooled using a quench tower to prevent the regeneration of dioxins.
[0068] The online monitoring method for the concentrations of preset gas one, preset gas two, and preset gas three includes the use of an online gas analyzer. The technologies used in the online gas analyzer include non-dispersive infrared (NDIR), digital laser absorption spectroscopy (TDLAS), and Fourier transform infrared spectroscopy (FTIR).
[0069] The model training module trains a critical prediction model based on feature data and critical values, which is used to predict critical values using feature data.
[0070] The training method for the criticality prediction model includes: taking feature data as input and pyrolysis critical index, initial combustion critical index, and secondary combustion critical index as output; performing feature engineering operations on the input to obtain standardized input; performing feature engineering operations on the output to obtain standardized output; a standardized input and its corresponding standardized output form a set of training data, and all training data form a training dataset; dividing the training dataset into a training set and a validation set, training the selected machine learning model, and finally obtaining a criticality prediction model that can predict pyrolysis critical index, initial combustion critical index, and secondary combustion critical index using feature data.
[0071] Machine learning models can include LightGBM, XGBoost, and Deep Neural Networks (DNNs).
[0072] The control module uses a critical prediction model to predict critical values based on real-time feature data; it then switches between reaction stages based on the critical values; and adjusts the temperature and atmosphere control parameters at each reaction stage.
[0073] The conversion method for the reaction stages includes: in the pyrolysis stage, the concentration of preset gas one is monitored online at a preset frequency; when the concentration of preset gas one equals the pyrolysis critical index, the primary combustion stage begins; in the primary combustion stage, the concentration of preset gas two is monitored online at a preset frequency; when the concentration of preset gas two equals the primary combustion critical index, the secondary combustion stage begins; in the secondary combustion stage, the concentration of preset gas three is monitored online at a preset frequency; when the concentration of preset gas three equals the secondary combustion critical index, the secondary combustion stage ends.
[0074] The adjustment methods for control parameters include: During the pyrolysis stage, maintaining the temperature within a preset temperature range one, such as 400~800℃, ensuring the pyrolysis chamber in the high-temperature incinerator is in an oxygen-deficient or oxygen-free environment, where the oxygen content is, for example, <3%; During the primary combustion stage, maintaining the temperature within a preset temperature range two, such as 850~1100℃, which needs to be higher than the ignition point of the organic components, and introducing oxygen-enriched air or oxygen into the combustion chamber of the high-temperature incinerator, for example, controlling the excess air coefficient to 1.2~1.5; During the secondary combustion stage, maintaining the temperature within a preset temperature range three, such as 1100~1200℃, to completely decompose dioxin precursors, and introducing oxygen and natural gas or diesel into the secondary combustion chamber of the high-temperature incinerator. These control parameter adjustment methods are existing technologies and will not be elaborated further here.
[0075] This embodiment proposes a composite atmosphere control system for the gasification treatment of waste circuit boards, which effectively solves the problems in the prior art where the characteristic data fluctuates greatly during the gasification treatment of waste circuit boards, and the residence time and control parameters in the reaction stage are fixed and cannot adapt to the diversity of characteristic data.
[0076] Specifically, this system is implemented through the following methods:
[0077] By collecting characteristic data from the crushed waste circuit board material after pretreatment, the critical values of each reaction stage (pyrolysis, primary combustion, and secondary combustion) during the heat treatment process are obtained through online monitoring. The critical values of each reaction stage under any characteristic data are predicted by machine learning models, thereby achieving real-time control and dynamic adjustment.
[0078] Compared with existing technologies, this system has significant advantages:
[0079] First, this invention precisely collects characteristic data (such as average particle size and composition ratio) of the pre-treated crushed waste circuit board material, and combines this with online gas monitoring to capture critical values at each reaction stage during the heat treatment process in real time. By using a machine learning model to train and predict the collected data, it achieves accurate determination of the critical states at each reaction stage of pyrolysis, primary combustion, and secondary combustion, effectively avoiding the problems of insufficient or excessive reaction caused by fixed parameters in traditional processes. This not only significantly improves the control precision of the reaction process but also ensures stable and efficient operation even when there are significant differences between batches of waste circuit boards.
[0080] Secondly, by dynamically adjusting key process parameters such as temperature and oxygen supply, the system can achieve precise stage transition control during heat treatment. This control strategy, based on real-time gas concentration changes and characteristic data prediction, not only reduces energy waste and improves heat treatment efficiency, but also significantly inhibits the formation of harmful substances (such as dioxin precursors) during combustion, thus better meeting environmental protection requirements.
[0081] Furthermore, through pre-established machine learning models, not only can cross-batch process adaptive adjustments be achieved, but real-time feedback information can also be provided for equipment debugging and process optimization, effectively shortening the process debugging cycle and improving the overall safety and stability of equipment operation.
[0082] Finally, the implementation of this invention organically combines traditional experience-based control methods with advanced data-driven technology, making the control of each reaction stage in heat treatment more intelligent and precise. This not only improves energy utilization and reduces production costs but also significantly reduces harmful gas emissions during combustion, achieving the goal of efficient, environmentally friendly, and economical comprehensive treatment of waste circuit boards. Through this system, the industrial sector has taken a significant step forward in addressing problems such as inaccurate reaction stages, energy waste, and environmental pollution encountered in the treatment of waste circuit boards.
[0083] Example 2
[0084] See Figure 3As shown, this embodiment provides a method for controlling the composite atmosphere in the gasification treatment of waste circuit boards. The method includes:
[0085] Collect characteristic data from each batch of crushed material and extract the characteristics of each batch of crushed material. One experimental sample;
[0086] The heat treatment process is divided into a pyrolysis stage, a primary combustion stage, and a secondary combustion stage; the critical values for pyrolysis, primary combustion, and secondary combustion are defined as critical values. Each experimental sample underwent a heat treatment process.
