Multi-source solid waste intelligent proportioning and homogenization pretreatment method based on assay data

Abstract

This invention relates to a method for intelligent proportioning and homogenization pretreatment of multi-source solid waste based on laboratory data, belonging to the field of solid waste resource utilization technology. The method includes: performing correlation analysis on the component correlation relationships among the multi-source waste laboratory data; ranking the solid waste components in the proportioning constraint parameter set according to homogenization sensitivity indicators to generate waste proportioning adaptation adjustment parameters; calculating the waste gradient fusion value of the non-steady-state diffusion proportions among solid wastes based on the homogenization migration driving function, and outputting a solid waste proportioning control curve; analyzing the resource reaction window during the solid waste homogenization stage, selecting a waste resource utilization processing mode, simulating and modeling the solid waste feeding and exchange process, determining the clustering boundary of the waste resource utilization processing mode, and generating a proportioning process adaptation interval; and adaptively adjusting the waste gradient fusion value based on solid waste treatment feedback data to generate a solid waste homogenization proportioning scheme.

Description

Technical Field

[0001] This invention belongs to the field of solid waste resource utilization technology, specifically relating to a method for intelligent proportioning and homogenization pretreatment of multi-source solid waste based on laboratory data. Background Technology

[0002] In the modern building materials industry, the greening, intelligentization, and efficiency transformation of the production process of building bricks, as a basic structural and enclosure material, is the core driving force for promoting the high-quality development of the industry. Traditional building brick production relies heavily on extensive management. From raw material storage and chemical testing to drying, batching and mixing, and then to high-temperature sintering and finished product inspection, each stage generally suffers from problems such as low automation, high energy consumption, weak pollution emission control, and insufficient product quality stability.

[0003] On the raw material side, traditional separate storage lacks precise control, easily leading to fluctuations in raw material moisture content and composition, affecting the accuracy of subsequent batching. The drying and mixing processes rely on manual experience, making it difficult to achieve homogenization under constant temperature and humidity conditions, resulting in fluctuations in the strength and density of the green body, creating potential quality hazards for subsequent sintering. In the forming stage, the automation level of processes such as vacuum extrusion and strip / green cutting is insufficient, and the recycling efficiency of semi-finished products and scraps is low, causing raw material waste and increased costs. As the core node for energy consumption and emissions, the sintering stage is often affected by traditional kilns employing extensive heating methods with insufficient precision in temperature and atmosphere field control, easily leading to uneven brick sintering and substandard strength. It also generates large amounts of sulfur- and dust-containing waste gas; if desulfurization, denitrification, and electrostatic dust removal facilities operate inefficiently, it will put significant pressure on the surrounding ecological environment.

[0004] Furthermore, in traditional production processes, finished product inspection relies on manual sampling, which is inefficient and has a high rate of missed inspections. The lack of closed-loop management in the disposal of defective products easily leads to secondary waste. The application of energy-saving processes such as waste heat recovery and dry-wet refining is insufficient, resulting in a long-term low level of energy utilization efficiency. As national environmental protection regulations become increasingly stringent, the shortcomings of traditional processes in terms of energy consumption, emissions, and quality stability are becoming increasingly apparent, making it difficult to meet the demands of modern construction projects for high-performance, low-carbon building materials.

[0005] Therefore, there is an urgent need to construct a smart proportioning and homogenization pretreatment method for multi-source solid waste based on laboratory data, such as... Figure 3 As shown, by introducing intelligent equipment such as robotic brick stacking, intelligent packaging, and waste heat recovery, combined with deep waste gas treatment and closed-loop recycling technology for defective products, the entire production process can be automated, decarbonized, and quality controlled, promoting the upgrading of building brick manufacturing from traditional extensive to green and intelligent, and providing solid support for the sustainable development of the construction industry. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a method for intelligent proportioning and homogenization pretreatment of multi-source solid waste based on laboratory data. The objective of this invention can be achieved through the following technical solutions: S1: Obtain multi-source waste test data, construct a solid waste component constraint system, perform correlation analysis on the component correlation relationship between the multi-source waste test data, obtain the ratio constraint parameter set of coupling state of different waste constraint components, and sort the solid waste components in the ratio constraint parameter set according to the ratio balance domain partitioning algorithm to generate waste ratio adaptation adjustment parameters. S2: Using the morphology and reaction changes of solid waste components before homogenization as input variables, a solid waste intelligent ratio control model is established. Based on the homogenization migration driving function, the waste gradient fusion value of the non-steady-state diffusion ratio between solid wastes is calculated, and the solid waste ratio control curve is output. S3: Based on the solid waste ratio control curve, the resource reaction window of the solid waste homogenization stage is analyzed. According to the waste ratio adaptation adjustment parameters, the waste resource utilization processing mode is selected. The solid waste feeding and exchange process is simulated and modeled. The phase transformation state vector of multi-source solid waste in the exchange process is extracted. The clustering boundary of the waste resource utilization processing mode is determined through the solid waste state critical discrimination mechanism to generate the ratio process adaptation range. S4: In the simulation modeling process, the gradient fusion value of the waste material is adaptively adjusted according to the solid waste treatment feedback data to obtain the solid waste treatment mapping space. The solid waste resource reflection matching weight is set in combination with the ratio process adaptation interval, and the material organization homogeneity of the solid waste components is calibrated to generate a solid waste homogenization ratio scheme.

