Lightweight aggregate preparation method using solid wastes

Abstract

The invention discloses a method for preparing a lightweight aggregate by using solid wastes, and relates to the technical field of solid waste utilization, and the method comprises the following steps: scanning the solid wastes by using a near infrared spectrometer to obtain spectral data, and determining an optimal mixing ratio of the solid wastes based on the spectral data; grinding the solid wastes proportioned according to the optimal mixing ratio into powder, and mixing and granulating with a foam stabilizer and a composite foaming agent to obtain a lightweight aggregate green body; the lightweight aggregate green body is subjected to two-stage sintering treatment, and the proportion of mixed gas in the sintering process is adjusted according to the content of organic matter in the solid waste; and according to a sintering result, adjusting quenching process parameters, and based on the adjusted quenching process parameters, carrying out quenching treatment on the sintered lightweight aggregate green body to obtain the lightweight aggregate. According to the method, the raw material utilization rate and the proportioning efficiency are remarkably improved, and a precise regulation and control technical path is provided for high-value utilization of a complex solid waste system.

Description

Technical Field

[0001] The invention relates to the technical field of solid waste utilization, and in particular to a method for preparing light aggregate using solid waste. Background Art

[0002] With the acceleration of industrialization and urbanization, the amount of solid waste (such as slag, fly ash, construction waste, etc.) has increased rapidly, causing great pressure on the environment. At present, a large amount of such waste is simply stored, which not only occupies land resources, but also causes environmental pollution and ecological damage. Therefore, how to effectively treat these wastes and convert them into valuable materials has become an important task in the field of environmental protection.

[0003] In response to this challenge, a method for preparing lightweight aggregate using solid waste as raw materials was proposed. This method not only helps to reduce the negative impact of solid waste on the environment, but also reduces the dependence on natural resources in traditional lightweight aggregate production, showing significant economic and environmental benefits.

[0004] However, although the technology of preparing lightweight aggregate from solid waste has shown certain application prospects, it still faces some limitations in actual operation. For example, in the preparation of lightweight aggregate, due to the wide sources of solid waste and complex and variable components, different types of waste may lead to inconsistent performance of the final product when preparing lightweight aggregate. This variability not only affects the quality of the product and its scope of application, but also increases the difficulty of quality control in the production process.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of the problems in the related art, the present invention proposes a method for preparing lightweight aggregate using solid waste to overcome the above-mentioned technical problems existing in the existing related art.

[0007] To this end, the specific technical solution adopted by the present invention is as follows: A method for preparing light aggregate using solid waste, comprising: S1. Scanning solid waste using a near infrared spectrometer to obtain spectral data, and determining an optimal mixing ratio of the solid waste based on the spectral data; S2, grinding the solid waste in the optimal mixing ratio into powder, combining with a foam stabilizer and a composite foaming agent for mixed granulation to obtain a lightweight aggregate green body; S3, subjecting the light aggregate green body to a two-stage sintering process, and adjusting the ratio of the mixed gas during the sintering process according to the organic matter content in the solid waste; S4. According to the sintering result, the quenching process parameters are adjusted, and based on the adjusted quenching process parameters, the sintered lightweight aggregate green body is quenched to obtain lightweight aggregate.

[0008] Optionally, scanning the solid waste using a near infrared spectrometer to obtain spectral data, and determining the optimal mixing ratio of the solid waste based on the spectral data includes: S11, using a near-infrared spectrometer with a wavelength range of 900-2500 nm to perform spectral scanning on the solid waste, obtain spectral data, and pre-process the spectral data through multivariate scattering correction; S12. Based on the preprocessed spectral data, a component prediction model is constructed using a partial least squares regression algorithm, and the component prediction model is trained using historical sample data; S13. Input the real-time spectral data into the trained component prediction model to perform component prediction. Based on the prediction results, use a genetic algorithm to optimize the mixing ratio of different solid wastes to determine the optimal mixing ratio of the solid wastes.

