Plasma gasification and fusion intelligent control system for solid waste treatment

Abstract

The invention provides a plasma gasification and melting intelligent control system for solid waste treatment, and relates to the technical field of intelligent control, and the system comprises an acquisition module which is used for acquiring material level distribution data of a plurality of different heights in a gasification furnace; the first determining module is used for determining a material level stability potential function according to the material level distribution data; the calculation module is used for calculating a vortex risk factor for describing the material level accumulation degree based on the material level stability potential function; the screening module is used for screening a material level abnormal area based on the vortex risk factors; the output module is used for outputting the local material level compensation amount in the material level abnormal area with the vortex risk factor smaller than the preset vortex risk factor as the target; the second determining module is used for determining plasma power based on the compensated material level distribution data; and the treatment module is used for treating the solid waste according to the plasma power. The problems of local accumulation, insufficient combustion and synthesis gas quality fluctuation are solved, and collaborative optimization of treatment efficiency and energy efficiency is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an intelligent control system for plasma gasification and melting in solid waste treatment. Background Technology

[0002] A gasifier is a device that uses high-temperature gasification technology to convert solid waste into gas. During the gasification process, under oxygen-deficient or limited oxygen conditions, the waste is converted into combustible gases, gaseous chemical substances, and solid residues through high-temperature pyrolysis. Gasifiers are widely used in waste treatment, energy production, and other fields.

[0003] The plasma gasification and melting process for solid waste treatment utilizes the high-temperature energy of plasma to efficiently convert solid waste into combustible gas, while simultaneously transforming residual materials into molten material, thus achieving waste reduction and resource recovery. By monitoring data such as temperature and pressure during the waste treatment process in real time and automatically adjusting the equipment's operating status based on the analysis results, the treatment process is ensured to be stable and efficient, reducing environmental pollution.

[0004] However, existing technologies generally lack the ability to monitor and adaptively control the dynamic distribution of material levels in solid waste treatment, making it difficult to accurately identify risk areas of material levels. This results in unreasonable plasma power distribution, low treatment efficiency, and problems such as local accumulation or incomplete combustion. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a plasma gasification and melting intelligent control system for solid waste treatment, which can solve the technical problems in the prior art that generally lack the ability to monitor and adaptively control the dynamic distribution of material level in solid waste treatment, making it difficult to accurately identify material level risk areas, resulting in unreasonable plasma power distribution, low treatment efficiency, and problems such as local accumulation or incomplete combustion.

[0006] This invention proposes an intelligent control system for plasma gasification and melting in solid waste treatment, applied to a gasification furnace. The system includes: The data acquisition module is used to collect data on the distribution of material levels at multiple different heights within the gasifier. The first determining module is used to determine the material level stability potential function based on the material level distribution data; The calculation module is used to calculate the vortex risk factor describing the degree of material accumulation based on the material level stability potential function; The screening module is used to screen areas with abnormal material levels based on vortex risk factors. The output module is used to output the local material level compensation amount in areas with abnormal material levels, with the goal of the vortex risk factor being less than the preset vortex risk factor. The second determining module is used to determine the plasma power based on the compensated material level distribution data; The processing module is used to process solid waste according to plasma power.

[0007] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this embodiment of the invention, by real-time acquisition of multi-height material level distribution data within the gasifier, combined with dynamic calculation of the material level stability potential function and vortex risk factor, abnormal material accumulation areas are accurately identified. Based on the risk factor threshold, local material level compensation is output, and plasma power allocation is simultaneously optimized. The multi-physics coupling effect of the material field, thermal field, and flow field is transformed into a quantifiable and controllable potential function model. Early warning and targeted intervention for abnormal areas are achieved through nonlinear mapping of the vortex risk factor. This avoids the limitations of traditional single-parameter threshold control. With dynamic compensation and adaptive power adjustment as the core, it significantly improves the uniformity of material layer distribution and thermal field stability, thereby solving problems such as local accumulation, incomplete combustion, and syngas quality fluctuations, achieving synergistic optimization of processing efficiency and energy efficiency. Attached Figure Description