[0007] Specifically, the method for constructing the solid waste component constraint system is as follows: multi-source waste test data is used as the feature input set in the solid waste component constraint system. The multi-source waste test data includes information on the proportion of impurity content and reaction transformation characteristics. Based on the solid waste component balance rule, the coupling relationship between different waste components is constrained, component combination regions that meet the homogenization reaction conditions are screened, and a solid waste component constraint system that matches the solid waste ratio state is constructed.

[0008] Specifically, the generation process of the ratio constraint parameter set is as follows: based on the continuity of solid waste coupling and transmission under adjacent processing nodes, the component migration gradient is calculated, and the ratio constraint is determined on the component migration gradient. When a nonlinear abrupt change is detected in the constraint relationship between the reaction characteristics of the target node and its neighboring nodes, a feasible region backtracking search is performed to obtain the ratio constraint parameter set.

[0009] Specifically, the waste ratio adaptation adjustment parameter, as a dynamic ratio constraint factor, includes component migration sensitivity and ratio reaction gain weight. It modulates the waste gradient fusion value output by the homogenization migration driving function, suppresses highly sensitive solid waste components, enhances low-sensitive solid waste components, and maintains the solid waste structure rearrangement driving potential field during the ratio evolution process.

[0010] Specifically, the intelligent solid waste proportioning and control model includes a solid waste component evolution structure and a homogenization decision-making and adjustment structure; The solid waste component evolution structure is used to perform mechanistic analysis on the waste state of the solid waste component constraint system before ratio control, reselect the migration and change of solid waste components, and establish a solid waste evolution mapping relationship between ratio change and system reaction behavior. The homogenization decision-making and adjustment structure is used to actively interfere with the proportion homogenization mode based on the solid waste evolution mapping relationship, embed the waste proportion adaptation adjustment parameter into the solid waste treatment mapping space, and apply convergence domain constraints in the proportion component evolution process, so that the proportion change is always within the feasible region of the target reaction window, maintain the solid waste conversion contribution ratio of the waste proportion adaptation adjustment parameter under the influence of material fluctuation, and output the solid waste proportion control curve.

[0011] Specifically, the process of calculating the waste gradient fusion value by the homogenization migration driving function is as follows: based on the energy level difference between each processing node, query the solid waste structure rearrangement driving potential field, perform nonlinear transfer allocation of the component participation weights of different waste processing nodes, and combine the solid waste internal state transition threshold and the tissue component relaxation time to generate the waste gradient fusion value by taking the solid waste homogenization state as the compression ratio distribution value.

[0012] Specifically, the solid waste ratio control curve uses the solid waste conversion response index as the vertical control characteristic and the solid waste gradient ratio as the horizontal ratio evolution variable to establish a ratio conversion structure. The waste gradient fusion value is then subjected to time-series expansion to obtain the ratio evolution state sequence, and the solid waste conversion gain parameter is extracted to reflect the contribution ratio of the solid waste ratio of adjacent conversion layers.

[0013] Specifically, the analysis process of the resource response window in the solid waste homogenization stage is as follows: based on the time series and solid waste level, the waste component response data is reconstructed in layers, the component change rate and cumulative effect of each waste mixing node in the continuous treatment cycle are quantified, the stability index of the proportioning components is generated, and based on the solid waste state critical discrimination mechanism, the time series response of the mixing nodes of adjacent solid waste layers is jointly constrained.

[0014] Specifically, the solid waste state criticality discrimination mechanism performs time-series analysis on the solid waste ratio distribution value based on the stability index of the ratio components, extracts the ratio evolution characteristics of high-priority reaction areas, calculates the conversion failure risk probability of each area based on the preset local reaction failure benchmark, and formulates adjustment strategies for conversion mode and ratio homogenization efficiency based on the conversion failure risk probability, and performs cluster boundary determination on the waste resource processing mode.