[0009] Optionally, the real-time spectral data is input into a trained component prediction model for component prediction. Based on the prediction results, a genetic algorithm is used to optimize the mixing ratio of different solid wastes. Determining the optimal mixing ratio of solid wastes includes: S131, collecting real-time spectrum data of solid waste, and preprocessing the real-time spectrum data; S132, inputting the preprocessed real-time spectral data into the trained component prediction model, and using the component prediction model to predict various components in the solid waste; S133, based on the result of component prediction, setting the parameters of the genetic algorithm to establish a weighted fitness function, and using the weighted fitness function to generate an initial ratio scheme; S134, constructing a reference sequence and a comparison sequence, calculating the correlation between the reference sequence and the comparison sequence using grey correlation analysis, and quantifying the initial matching scheme; S135. Based on the quantitative results of the initial mixing scheme, perform adaptive genetic operations to gradually optimize the mixing ratio of solid waste and determine the optimal mixing ratio.

[0010] Optionally, the solid wastes prepared in an optimal mixing ratio are ground into powder, and mixed and granulated with a foam stabilizer and a composite foaming agent to obtain a lightweight aggregate green body, including: S21, grinding the solid waste prepared according to the optimal mixing ratio using a grinder to obtain powdered waste; S22, in a double-screw conical mixer, uniformly mixing the powdered waste, the foam stabilizer and the composite foaming agent according to a preset ratio, stirring for 0.2-0.3 min, to generate a mixture with a pre-foamed structure; S23, granulating the mixed material by a centrifugal atomizing granulator to prepare primary particles, and drying the primary particles by a hot air blower; S24. Introduce modified fly ash slurry into the fluidized bed, and control the spraying pressure and fluidizing wind speed of the fluidized bed to form a secondary coating layer with a thickness of 30-50 μm on the surface of the primary particles to obtain a lightweight aggregate green body.

[0011] Optionally, the diameter of the primary particles is 5-8 mm; the diameter of the secondary coating is 10-12 mm.

[0012] Optionally, the modified fly ash slurry is a fly ash slurry comprising 5% calcium stearate.

[0013] Optionally, the composite foaming agent includes aluminum powder, hydrogen peroxide and silicon carbide.

[0014] Optionally, the rotation speed of the centrifugal atomizing granulator is 1800-2500 rpm.

[0015] Optionally, the lightweight aggregate green body is subjected to a two-stage sintering process, and the mixed gas ratio during the sintering process is adjusted according to the organic matter content in the solid waste, including: S31, placing the light aggregate green body in a push plate kiln, pre-sintering the body at a temperature range of 300-600° C. for a holding time of 1-3 hours, and introducing a mixed gas of a preset proportion during the pre-sintering process to prevent oxidation of organic matter in the light aggregate green body; S32, the pre-fired light aggregate green body is transferred into a roller kiln for sintering. The temperature range of the sintering process is 900-1200°C and the holding time is 2-5h to promote the melting and bonding of various components in the light aggregate green body; S33. Use an organic carbon analyzer to obtain the organic matter content of the light aggregate green body during the sintering process, and adjust the mixed gas ratio during the sintering process based on the obtained organic content.

[0016] Optionally, the mixed gas is a mixed gas of nitrogen and carbon dioxide.

[0017] The beneficial effects of the present invention are: 1. The present invention combines spectral analysis with intelligent algorithms to achieve rapid analysis of solid waste components and dynamic optimization of mixing ratios, significantly improving raw material utilization and ratio efficiency, and providing a precise control technology path for the high-value utilization of complex solid waste systems.

[0018] 2. The present invention constructs a gradient structure through a multi-stage granulation and coating process, and combines the synergistic effect of a composite foaming agent to enable the lightweight aggregate to have both lightweight and high strength as well as thermal insulation properties, significantly improving the compressive strength of the product, reducing water absorption, and achieving synergistic optimization of thermal and physical properties.

[0019] 3. The present invention combines two-stage dynamic oxygen-controlled sintering with waste heat recovery technology to significantly reduce energy consumption and carbon emissions, promote the deep integration of solid waste resource utilization and green building materials production, and promote low-carbon transformation and circular economy development in the building materials field. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 The present invention is a flowchart of a method for preparing light aggregate using solid waste according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0023] According to an embodiment of the present invention, a method for preparing lightweight aggregate using solid waste is provided.

[0024] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, a method for preparing light aggregate using solid waste according to an embodiment of the present invention comprises: S1. Scan the solid waste using a near-infrared spectrometer to obtain spectral data, and determine the optimal mixing ratio of the solid waste based on the spectral data.

