A method for designing a full-solid waste cementing material based on a reactive ion budget and phase topology coupling and application

Abstract

The application discloses a method for designing full-solid-waste cementing material based on coupling of reactive ion budget and phase topology and application thereof, and belongs to the technical field of industrial solid waste resource utilization and low-carbon building materials. The application obtains basic physicochemical parameters of candidate industrial solid wastes, establishes a reactive ion fingerprint vector, and establishes a target product vector according to the use; a reactive ion budget model, a time-dimension ion budget function and a phase topology index model are constructed; a full-solid-waste cementing material is prepared by solving a matching scheme meeting a constraint condition, and model parameters are corrected based on performance feedback; the application improves solid waste matching from experience matching to computable design of "effective ion supply-time path matching-space topology continuity", realizes directional design and preparation of high-strength, fast-setting, low-carbon / pollution-resistant full-solid-waste cementing material without relying on an additional strong-alkali activator.

Description

Technical Field

[0001] This invention relates to the field of industrial solid waste resource utilization and low-carbon building materials technology, specifically to a method and application of compatibility design for all-solid waste cementitious materials based on reactive ion budget and phase topology coupling design. Background Technology

[0002] With the rapid development of industries such as metallurgy, power, chemicals, non-ferrous metals, and environmental governance, the generation of industrial solid wastes, including granulated blast furnace slag, steel slag, red mud, carbide slag, phosphogypsum, desulfurization gypsum, electrolytic manganese slag, lithium slag, phosphorus slag, and fly ash, continues to increase. Large-scale stockpiling of solid waste not only occupies land resources but also poses risks such as dust and leachate pollution. Co-preparing cementitious materials from industrial solid waste is an important way to achieve resource utilization, high-value extraction, and low-carbon building materials.

[0003] Existing methods for the compatibility design of industrial solid waste cementitious materials have the following main shortcomings:

[0004] First, the design is mostly based on total chemical composition or empirical formulation. Technicians often design formulations based on the total content of CaO, SiO2, Al2O3, SO3, etc., or through repeated screening via single-factor experiments and orthogonal experiments. However, even if the total oxide composition of slag, steel slag, red mud, gypsum waste, and fly ash from different sources and batches is similar, their active calcium, active silicon, active aluminum, soluble sulfate, and effective alkalinity may vary significantly, leading to large fluctuations in the final material properties and poor transferability.

[0005] Second, it neglects the timing of the reaction process. The formation of industrial solid waste gelling materials involves multiple stages, including early ion dissolution, mid-term framework phase formation, and late-stage gel densification. Current technologies only focus on the strength at a certain age, without considering the concentrations of Ca, Si, Al, and SO4 in different stages such as 0–24 h, 1–7 d, and 7–28 d. 2- The matching relationship between alkalinity release and coagulation makes it difficult to simultaneously consider coagulation time, early strength, late strength, and microstructure densification.

[0006] Third, there is a lack of systematic quantification of the spatial continuity and microstructure topological characteristics of the reaction products. Macroscopic performance depends not only on the formation of C-(A)-SH gels and AFt phases, but also on the continuity of the gel, the uniformity of the skeletal phase distribution, the refinement of pore size, and the density of the interfacial transition zone. Traditional empirical formulation and simple component design cannot incorporate these factors into a unified evaluation framework.

[0007] Fourth, it relies on the addition of high-alkali activators or cement clinker. Although some systems contain a high proportion of solid waste, they require the addition of NaOH, water glass, or cement clinker as the main activating components. They are not truly solid waste cementitious materials. Moreover, the addition of high-alkali activators has problems such as high cost, high safety risks, and high carbon emissions.

[0008] Fifth, there is a lack of a unified and transferable design framework for different applications. High-strength, dense, fast-setting skeleton, and low-carbon curing / fouling-resistant materials have different requirements for reaction products, ion budgets, and pore structures, and existing methods are difficult to take into account multiple application scenarios.

[0009] Therefore, there is an urgent need to provide a compatibility design method for all-solid waste cementitious materials that can be based on the actual reactivity of raw materials and take into account both time path and spatial topological continuity. Summary of the Invention

[0010] Purpose of the Invention: The purpose of this invention is to address the shortcomings of existing technologies by providing a method and its application for designing the compatibility of solid waste cementitious materials based on reactive ion budget and phase topology coupling. This method solves the technical problems of existing methods that rely solely on total chemical composition, neglect reaction pathway matching, lack microstructure continuity constraints, depend on external alkali activators, and are difficult to transfer to different applications. This invention starts with the testing of basic physicochemical parameters of candidate industrial solid waste raw materials and establishes a reactive ion fingerprint vector W. i The method combines the target product vector P, the reactive ion budget model B, the time-dimensional ion budget function B(t), and the phase topological index model T with actual ingredient preparation and performance back-substitution correction to form a complete technical closed loop from raw material testing to formulation implementation. This provides a unified method for the directional design of high-strength dense, fast-setting skeleton, and low-carbon curing / pollution-resistant all-solid waste cementitious materials.

[0011] Technical solution: The present invention provides a method for designing the compatibility of solid waste cementitious materials based on reactive ion budget and phase topology coupling, comprising the following steps:

[0012] S1. Select candidate industrial solid waste raw materials and determine the basic physicochemical parameters of each raw material. The basic physicochemical parameters include at least chemical composition, mineral composition, amorphous phase content, particle size distribution, specific surface area, leachate pH, water requirement, alkali-soluble active silicon content, alkali-soluble active aluminum content, acid-soluble or complex-soluble active calcium content, and soluble sulfate content.

[0013] The performance of all-solid-waste cementitious materials depends on the actual reactivity of the raw materials, not the nominal composition. Even with similar total oxides (CaO, SiO2, Al2O3, SO3), slag, steel slag, red mud, phosphogypsum, and other materials from different sources may exhibit significant differences in the release rate and amount of active components. Therefore, standardized testing is essential to obtain fundamental parameters that reflect the "actual reactivity."

[0014] S2. Based on the measurement results of S1, each raw material is characterized as a reactive ionic fingerprint vector. The It should include at least the effective calcium equivalent, effective silicon equivalent, effective aluminum equivalent, effective sulfate equivalent, effective alkalinity equivalent, effective water demand coefficient, and environmental or volume stability risk coefficient;

[0015] Preferably, the reactive ion fingerprint vector Wi is represented as:

[0016]

[0017] in, The effective calcium equivalent that can be released per unit mass of raw material within the target age period. This refers to the effective silicon equivalent that can be released per unit mass of raw material within the target age. This refers to the effective aluminum equivalent that can be released per unit mass of raw material within the target age period. The effective sulfate equivalent that can be released per unit mass of raw material within the target age period. This refers to the effective alkalinity equivalent that can be released per unit mass of raw material within the target age period. The effective water demand coefficient per unit mass of raw material. The environmental risk coefficient and / or volume stability risk coefficient per unit mass of raw material.

[0018] Traditional designs use total oxide content (such as total CaO and total SiO2), but most calcium in steel slag exists as an inert silicate phase and is almost not released within 28 days; while calcium in carbide slag exists almost entirely as Ca(OH)2 and can be released rapidly within hours. The total CaO of both may be the same, but their contributions to the gelation reaction are drastically different. S2 condenses each raw material into a seven-dimensional fingerprint vector, where each dimension is an "effective equivalent," i.e., the amount of ions that can actually be released and participate in the reaction within the target age, rather than the total content. The effective alkalinity equivalent is used to quantify the ability of the raw material to raise the pH of the system, the effective water demand coefficient affects workability and pore structure, and the risk coefficient directly incorporates environmental safety and volume stability into the design variables.

[0019] S3. Establish the target product vector based on the intended use. The target product vector It contains at least one or more of the following: C-(A)-SH gel, AFt phase, N-(C)-ASH gel, AFm phase, LDH phase, and carbonate dense phase;

[0020] The macroscopic properties of cementitious materials (strength, setting time, durability) are determined by their microscopic products. Different applications require different product compositions.