[0015] Specifically, the process matching range is used as the state reference benchmark domain in the solid waste intelligent ratio control model. It constrains the process feasibility of the component organization state and phase transformation mode of multi-source solid waste in the pre-homogenization stage. By limiting the ratio transformation matching degree of the solid waste component constraint system, the solid waste ratio control process is transformed from single component standard control to dynamic range constraint control of the multi-source coupled state feasible domain.

[0016] Specifically, the solid waste treatment mapping space includes a material state characterization layer and a process conversion mapping layer; The material state characterization layer: structural coupling deconstructs the physicochemical state of multi-source solid waste in the pre-homogenization stage, constructs a multi-source coupled state feature set, which includes component ratio stability features and material organization homogeneity features, and obtains the state vectorization expression basis of solid waste material state in high-dimensional mechanism feature space based on the migration and distribution law of waste components. The process conversion mapping layer introduces a material migration driving factor, establishes a state vectorization expression basis, defines the conversion mapping relationship between process response behaviors, and divides the corresponding process behavior response regions in the solid waste treatment mapping space according to the solid waste homogenization stability margin.

[0017] Specifically, the method for generating the solid waste homogenization ratio scheme is as follows: based on the solid waste treatment mapping space, the homogeneity of the material organization is calibrated, the ratio characteristics of the high homogeneity area are extracted, and combined with the preset homogenization failure benchmark, the homogenization ratio risk probability of each area is calculated. Based on the homogenization ratio risk probability, an adjustment strategy for the solid waste ratio components and treatment frequency is formulated to generate the solid waste homogenization ratio scheme.

[0018] The beneficial effects of this invention are as follows: This invention constructs a data-driven intelligent proportioning and homogenization pretreatment control method for multi-source solid waste. This method achieves precise analysis and dynamic adjustment of the compositional differences, reaction characteristics, and structural organization of solid waste from complex sources. It enables different types of solid waste to undergo synergistic homogenization and reaction potential matching before entering resource utilization processes, thereby significantly improving the resource substitution ratio and finished product performance stability of solid waste in the preparation of building materials such as sintered bricks. Furthermore, relying on an intelligent control method that couples a mechanistic evolution model and an adaptive decision-making and adjustment model, a novel intelligent solid waste batching and homogenization treatment device system for continuous production is constructed, improving material efficiency. By improving the homogeneity of the organization and the controllability of the production process, a digital and model-based solid waste utilization process mode that conforms to the characteristics of new quality productivity is formed. At the same time, by constraining the proportion of highly volatile and harmful components and controlling the reaction window, the risk of harmful gas emissions and energy consumption fluctuations during the sintering process are effectively reduced, the synergistic level of solid waste reduction, harmlessness and resource utilization is improved, and the environmental protection effect is enhanced. Furthermore, the proportion control model of this invention can be integrated with artificial intelligence algorithms to achieve self-learning optimization, trend prediction and adaptive updating of process parameters based on multi-source solid waste characteristic data, further improving the system's adaptability to raw material fluctuations and long-term operational stability. Attached Figure Description

[0019] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0020] Figure 1 This is a schematic diagram of the framework of a multi-source solid waste intelligent proportioning and homogenization pretreatment method based on laboratory data according to the present invention.

[0021] Figure 2 This is a schematic diagram illustrating the execution of the intelligent solid waste proportioning and homogenization pretreatment method based on laboratory data in the present invention.

[0022] Figure 3 This is a flowchart of a pretreatment method for intelligent proportioning and homogenization of multi-source solid waste based on laboratory data, according to the present invention. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0024] Please see Figure 1 A method for intelligent proportioning and homogenization pretreatment of multi-source solid waste based on laboratory data: S1: Obtain multi-source waste test data, construct a solid waste component constraint system, perform correlation analysis on the component correlation relationship between the multi-source waste test data, obtain the ratio constraint parameter set of coupling state of different waste constraint components, and sort the solid waste components in the ratio constraint parameter set according to the ratio balance domain partitioning algorithm to generate waste ratio adaptation adjustment parameters. S2: Using the morphology and reaction changes of solid waste components before homogenization as input variables, a solid waste intelligent ratio control model is established. Based on the homogenization migration driving function, the waste gradient fusion value of the non-steady-state diffusion ratio between solid wastes is calculated, and the solid waste ratio control curve is output. S3: Based on the solid waste ratio control curve, the resource reaction window of the solid waste homogenization stage is analyzed. According to the waste ratio adaptation adjustment parameters, the waste resource utilization processing mode is selected. The solid waste feeding and exchange process is simulated and modeled. The phase transformation state vector of multi-source solid waste in the exchange process is extracted. The clustering boundary of the waste resource utilization processing mode is determined through the solid waste state critical discrimination mechanism to generate the ratio process adaptation range. S4: In the simulation modeling process, the gradient fusion value of the waste material is adaptively adjusted according to the solid waste treatment feedback data to obtain the solid waste treatment mapping space. The solid waste resource reflection matching weight is set in combination with the ratio process adaptation interval, and the material organization homogeneity of the solid waste components is calibrated to generate a solid waste homogenization ratio scheme.