[0025] Preferably, the solid waste is scanned using a near infrared spectrometer to obtain spectral data, and determining the optimal mixing ratio of the solid waste based on the spectral data includes: S11. Use a near-infrared spectrometer with a wavelength range of 900-2500nm to perform spectral scanning on solid waste to obtain spectral data, and pre-process the spectral data through multivariate scattering correction.

[0026] S12. Based on the preprocessed spectral data, a component prediction model is constructed using a partial least squares regression algorithm, and the component prediction model is trained using historical sample data.

[0027] S13. Input the real-time spectral data into the trained component prediction model to perform component prediction. Based on the prediction results, use a genetic algorithm to optimize the mixing ratio of different solid wastes to determine the optimal mixing ratio of the solid wastes.

[0028] It should be noted that a specific embodiment of using a near-infrared spectrometer to scan solid waste to obtain spectral data and determining the optimal mixing ratio of solid waste based on the spectral data is as follows: Experimental data: fly ash (FA), SiO252.3%, Al2O328.1% (primary ash); slag (SL), SiO239.8%, CaO38.5% (S95 grade); historical samples, 10 groups of chemical composition data with different ratios (FA:SL=0:100 to 100:0).

[0029] Step 1: Spectral scanning and preprocessing (1) Instrument parameter setting Wavelength range: 900-2500nm (corresponding wave number 4000-11111cm⁻¹).

[0030] Resolution: 8cm⁻¹, number of scans: 64 times, aperture: 5mm.

[0031] Reference background: The integrating sphere has a built-in reference, and the background spectrum is deducted before each sample is collected.

[0032] (2) Sample preparation and scanning Mix FA and SL in a mass ratio (e.g. FA:SL=50:50), grind to 200 mesh (75μm), and ensure that there are no bubbles when loading the sample; collect 3 spectra for each ratio and take the average spectrum as the final data.

[0033] (3) Multivariate Scatter Correction (MSC) Segment processing: The spectral data is segmented at 100 nm intervals (16 segments in total).

[0034] Correction formula: MSC=(xᵢ-b0) / b, where b0 is the piecewise linear regression intercept and b is the slope.

[0035] After correction, the spectral signal-to-noise ratio is improved by 42% (taking the 1450nm Al-O-Al peak as an example).

[0036] Step 2: Component prediction model construction and training (1) Characteristic wavelength screening Filter key wavelengths by VIP score (Variable Importance in Projection): Si-O-Ti bond: 960nm (VIP=1.32).

[0037] Al-O-Al bond: 1450nm (VIP=1.45).

[0038] Si-O-Si bond: 2200nm (VIP=1.28).

[0039] (2) Model training Input variables: 16 MSC-calibrated spectra (16×1 wavelength points); Output variables: SiO2, Al2O3, CaO content (laboratory chemical analysis benchmark).

[0040] Parameter optimization: number of principal components (PCs), determined by Q² cumulative value (PCs=5 when Q²=0.82); cross-validation, ten-fold cross-validation, RMSEP (sum of squared prediction residuals) ≤1.5%.

[0041] Step 3: Genetic algorithm to optimize the mixing ratio (1) Objective function setting Objective 1: Maximize the gradient porosity (constraint: porosity ≥ 40%).

[0042] Goal 2: Minimize raw material costs (fly ash unit price ￥120 / ton, slag ￥150 / ton).

[0043] Constraints: CaO / SiO2 molar ratio ≤ 1.2 (to avoid insufficient slag activity).

[0044] (2) Genetic algorithm parameters Population size: 80, number of iterations: 150 generations; crossover probability: 0.85, mutation rate: 0.05.

[0045] The fitness function is: ; Where, Fitness represents the fitness function; P max Indicates the maximum porosity; P actual Indicates the actual porosity; P min Indicates the minimum porosity; C max represents the maximum cost; C actual Indicates the actual cost; C min Represents the minimum cost.

[0046] (3) Optimization results a. Optimal ratio: FA:SL=55:45 (mass ratio).

[0047] b. Performance Verification Porosity: 43.2% (target achievement rate 108%).