[0021] High-strength, dense structures require a C-(A)-SH gel as the primary component, which forms the core of strength; AFt acts as an auxiliary framework to provide early support; N-(C)-ASH and LDH assist in densification. Fast-setting framework structures require a large amount of AFt to rapidly form the framework, with early gel filling and later reinforcement by C-(A)-SH. Low-carbon curing / fouling-resistant structures require a synergistic effect of C-(A)-SH, LDH, AFt, and carbonates to achieve physical encapsulation, chemical fixation, and pore structure closure.

[0022] Preferably, the target product vector P includes not only the types of target reaction products, but also the target volume fraction range of each target product at the target age. The target volume fraction range is preferably determined through the following steps:

[0023] (1) Determine the target performance indicators based on the intended use;

[0024] (2) Select similar systems with qualified performance as reference samples, and obtain the mass fraction or area fraction of the main reaction product by XRD-Rietveld, TG / DTG, MIP and SEM / BSE image analysis;

[0025] (3) Convert the mass fraction to volume fraction based on the density of each product, or approximate the area fraction obtained from image segmentation as the volume fraction;

[0026] (4) The target volume fraction range is iteratively corrected by combining the effective calcium, effective silicon, effective aluminum, effective sulfate and effective alkalinity supply capacity of the raw material system.

[0027] Preferably, in the high-strength and dense system, the target product vector P is set to form a continuous C-(A)-SH gel network, with an appropriate amount of AFt providing early skeletal support; in the fast-setting skeletal system, the target product vector P is set to form a rapid skeletal structure with early AFt and rapid gel filling; in the low-carbon curing / fouling-resistant system, the target product vector P is set to form a synergistic encapsulation and fixation effect of C-(A)-SH, LDH, AFt and carbonate phases.

[0028] S4. Establish a reactive ion budget model. Synchronous topological index model ,in For the first The mass fraction of the raw materials, the phase topology index model It is constructed from two or more of the following: gel phase volume fraction, gel connectivity, uniformity of framework phase distribution, critical pore size, continuity of interface transition zone, and micro-region modulus dispersion.

[0029] It is a linear conservation equation. Given the target product P, the minimum total amount of effective ions required to form these products can be calculated. The strength of the material designed in this invention depends not only on "how many products are generated" but also on whether these products are spatially continuous. T is a comprehensive index that quantifies six microstructural characteristics—gel volume fraction, connectivity, framework uniformity, pore size refinement, interface continuity, and modulus uniformity—into a value between 0 and 1. The higher the T, the more continuous the gel network, the fewer the pores, and the denser the structure.

[0030] For any target product vector P, its corresponding total target ion demand vector D is defined as:

[0031]

[0032] The components are obtained by conversion using the volume fraction of the target product, the density of each target product, and stoichiometric relationships. Preferably, the volume fraction of the target product is first converted into the target mass of each target product per unit volume of gel, and then the total required amount of effective calcium, effective silicon, effective aluminum, effective sulfate, and effective alkalinity to form the target product is calculated based on the characteristic chemical composition and stoichiometric relationships of each target product.

[0033] Preferably, the target quality of the j-th target product is:

[0034]

[0035] in, Let j be the target volume fraction of the target product. Let be the density of the j-th target product.

[0036] For ionic component x, the total demand is:

[0037]

[0038] in, The coefficient of ionic component x required per unit mass of product to form the j-th target product.

[0039] S5. Mass fraction of candidate raw materials The solution is performed to ensure that the resulting formulation simultaneously satisfies the following constraints: effective calcium / effective silicon molar ratio, effective aluminum / effective silicon molar ratio, effective sulfate / effective aluminum molar ratio, initial effective basicity, water-cement ratio, and phase topological index not lower than a preset threshold. The risk factor is not higher than the preset threshold. ;

[0040] S6. According to the formula obtained by the solution, the industrial solid waste raw materials are batched, mixed, water is added, molded and cured to obtain a solid waste cementitious material. The cementitious solid components, except for the mixing water, are all derived from industrial solid waste.

[0041] This step involves two core constraints. First, apart from the mixing water, all cementitious solid components must originate entirely from industrial solid waste; cement clinker or commercial alkali activators (NaOH, water glass, etc.) cannot be added as the main activating components. This ensures the method's low-carbon nature and cost advantage. Second, the raw materials are divided into four categories: A (main precursor), B (endogenous alkali calcium source), C (sulfate source), and D (auxiliary regulation), with dosage windows provided for each category's intended use. These windows are derived from statistical summarization of extensive experimental data and represent empirical boundaries ensuring the system's functional balance.

[0042] S7. Perform performance testing on the prepared solid waste cementitious material and feed back the test results to correct the model parameters in S2 to S5.

[0043] Industrial solid waste originates from complex sources and exhibits significant batch-to-batch fluctuations, making it impossible for any offline model to fully predict its composition. S7 constructed a closed-loop optimization system: material preparation – measured performance – comparison with model predictions – reverse correction of parameters in Wi, B(t), and T – re-solution. After 1-3 iterations, the model converges to the optimal formulation for that batch of raw materials.

[0044] Preferably, the model parameter back-substitution correction adopts an iterative method of "error discrimination - parameter positioning - directional correction - re-solution". If the measured early intensity is lower than the predicted value, the parameters related to early effective alkalinity and effective calcium release are corrected first; if the measured later intensity is lower than the predicted value, the age-weighted parameters of effective silicon and effective aluminum are corrected first; if the pore structure or interface continuity does not meet the requirements, the relevant weight parameters in the phase topology index model T are corrected first; if the environmental or volume stability indicators do not meet the requirements, the corresponding weight parameters in the risk coefficient model are corrected first.

[0045] Preferably, the parameter iteration adopts the following formula:

[0046]

[0047] Where θ is the parameter to be corrected, and λ is the learning rate, preferably between 0.05 and 0.30. These are measured performance values. To predict performance values, ε is a small quantity to prevent zero. The parameter is the sensitivity direction coefficient.

[0048] Furthermore, the reactive ion fingerprint vector described in S2 for:

[0049] ,

[0050] In the formula, The effective calcium equivalent that can be released per unit mass of raw material within the target age period. This refers to the effective silicon equivalent that can be released per unit mass of raw material within the target age. This refers to the effective aluminum equivalent that can be released per unit mass of raw material within the target age period. This refers to the effective sulfate equivalent that can be released per unit mass of raw material within the target age period. H represents the effective alkalinity equivalent that can be released per unit mass of raw material within the target age period. i K is the effective water demand coefficient per unit mass of raw material. i The environmental risk coefficient and / or volume stability risk coefficient per unit mass of raw material.

[0051] Furthermore, the target application described in S3 includes one of the following: high-strength dense type, fast-setting skeleton type, and low-carbon curing / stain-resistant type, wherein the target product vector of the high-strength dense type... The target product vector of the rapidly solidifying framework is characterized by a C-(A)-SH gel as the main phase, an AFt phase as the auxiliary framework phase, and an N-(C)-ASH gel and / or an LDH phase as the auxiliary dense phase. The product is dominated by the AFt phase and the early gel phase, with a continuous gel reinforcement structure formed later; the target product vector of the low-carbon curing / pollution-resistant type is... It is mainly composed of C-(A)-SH gel, LDH phase, AFt phase and / or carbonate dense phase.

[0052] Furthermore, the constraints described in S5 satisfy the following conditions for different applications:

[0053] The high-strength and dense type of constraint conditions satisfy the following: effective calcium / effective silicon molar ratio 0.80-1.80, effective aluminum / effective silicon molar ratio 0.08-0.45, effective sulfate / effective aluminum molar ratio 0.15-1.50, initial effective alkalinity pH 11.0-13.5, and water-gel ratio 0.18-0.40.

[0054] The constraints of the fast-setting skeleton type are: effective calcium / effective silicon molar ratio of 0.90 to 2.00, effective aluminum / effective silicon molar ratio of 0.10 to 0.55, effective sulfate / effective aluminum molar ratio of 0.50 to 2.20, initial effective alkalinity of pH 11.2 to 13.8, and water-cement ratio of 0.22 to 0.55.

[0055] The constraints for the low-carbon curing / pollution-resistant type are: effective calcium / effective silicon molar ratio of 0.60 to 1.60, effective aluminum / effective silicon molar ratio of 0.08 to 0.50, effective sulfate / effective aluminum molar ratio of 0.10 to 1.80, initial effective alkalinity pH of 10.8 to 13.5, and water-to-gel ratio of 0.25 to 0.80.