[0008] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0009] Figure 1 This is a schematic diagram of the structure of a plasma gasification and melting intelligent control system for solid waste treatment provided in an embodiment of the present invention. Detailed Implementation

[0010] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions 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, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0011] The plasma gasification and melting intelligent control system for solid waste treatment provided in this invention will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0012] Reference manual attached Figure 1The diagram shows a schematic of the structure of a plasma gasification and melting intelligent control system for solid waste treatment provided in an embodiment of the present invention.

[0013] This invention provides an intelligent control system for plasma gasification and melting of solid waste, applied to a gasification furnace. The system includes: Acquisition module 1 is used to collect material level distribution data at multiple different heights inside the gasifier.

[0014] Among them, the material level distribution data refers to the filling status and dynamic distribution information of the material in the vertical space (different height layers) of the gasifier.

[0015] In one possible implementation, the acquisition module 1 is specifically used for: Material level distribution data is collected by a high-frequency radar level gauge. Specifically, the material level distribution data includes multiple material level heights at different heights and circumferential angles within the gasifier.

[0016] Understandably, high-frequency radar level gauges can acquire three-dimensional material level distribution data of gasifiers in real time with high precision and non-contact. Combined with circumferential angle measurement, they can accurately capture local accumulation and vortex dynamics, providing a reliable basis for stability control and significantly improving monitoring accuracy and system response speed under complex working conditions.

[0017] The first determining module 2 is used to determine the material level stability potential function based on the material level distribution data.

[0018] The material level stability potential function is a function that quantifies the stability of material distribution at different heights within the gasifier through mathematical modeling. Its core is the calculation of the material level gradient, curvature, and local energy distribution, reflecting the dynamic equilibrium state of material accumulation. The material level stability potential function can quantify the dynamic equilibrium state of the material layer in real time and accurately identify potential vortex risk areas.

[0019] In one possible implementation, the first determining module 2 is specifically used for: Calculate the material level tilt factor and material level curvature factor based on the material level distribution data.

[0020] In one possible implementation, the material level tilt factor is the ratio of the gradient modulus of the material level distribution data to the gradient modulus of the maximum material level distribution data in the gasifier, and the material level curvature factor is the ratio between the gradient of the material level distribution data and the Laplacian operator corresponding to the gradient of the maximum material level distribution data in the gasifier.

[0021] The specific formulas for calculating the material level tilt factor and the material level curvature factor are as follows: .

[0022] .

[0023] .

[0024] in, Indicates the circumferential angle of the gasifier. Represents a time variable. Indicates the height of the gasifier z Circumferential angle of gasifier and time variables Corresponding material level distribution data, This represents the gradient magnitude of the material level distribution data. This represents the gradient modulus of the maximum material level distribution data in the gasifier. This indicates finding the partial derivative. and These represent the Laplace operators corresponding to the gradient of the material level distribution data and the gradient of the maximum material level distribution data in the gasifier, respectively. and Respectively represent and and The relevant material level tilt factor and material level curvature factor.

[0025] By normalizing the gradient magnitude and Laplacian operator, the relative changes in the tilt and curvature of the material layer can be quantified, and multi-dimensional feature extraction can be standardized to achieve highly sensitive identification of anomalies such as local vortices and collapses, thereby improving the accuracy of stability assessment and the pertinence of compensation control under dynamic working conditions.

[0026] By nonlinearly coupling the material level tilt factor and the material level curvature factor, the material level stability potential function is obtained.

[0027] The specific formula for calculating the material level stability potential function is as follows: .

[0028] in, Indicates and and The relevant material level stability potential function value.