[0048] Cost: ￥132 / ton (12% lower than pure slag formula).

[0049] Compressive strength: 12.5MPa (meets GB / T 8077 standard).

[0050] Preferably, the real-time spectral data is input into a trained component prediction model for component prediction. Based on the prediction results, a genetic algorithm is used to optimize the mixing ratio of different solid wastes. Determining the optimal mixing ratio of solid wastes includes: S131, collecting real-time spectrum data of solid waste, and preprocessing the real-time spectrum data; S132, inputting the preprocessed real-time spectral data into the trained component prediction model, and using the component prediction model to predict various components in the solid waste; S133, based on the result of component prediction, setting the parameters of the genetic algorithm to establish a weighted fitness function, and using the weighted fitness function to generate an initial ratio scheme; S134, constructing a reference sequence and a comparison sequence, calculating the correlation between the reference sequence and the comparison sequence using grey correlation analysis, and quantifying the initial matching scheme; S135. Based on the quantitative results of the initial mixing scheme, perform adaptive genetic operations to gradually optimize the mixing ratio of solid waste and determine the optimal mixing ratio.

[0051] It should be noted that the real-time spectral data is input into the trained component prediction model for component prediction. Based on the prediction results, the genetic algorithm is used to optimize the mixing ratio of different solid wastes. The specific embodiment of determining the optimal mixing ratio of solid wastes is as follows: Experimental background: Fly ash (FA) and construction waste recycled aggregate (CFA) are used as raw materials. The goal is to prepare gradient porosity lightweight aggregate, with a porosity of ≥40% and a compressive strength of ≥12MPa. Near infrared spectroscopy (NIRS) is used to quickly predict the composition, and the genetic algorithm (GA) is used to optimize the mixing ratio.

[0052] Step 1: Spectral data collection and preprocessing (1) Experimental parameters Instrument: Ocean Insight FX-Pro fiber optic spectrometer, wavelength range 900-2500nm, resolution 3nm.

[0053] Sample preparation: FA and CFA were mixed in the mass ratio (0:100, 25:75, 50:50, 75:25, 100:0) and ground to 200 mesh (75 μm).

[0054] Scanning conditions: Scan 3 points on each sample, light source intensity 80%, integration time 30ms, ambient temperature 25℃.

[0055] (2) Preprocessing process MSC correction: eliminate the influence of uneven particle size, the formula is: ; Where MSC represents the correction function; x i Represents the i-th data; represents the sample mean; s represents the standard deviation.

[0056] Savitzky-Golay smoothing: window width = 15, polynomial order = 3, noise removal.

[0057] (3) Experimental results The original spectral signal-to-noise ratio (SNR) = 52dB, which is increased to 78dB after MSC correction.

[0058] Key characteristic peaks: 1450nm (Al-O-Al bond), 2200nm (Si-O-Si bond), signal-to-noise ratio improved by 45%.

[0059] Step 2: Model construction Dataset: 100 sets of historical samples (including SiO2, Al2O3, and CaO content of FA and CFA), divided into 80% training set and 20% validation set.

[0060] Feature extraction: VIP scoring screens key wavelengths (960nm, 1450nm, 2200nm).

[0061] PLS modeling: number of principal components = 5, cross-validation Q² = 0.86, RMSEP = 1.2%.

[0062] Input preprocessed spectral data and output component prediction values.

[0063] Step 3: Genetic algorithm parameter setting and initial ratio generation (1) Objective function Objective 1: Maximize gradient porosity (weight 0.5).

[0064] Objective 2: Minimize costs (weight 0.3, FA unit price ￥80 / ton, CFA ￥120 / ton).

[0065] Constraint: CaO / SiO2 molar ratio ≤1.0 (avoid alkali aggregate reaction).

[0066] Genetic algorithm parameters: population size = 80, number of iterations = 200 generations; crossover probability = 0.8, mutation rate = 0.05.

[0067] The fitness function is: ; Where, Fitness represents the fitness function; P max Indicates the maximum porosity; P actual Indicates the actual porosity; P min Indicates the minimum porosity; C max represents the maximum cost; C actual Indicates the actual cost; C min Represents the minimum cost.

[0068] The initial ratio scheme is shown in Table 1.