[0056] The compatibility problem is a multi-objective, multi-constraint optimization problem. S5 incorporates the following constraints into its solution framework: three sets of molar ratio windows (effective Ca / Si, Al / Si, SO4 / Al): corresponding to the formation window of C-(A)-SH, the degree of gel crosslinking, and the stoichiometry of AFt formation, respectively. These windows are derived from a summary of extensive literature and experimental data; deviation from any window will lead to performance degradation. Initial effective alkalinity (pH window): too low, and the precursor will not dissolve sufficiently; too high, and the encapsulated particles may be momentarily passivated. Gel ratio window: too low, and the reaction will be incomplete; too high, and the porosity will increase and the strength will decrease. Phase topological index threshold T0: ensures that the microstructure meets the minimum densification requirements for the target application. Risk coefficient threshold K0: ensures environmental safety and volume stability.

[0057] Furthermore, a time-dimensional ion budget function is also established in S5. Ion release matching verification was performed for three stages: 0–24 h, 1–7 d, and 7–28 d. For any ion component, the cumulative release within the time interval [0, t] was defined as:

[0058] 5.

[0059] mi is the mass fraction of the i-th raw material, r i,x (τ) represents the instantaneous release rate of the ionic component x of the i-th raw material at time τ; the required cumulative release of effective alkalinity equivalent Q during the 0–24 h period. Alk (24h) Not less than 40% of the total demand DAlk, the cumulative release of effective calcium equivalent QCa (24h) Not less than 30% of the total demand DCa, and the cumulative release of effective silicon and effective aluminum equivalents Not less than 10% of their respective total demands; During the 1-7 day period, the cumulative release of effective silicon, effective aluminum, and effective calcium equivalents is required to reach more than 60% of their respective total demands, and the cumulative release of effective sulfate equivalents is maintained between 40% and 80% of the total demand; During the 7-28 day period, there is still a new release of effective silicon and effective aluminum equivalents, and the cumulative release over 28 days is no less than 15% higher than the cumulative release over 7 days.

[0060] Furthermore, the phase topology index model described in S4 It is expressed by the following formula: ,in, This is the normalized value for the volume fraction of the gel phase. This is the normalized value of gel connectivity. This is the normalized value for the uniformity of the skeletal phase distribution. This is the normalized value of the critical aperture. This is the normalized value for the continuity of the interface transition area. This represents the normalized value of the micro-region modulus dispersion. These are the weighting coefficients, and their sum is 1.

[0061] Furthermore, the industrial solid waste raw materials mentioned in S6 are functionally classified into Category A main precursor raw materials, Category B endogenous calcium-alkali activating raw materials, Category C sulfate regulating raw materials, and Category D auxiliary densifying or environmental regulating raw materials. Category A main precursor raw materials are selected from one or more of granulated blast furnace slag, lithium slag, phosphorus slag, fly ash, and coal gangue calcination products. Category B endogenous calcium-alkali activating raw materials are selected from one or more of carbide slag, red mud, steel slag, alkali slag, desulfurization ash, and white mud. Category C sulfate regulating raw materials are selected from one or more of phosphogypsum, desulfurization gypsum, electrolytic manganese slag, and titanium gypsum. Category D auxiliary densifying or environmental regulating raw materials are selected from one or more of steel slag, fly ash, red mud, carbonized slag, and stabilized fly ash.

[0062] Furthermore, when the target application is high-strength and dense, type A accounts for 45%–80%, type B accounts for 5%–30%, and type C accounts for 2%–20%; when the target application is fast-setting skeleton type, type A accounts for 25%–70%, type B accounts for 5%–35%, and type C accounts for 10%–45%; when the target application is low-carbon curing / stain-resistant type, type A accounts for 25%–75%, type B accounts for 5%–35%, type C accounts for 2%–25%, and type D accounts for 5%–35%.

[0063] A solid waste cementitious material is prepared using the method described above.

[0064] The above-mentioned solid waste cementitious materials are used in precast components, grouting materials, repair materials, road engineering materials, mine filling materials, contaminated site solidification and stabilization materials, or 3D printed building materials.

[0065] Beneficial effects: Compared with the prior art, the advantages of the present invention are as follows:

[0066] (1) The industrial solid waste formulation design is upgraded from the "total composition empirical method" to the "effective reactivity constraint method". Existing technologies usually design formulations based on total CaO, SiO2, Al2O3 or empirical trial formulations, which is difficult to accurately reflect the true usability of active components in different industrial solid waste raw materials. This invention establishes a reactive ion fingerprint vector Wi, incorporating active calcium, active silicon, active aluminum, soluble sulfate, effective alkalinity, water requirement characteristics and risk factors into a unified parameter system, which can more realistically characterize the contribution of different industrial solid waste raw materials to the gelation reaction.

[0067] (2) Simultaneously considering ion budget, reaction pathway, and microstructure continuity improves the accuracy of formulation design. This invention not only establishes a reactive ion budget model B to constrain the total amount of raw materials, but also establishes a time-dimensional ion budget function B(t) to match and verify the ion release pathways at each stage from 0 to 24 h, 1 to 7 d, and 7 to 28 d. Furthermore, it constrains gel continuity, framework phase distribution, pore structure refinement, and interfacial transition zone compaction through a phase topology index model T, thereby enabling the formulation design to take into account early reactions, later compaction, and final performance.

[0068] (3) It can realize the targeted design of solid waste cementitious materials for different applications. The present invention establishes different target product vectors P according to the target application, which can be used to design high-strength dense, fast-setting skeleton, and low-carbon curing / pollution-resistant solid waste cementitious materials, respectively, and has strong pertinence and versatility. For high-strength dense materials, higher 28-day compressive strength and lower porosity can be obtained; for fast-setting skeleton materials, faster setting and higher early strength can be taken into account; for low-carbon curing / pollution-resistant materials, medium strength, densification and pollutant curing and stabilization effects can be taken into account.

[0069] (4) Improve the adaptability to raw material fluctuations and the feasibility of engineering. Industrial solid waste has complex sources and large batch fluctuations. This invention takes raw material testing as a premise, model solving as an intermediate link, and actual preparation and performance back-substitution correction as a closed loop. It can dynamically modify the formula according to the differences in raw materials, thereby improving the adaptability of the design results to industrial solid waste from different sources and the feasibility of engineering.

[0070] (5) It is conducive to achieving true solid waste treatment and low admixture. In the method described in this invention, all the cementitious solid components of the solid waste cementitious material, except for the mixing water, are derived from industrial solid waste. It is preferably carried out under the condition that NaOH, KOH, water glass or cement clinker are not introduced as the main activating components, which is conducive to reducing the dependence on admixtures, reducing costs and carbon emissions, and improving the high-value utilization level of industrial solid waste.

[0071] (6) The complete method chain is more conducive to patent implementation and industrial transformation. This invention is not a simple mathematical calculation method, but a complete technical solution including the testing of basic physical and chemical parameters of raw materials, construction of reactive ion fingerprints, solution of reactive ion budgets, phase topological constraints, actual batch preparation, performance testing and model back-substitution correction. It has a clear material preparation object and engineering application scenario, and is suitable for industrial promotion. Attached Figure Description

[0072] Figure 1 This is a flowchart of the method of the present invention, which shows the complete process from the selection of candidate industrial solid waste raw materials, testing of basic physicochemical parameters, construction of reactive ion fingerprint vector Wi, establishment of target product vector P and its corresponding total ion demand vector D, calculation of reactive ion budget model B, calculation of time-dimensional ion budget function B(t), comparison of stage cumulative release amount with target total demand, evaluation of phase topology index model T, to raw material compatibility, material preparation, performance testing and model back-substitution correction. Detailed Implementation

[0073] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the embodiments described.

[0074] To ensure the reproducibility of model parameters and the comparability of embodiments, the basic physical and chemical parameters in this invention are determined using the following unified method.

[0075] 1. Detection Method

[0076] 1.1 Chemical Composition of Raw Materials

[0077] The contents of CaO, SiO2, Al2O3, SO3, Fe2O3, MgO, Na2O, K2O and other major oxides in various industrial solid waste raw materials were determined by X-ray fluorescence spectrometry (XRF).