[0025] In this embodiment, the method for constructing the solid waste component constraint system is as follows: multi-source waste test data is used as the feature input set in the solid waste component constraint system. The multi-source waste test data includes impurity content ratio information and reaction transformation characteristics. Based on the solid waste component balance rule, the coupling relationship between different waste components is constrained, component combination regions that meet the homogenization reaction conditions are screened, and a solid waste component constraint system that matches the solid waste ratio state is constructed.

[0026] In this embodiment, the generation process of the ratio constraint parameter set is as follows: based on the continuity of solid waste coupling and transmission under adjacent processing nodes, the component migration gradient is calculated, and the ratio constraint is determined on the component migration gradient. When a nonlinear abrupt change is detected in the constraint relationship between the reaction characteristics of the target node and its neighboring nodes, a feasible region backtracking search is performed to obtain the ratio constraint parameter set.

[0027] In this embodiment, the waste ratio adaptation adjustment parameter serves as a dynamic ratio constraint factor, including component migration sensitivity and ratio reaction gain weight. It modulates the waste gradient fusion value output by the homogenization migration driving function, suppresses highly sensitive solid waste components, enhances low-sensitive solid waste components, and maintains the solid waste structure rearrangement driving potential field during the ratio evolution process.

[0028] In this embodiment, a solid waste treatment workshop in a steel reservoir is used as an example. This workshop processes multi-source solid waste, including blast furnace slag (Slag A, containing 25% FeO and 40% CaO), converter slag (Slag B, containing 15% FeO and 20% MgO), construction waste (Waste C, containing 50% SiO2 and 10% Al2O3), and fly ash (Ash D, containing 30% C and 40% SiO2). The goal is to achieve homogenized pretreatment of solid waste through intelligent proportioning based on laboratory data, thereby improving resource utilization efficiency (such as in the preparation of cement clinker) and reducing environmental pollution. The system has a processing capacity of 200 tons / day, and the laboratory data comes from an XRF analyzer (X-ray Fluorescence, collecting component proportions) and a DSC thermal analyzer (Differential Scanning Calorimetry, collecting reaction transformation characteristics).

[0029] The system uses Industrial Internet of Things (IIoT) sensors to collect data in real time, including component content, temperature (200-800°C), and stirring speed (50-100 rpm). The entire process is implemented through a Python-based algorithm running on a central scheduling server, integrating the SciPy library for gradient calculation, the NetworkX library for constraint system modeling, and the SymPy library for symbolic equation solving. The digital twin simulation uses MATLAB Simulink to construct the solid waste exchange process and performs simulations using real-time data. Example data: Laboratory dataset {Slag A: [FeO=0.25, CaO=0.4], Slag B: [FeO=0.15, MgO=0.2], Waste C: [SiO2=0.5, Al2O3=0.1], Ash D: [C=0.3, SiO2=0.4]}.

[0030] The implementation process is as follows: Acquire multi-source waste test data, construct a solid waste composition constraint system, perform correlation analysis, and generate waste ratio adaptation adjustment parameters; Construction of a solid waste component constraint system: Laboratory data is used as the feature input set, based on balance rules (e.g., CaO / SiO2 > 1.2 ensures reactivity). Constraint coupling relationships are identified by selecting combined regions, such as the combination of Slag A and Waste C (CaO + SiO2 > 0.7). A graph G = nx.Graph() is constructed using NetworkX, where nodes represent components (e.g., G.add_node('FeO', attr={'ratio':0.25})) and edges represent couplings (e.g., G.add_edge('FeO', 'CaO', weight=0.8)).

[0031] Generation of the proportion constraint parameter set: Calculate the component migration gradient ∇c = (c_{node i} - c_{node j}) / dist_ij, e.g., FeO gradient = 0.1 / ton. For nonlinear mutation detection (|∇c|>0.05 threshold), backtrack to search the feasible region (using SciPy optimize.minimize). The resulting parameter set is: {Coupling states: [FeO-CaO=0.8, SiO2-Al2O3=0.6]}.

[0032] Ranking of homogenization sensitivity indicators and generation of adjustment parameters: Based on the ratio balance domain partitioning algorithm (K-means clustering, k=4), the ranking sensitivity indicators are (FeO highest 0.9, C lowest 0.4). Adjustment parameters are generated: component migration sensitivity s=0.7, gain weight w=1.2.