[0029] It should be noted that the material level stability potential function, by nonlinearly coupling the material level tilt factor (gradient magnitude normalization) and the curvature factor (Laplacian operator normalization), combined with the dynamic rate of change term, comprehensively quantifies the local tilt degree, curvature characteristics, and temporal evolution trend of the material level distribution, thereby accurately characterizing the stability of the material layer. Simultaneously, it captures the spatial gradient (tilt) and curvature (convexity / concavity) characteristics of the material level, and responds to the changing trend in real time through the dynamic derivative term, which can significantly improve the accuracy of identifying anomalies such as vortex accumulation and local collapse.

[0030] Calculation module 3 is used to calculate the vortex risk factor describing the degree of material accumulation based on the material level stability potential function.

[0031] Among them, the vortex risk factor is an indicator that quantifies the possibility of uneven material accumulation or vortex formation in a local area by analyzing the gradient and curvature changes of the material level stability potential function. It is used to identify potential vortex risk areas. The vortex risk factor can accurately locate high accumulation risk areas, provide early warning of vortex formation trends, provide dynamic threshold basis for local compensation, effectively avoid oxygen-deficient / oxygen-excessive combustion, and significantly improve syngas quality and system energy efficiency.

[0032] In one possible implementation, the computing module 3 is specifically used for: The circumferential cross-section of the gasifier is divided into multiple evenly distributed sector-shaped control units.

[0033] Calculate the circulation integral of the material level stability potential function along the circumferential closed boundary of the sector control unit.

[0034] The formula for calculating the circulation integral is as follows: .

[0035] .

[0036] in, and These represent the circumferential and radial components of the material level stability potential function value, respectively. Indicates the radius of the gasifier. Indicates the circumferential angle width of the sector control unit. Represents the path infinitesimal components, The closed curve corresponding to the sector control unit C The points, Indicates and and The relevant material level stability potential function value, Represents a closed curve C A directed infinitesimal vector, This indicates the initial circumferential angle corresponding to the sector control unit. Representing the angle element, Represents a radial infinitesimal element. and These represent the inner and outer radii of the sector control unit, respectively.

[0037] The circumferential component represents the rate of change of the potential function in the circumferential direction (the direction around the central axis of the gasifier), i.e., the gradient of the potential function along the circumferential angle. It reflects the non-uniformity of the material layer distribution in the circumferential direction (such as local vortices, accumulation, or depressions). For example, if the circumferential gradient is large in a certain area, it indicates that the stability of the material layer at that location fluctuates drastically in the circumferential direction. The radial component represents the rate of change of the potential function in the radial direction (the radial direction from the center to the edge of the gasifier), i.e., the gradient of the potential function along the radius. It reflects the gradient characteristics of the material layer distribution in the radial direction. For example, abrupt changes in the radial gradient may indicate abnormal accumulation or voids in the material near the furnace wall or in the central region, affecting the uniformity of the thermal field.

[0038] It should be noted that by dividing the fractal control unit and calculating circulation integrals, this method can accurately capture the rotational effect of the material level stability potential function on a closed path, quantifying the intensity and distribution characteristics of local vortices. Combining the integration of circumferential and radial gradient components, it dynamically characterizes the rotational trend and energy distribution of abnormal areas in the material layer, significantly improving the sensitivity and spatial resolution for identifying complex material level vortices, local collapses, and other non-uniform phenomena. This provides a high-spatiotemporal-precision mathematical basis for subsequent compensation control, effectively avoiding the lag and blind-zone problems of traditional single-point monitoring.

[0039] The vortex risk factor for each sector control unit is calculated by combining circulation integrals.

[0040] The specific formula for calculating the vortex risk factor is as follows: .

[0041] .

[0042] in, Indicates and The vortex risk factor of the relevant sector control unit, This represents the area of ​​a single sector control unit. This represents the circumferential cross-sectional area of ​​the gasifier. This indicates the total number of sector control unit divisions.