[0078] 1.2 Composition of raw material minerals and content of amorphous phase

[0079] The mineral composition was determined by X-ray diffraction (XRD), and the content of amorphous phases was estimated by internal or external standard methods.

[0080] 1.3 Particle size distribution and specific surface area

[0081] D10, D50, and D90 were determined using laser particle size analysis; specific surface area was determined using the Blaine permeability method or the BET method.

[0082] 1.4 pH and conductivity of raw material leachate

[0083] Weigh the sample at a liquid-to-solid ratio of 10:1 (mL:g), shake at room temperature for 2 h, and then let it stand. Measure the pH and conductivity of the leachate.

[0084] 1.5 Determination of active calcium, active silicon, active aluminum and soluble sulfate ions

[0085] (1) Active silicon and active aluminum: Weigh 1.00 g of sample, add 100 mL of 1 mol / L NaOH solution, and shake and extract at 25℃ for 6 h and 24 h. After filtration, determine the Si and Al content in the filtrate respectively. Use the value measured at 24 h as the base value of effective silicon equivalent and effective aluminum equivalent in this invention, and introduce age weighting coefficient as needed.

[0086] (2) Active calcium: Weigh 1.00 g of sample, add 100 mL of 0.5 mol / L NH4Cl solution, shake and extract at 25℃ for 2 h, filter and determine the Ca content in the filtrate; if necessary, the results of weak acid extraction can be corrected.

[0087] (3) Soluble sulfate: Weigh 1.00 g of sample, add 100 mL of deionized water, shake and extract at 25℃ for 2 h, filter and determine the SO4²⁻ content in the filtrate; if necessary, the results of weak acid extraction can be supplemented.

[0088] 1.6 Effective water demand coefficient Hi

[0089] Each raw material is separately prepared into a paste. The effective water demand coefficient Hi is calculated by converting the amount of water required to achieve the predetermined fluidity, and then normalized according to the following formula:

[0090]

[0091] Where W_i is the amount of water required for the raw material to reach the predetermined fluidity, and W_{\text{ref}} is the amount of water required for the reference slag to reach the same fluidity.

[0092] 1.7 Environmental risk coefficient and volumetric stability risk coefficient Ki

[0093] Ki is constructed using one or more of the following: free CaO content, free MgO content, normalized value of heavy metal leaching concentration, soluble salt content, stability test expansion value, and 90-day length change rate. Preferably, it is constructed as follows:

[0094]

[0095] Wherein, F_{\text{ca}} is the normalized value of free CaO, F_{\text{mg}} is the normalized value of free MgO, L_e is the normalized value of leaching risk, E_x is the normalized value of length change rate, S_s is the normalized value of soluble salt, and b_1 to b_5 are weighting coefficients and their sum is 1.

[0096] 1.8 Reactive Ion Fingerprint Vector Wi

[0097] Each raw material was characterized as follows:

[0098]

[0099] in:

[0100] The active calcium values ​​obtained by 1.5(2) were weighted by age;

[0101] and The 24-hour values ​​obtained by 1.5(1) were weighted by age;

[0102] The value obtained using 1.5(3) is used;

[0103] The calculation is based on a combination of the leachate's pH, alkalinity titration value, and soluble alkali metal content.

[0104] H i Measured according to 1.6;

[0105] K i Build according to 1.7.

[0106] Preferably, each effective equivalent parameter in Wi can be represented in a phased weighted form according to the target age, that is:

[0107]

[0108] in, Let be the comprehensive effective equivalent of the i-th raw material for a certain effective ionic component. For the effective equivalent of the nth age stage, For the corresponding age option weighting coefficient, and .

[0109] 1.9 Time-dimensional ion budget function B(t)

[0110] Using 0–24 h, 1–7 d, and 7–28 d as three budget stages, and employing the 24 h activity value, the 3 d heat of reaction or intensity correction value, and the 28 d residual unreacted proportion correction value respectively, W_i is discretized over time to obtain W_i(t), and then the following is established:

[0111]

[0112] For any ionic component x, its cumulative release over the time interval [0, t] is defined as:

[0113]

[0114] in, Let be the cumulative release amount up to time t, and let mi be the mass fraction of the i-th raw material. Let be the instantaneous release rate of the i-th raw material for ionic component x at time τ.

[0115] In the engineering calculations of discrete time steps, the cumulative release amount can also be expressed as a piecewise summation as follows:

[0116]

[0117] in, Let x be the increment of ionic component x released by the i-th raw material in the k-th time step.

[0118] 1.10 Phase topological index T

[0119] The phase topology index T is constructed from four or more of the following six factors: gel phase volume fraction Gv, gel connectivity Gc, framework phase distribution uniformity Eu, critical pore size Pc, interfacial transition zone continuity I{tz}, and microregion modulus dispersion Cd. The following formula is preferred:

[0120]

[0121] Among them, G v G c E u P c I tz C d All are normalized to 0 to 1, and a1 to a6 are weight coefficients with a total sum of 1.

[0122] The preferred weights are: a1=0.22, a2=0.20, a3=0.14, a4=0.18, a5=0.16, a6=0.10.

[0123] The parameters are preferably obtained in the following manner:

[0124] G v The results were obtained through thermogravimetric analysis, XRD-Rietveld quantitative analysis, and mass balance conversion, and normalized to 0–1.

[0125] Gc The proportion of gel-connected connected regions after BSE image segmentation was calculated and normalized to 0-1;

[0126] E u The area distribution dispersion of the skeleton phase in multiple fields of view is reverse-converted and normalized to 0 to 1.

[0127] P c The critical pore size, measured by mercury porosimetry, is normalized to 0–1 by a range linear transformation relative to the reference system.

[0128] I tz The grayscale continuity or nanoindentation modulus gradient dispersion in the interface transition region is converted and normalized to 0 to 1.

[0129] C d The values ​​are converted using the standard deviation or coefficient of variation of the micro-region modulus and normalized to 0–1.

[0130] Preferably, when some microscopic test data is missing, alternative parameters can be used for approximate evaluation, but at least two of the following should be included: gel phase volume fraction, critical pore size, and interfacial continuity.

[0131] 1.11 Setting time, fluidity, mechanical properties and durability

[0132] Flowability: Tested according to the current method for testing the flowability of cementitious slurry or mortar; Setting time: Tested according to the current method for testing the setting time of cementitious materials; Compressive strength and flexural strength: Tested according to the current method for testing cementitious mortar or mortar specimens; Volume stability: Tested according to the current method for testing the stability of cementitious materials and the method for testing the change in length; Total porosity and critical pore size: Tested using mercury intrusion porosimetry (MIP); Microstructure: Characterized using XRD and SEM / BSE; Heavy metal leaching: Tested using the current method for testing the leaching of solid waste.

[0133] 2. General steps for solving the model

[0134] In this invention, the reactive ion fingerprint vector Wi is used to characterize the effective reaction potential of a single raw material within a target age; the reactive ion budget model B is used to summarize the total reaction resources available to the entire system under a given ratio; the time-dimensional ion budget function B(t) is used to describe the release and availability of the above total reaction resources at different age stages; the target product vector P is used to define the target reaction product system required for materials of different uses; and the phase topology index model T is used to evaluate the continuity and compaction of the reaction product network formed under the constraints of the reaction resources.

[0135] Preferably, the unified calculation logic of the present invention is as follows: First, construct Wi based on the basic physicochemical parameters of each candidate raw material; then calculate the total budget B and dynamic budget B(t) based on the raw material mass fraction mi; then compare B and B(t) with the total ion demand vector D corresponding to the target product vector P to determine whether the ion supply matches the target product formation demand; finally, evaluate the structure formation quality through the phase topology index model T, and complete the compatibility screening and back-substitution correction accordingly.