[0033] Establish a solid waste intelligent proportioning and control model, calculate the waste gradient fusion value, and output the proportioning and control curve; Intelligent proportioning and control model for solid waste: Solid waste composition evolution structure: Mechanistic analysis of waste state, selection of migration and change (e.g., FeO→Fe2O3 mapping). Establishment of evolution mapping: f(ratio) = k * Δreaction, k=0.5.

[0034] Homogenization decision adjustment structure: Embedded adjustment parameters are used to impose convergence region constraints (|fluctuation| < 0.1) on the ratio. The conversion contribution ratio is maintained (FeO: 0.4, CaO: 0.3).

[0035] Homogenization migration driving function: Based on energy level differences (e.g., Slag A level is 0.2 eV higher), query the potential field (using SymPy solve equations). Nonlinearly assign participation weights (weights [0.4, 0.3, 0.2, 0.1]), combined with a threshold (transition threshold 0.05) and relaxation time τ = 5 min. Compress the ratio values ​​to generate the fusion value: ffusion = ∑w_i * ∇c_i = 0.65.

[0036] Solid waste ratio control curve: vertical axis conversion response index r=0.8, horizontal axis gradient ratio p=0-1. Time series unrolling sequence [t=0: r=0.5, t=10: r=0.7], extract gain parameter g=0.15, output curve: {conversion layer contribution: [layer 1:0.4, layer 2:0.3]}.

[0037] Based on the control curve, the resource response window is analyzed, the processing mode is selected, simulation modeling is performed, and the process matching range is generated. Resource response window analysis: Hierarchical reconstruction of time series (t=0-30 min, hierarchy: stirring nodes 1-4), quantified change rate dr / dt=0.02 / min, cumulative effect ∑r=2.5. Stability index stab=0.85 is generated. Adjacent nodes are jointly constrained (e.g., response difference between nodes 1-2 <0.1).

[0038] Criticality determination mechanism for solid waste: Time-series analysis of proportion distribution to extract high-priority features (FeO region). Failure baseline = 0.2, risk probability P = 1 - exp(-stab / 0.1) = 0.15. Adjustment strategy: Cluster boundary determination (DBSCAN, eps = 0.1), selection of processing mode (e.g., melting mode for high FeO).

[0039] Simulation modeling and adaptation range: Simulink simulation of material exchange (e.g., exchange rate of Slag A and Waste C 0.1 ton / min), extract phase vector [solid phase: 0.6, liquid phase: 0.4]. Process feasibility constraints (matching degree > 0.7), generation range: [ratio 0.2-0.4 for FeO, 0.3-0.5 for SiO2].

[0040] Adaptively adjust the fusion value to obtain the processing mapping space, perform state calibration, and generate a solid waste homogenization ratio scheme. Solid waste treatment mapping space: Material state characterization layer: Deconstruct the physicochemical state and construct a feature set {stability: 0.85, homogeneity: 0.88}. Vectorized representation: V = [0.25, 0.4, 0.5, 0.3] (high-dimensional space).

[0041] Process transition mapping layer: Introduce a driving factor d=0.6 and establish a mapping f(V)=d*response. Divide the response region (stability margin 0.9).

[0042] Adaptive adjustment and state calibration: Adjust the fusion value (+0.05 based on feedback) and calibrate homogeneity (error <0.02).

[0043] Solid waste homogenization ratio generation: Extracting characteristics of highly homogeneous areas (homogeneity > 0.85). Failure baseline = 0.1, risk probability Q = 0.12. Adjustment strategy: Reduce FeO content by 0.05, increase treatment frequency by 20%. Generated scheme: {Slag A: 30%, Slag B: 25%, Waste C: 25%, Ash D: 20%; Homogenization time: 15 min}.

[0044] In this embodiment, as Figure 2As shown, the intelligent solid waste proportioning and control model includes a solid waste component evolution structure and a homogenization decision-making and adjustment structure; The solid waste component evolution structure is used to perform mechanistic analysis on the waste state of the solid waste component constraint system before ratio control, reselect the migration and change of solid waste components, and establish a solid waste evolution mapping relationship between ratio change and system reaction behavior. The homogenization decision-making and adjustment structure is used to actively interfere with the proportion homogenization mode based on the solid waste evolution mapping relationship, embed the waste proportion adaptation adjustment parameter into the solid waste treatment mapping space, and apply convergence domain constraints in the proportion component evolution process, so that the proportion change is always within the feasible region of the target reaction window, maintain the solid waste conversion contribution ratio of the waste proportion adaptation adjustment parameter under the influence of material fluctuation, and output the solid waste proportion control curve.