[0043] It should be noted that by using the normalized circulation integral and sector area, the vortex risk factor can achieve standardized assessment of vortex intensity in regions of different sizes, accurately quantify the degree of local anomalies, and, combined with dynamic zoning control, significantly improve the system's real-time identification and compensation accuracy of material level vortices, thus avoiding combustion imbalance caused by local accumulation.

[0044] Specifically, by dividing the circumferential cross-section of the gasifier into sector-shaped units, the local vortex effect is quantified based on the circulation integral of the material level stability potential function. The circulation integral reflects the cumulative rotation of the potential function gradient along a closed path; the larger its absolute value, the more unstable the material level distribution in that region, and the higher the vortex risk. After normalizing the sector area, the vortex risk factor can accurately characterize the degree of anomaly in each unit. Through mathematical modeling of the fractal control unit and circulation integral, the complex vortex characteristics of the three-dimensional material level distribution are transformed into quantifiable local risk indicators, significantly improving the accuracy and response speed of identifying anomalies such as local accumulation and collapse. This provides a high spatiotemporal resolution decision-making basis for dynamic compensation, effectively avoiding oxygen-deficient / oxygen-excess combustion, and improving syngas quality and system energy efficiency.

[0045] Screening module 4 is used to screen abnormal material level areas based on vortex risk factors.

[0046] The abnormal material level zone is the area where material accumulates. The gasification reaction relies on a uniform material layer distribution to provide a stable thermal environment. The presence of the abnormal material level zone will directly disrupt this balance, leading to localized oxygen-deficient combustion (producing incomplete products such as CO) or oxygen-over-oxygen combustion (producing excessive CO). ), syngas quality fluctuations.

[0047] In one possible implementation, the filtering module 4 is specifically used for: The sector control unit corresponding to the risk factor of the vortex is identified as the abnormal material level area.

[0048] Among them, the preset vortex risk factor is a threshold determined through experiments, historical data statistics or theoretical models during system operation, used to distinguish between normal and abnormal material level distributions.

[0049] It should be noted that those skilled in the art can set the magnitude of the preset vortex risk factor according to actual needs, and this invention does not limit this.

[0050] Output module 5 is used to output local material level compensation amount in areas with abnormal material level, with the goal of the vortex risk factor being less than the preset vortex risk factor.

[0051] Local material level compensation refers to the numerical target of dynamically increasing / decreasing the material filling amount to balance the material layer distribution in areas with abnormal material levels identified within the gasifier, by adjusting the feed rate or distribution device. Its core purpose is to eliminate local instability caused by excessive vortex risk factors. Local compensation can precisely intervene in the material distribution in abnormal areas, dynamically eliminate vortex risks, and avoid local combustion imbalances.

[0052] In one possible implementation, output module 5 is specifically used for: By combining the vortex risk factor and the preset vortex risk factor, the compensation priority for each abnormal area of ​​the material level is determined.

[0053] The specific formula for calculating compensation priority is as follows: .

[0054] in, This indicates a preset vortex risk factor. Indicates circumferential angle The corresponding vortex risk factor for the sector control unit, Indicates circumferential angle The corresponding rate of change of the vortex risk factor, Indicates circumferential angle The compensation priority of the corresponding sector control unit.

[0055] It should be noted that the compensation priority is calculated by combining the vortex risk factor and the rate of change to dynamically quantify the deterioration trend and risk level of the abnormal area, and prioritize the response to high-risk and rapidly deteriorating areas, so as to achieve precise allocation of compensation resources, significantly improve the system's response speed and processing efficiency to sudden anomalies, and avoid combustion imbalance and syngas quality fluctuations caused by local accumulation.

[0056] Based on the compensation priority, the instantaneous compensation amount of local material level is allocated according to the negative direction of the gradient of the material level stability potential function.

[0057] The specific formula for calculating the instantaneous compensation amount of local material level is as follows: .