[0136] 2.1 Functional Classification of Raw Materials

[0137] Candidate industrial solid wastes are classified into four categories based on their chemical function in the gelation system: Category A (Main Precursor Raw Materials): Provides active Si, Al, and some Ca, serving as the main source of the gel network. Selected from one or more of granulated blast furnace slag, lithium slag, phosphorus slag, fly ash, and coal gangue calcination products. Category B (Endogenous Calcium-Alkali Activating Raw Materials): Provides Ca(OH)₂ or alkaline oxides, increasing the system pH and promoting precursor dissolution. Selected from one or more of carbide slag, red mud, steel slag, alkaline slag, desulfurization ash, and white mud. Category C (Sulfate Controlling Raw Materials): Provides soluble SO₄²⁻, controlling the formation of framework phases such as AFt. Selected from one or more of phosphogypsum, desulfurization gypsum, electrolytic manganese slag, and titanium gypsum. Category D (Auxiliary Densification or Environmental Control Raw Materials): Adjusts particle size distribution, fills pores, or immobilizes pollutants. Selected from one or more of steel slag, fly ash, red mud, carbonized slag, and stabilized fly ash.

[0138] 2.2 Establish the target product vector P

[0139] Based on the target application (high-strength dense type, fast-setting skeleton type, or low-carbon curing / stain-resistant type), determine the required dominant reaction products and their target volume fraction range, and form the target product vector P.

[0140] The volume fraction of each product phase in the target product vector P is preferably calculated or estimated using the following method:

[0141] (1) First, determine the target performance indicators based on the intended use, including strength grade, setting time range, pore structure density and pollution resistance requirements;

[0142] (2) Select similar systems that achieve the corresponding performance targets as reference samples, and use XRD-Rietveld quantitative analysis, TG / DTG, MIP and SEM / BSE image analysis to obtain the mass fraction or area fraction of the main reaction products;

[0143] (3) Based on the density of each product, convert the mass fraction of each product into a volume fraction, or approximate the area fraction obtained by segmenting the BSE image as a volume fraction;

[0144] (4) The volume fraction range is iteratively corrected by combining the target performance with the achievable effective ion supply capability.

[0145] For the j-th target product, its target volume fraction It can be converted using the following formula:

[0146]

[0147] in, For the target quality of the j-th target product, Let be the density of the j-th target product.

[0148] Specifically as follows:

[0149] High-strength and dense type: The target volume fraction of C-(A)-SH gel in P is set at 45%–75%, AFt phase at 5%–20%, and N-(C)-ASH and / or LDH phase at 5%–20%. Preferably, C-(A)-SH is the main phase, AFt is the auxiliary framework phase, and N-(C)-ASH and LDH are the auxiliary dense phases.

[0150] Rapid-setting skeleton type: The target volume fraction of AFt phase in P is set at 20%–45%, early gel phase at 20%–40%, and later reinforcing gel phase at 10%–30%. The early stage requires AFt and initial gel to form the skeleton, with later stage reinforcement by C-(A)-SH and / or N-(C)-ASH. Low-carbon curing / pollution-resistant type: The total integral of C-(A)-SH gel, LDH phase, AFt phase, and carbonate dense phase in P is set at no less than 60%. The preferred volume fraction ranges for each product phase are: C-(A)-SH gel 25%–50%, LDH phase 5%–20%, AFt phase 5%–20%, and carbonate dense phase 5%–20%. A composite structure is required to achieve physical encapsulation and chemical fixation of pollutants.

[0151] 2.3 Reactive Ion Fingerprint Vector and Budget Model

[0152] The active components of each raw material were determined according to the methods described in 1.5 to 1.10, and a reactive ionic fingerprint vector was established.

[0153]

[0154] Then, a total ion budget model is established:

[0155]

[0156] Where mi is the mass fraction of the i-th raw material.

[0157] For any target product vector P, its corresponding total target ion demand vector is defined as:

[0158]

[0159] in, The total required amount of ionic component x to form the target product vector P. Preferably, the target mass of each target product is first calculated based on the volume fraction and density of the target product, and then the corresponding mass is calculated based on the characteristic chemical composition and stoichiometry of each target product. and .

[0160] Therefore, when solving for the formulation, it should be ensured that the total effective ion supply is not less than the minimum ion budget required to form the target product vector P.

[0161] 2.4 Matching and Solving Constraints

[0162] Raw material mass fraction Solve the problem to obtain a formula that simultaneously satisfies the following constraints:

[0163] Effective calcium / effective silicon molar ratio ;

[0164] Effective aluminum / effective silicon molar ratio ;

[0165] effective sulfate / effective aluminum molar ratio ;

[0166] Initial effective alkalinity (based on leachate pH);

[0167] Water-to-binder ratio;

[0168] Phase topology index ( The preset threshold is set according to the application, such as high-strength and dense type. Fast-setting skeleton type Low-carbon curing / stain-resistant type );

[0169] Risk coefficient ( For a preset threshold, such as ).

[0170] The optimal formulation solution is performed in the following order: First, determine the target product vector P and its corresponding total target ion demand vector D based on the target application; then, calculate the total budget B based on the reactive ion fingerprint vector Wi and the raw material mass fraction mi; next, calculate the cumulative release amount Qx(t) at each stage based on the time-dimensional ion budget function B(t), and check whether it meets the stage release ratio requirements corresponding to the total target demand Dx; finally, calculate the phase topology index T based on the predicted product formation state, and compare it with the preset threshold T0 to screen candidate formulations that meet the constraints.

[0171] 2.5 Time-dimensional ion release matching verification

[0172] Establish the time-dimensional ion budget function:

[0173]

[0174] in It is obtained by discretization using the method in Section 1.9.

[0175] For any ionic component x, its cumulative release over the time interval [0, t] is defined as:

[0176]

[0177] in, Let be the cumulative release amount up to time t, and let mi be the mass fraction of the i-th raw material. Let be the instantaneous release rate of the i-th raw material for ionic component x at time τ.

[0178] For engineering calculations at discrete time steps, the cumulative release amount can also be expressed as:

[0179]

[0180] in, Let x be the increment of ionic component x released by the i-th raw material in the k-th time step.

[0181] For any target product vector P, its corresponding total target ion demand vector is defined as:

[0182]

[0183] in, The total amount of ionic component x required to form the target product vector P.

[0184] The results were verified for three phases: 0–24 h, 1–7 d, and 7–28 d.

[0185] 0–24 h phase: Required cumulative release of effective alkalinity equivalent Not less than the total demand 40% of the effective calcium equivalent The cumulative release amount is not less than the total demand. 30% of the total demand is required, and the cumulative release of effective silicon and effective aluminum equivalents is not less than 10% of their respective total demand, in order to meet the requirements for nucleation and dissolution initiation.

[0186] Stage 1–7: The cumulative release of effective silicon, effective aluminum, and effective calcium equivalents should reach more than 60% of their respective total requirements, and the cumulative release of effective sulfate equivalents should be maintained between 40% and 80% of the total requirements to ensure the synergistic formation of the main gel and the framework.

[0187] 7–28 d stage: It is required that there is still a new release of effective silicon and effective aluminum equivalent, and the cumulative release in 28 days is no less than 15% higher than the cumulative release in 7 days, so as to form a secondary gel layer around the unreacted particles and achieve densification in the later stage.

[0188] If the conditions of any of the above stages are not met, the raw material ratio should be adjusted or the types of raw materials should be changed, and the solution should be obtained again.

[0189] 2.6 Preparation and Back-substitution

[0190] Materials were prepared according to the obtained formula and their properties were tested (see Examples 1-3). If the measured properties (such as strength, porosity, setting time, leaching concentration, etc.) deviated from the target values, the parameters in Wi, B(t), and T were corrected by back substitution using the following rules, and the iteration was repeated 1-3 times:

[0191] (1) If the measured early strength is more than 10% lower than the predicted value, and the condensation time is too long and the heat flow peak is delayed, then the weighting coefficient or release rate coefficient related to the early effective alkalinity and early effective calcium release should be increased first.

[0192] (2) If the measured 28-day strength is more than 10% lower than the predicted value, and TG / XRD indicates that the main gel product is not formed enough, then the age weighting coefficient of effective silicon and effective aluminum should be increased first, or the target product formation efficiency coefficient should be corrected.

[0193] (3) If SEM / BSE or MIP indicates that the pore structure is coarsened and the interface continuity is insufficient, then adjust the weight parameters related to gel connectivity, critical pore size and interface continuity in the phase topology index model T.

[0194] (4) If the volume stability or leaching performance does not meet the requirements, the weight of the corresponding risk item in the risk coefficient model shall be increased and the mass fraction constraint of the relevant raw materials shall be tightened.