[0045] In this embodiment, the process of calculating the waste gradient fusion value by the homogenization migration driving function is as follows: based on the energy level difference between each processing node, query the solid waste structure rearrangement driving potential field, perform nonlinear transfer allocation of the component participation weights of different waste processing nodes, and combine the solid waste internal state transition threshold and the tissue component relaxation time to generate the waste gradient fusion value by using the solid waste homogenization state as the compression ratio distribution value.

[0046] In this embodiment, the ratio equilibrium domain partitioning algorithm constructs a normalized distance function between the target component range of solid waste and the actual ratio result to quantitatively map the position of the ratio state in the multi-component constraint space. Based on the deviation magnitude, it further divides the ratio space into stable, transitional, and imbalanced regions. The specific algorithm formula is as follows: , The first term is the linear coupling stable energy term, the second term is the large deviation penalty nonlinear term, the third term is the gradient smoothing constraint term for the proportion change, λ and η are adjustment coefficients, ∇x is the gradient operator of the proportion variable, and ε T W ε This represents the component coupling stability energy.

[0047] The criteria for dividing the region are: Stable equilibrium region: F(x) ≤ θ1; Metastable transition region: θ1 <F(x)≤θ2; Instability risk domain: F(x)>θ2.

[0048] The phase transformation state vector is a multidimensional characteristic description vector of the generation, transformation and residual states of each major phase during the homogenization and subsequent heat treatment (such as sintering) of solid waste. It is used to reflect the staged state of the system's evolution from the original mixed state to the target stable phase structure, including the proportion of liquid phase generation, the degree of formation of the target crystalline phase, and the residual unreacted active phase.

[0049] The core of the solid waste component balance rule lies in mapping the chemical component content, potential reactivity, and thermal transformation behavior of each waste material to the component evolution space. By constructing a multi-level matching relationship between the target reaction window and the actual mixing state, the participation of components is structurally constrained, ensuring that the mixing system is within the feasible region of components that can enter a stable reaction path before homogenization. This avoids imbalances such as runaway high activity, inert accumulation, or abnormal enrichment of harmful components. Essentially, this rule is a component synergistic balance mechanism that balances conservation, transformation, and reaction adaptation, providing a stable initial state benchmark for subsequent proportioning control models.

[0050] The probability of conversion failure is used to characterize the likelihood that multi-source solid waste deviates from the stable reaction path during homogenization and subsequent thermal treatment, leading to abnormal phase transformation or excessive harmful releases. Its calculation is based on a synergistic coupling model of phase state, compositional deviation, and migration instability behavior. The specific calculation formula is as follows: , Where, Φ sys Let ξ be the total free energy potential function of the system. r Let ΔG be the reaction progress variable. r To reflect the free energy in real time, ΔG stable The free energy corresponding to the stable reaction pathway.

[0051] Harmful release drivers: , in, Let θ be the vapor partial pressure of component j. redox Let ζ be the redox state parameter of the system. j The toxicity weighting coefficient represents the risk of abnormal volatilization and release caused by temperature-atmosphere coupling.

[0052] The migration and distribution law of the waste components refers to the evolutionary description rules of the spatial redistribution and participation in weight reconstruction behavior of each chemical component in the material unit due to differences in structural energy levels, organizational rearrangement trends and reaction-driven effects during solid waste homogenization and subsequent heat treatment. Through this law, the distribution trend of components migrating from local enriched state to cooperative stable state, as well as the transfer direction of high-energy components to low-energy stable structure absorption and fusion, can be characterized, thereby providing a dynamic evolutionary basis for fusion order parameter calculation, ratio control and risk prediction.

[0053] In this embodiment, the solid waste ratio control curve uses the solid waste conversion response index as the vertical control characteristic quantity and the solid waste gradient ratio as the horizontal ratio evolution variable to establish a ratio conversion structure. The waste gradient fusion value is then subjected to time-series expansion to obtain the ratio evolution state sequence, and the solid waste conversion gain parameter is extracted to reflect the contribution ratio of the solid waste ratio of adjacent conversion layers.

[0054] In this embodiment, the analysis process of the resource response window in the solid waste homogenization stage is as follows: based on the time series and solid waste level, the waste component response data is reconstructed in layers, the component change rate and cumulative effect of each waste mixing node in the continuous processing cycle are quantified, the stability index of the proportioning components is generated, and based on the solid waste state critical discrimination mechanism, the time series response of the mixing nodes of adjacent solid waste layers is jointly constrained.