[0058] in, Indicates the reference fabric rate. This represents the natural exponential function. This represents the gradient of the potential function for material level stability. This represents the gradient of the critical level stability potential function. Represents the hyperbolic tangent function. Indicates circumferential angle The corresponding area is t Instantaneous compensation amount of local material level at a given moment.

[0059] The gradient of the critical level stability potential function can be set as a preset proportion of the gradient of the level stability potential function. Optionally, the preset proportion can be set to 0.8. It should be noted that those skilled in the art can set the size of the preset proportion according to actual needs.

[0060] It should be noted that by dynamically allocating the compensation amount in the negative direction of the gradient of the material level stability potential function, combined with the exponential decay function to suppress overcompensation in the high gradient region, and using the hyperbolic tangent function to nonlinearly correlate the compensation intensity with the priority, the spatiotemporal adaptive control of the compensation amount can be achieved, which significantly improves the response accuracy and system stability in the local abnormal region and avoids secondary imbalance caused by overcompensation or undercompensation.

[0061] The dwell time of the rotating distributor of the gasifier in different circumferential angular directions is determined by combining the instantaneous compensation amount of local material level.

[0062] In one possible implementation, the dwell time is specifically the product of the baseline dwell time and the instantaneous compensation amount of the local material level, divided by the baseline material distribution rate.

[0063] Specifically, the baseline dwell time is the standard material feeding time of the material feeder in each direction when there are no abnormalities.

[0064] It should be noted that this dwell time formula dynamically links the compensation amount with the reference rate, enabling precise allocation of the fabric distributor's dwell time in abnormal areas. This ensures that the compensation amount matches the demand while avoiding excessive dwell time that could lead to decreased efficiency, thus significantly improving compensation accuracy and system operating efficiency.

[0065] The cumulative compensation amount of the instantaneous compensation amount of the local material level within the corresponding residence time is output as the local material level compensation amount.

[0066] Specifically, the output module dynamically calculates compensation priorities (combining vortex risk factors and rates of change) and allocates compensation amounts based on the negative direction of the gradient of the material level stability potential function. It balances compensation intensity and gradient sensitivity using exponential decay functions and hyperbolic tangent functions, ultimately achieving precise local material level compensation through dynamic adjustment of the dwell time of the rotating distributor. This method achieves spatiotemporal adaptability of the compensation strategy through multidimensional mathematical modeling (priority, gradient direction, and dynamic compensation function), prioritizing responses to high-risk areas and dynamically suppressing vortex evolution. This significantly improves the uniformity of material layer distribution and thermal field stability, reduces syngas quality fluctuations, and avoids overcompensation or response lag through coordinated control of dwell time and cumulative compensation amount, achieving efficient and precise intelligent regulation.

[0067] The second determining module 6 is used to determine the plasma power based on the compensated material level distribution data.

[0068] Plasma power refers to the plasma energy input intensity dynamically adjusted according to the compensated material level distribution. It is used to match the pyrolysis and melting requirements of materials in the gasifier, ensuring a dynamic balance between the reaction temperature field and gasification efficiency. By dynamically matching the compensated material level distribution to optimize plasma power, the uniformity of the thermal field can be precisely controlled, energy consumption can be reduced, and the quality of syngas can be improved, significantly enhancing the system's adaptability and stability to complex operating conditions.

[0069] In one possible implementation, the second determining module 6 is specifically used for: Obtain the temperature of the melting zone inside the gasifier.

[0070] The temperature decision coefficient is determined by combining the melting zone temperature and the compensated material level distribution data.

[0071] The specific formula for calculating the temperature decision coefficient is as follows: .

[0072] in, Represents the temperature decision coefficient. This indicates the compensated material level height. Indicates the temperature of the melting zone. and These represent the minimum and maximum material levels specified in the gasifier's user manual, respectively. This indicates the optimal melting zone reaction temperature specified in the gasifier's user manual.