[0195] (5) Preferably, the parameter back-substitution correction adopts the following iterative form:

[0196]

[0197] Where θ is the parameter to be corrected, and λ is the learning rate, preferably between 0.05 and 0.30. These are measured performance values. To predict performance values, ε is a small quantity to prevent zero. The parameter is the sensitivity direction coefficient.

[0198] Preferably, the back-substitution correction preferentially applies to the age-weighted coefficient in Wi, the stage release ratio parameter in B(t), and the weight parameter in T, and the correction magnitude in a single iteration does not exceed 20% of the original parameter value.

[0199] 3. Example 1: High-strength, dense, all-solid waste cementitious material

[0200] 3.1 Raw Materials

[0201] The industrial solid wastes used include: granulated blast furnace slag, lithium slag, carbide slag, red mud, phosphogypsum, steel slag, and fly ash. All raw materials are dried and passed through a 0.15 mm sieve before use. Slag and lithium slag serve as the main precursors, carbide slag and red mud as endogenous calcium-alkali activating components, phosphogypsum as a sulfate regulating component, and steel slag and fly ash as auxiliary densifying components.

[0202] 3.2 Basic parameters of raw materials

[0203] The main basic parameters measured are as follows:

[0204] (1) Granulated blast furnace slag: D50=12.8 μm, Blaine specific surface area 432 m² / kg, leachate pH=10.7; main oxides: CaO 39.6%, SiO2 33.1%, Al2O3 14.3%.

[0205] (2) Lithium slag: D50=15.4 μm, Blaine specific surface area 405 m² / kg, leachate pH=9.8; main oxides: SiO2 52.4%, Al2O3 19.5%, CaO 8.1%, SO3 4.3%.

[0206] (3) Carbide slag: D50=18.1 μm, leachate pH=12.6; the main components are Ca(OH)2 and CaCO3, with CaO accounting for about 62.8%.

[0207] (4) Red mud: D50=9.7 μm, BET specific surface area 9.6 m² / g, leachate pH=10.9; main oxides: Fe2O3 34.2%, Al2O3 19.8%, CaO 11.6%, SiO2 14.7%.

[0208] (5) Phosphogypsum: D50=21.3 μm, leachate pH=5.2; mainly CaSO4·2H2O, with high soluble SO4²⁻.

[0209] (6) Steel slag: D50=17.6 μm, Blaine specific surface area 368 m² / kg, leachate pH=11.3; main oxides: CaO 41.2%, SiO2 14.8%, Fe2O3 22.7%, MgO 6.9%.

[0210] (7) Fly ash: D50=19.2 μm, Blaine specific surface area 356 m² / kg, leachate pH=9.6; main oxides: SiO2 51.7%, Al2O3 27.4%, CaO 7.5%.

[0211] 3.3 Reactive Ion Fingerprint and Model Parameters

[0212] The key model parameters corresponding to the formulation in this embodiment were obtained by measuring and converting according to the methods described in 1.5 to 1.10 as follows:

[0213] = 1.08

[0214] = 0.19

[0215] = 0.42

[0216] Initial effective alkalinity pH = 12.1

[0217] Hi overall score = 1.06

[0218] K = 0.31

[0219] T = 0.72

[0220] T0 is set to 0.65 and K0 is set to 0.45.

[0221] 3.4 Formulation

[0222] By mass percentage of solid components: granulated blast furnace slag: 46%; lithium slag: 10%; calcium carbide slag: 8%; red mud: 7%; phosphogypsum: 9%; steel slag: 10%; fly ash: 10%; water-cement ratio: 0.28. No NaOH, water glass, or cement clinker are added as main activating components.

[0223] 3.5 Preparation method

[0224] After dry mixing the above solid raw materials for 5 minutes, add mixing water and mechanically stir for 4 minutes to prepare neat slurry and mortar samples. After molding, the samples are cured at 40℃ and relative humidity not less than 95% for 24 hours. After demolding, they are transferred to 20℃ and relative humidity not less than 95% for curing until the test age.

[0225] 3.6 Test Results

[0226] Slurry fluidity: 162 mm

[0227] Initial setting time: 118 min

[0228] Final setting time: 196 min

[0229] 1-day compressive strength: 17.8 MPa

[0230] 3-day compressive strength: 31.6 MPa

[0231] 7-day compressive strength: 45.9 MPa

[0232] 28-day compressive strength: 58.4 MPa

[0233] 90-day compressive strength: 64.1 MPa

[0234] 28-day flexural strength: 8.1 MPa

[0235] 90-day length change rate: 0.036%

[0236] Total porosity: 15.2%

[0237] Critical pore size: 31 nm

[0238] 3.7 Microstructure Characterization

[0239] XRD analysis showed that the main reaction product was C-(A)-SH gel, with a small number of AFt characteristic peaks; SEM / BSE observation showed that a continuous gel coating layer was formed around the slag and lithium slag particles, with few pores; MIP test showed that the pore structure was mainly distributed with small pore size and the critical pore size was small.

[0240] In this embodiment, the compressive strength at 28 days is consistently above 50 MPa, the strength at 90 days continues to increase, and the length change rate at 90 days is low, indicating that it has good high strength and densification characteristics.

[0241] 4. Example 2: Rapid-setting skeleton-type all-solid waste cementitious material

[0242] 4.1 Raw Materials

[0243] The industrial solid wastes used include: granulated blast furnace slag, carbide slag, red mud, phosphogypsum, desulfurized gypsum, electrolytic manganese slag, steel slag, and fly ash. Among them, phosphogypsum, desulfurized gypsum, and electrolytic manganese slag together serve as sulfate regulating components; carbide slag and red mud serve as endogenous calcium-base activating components; and blast furnace slag serves as the main precursor.

[0244] 4.2 Basic parameters of raw materials

[0245] All raw materials were dried, crushed, and sieved before use. The leaching solution of electrolytic manganese slag had a pH of 6.3 and a D50 of 16.8 μm, with a high SO3 content among the main oxides; the desulfurized gypsum had a D50 of 19.5 μm; the basic parameters of the remaining slag, carbide slag, red mud, steel slag, and fly ash were similar to or close to those of batches in Example 1.

[0246] 4.3 Model Parameters

[0247] After measurement and solution, the key model parameters are:

[0248] = 1.34

[0249] = 0.24

[0250] = 1.08

[0251] Initial effective alkalinity pH = 12.4

[0252] H_i composite value = 1.09

[0253] K = 0.38

[0254] T = 0.63

[0255] Among them, the target for rapid coagulation skeleton type is set to T_0 as 0.58 and K_0 as 0.50.

[0256] 4.4 Formula

[0257] Based on the mass percentage of solid components:

[0258] Granulated blast furnace slag: 34%

[0259] Calcium carbide slag: 9%

[0260] Red mud: 7%

[0261] Phosphogypsum: 16%

[0262] Desulfurized gypsum: 10%

[0263] Electrolytic manganese slag: 8%

[0264] Steel slag: 8%

[0265] Fly ash: 8%

[0266] The water-cement ratio is 0.33. No NaOH, water glass, or cement clinker are added as the main activating components.

[0267] 4.5 Preparation method

[0268] Dry mix all solid raw materials for 4 minutes, add water and stir at high speed for 3 minutes, then immediately form the sample. Cur the sample at 20℃ and relative humidity not less than 95% until the test age.

[0269] 4.6 Test Results

[0270] Initial fluidity of the slurry: 176 mm

[0271] Flowability at 15 min: 154 mm

[0272] 30-minute flowability: 139 mm

[0273] Initial setting time: 26 min

[0274] Final setting time: 43 min

[0275] 30-minute compressive strength: 1.6 MPa

[0276] 1-hour compressive strength: 3.4 MPa

[0277] 3-hour compressive strength: 7.9 MPa

[0278] 24-hour compressive strength: 18.6 MPa

[0279] 7-day compressive strength: 31.7 MPa

[0280] 28-day compressive strength: 39.8 MPa

[0281] 28-day flexural strength: 6.1 MPa

[0282] 28-day length change rate: 0.061%

[0283] 4.7 Microstructure Characterization

[0284] XRD analysis showed that the AFt characteristic peak could be detected at 6 h, and the AFt characteristic was enhanced after 24 h, while a broad diffuse peak appeared, indicating that the gel phase gradually formed. SEM observation showed that in the early stage, there was a needle-like skeleton phase and a filling gel together forming a supporting structure. The flowability loss curve showed that the flowability remained at a workable level within 30 min, and no obvious false setting phenomenon was observed.