[0087] During the pyrolysis stage, critical pyrolysis parameters are collected.
[0088] During the initial combustion stage, critical initiation indicators are collected.
[0089] During the secondary combustion stage, critical secondary combustion indicators are collected.
[0090] A critical prediction model is trained based on feature data and critical values, which is used to predict critical values using feature data.
[0091] Based on real-time feature data, a critical prediction model is used to predict critical values; the reaction stages are then transitioned according to the critical values; and temperature and atmosphere control parameters are adjusted at each reaction stage.
[0092] For any parts not mentioned in this application, existing technologies may be used or referenced.
[0093] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0094] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims.
Claims
1. A method for controlling the composite atmosphere in the gasification treatment of waste circuit boards, characterized in that, include: Collect characteristic data of each batch of crushed material and extract individual experimental samples of each batch of crushed material; the characteristic data includes average particle size and composition ratio. The composition ratio is the mass ratio of the metal part, glass fiber part and organic component part in the waste circuit board; The heat treatment process is divided into a pyrolysis stage, a primary combustion stage, and a secondary combustion stage; the critical values of pyrolysis, primary combustion, and secondary combustion are defined as critical values; and the heat treatment process is performed on each experimental sample. During the pyrolysis stage, critical pyrolysis parameters are collected. During the initial combustion stage, critical initiation indicators are collected. During the secondary combustion stage, critical secondary combustion indicators are collected. Methods for obtaining pyrolysis critical indicators, initial combustion critical indicators, and secondary combustion critical indicators include: The concentration of preset gas one is monitored online at a preset frequency; When the rate of change of the concentration of preset gas 1 is less than the preset threshold, the instantaneous concentration of preset gas 1 is used as the critical index for pyrolysis. The concentration of preset gas 2 is monitored online at a preset frequency; when the rate of change of the concentration of preset gas 2 is less than a preset threshold, the instantaneous concentration of preset gas 2 is used as the critical index for initial combustion. The concentration of the preset gas 3 is monitored online at a preset frequency; when the rate of change of the preset gas 3 concentration is less than the preset threshold, the instantaneous concentration of the preset gas 3 is used as the critical index for secondary combustion. A critical prediction model is trained based on feature data and critical values, which is used to predict critical values using feature data. Based on real-time feature data, a critical prediction model is used to predict critical values; the reaction stages are then transitioned according to the critical values; and temperature and atmosphere control parameters are adjusted at each reaction stage.
2. The composite atmosphere control method for waste circuit board gasification treatment according to claim 1, characterized in that, The methods for extracting experimental samples include: The mass of the crushed material processed in a single high-temperature incineration process is taken as the mass of a single sample. For each batch of crushed material, a sample of the crushed material is extracted according to the mass of the single sample and marked as an experimental sample.
3. The composite atmosphere control method for waste circuit board gasification treatment according to claim 1, characterized in that, Methods for switching reaction stages include: During the pyrolysis stage, the concentration of preset gas one is monitored online at a preset frequency. When the concentration of preset gas one equals the pyrolysis critical index, the primary combustion stage begins. During the primary combustion stage, the concentration of preset gas two is monitored online at a preset frequency. When the concentration of preset gas two equals the primary combustion critical index, the secondary combustion stage begins. During the secondary combustion stage, the concentration of preset gas three is monitored online at a preset frequency. When the concentration of preset gas three equals the secondary combustion critical index, the secondary combustion stage ends.
4. The composite atmosphere control method for waste circuit board gasification treatment according to claim 1, characterized in that, The preset gases include carbon monoxide (CO), methane (CH4), and ethylene (C2H4).
5. The composite atmosphere control method for waste circuit board gasification treatment according to claim 1, characterized in that, Preset gas 2 includes carbon monoxide (CO) and oxygen (O2); if it is oxygen (O2), then the rate of change of the concentration of preset gas 2 is replaced by the rate of change of the oxygen concentration difference between the inlet and outlet of the combustion chamber in the high-temperature incineration equipment.
6. The composite atmosphere control method for waste circuit board gasification treatment according to claim 1, characterized in that, The preset gas three includes dioxin precursors; dioxin precursors include 2,4,6-trichlorophenol (C6H3Cl3O) and chlorobenzene (C6H5Cl).
7. A composite atmosphere control system for the gasification treatment of waste circuit boards, characterized in that, The system for implementing the composite atmosphere control method for waste circuit board gasification treatment as described in claim 1 includes: The first acquisition module is used to collect the characteristic data of each batch of crushed material and extract each experimental sample of each batch of crushed material. The module is configured to divide the heat treatment process into a pyrolysis stage, a primary combustion stage, and a secondary combustion stage; the critical values of pyrolysis in the pyrolysis stage, primary combustion in the primary combustion stage, and secondary combustion in the secondary combustion stage are defined as critical values; and the heat treatment process is performed on each experimental sample. The second acquisition module acquires critical pyrolysis indicators during the pyrolysis stage; The third acquisition module collects the critical indicators for initial combustion during the primary combustion stage; The fourth data acquisition module collects critical parameters for secondary combustion during the secondary combustion stage. The model training module trains a critical prediction model based on feature data and critical values, which is used to predict critical values using feature data. The control module uses a critical prediction model to predict critical values based on real-time feature data; it then switches between reaction stages based on the critical values; and adjusts the temperature and atmosphere control parameters at each reaction stage.