[0073] The temperature decision coefficient dynamically couples the synergistic effects of material level height and melting zone temperature through the product of a normalized level term and a temperature deviation penalty term. The level term reflects the thickness of the protective layer, while the temperature term quantifies the degree of thermodynamic deviation through an exponential function, enabling sensitive detection and comprehensive assessment of level anomalies or temperature imbalances. By coupling these two terms, multi-field information is condensed into dimensionless coefficients, balancing control accuracy and engineering practicality. It retains the physical essence of the thermal conductivity scaling law while achieving a balance between sensitivity to small deviations and strong robustness to extreme anomalies through exponential decay characteristics, avoiding the misjudgment defects of traditional single-parameter control. Furthermore, its dimensionless design allows it to transcend the limitations of specific furnace parameters, possessing universal control capabilities.

[0074] The plasma power is determined by the temperature decision coefficient based on the scaling law of the heat conduction equation.

[0075] The specific formula for calculating plasma power is as follows: .

[0076] in, Indicates plasma power. This indicates the plasma reference power.

[0077] Specifically, this method compensates for the synergistic effect of material level height and melting zone temperature through dynamic coupling compensation of temperature decision coefficients. The material level term is normalized to reflect the thickness of the protective layer, while the temperature term quantifies the degree of thermodynamic deviation using an exponential function. The product of these two terms forms a dimensionless coefficient. Combined with the thermal conductivity scaling law, multi-field information is converted into plasma power adjustment quantities, achieving adaptive synergistic control of the thermal field and material layer.

[0078] Through multi-parameter coupling and dimensionless design, the requirements for material level protection and temperature optimization can be precisely balanced, avoiding the misjudgment defects of single-parameter control. The exponential function combines sensitivity to small deviations with robustness to extreme anomalies, significantly improving the system's adaptability and stability under complex operating conditions. The scaling law mapping preserves the physical essence of heat conduction, taking into account both engineering practicality and universality, and is suitable for the dynamic control needs of different furnace types.

[0079] Processing module 7 is used to process solid waste according to plasma power.

[0080] In one possible implementation, it also includes: The repeat module 8 is used to repeatedly collect data from the module and process the module at a preset cycle until the preset time is reached or the solid waste is completely processed.

[0081] It should be noted that those skilled in the art can set the size of the preset cycle according to actual needs, and this invention does not limit this.

[0082] In practical applications, this system acquires real-time three-dimensional material level distribution data within the gasifier using high-frequency radar. Combining this with the material level stability potential function (quantifying the material layer gradient, curvature, and dynamic changes) and the vortex risk factor (assessing local vortex intensity based on circulation integral), it accurately identifies abnormal accumulation areas. Through compensation priority calculation and a gradient negative direction compensation strategy, it dynamically adjusts the residence time and compensation amount of the rotating material distributor, achieving spatiotemporal adaptive control of the material layer distribution. Simultaneously, by combining the molten zone temperature and compensated material level data, it dynamically optimizes plasma power using temperature decision coefficients and the heat conduction scaling law, achieving coordinated control of the thermal field and the material layer.

[0083] By coupling multiple parameters (level, temperature, gradient) and mathematical modeling (nonlinear potential function, circulation integral, scaling law mapping), intelligent regulation from anomaly identification to compensation control is achieved, significantly improving the uniformity of the material bed and the stability of the thermal field. The compensation algorithm using exponential and hyperbolic tangent functions balances sensitivity and robustness, avoiding overcompensation or response lag. Dimensionless design and scaling law mapping make the system universally applicable, adaptable to different furnace types and operating conditions, reducing energy consumption and improving syngas quality, effectively suppressing anaerobic / superaerobic combustion, and significantly enhancing the efficiency and environmental friendliness of solid waste treatment.