[0069] Table 1: Initial ratio scheme (partial) Step 4: Grey correlation analysis to quantify the matching scheme Reference sequence: ideal ratio (FA: 75%, CFA: 25%, porosity 45%, cost ￥100).

[0070] Comparison series: Actual performance of the initial 50 mixes.

[0071] Correlation calculation: ; In the formula, ζ represents the correlation value; i represents the comparison sequence number; k represents the index number; x0(k) represents the reference sequence; x i (k) represents the comparison sequence; ρ represents the resolution coefficient.

[0072] Step 5: Adaptive genetic operation and optimal solution determination (1) Adaptive strategy Population diversity monitoring: If the population fitness standard deviation is less than 0.05, the trigger mutation rate will be increased to 0.1.

[0073] Elite retention: retain the top 5% high fitness individuals in each generation.

[0074] (2) Optimization process Iteration 50: The mean fitness increased from 0.68 to 0.85.

[0075] Convergence condition: The fitness change rate is less than 1% for 10 consecutive generations.

[0076] (3) Optimal ratio: FA: 73%, CFA: 27%.

[0077] Preferably, the solid wastes prepared in an optimal mixing ratio are ground into powder, and mixed and granulated with a foam stabilizer and a composite foaming agent to obtain a lightweight aggregate green body, including: S21. Grinding the solid waste prepared according to the optimal mixing ratio using a grinder to obtain powdered waste.

[0078] S22. In a double-screw conical mixer, the powdered waste, foam stabilizer and composite foaming agent are uniformly mixed in a preset ratio, and stirred for 0.2-0.3 minutes to generate a mixture with a pre-foamed structure.

[0079] Preferably, the composite foaming agent includes aluminum powder, hydrogen peroxide and silicon carbide.

[0080] S23, granulating the mixed material with a centrifugal atomizing granulator to prepare primary particles, and drying the primary particles with a hot air blower.

[0081] Preferably, the rotation speed of the centrifugal atomizing granulator is 1800-2500 rpm.

[0082] S24. Introduce modified fly ash slurry into the fluidized bed, and control the spraying pressure and fluidizing wind speed of the fluidized bed to form a secondary coating layer with a thickness of 30-50 μm on the surface of the primary particles to obtain a lightweight aggregate green body.

[0083] Preferably, the diameter of the primary particles is 5-8 mm; the diameter of the secondary coating is 10-12 mm.

[0084] Preferably, the modified fly ash slurry is a fly ash slurry comprising 5% calcium stearate.

[0085] S3. The light aggregate green body is subjected to a two-stage sintering treatment, and the ratio of the mixed gas during the sintering process is adjusted according to the organic matter content in the solid waste.

[0086] Preferably, the light aggregate green body is subjected to a two-stage sintering process, and the mixed gas ratio during the sintering process is adjusted according to the organic matter content in the solid waste, including: S31, placing the light aggregate green body in a push plate kiln, pre-sintering the body at a temperature range of 300-600° C. for a holding time of 1-3 hours, and introducing a mixed gas of a preset proportion during the pre-sintering process to prevent oxidation of organic matter in the light aggregate green body; S32, the pre-fired light aggregate green body is transferred into a roller kiln for sintering. The temperature range of the sintering process is 900-1200°C and the holding time is 2-5h to promote the melting and bonding of various components in the light aggregate green body; S33. Use an organic carbon analyzer to obtain the organic matter content of the light aggregate green body during the sintering process, and adjust the mixed gas ratio during the sintering process based on the obtained organic content.

[0087] Preferably, the mixed gas is a mixed gas of nitrogen and carbon dioxide.

[0088] S4. According to the sintering result, the quenching process parameters are adjusted, and based on the adjusted quenching process parameters, the sintered lightweight aggregate green body is quenched to obtain lightweight aggregate.

[0089] The specific implementation mode of the present invention is further described below in conjunction with embodiments: Example 1 (1) Use a near-infrared spectrometer (900-2500nm) to scan solid waste, and improve the spectral signal-to-noise ratio through multivariate scattering correction (MSC) preprocessing. Based on the partial least squares regression (PLS) algorithm, a component prediction model is constructed, and combined with historical sample training, a rapid prediction of key components such as SiO2 and Al2O3 (error ≤ 1.5%) is achieved. A genetic algorithm is further used to optimize the mixing ratio, with porosity and cost as the objective function, to dynamically adjust the ratio of fly ash and construction waste recycled aggregate (such as FA:CFA=55:45), significantly improving the formula efficiency.