[0285] This embodiment takes into account early rapid setting, high early strength, and certain late-stage reinforcement capabilities, and is suitable for rapid-setting skeleton-type engineering application scenarios.

[0286] 5. Example 3: Low-carbon curing / pollution-resistant all-solid waste cementitious material

[0287] 5.1 Raw Materials

[0288] The industrial solid wastes used include: granulated blast furnace slag, red mud, steel slag, carbide slag, desulfurization gypsum, phosphogypsum, fly ash, and simulated contaminated soil containing heavy metals. The simulated contaminated soil was prepared by using natural sandy soil as a matrix and adding P... b C d and C r Salt preparation to reduce P in the initial leachate of contaminated soil b C d C r It has measurable concentration.

[0289] 5.2 Simulation of initial properties of contaminated soil

[0290] The initial total amount of pollutants in the simulated contaminated soil is: P b 520 mg / kg, C d 68 mg / kg, C r 410 mg / kg. The leaching concentration of the unconsolidated simulated contaminated soil, measured according to the leaching test method, was: P b 8.4 mg / L, C d 1.7 mg / L, C r 5.6 mg / L.

[0291] 5.3 Basic parameters of other raw materials

[0292] The basic parameters of slag, red mud, steel slag, carbide slag, desulfurized gypsum, phosphogypsum and fly ash are the same as or similar to those of the batch in Example 1.

[0293] 5.4 Model Parameters

[0294] After measurement and solution, the key model parameters are:

[0295] = 0.98

[0296] = 0.21

[0297] = 0.36

[0298] Initial effective alkalinity pH = 11.7

[0299] Hi overall score = 1.11

[0300] K = 0.40

[0301] T = 0.60

[0302] Among them, the target for low-carbon curing / pollution resistance is set to T_0 as 0.55 and K_0 as 0.52.

[0303] 5.5 Formula

[0304] Based on the mass percentage of solid components:

[0305] Granulated blast furnace slag: 34%

[0306] Red mud: 14%

[0307] Steel slag: 16%

[0308] Calcium carbide slag: 6%

[0309] Desulfurized gypsum: 8%

[0310] Phosphogypsum: 4%

[0311] Fly ash: 12%

[0312] Simulated contaminated soil: 6%

[0313] The water-cement ratio is 0.40. No NaOH, water glass, or cement clinker are added as the main activating components.

[0314] 5.6 Preparation method

[0315] After dry mixing all solid raw materials evenly, water is added and stirred for 4 minutes to form the sample. The sample is cured at 20℃ and relative humidity not less than 95% for 48 hours, then carbonized in a 10% CO2 environment for 8 hours, and then transferred to room temperature for wet curing until the test age.

[0316] 5.7 Test Results

[0317] Slurry fluidity: 171 mm

[0318] Initial setting time: 82 min

[0319] Final setting time: 151 min

[0320] 3-day compressive strength: 10.4 MPa

[0321] 7-day compressive strength: 17.9 MPa

[0322] 28-day compressive strength: 27.6 MPa

[0323] 90-day compressive strength: 31.2 MPa

[0324] 28-day flexural strength: 4.9 MPa

[0325] Total porosity after 28 days: 18.9%

[0326] 28-day critical pore size: 48 nm

[0327] Weight gain due to carbonization: 2.6%

[0328] 5.8 Leaching and Stain Resistance

[0329] The leaching concentrations of the solidified specimens after 28 days were: Pb 0.42 mg / L, Cd 0.09 mg / L, and Cr 0.31 mg / L. Compared to the unsolidified simulated contaminated soil, the leaching concentrations of Pb, Cd, and Cr decreased by approximately 95.0%, 94.7%, and 94.5%, respectively. After immersion in a 5% sodium sulfate solution for 60 days, the specimen strength retention rate was 87%.

[0330] 5.9 Microstructure Characterization

[0331] XRD analysis showed that C-(A)-SH gel, a small amount of AFt and carbonate phase were formed in the material; SEM observation showed that the contaminated soil particles were wrapped by gel and reaction products, and the degree of pore connectivity was reduced; leaching results showed that the system has a significant solidification and stabilization effect on simulated heavy metal pollutants.

[0332] This embodiment avoids the broad description of "any heavy metal waste residue" and uses simulated contaminated soil with clear sources and initial concentrations, demonstrating the comparability and repeatability of the embodiment.

[0333] 6. Comparative Example 1: High-intensity model without constraint optimization using the RIB-PT model

[0334] 6.1 Formula

[0335] Based on the mass percentage of solid components: granulated blast furnace slag 40%, steel slag 16%, red mud 10%, phosphogypsum 14%, fly ash 12%, carbide slag 8%, with a water-cement ratio of 0.28. This comparative example only determined the formula based on the total oxide ratio and empirical trial mixing, without establishing constraints on W_i, B(t), and T.

[0336] 6.2 Results

[0337] Slurry fluidity: 158 mm

[0338] 7-day compressive strength: 30.7 MPa

[0339] 28-day compressive strength: 42.5 MPa

[0340] 90-day compressive strength: 46.1 MPa

[0341] Total porosity: 20.8%

[0342] Critical aperture: 67 nm

[0343] 90-day length change rate: 0.088%

[0344] 6.3 Analysis

[0345] Compared with Example 1, although Comparative Example 1 has a similar overall chemical composition, its strength, pore structure and volume stability are poor because it did not perform constraint optimization on effective ion budget, time-dimensional ion release and phase topological continuity, indicating that the method of the present invention has substantial effect.

[0346] 7. Comparative Example 2: Comparison of fast-setting type without sulfate pathway control

[0347] 7.1 Formula

[0348] Based on Example 2, the total amount of phosphogypsum, desulfurized gypsum and electrolytic manganese slag was reduced to 18%, with the remainder made up with fly ash.

[0349] 7.2 Results

[0350] Initial setting time: 61 min

[0351] Final setting time: 104 min

[0352] 30-minute compressive strength: 0.4 MPa

[0353] 3-hour compressive strength: 3.1 MPa

[0354] 24-hour compressive strength: 11.2 MPa

[0355] 28-day compressive strength: 35.3 MPa

[0356] 7.3 Analysis

[0357] When the available sulfate budget is insufficient, the formation of the AFt framework phase is limited, and the effect of the rapid-setting framework is significantly reduced, indicating that window control of R_{SO_4 / Al}^{act} is necessary for this type of material.

[0358] 8. Comparative Example 3: Comparison of pollution-resistant types without carbonization assistance and risk control

[0359] 8.1 Formula

[0360] Same as Example 3, but CO2-assisted maintenance is cancelled, and K-value constraint optimization is not performed.

[0361] 8.2 Results

[0362] 28-day compressive strength: 23.1 MPa

[0363] Pb leaching concentration: 0.93 mg / L

[0364] Cd leaching concentration: 0.18 mg / L

[0365] Cr leaching concentration: 0.74 mg / L

[0366] 60-day strength retention rate: 79%

[0367] 8.3 Analysis

[0368] After removing carbonization assistance and risk constraints, the densification degree and leaching control effect of the material both decreased, indicating that K-value control and process path control in the model of this invention are necessary.

[0369] 9. Conclusions of the Examples

[0370] As can be seen from Examples 1-3 and Comparative Examples 1-3, this invention, by constructing a reactive ion fingerprint vector W_i, a reactive ion budget model B, a time-dimensional ion budget function B(t), and a phase topology index model T, and combining this with a risk coefficient K to constrain and optimize the raw materials, can obtain stable all-solid waste cementitious materials for different applications. Wherein:

[0371] The high-strength and dense type embodiment exhibits a stable compressive strength exceeding 50 MPa at 28 days under conditions of low water-cement ratio and moderate heat and humidity curing, and its strength continues to increase at 90 days with a relatively dense pore structure.

[0372] The fast-setting skeleton type embodiment combines rapid setting, workability and flowability, and high early strength, making it more suitable for engineering application scenarios;

[0373] The low-carbon curing / pollution-resistant embodiment exhibits high curing and stabilization efficiency for simulated heavy metal pollutants while maintaining moderate strength.