[0084] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this embodiment of the invention, by real-time acquisition of multi-height material level distribution data within the gasifier, combined with dynamic calculation of the material level stability potential function and vortex risk factor, abnormal material accumulation areas are accurately identified. Based on the risk factor threshold, local material level compensation is output, and plasma power allocation is simultaneously optimized. The multi-physics coupling effect of the material field, thermal field, and flow field is transformed into a quantifiable and controllable potential function model. Early warning and targeted intervention for abnormal areas are achieved through nonlinear mapping of the vortex risk factor. This avoids the limitations of traditional single-parameter threshold control. With dynamic compensation and adaptive power adjustment as the core, it significantly improves the uniformity of material layer distribution and thermal field stability, thereby solving problems such as local accumulation, incomplete combustion, and syngas quality fluctuations, achieving synergistic optimization of processing efficiency and energy efficiency.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A plasma gasification and melting intelligent control system for solid waste treatment, characterized in that, Applied to gasifiers, the system includes: The data acquisition module is used to collect data on the distribution of material levels at multiple different heights within the gasifier. The first determining module is used to determine the material level stability potential function based on the material level distribution data; The calculation module is used to calculate the vortex risk factor describing the degree of material accumulation based on the material level stability potential function. The screening module is used to screen areas with abnormal material levels based on the vortex risk factor. The output module is used to output a local material level compensation amount in the abnormal material level area, with the vortex risk factor being less than a preset vortex risk factor as the target. The second determining module is used to determine the plasma power based on the compensated material level distribution data; A processing module for processing the solid waste according to the plasma power.

2. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 1, characterized in that, The acquisition module is specifically used for: The material level distribution data is collected by a high-frequency radar level gauge, wherein the material level distribution data specifically refers to multiple material level heights at different heights and different circumferential angles within the gasifier.

3. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 1, characterized in that, The first determining module is specifically used for: Based on the material level distribution data, calculate the material level tilt factor and the material level curvature factor; The material level tilt factor and the material level curvature factor are nonlinearly coupled to obtain the material level stability potential function.

4. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 3, characterized in that, The material level tilt factor is the ratio of the gradient modulus of the material level distribution data to the gradient modulus of the maximum material level distribution data in the gasifier, and the material level curvature factor is the ratio between the gradient of the material level distribution data and the Laplacian operator corresponding to the gradient of the maximum material level distribution data in the gasifier.

5. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 1, wherein the calculation module is specifically used for: The circumferential cross-section of the gasifier is divided into multiple evenly distributed sector-shaped control units; Calculate the circulation integral of the material level stability potential function along the circumferential closed boundary of the sector control unit; The vortex risk factor for each sector control unit is calculated by combining the circulation integral.

6. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 1, characterized in that, The filtering module is specifically used for: The sector control unit corresponding to the risk factor of the preset vortex is identified as the abnormal material level area.

7. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 1, characterized in that, The output module is specifically used for: By combining the vortex risk factor and the preset vortex risk factor, the compensation priority for each of the abnormal material level regions is determined; Based on the aforementioned compensation priority, the instantaneous compensation amount for local material level is allocated according to the negative direction of the gradient of the material level stability potential function. The dwell time of the rotating distributor of the gasifier in different circumferential angular directions is determined by combining the instantaneous compensation amount of the local material level. The cumulative compensation amount of the instantaneous compensation amount of the local material level within the corresponding residence time is output as the local material level compensation amount.

8. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 7, characterized in that, The dwell time is specifically the product of the baseline dwell time and the instantaneous compensation amount of the local material level, divided by the baseline material distribution rate.

9. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 1, characterized in that, The second determining module is specifically used for: To obtain the temperature of the melting zone inside the gasifier; The temperature decision coefficient is determined by combining the melting zone temperature and the compensated material level distribution data. The plasma power is determined by the temperature decision coefficient based on the scaling law of the heat conduction equation.

10. The intelligent control system for plasma gasification and melting of solid waste treatment according to claim 1, characterized in that, Also includes: The repeat module is used to repeat the acquisition module to the processing module at a preset period until the preset time is reached or the solid waste is completely processed.

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