[0090] (2) Grind to 200 mesh according to the optimal ratio, add foam stabilizer (sodium dodecyl sulfate) and composite foaming agent (aluminum powder + hydrogen peroxide + silicon carbide), and evenly disperse through a double-helix cone mixer. Use a centrifugal atomization granulator (speed 1800-2500rpm) to make primary particles (5-8mm), and then spray fly ash slurry containing 5% calcium stearate through a fluidized bed to form a secondary coating layer (10-12mm), and finally obtain a lightweight aggregate green body with a bulk density as low as 0.65g / cm³.

[0091] (3) In the pre-burning stage (300-600℃), a mixed gas of N2:CO2=7:3 is introduced to inhibit the oxidation of organic matter; in the main sintering stage (900-1200℃), the CO2 ratio is dynamically adjusted according to the residual organic matter (for every 0.1% increase in the residual amount, the CO2 ratio increases by 5%) to promote melt bonding. After sintering, the porosity reaches 42.1%, the compressive strength increases to 12.3MPa, and the energy consumption is reduced by 22% compared with the traditional process.

[0092] (4) A step quenching strategy was adopted: water mist cooling (0.3℃ / min) in the initial stage (0-30min), air cooling (1.0℃ / min) in the later stage (30-60min), and the end temperature was 120℃. Compared with natural cooling, the surface crack rate dropped from 15.2% to 2.8%, the closed-pore rate increased to 68%, and the comprehensive energy consumption was reduced by 23%. Industrial-level verification showed that the qualified rate of continuous production of 1000kg lightweight aggregate reached 95%, and the density was reduced by 17% (1.12g / cm³).

[0093] Example 2 (1) Use a near-infrared spectrometer (900-2500nm) to scan solid waste, and improve the spectral signal-to-noise ratio through multivariate scattering correction (MSC) preprocessing. Based on the partial least squares regression (PLS) algorithm, a component prediction model is constructed, and combined with historical sample training, a rapid prediction of key components such as SiO2 and Al2O3 (error ≤ 1.2%) is achieved. A genetic algorithm is further used to optimize the mixing ratio, with porosity and cost as the objective function, to dynamically adjust the ratio of fly ash and construction waste recycled aggregate (such as FA:CFA=60:40), significantly improving the formula efficiency.

[0094] (2) Grind to 180 mesh according to the optimal ratio, add foam stabilizer (sodium dodecyl sulfate) and composite foaming agent (aluminum powder + hydrogen peroxide + silicon carbide), and evenly disperse through a double-screw cone mixer. Use a centrifugal atomization granulator (speed 2000-2500rpm) to make primary particles (5-7mm), and then spray fly ash slurry containing 8% calcium stearate through a fluidized bed to form a secondary coating layer (9-11mm), and finally obtain a lightweight aggregate green body with a bulk density as low as 0.58g / cm³.

[0095] (3) During the pre-burning stage (300-600℃), a 6:4 mixed gas of N2:CO2 is introduced to inhibit the oxidation of organic matter; during the main sintering stage (900-1200℃), the CO2 ratio is dynamically adjusted according to the residual organic matter (for every 0.1% increase in the residual amount, the CO2 ratio increases by 6%) to promote melt bonding. After sintering, the porosity reaches 43.5%, the compressive strength increases to 13.0MPa, and the energy consumption is reduced by 25% compared with the traditional process.

[0096] (4) A step quenching strategy was adopted: water mist cooling (cooling rate 0.4℃ / min) in the initial stage (0-30min), air cooling (cooling rate 1.2℃ / min) in the later stage (30-60min), and the termination temperature was 125℃. Compared with natural cooling, the surface crack rate dropped from 16.5% to 3.1%, the closed-pore rate increased to 70%, and the comprehensive energy consumption was reduced by 25%. Industrial-level verification showed that when 1000kg of lightweight aggregate was produced continuously, the qualified rate reached 96% and the density was reduced by 18% (1.08g / cm³).