[0374] Therefore, the method of the present invention not only has theoretical compatibility guidance significance, but also has clear operability and feasibility.

[0375] As described above, although the invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for designing the compatibility of all-solid waste cementitious materials based on reactive ion budget and phase topological coupling, characterized in that... Includes the following steps: S1. Select candidate industrial solid waste raw materials and determine the basic physicochemical parameters of each raw material. The basic physicochemical parameters include at least chemical composition, mineral composition, amorphous phase content, particle size distribution, specific surface area, leachate pH, water requirement, alkali-soluble active silicon content, alkali-soluble active aluminum content, acid-soluble or complex-soluble active calcium content, and soluble sulfate content. S2. Based on the measurement results of S1, each raw material is characterized as a reactive ionic fingerprint vector. The It should include at least the effective calcium equivalent, effective silicon equivalent, effective aluminum equivalent, effective sulfate equivalent, effective alkalinity equivalent, effective water demand coefficient, and environmental or volumetric stability risk coefficient. S3. Establish the target product vector based on the intended use. The target product vector It contains at least one or more of the following: C-(A)-SH gel, AFt phase, N-(C)-ASH gel, AFm phase, LDH phase, and carbonate dense phase; S4. Establish a reactive ion budget model. Synchronous topological index model ,in For the first The mass fraction of the raw materials, the phase topology index model It is constructed from two or more of the following: gel phase volume fraction, gel connectivity, uniformity of framework phase distribution, critical pore size, continuity of interface transition zone, and micro-region modulus dispersion. S5. Mass fraction of candidate raw materials The solution is performed to ensure that the resulting formulation simultaneously satisfies the following constraints: effective calcium / effective silicon molar ratio, effective aluminum / effective silicon molar ratio, effective sulfate / effective aluminum molar ratio, initial effective basicity, water-cement ratio, and phase topological index not lower than a preset threshold. The risk factor is not higher than the preset threshold. ; S6. According to the formula obtained by the solution, the industrial solid waste raw materials are batched, mixed, water is added, molded and cured to obtain a solid waste cementitious material. The cementitious solid components, except for the mixing water, are all derived from industrial solid waste. S7. Perform performance testing on the prepared solid waste cementitious material and feed back the test results to correct the model parameters in S2 to S5.

2. The method according to claim 1, characterized in that: The reactive ion fingerprint vector described in S2 for: ， In the formula, The effective calcium equivalent that can be released per unit mass of raw material within the target age period. This refers to the effective silicon equivalent that can be released per unit mass of raw material within the target age. This refers to the effective aluminum equivalent that can be released per unit mass of raw material within the target age period. This refers to the effective sulfate equivalent that can be released per unit mass of raw material within the target age period. H represents the effective alkalinity equivalent that can be released per unit mass of raw material within the target age period. i K is the effective water demand coefficient per unit mass of raw material. i The environmental risk coefficient and / or volume stability risk coefficient per unit mass of raw material.

3. The method according to claim 1, characterized in that: The target application described in S3 includes one of the following: high-strength dense type, fast-setting skeleton type, and low-carbon curing / stain-resistant type. The target product vector of the high-strength dense type... The target product vector of the rapidly solidifying framework is characterized by a C-(A)-SH gel as the main phase, an AFt phase as the auxiliary framework phase, and an N-(C)-ASH gel and / or an LDH phase as the auxiliary dense phase. The product is dominated by the AFt phase and the early gel phase, with a continuous gel reinforcement structure formed later; the target product vector of the low-carbon curing / pollution-resistant type is... It is mainly composed of C-(A)-SH gel, LDH phase, AFt phase and / or carbonate dense phase.

4. The method according to claim 1 or 3, characterized in that: The constraints described in S5 satisfy the following conditions for different uses: The high-strength, dense confinement conditions satisfy the following: effective calcium / effective silicon molar ratio 0.80–1.80, effective aluminum / effective silicon molar ratio 0.08–0.45, and effective sulfate / effective aluminum molar ratio. 0.15~1.50, initial effective alkalinity pH 11.0~13.5, water-to-gel ratio 0.18~0.40; The constraints of the fast-setting skeleton type are: effective calcium / effective silicon molar ratio of 0.90 to 2.00, effective aluminum / effective silicon molar ratio of 0.10 to 0.55, effective sulfate / effective aluminum molar ratio of 0.50 to 2.20, initial effective alkalinity of pH 11.2 to 13.8, and water-cement ratio of 0.22 to 0.

55. The constraints for the low-carbon curing / stain-resistant type meet the following requirements: effective calcium / effective silicon molar ratio 0.60–1.60, effective aluminum / effective silicon molar ratio 0.08–0.50, and effective sulfate / effective aluminum molar ratio. 0.10~1.80, initial effective alkalinity pH 10.8~13.5, water-to-gel ratio 0.25~0.

80.

5. The method according to claim 1 or 3, characterized in that: S5 also establishes a time-dimensional ion budget function. Ion release matching verification was performed for three stages: 0–24 h, 1–7 d, and 7–28 d. For any ion component, the cumulative release within the time interval [0, t] was defined as: mi is the mass fraction of the i-th raw material, r i,x (τ) represents the instantaneous release rate of the ionic component x of the i-th raw material at time τ; the required cumulative release of effective alkalinity equivalent Q during the 0–24 h period. Alk (24h) Not less than 40% of the total demand DAlk, the cumulative release of effective calcium equivalent QCa (24h) Not less than 30% of the total demand DCa, and the cumulative release of effective silicon and effective aluminum equivalents Not less than 10% of their respective total demands; During the 1-7 day period, the cumulative release of effective silicon, effective aluminum, and effective calcium equivalents is required to reach more than 60% of their respective total demands, and the cumulative release of effective sulfate equivalents is maintained between 40% and 80% of the total demand; During the 7-28 day period, there is still a new release of effective silicon and effective aluminum equivalents, and the cumulative release over 28 days is no less than 15% higher than the cumulative release over 7 days.

6. The method according to claim 1 or 3, characterized in that: The phase topology index model described in S4 It is expressed by the following formula: , in, This is the normalized value of the gel phase volume fraction. This is the normalized value of gel connectivity. This is the normalized value for the uniformity of the skeletal phase distribution. This is the normalized value of the critical aperture. This is the normalized value for the continuity of the interface transition area. This represents the normalized value of the micro-region modulus dispersion. These are the weighting coefficients, and their sum is 1.

7. The method according to claim 1 or 3, characterized in that: The industrial solid waste raw materials described in S6 are classified into four categories according to their functions: Category A: main precursor raw materials; Category B: endogenous calcium-base activating raw materials; Category C: sulfate regulating raw materials; and Category D: auxiliary densification or environmental regulation raw materials. The Class A main precursor raw materials are selected from one or more of granulated blast furnace slag, lithium slag, phosphorus slag, fly ash, and coal gangue calcination products; The type B endogenous calcium-alkali activating raw materials are selected from one or more of the following: carbide slag, red mud, steel slag, alkali slag, desulfurization ash, and white mud. The C-type sulfate regulating raw material is selected from one or more of phosphogypsum, desulfurized gypsum, electrolytic manganese slag, and titanium gypsum; The Class D auxiliary densification or environmental control raw materials are selected from one or more of steel slag, fly ash, red mud, carbonized slag, and stabilized fly ash.

8. The method according to claim 7, characterized in that: When the target application is high-strength and dense, type A accounts for 45%–80%, type B accounts for 5%–30%, and type C accounts for 2%–20%; when the target application is fast-setting skeleton type, type A accounts for 25%–70%, type B accounts for 5%–35%, and type C accounts for 10%–45%; when the target application is low-carbon curing / stain-resistant type, type A accounts for 25%–75%, type B accounts for 5%–35%, type C accounts for 2%–25%, and type D accounts for 5%–35%.

9. A solid waste cementitious material, characterized in that, It is prepared by the method described in any one of claims 1 to 8.

10. The application of the all-solid waste cementitious material according to claim 9 in precast components, grouting materials, repair materials, road engineering materials, mine filling materials, contaminated site solidification and stabilization materials, or 3D printed building materials.

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