[0055] In this embodiment, the solid waste state criticality discrimination mechanism performs time-series analysis on the solid waste ratio distribution value based on the stability index of the ratio components, extracts the ratio evolution characteristics of high-priority reaction areas, calculates the conversion failure risk probability of each area based on the preset local reaction failure benchmark, and formulates adjustment strategies for conversion mode and ratio homogenization efficiency according to the conversion failure risk probability, and performs cluster boundary determination on the waste resource processing mode.

[0056] In this embodiment, the process adaptation range is used as the state reference benchmark domain in the solid waste intelligent proportioning and control model. It constrains the process feasibility of the component organization state and phase transformation mode of multi-source solid waste in the pre-homogenization stage. By limiting the proportioning and transformation matching degree of the solid waste component constraint system, the solid waste proportioning and control process is transformed from single component compliance control to dynamic range constraint control of the multi-source coupled state feasible domain.

[0057] In this embodiment, the solid waste treatment mapping space material state characterization layer and process conversion mapping layer are described. The material state characterization layer: structural coupling deconstructs the physicochemical state of multi-source solid waste in the pre-homogenization stage, constructs a multi-source coupled state feature set, which includes component ratio stability features and material organization homogeneity features, and obtains the state vectorization expression basis of solid waste material state in high-dimensional mechanism feature space based on the migration and distribution law of waste components. The process conversion mapping layer introduces a material migration driving factor, establishes a state vectorization expression basis, defines the conversion mapping relationship between process response behaviors, and divides the corresponding process behavior response regions in the solid waste treatment mapping space according to the solid waste homogenization stability margin.

[0058] In this embodiment, the method for generating the solid waste homogenization ratio scheme is as follows: based on the solid waste treatment mapping space, the homogeneity of the material organization is calibrated, the ratio characteristics of the high homogeneity area are extracted, and combined with the preset homogenization failure benchmark, the homogenization ratio risk probability of each area is calculated. Based on the homogenization ratio risk probability, an adjustment strategy for the solid waste ratio components and treatment frequency is formulated to generate the solid waste homogenization ratio scheme.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent proportioning and homogenization pretreatment of multi-source solid waste based on laboratory data, characterized in that, include: S1: Obtain multi-source waste test data, construct a solid waste component constraint system, perform correlation analysis on the component correlation relationship between the multi-source waste test data, obtain the ratio constraint parameter set of coupling state of different waste constraint components, and sort the solid waste components in the ratio constraint parameter set according to the ratio balance domain partitioning algorithm to generate waste ratio adaptation adjustment parameters. S2: Using the morphology and reaction changes of solid waste components before homogenization as input variables, a solid waste intelligent ratio control model is established. Based on the homogenization migration driving function, the waste gradient fusion value of the non-steady-state diffusion ratio between solid wastes is calculated, and the solid waste ratio control curve is output. S3: Based on the solid waste ratio control curve, the resource reaction window of the solid waste homogenization stage is analyzed. According to the waste ratio adaptation adjustment parameters, the waste resource utilization processing mode is selected. The solid waste feeding and exchange process is simulated and modeled. The phase transformation state vector of multi-source solid waste in the exchange process is extracted. The clustering boundary of the waste resource utilization processing mode is determined through the solid waste state critical discrimination mechanism to generate the ratio process adaptation range. S4: In the simulation modeling process, the gradient fusion value of the waste material is adaptively adjusted according to the solid waste treatment feedback data to obtain the solid waste treatment mapping space. The solid waste resource reflection matching weight is set in combination with the ratio process adaptation interval, and the material organization homogeneity of the solid waste components is calibrated to generate a solid waste homogenization ratio scheme.

2. The method according to claim 1, characterized in that, The method for constructing the solid waste component constraint system is as follows: multi-source waste test data is used as the feature input set in the solid waste component constraint system. The multi-source waste test data includes information on the proportion of impurity content and reaction transformation characteristics. Based on the solid waste component balance rule, the coupling relationship between different waste components is constrained, component combination regions that meet the homogenization reaction conditions are screened, and a solid waste component constraint system that matches the solid waste ratio state is constructed.

3. The method according to claim 1, characterized in that, The generation process of the ratio constraint parameter set is as follows: based on the continuity of solid waste coupling and transmission under adjacent processing nodes, the component migration gradient is calculated, and the ratio constraint is determined on the component migration gradient. When a nonlinear abrupt change is detected in the constraint relationship between the reaction characteristics of the target node and its neighboring nodes, a feasible region backtracking search is performed to obtain the ratio constraint parameter set.