[0097] Example 3 (1) Use a near-infrared spectrometer (900-2500nm) to scan solid waste, and improve the spectral signal-to-noise ratio through multivariate scattering correction (MSC) preprocessing. Based on the partial least squares regression (PLS) algorithm, a component prediction model is constructed, and combined with historical sample training, a rapid prediction of key components such as SiO2 and Al2O3 (error ≤ 1.0%) is achieved. A genetic algorithm is further used to optimize the mixing ratio, with porosity and cost as the objective function, to dynamically adjust the ratio of fly ash and construction waste recycled aggregate (such as FA:CFA=65:35), significantly improving the formula efficiency.

[0098] (2) Grind to 150 mesh according to the optimal ratio, add foam stabilizer (sodium dodecyl sulfate) and composite foaming agent (aluminum powder + hydrogen peroxide + silicon carbide), and evenly disperse through a double-helix cone mixer. Use a centrifugal atomization granulator (speed 2200-2500rpm) to make primary particles (4-6mm), and then spray fly ash slurry containing 10% calcium stearate through a fluidized bed to form a secondary coating layer (8-10mm), and finally obtain a lightweight aggregate green body with a bulk density as low as 0.60g / cm³.

[0099] (3) During the pre-burning stage (300-600℃), a mixed gas of N2:CO2=7:3 is introduced to inhibit the oxidation of organic matter; during the main sintering stage (900-1200℃), the CO2 ratio is dynamically adjusted according to the residual organic matter (for every 0.1% increase in the residual amount, the CO2 ratio increases by 5%) to promote melt bonding. After sintering, the porosity reaches 44.0%, the compressive strength increases to 13.5MPa, and the energy consumption is reduced by 28% compared with the traditional process.

[0100] (4) A step quenching strategy was adopted: water mist cooling (cooling rate 0.5℃ / min) in the initial stage (0-30min), air cooling (cooling rate 1.5℃ / min) in the later stage (30-60min), and the termination temperature was 130℃. Compared with natural cooling, the surface crack rate dropped from 17.0% to 2.5%, the closed-pore rate increased to 72%, and the comprehensive energy consumption was reduced by 28%. Industrial-level verification showed that when 1000kg of lightweight aggregate was produced continuously, the qualified rate reached 97% and the density was reduced by 20% (1.05g / cm³).

[0101] In summary, with the aid of the above-mentioned technical scheme of the present invention, by combining spectral analysis with intelligent algorithms, rapid analysis of solid waste components and dynamic optimization of mixing ratios can be achieved, which significantly improves raw material utilization and ratio efficiency, and provides a precise control technology path for the high-value utilization of complex solid waste systems. By constructing a gradient structure through multi-stage granulation and coating processes, combined with the synergistic effect of composite foaming agents, lightweight aggregates have both lightweight and high strength as well as thermal insulation properties, significantly improving the compressive strength of products, reducing water absorption, and achieving synergistic optimization of thermal and physical properties. By combining two-stage dynamic oxygen-controlled sintering with waste heat recovery technology, energy consumption and carbon emissions can be greatly reduced, promoting the deep integration of solid waste resource utilization and green building materials production, and promoting low-carbon transformation and circular economic development in the building materials field.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for preparing light aggregate using solid waste, characterized in that: include: S1. Scanning solid waste using a near infrared spectrometer to obtain spectral data, and determining an optimal mixing ratio of the solid waste based on the spectral data; S2, grinding the solid waste in the optimal mixing ratio into powder, combining with a foam stabilizer and a composite foaming agent for mixed granulation to obtain a lightweight aggregate green body; S3, subjecting the light aggregate green body to a two-stage sintering process, and adjusting the ratio of the mixed gas during the sintering process according to the organic matter content in the solid waste; S4. According to the sintering result, the quenching process parameters are adjusted, and based on the adjusted quenching process parameters, the sintered lightweight aggregate green body is quenched to obtain lightweight aggregate.

2. The method for preparing light aggregate using solid waste according to claim 1, characterized in that: The method of scanning the solid waste with a near infrared spectrometer to obtain spectral data and determining the optimal mixing ratio of the solid waste based on the spectral data includes: S11, using a near-infrared spectrometer with a wavelength range of 900-2500 nm to perform spectral scanning on the solid waste, obtain spectral data, and pre-process the spectral data through multivariate scattering correction; S12. Based on the preprocessed spectral data, a component prediction model is constructed using a partial least squares regression algorithm, and the component prediction model is trained using historical sample data; S13. Input the real-time spectral data into the trained component prediction model to perform component prediction. Based on the prediction results, use a genetic algorithm to optimize the mixing ratio of different solid wastes to determine the optimal mixing ratio of the solid wastes.

3. The method for preparing light aggregate using solid waste according to claim 2, characterized in that: The real-time spectral data is input into the trained component prediction model to perform component prediction, and based on the prediction results, a genetic algorithm is used to optimize the mixing ratio of different solid wastes to determine the optimal mixing ratio of solid wastes, including: S131, collecting real-time spectrum data of solid waste, and preprocessing the real-time spectrum data; S132, inputting the preprocessed real-time spectral data into the trained component prediction model, and using the component prediction model to predict various components in the solid waste; S133, based on the result of component prediction, setting the parameters of the genetic algorithm to establish a weighted fitness function, and using the weighted fitness function to generate an initial ratio scheme; S134, constructing a reference sequence and a comparison sequence, calculating the correlation between the reference sequence and the comparison sequence using grey correlation analysis, and quantifying the initial matching scheme; S135. Based on the quantitative results of the initial mixing scheme, perform adaptive genetic operations to gradually optimize the mixing ratio of solid waste and determine the optimal mixing ratio.

4. The method for preparing light aggregate using solid waste according to claim 1, characterized in that: The solid wastes prepared in the optimal mixing ratio are ground into powder, and mixed and granulated with a foam stabilizer and a composite foaming agent to obtain a lightweight aggregate green body, which includes: S21, grinding the solid waste prepared according to the optimal mixing ratio using a grinder to obtain powdered waste; S22, in a double-screw conical mixer, uniformly mixing the powdered waste, the foam stabilizer and the composite foaming agent according to a preset ratio, stirring for 0.2-0.3 min, to generate a mixture with a pre-foamed structure; S23, granulating the mixed material by a centrifugal atomizing granulator to prepare primary particles, and drying the primary particles by a hot air blower; S24. Introduce modified fly ash slurry into the fluidized bed, and control the spraying pressure and fluidizing wind speed of the fluidized bed to form a secondary coating layer with a thickness of 30-50 μm on the surface of the primary particles to obtain a lightweight aggregate green body.

5. The method for preparing light aggregate using solid waste according to claim 4, characterized in that: The diameter of the primary particles is 5-8 mm; the diameter of the secondary coating is 10-12 mm.

6. The method for preparing light aggregate using solid waste according to claim 5, characterized in that: The modified fly ash slurry is a fly ash slurry containing 5% calcium stearate.

7. The method for preparing light aggregate using solid waste according to claim 6, characterized in that: The composite foaming agent comprises aluminum powder, hydrogen peroxide and silicon carbide.

8. The method for preparing lightweight aggregate using solid waste according to claim 7, characterized in that: The rotation speed of the centrifugal atomizing granulator is 1800-2500rpm.

9. The method for preparing lightweight aggregate using solid waste according to claim 1, characterized in that: The two-stage sintering of the light aggregate green body and adjusting the ratio of the mixed gas in the sintering process according to the organic matter content in the solid waste include: S31, placing the light aggregate green body in a push plate kiln, pre-sintering the body at a temperature range of 300-600° C. for a holding time of 1-3 hours, and introducing a mixed gas of a preset proportion during the pre-sintering process to prevent oxidation of organic matter in the light aggregate green body; S32, the pre-fired light aggregate green body is transferred into a roller kiln for sintering. The temperature range of the sintering process is 900-1200°C and the holding time is 2-5h to promote the melting and bonding of various components in the light aggregate green body; S33. Use an organic carbon analyzer to obtain the organic matter content of the light aggregate green body during the sintering process, and adjust the mixed gas ratio during the sintering process based on the obtained organic content.

10. The method for preparing lightweight aggregate using solid waste according to claim 9, characterized in that: The mixed gas is a mixed gas of nitrogen and carbon dioxide.

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