4. The method according to claim 1, characterized in that, The waste ratio adaptation adjustment parameters serve as dynamic ratio constraint factors, including component migration sensitivity and ratio reaction gain weights. They are used to modulate the waste gradient fusion value output by the homogenization migration driving function, suppressing highly sensitive solid waste components, enhancing low-sensitive solid waste components, and maintaining the solid waste structure rearrangement driving potential field during the ratio evolution process.

5. The method according to claim 1, characterized in that, The intelligent solid waste proportioning and control model includes a solid waste component evolution structure and a homogenization decision-making and adjustment structure. The solid waste component evolution structure is used to perform mechanistic analysis on the waste state of the solid waste component constraint system before ratio control, reselect the migration and change of solid waste components, and establish a solid waste evolution mapping relationship between ratio change and system reaction behavior. The homogenization decision-making and adjustment structure is used to actively interfere with the proportion homogenization mode based on the solid waste evolution mapping relationship, embed the waste proportion adaptation adjustment parameter into the solid waste treatment mapping space, and apply convergence domain constraints in the proportion component evolution process, so that the proportion change is always within the feasible region of the target reaction window, maintain the solid waste conversion contribution ratio of the waste proportion adaptation adjustment parameter under the influence of material fluctuation, and output the solid waste proportion control curve.

6. The method according to claim 3, characterized in that, The process of calculating the waste gradient fusion value by the homogenization migration driving function is as follows: based on the energy level difference between each processing node, query the solid waste structure rearrangement driving potential field, perform nonlinear transfer allocation of the component participation weights of different waste processing nodes, and combine the solid waste internal state transition threshold and the tissue component relaxation time to generate the waste gradient fusion value by taking the solid waste homogenization state as the compression ratio distribution value.

7. The method according to claim 1, characterized in that, The solid waste ratio control curve uses the solid waste conversion response index as the vertical control characteristic and the solid waste gradient ratio as the horizontal ratio evolution variable to establish a ratio conversion structure. The waste gradient fusion value is expanded over time to obtain the ratio evolution state sequence, and the solid waste conversion gain parameter is extracted to reflect the contribution ratio of the solid waste ratio of adjacent conversion layers.

8. The method according to claim 1, characterized in that, The analysis process of the resource response window in the solid waste homogenization stage is as follows: based on the time series and solid waste level, the waste component response data is reconstructed in layers, the component change rate and cumulative effect of each waste mixing node in the continuous processing cycle are quantified, the stability index of the proportioning components is generated, and based on the solid waste state critical discrimination mechanism, the time series response of the mixing nodes of adjacent solid waste layers is jointly constrained.

9. The method according to claim 8, characterized in that, The solid waste state criticality discrimination mechanism is based on the stability index of the proportioning components. It performs time-series analysis on the solid waste proportioning distribution value, extracts the proportioning evolution characteristics of high-priority reaction areas, calculates the conversion failure risk probability of each area based on the preset local reaction failure benchmark, and formulates adjustment strategies for conversion mode and proportioning homogenization efficiency according to the conversion failure risk probability. It also performs cluster boundary determination on the waste resource processing mode.

10. The method according to claim 5, characterized in that, The process adaptation range is used as the state reference benchmark domain in the solid waste intelligent proportioning and control model. It constrains the process feasibility of the component organization state and phase transformation mode of multi-source solid waste in the pre-homogenization stage. By limiting the proportioning and transformation matching degree of the solid waste component constraint system, the solid waste proportioning and control process is transformed from single component standard control to dynamic range constraint control of the multi-source coupled state feasible domain.

11. The method according to claim 1, characterized in that, The solid waste treatment mapping space material state characterization layer and process conversion mapping layer; The material state characterization layer: structural coupling deconstructs the physicochemical state of multi-source solid waste in the pre-homogenization stage, constructs a multi-source coupled state feature set, which includes component ratio stability features and material organization homogeneity features, and obtains the state vectorization expression basis of solid waste material state in high-dimensional mechanism feature space based on the migration and distribution law of waste components. The process conversion mapping layer introduces a material migration driving factor, establishes a state vectorization expression basis, defines the conversion mapping relationship between process response behaviors, and divides the corresponding process behavior response regions in the solid waste treatment mapping space according to the solid waste homogenization stability margin.

12. The method according to claim 1, characterized in that, The method for generating the solid waste homogenization ratio scheme is as follows: based on the solid waste treatment mapping space, the homogeneity of the material organization is calibrated, the ratio characteristics of the high homogeneity area are extracted, and combined with the preset homogenization failure benchmark, the homogenization ratio risk probability of each area is calculated. Based on the homogenization ratio risk probability, an adjustment strategy for the solid waste ratio components and treatment frequency is formulated to generate the solid waste homogenization ratio scheme.

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