Method for solidifying sludge

Abstract

The application provides a sludge solidification treatment method, and belongs to the technical field of soft soil foundation. The method comprises the following steps: obtaining a plurality of sample parameter groups and manufacturing a plurality of test samples, each sample parameter group comprising a sludge moisture content, a cement mixing amount and a slag replacement cement ratio, testing the compressive strength of the plurality of test samples, establishing a linear model, a second-order interaction model, a quadratic polynomial model and a cubic polynomial model based on the plurality of test parameter groups, and obtaining the fitting coefficients of each model for the plurality of test parameter groups; determining the model with the largest fitting coefficient as a target model, performing response surface analysis on the compressive strength of the test sample based on the target model, obtaining a plurality of initial parameter groups with the sum of the compressive strength of the manufactured test sample on the nth day and the 4nth day being greater than a specified value, and determining a target parameter group from the plurality of initial parameter groups, so that the beneficial effect of simultaneously meeting the early strength and long-term stability of the solidified sludge is achieved.

Description

Technical Field

[0001] This application relates to the field of soft soil foundation technology, and more specifically, to a method for solidifying silt. Background Technology

[0002] In the construction of urban expressways in southeastern coastal areas of my country, high-moisture-content silty soft soil is widely distributed. This type of soil has high natural moisture content, large porosity, low strength, high compressibility, and poor permeability. This type of soft soil is particularly prominent in projects such as urban expressways in coastal cities, where the silt moisture content of the site reaches as high as 53.9% to 145%, resulting in extremely low natural bearing capacity. Without effective solidification treatment, excessive and uneven settlement is prone to occur under embankment loads and long-term traffic loads, which will seriously affect the safety of road construction and long-term operational stability. Currently, cement is commonly used as the main solidifying agent for soft soil solidification treatment. The cementitious material generated by cement hydration binds loose soil particles into a whole, thereby improving the soil strength and deformation stability.

[0003] To reduce cement usage, decrease carbon emissions, and improve curing efficiency, existing technologies often employ a method of replacing cement with slag and co-curing with cement. This utilizes the pozzolanic activity and water absorption properties of slag to participate in the secondary hydration reaction, thereby achieving the resource utilization of industrial solid waste while ensuring strength.

[0004] However, most existing soft soil consolidation methods use single-factor or orthogonal test designs to design mix proportions, and do not consider the impact of fluctuations in on-site construction parameters on strength stability. This results in insufficient strength prediction accuracy, poor applicability of mix proportions, and difficulty in simultaneously meeting the engineering requirements of early strength and long-term stability.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This application provides a method for solidifying sludge, which solves the problem in related technologies that it is difficult to simultaneously meet the engineering requirements of early strength and long-term stability. The technical solution is as follows: According to one aspect of this application, a method for solidifying sludge is provided, the method comprising: Multiple sample parameter groups are obtained, each sample parameter group includes three parameters, namely, sludge moisture content, cement content and slag replacement ratio of cement, and at least one parameter in any two sample parameter groups is different. Multiple test samples are manufactured based on the aforementioned multiple sample parameter sets; The multiple test samples are tested to obtain the compressive strength of the multiple test samples on day n and day 4n, where n is greater than or equal to 1; Multiple initial models are established based on multiple test parameter sets, including linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models. Each test parameter set includes a sample parameter set and the compressive strength of the test sample manufactured based on the sample parameter set on day n and day 4n. The initial model is used to output the compressive strength of the test sample based on the sludge moisture content, cement content, and the proportion of slag replacing cement. Obtain the fitting coefficients of each of the linear model, second-order interactive model, quadratic polynomial model, and cubic polynomial model for the multiple test parameter sets; The initial model with the largest fitting coefficient is determined as the target model; Based on the target model, the compressive strength of the test sample is analyzed by response surface analysis to obtain multiple initial parameter sets in which the sum of the compressive strength of the manufactured test sample on day n and day 4n is greater than a specified value. Each initial parameter set includes sludge moisture content, cement content, and slag replacement ratio of cement. Determine a target parameter set from the plurality of initial parameter sets; The sludge is solidified based on the target parameter set.

[0007] Optionally, determining the initial model with the largest fitting coefficient as the target model includes: An analysis of variance was performed on the initial model with the largest fitting coefficient to obtain the first significance probability of the initial model with the largest fitting coefficient, the second significance probability of each parameter among the silt moisture content, the cement content and the proportion of slag replacing cement, the third significance probability of the product of every two parameters among the three parameters, and the fourth significance probability of the square of each parameter among the three parameters. Determine whether the first significance probability, the second significance probability, the third significance probability, and the fourth significance probability are all less than the target significance probability; When the first significance probability, the second significance probability, the third significance probability, and the fourth significance probability are all less than the target significance probability, the initial model with the largest fitting coefficient is determined as the target model; When the first significance probability, the second significance probability, the third significance probability, or the fourth significance probability is greater than or equal to the target significance probability, the step of testing the plurality of test samples is performed.

[0008] Optionally, when the initial model with the largest fitting coefficient is the quadratic polynomial model, the quadratic regression equations for the compressive strength on day n and day 28 of the quadratic polynomial model include: y1=1.65875-0.005125x1+0.042751x2+0.04275x3-0.00018x1x2+0.000255x1x3+0.000375x2x3+0.000020x1 2 +0.0012x2 2 -0.002525x3 2 ; y2=2.91625-0.004675x1-0.067375x2+0.03075x3-0.00018x1x2+0.000265x1x3+0.000625x2x3+0.000017x1 2 +0.001325x2 2 -0.0024x3 2 ; Where y1 is the compressive strength on day n, y2 is the compressive strength on day 4n, x1 is the moisture content of the silt, x2 is the cement content, and x3 is the proportion of slag replacing cement.

[0009] Optionally, determining a target parameter set from the plurality of initial parameter sets includes: Determine the first average compressive strength of the test samples manufactured from the plurality of initial parameter sets on day n, and the second average compressive strength on day 4n; Remove the initial parameter groups from the multiple initial parameter groups where the compressive strength of the manufactured test sample on day n is less than the first average value, and the initial parameter groups where the compressive strength on day 4n is less than the second average value, to obtain the remaining initial parameter groups. When the number of remaining initial parameter sets is greater than 1, the parameter set with the largest sum of the compressive strength of the manufactured test sample on day n and the compressive strength on day 4n among the remaining multiple initial parameter sets is determined as the target parameter set; When the number of remaining initial parameter sets is equal to 1, the remaining initial parameter set is determined as the target parameter set.

[0010] Optionally, the solidification of sludge based on the target parameter set includes: Generate target samples based on the target parameter set; The compressive strength of the target sample was measured on day n and day 4n. When the difference between the measured compressive strength of the target sample on day n and day 4n and the compressive strength output by the target model based on the target parameter set is less than the strength threshold, the sludge is solidified based on the target parameter set. When the difference between the measured compressive strength of the target sample on day n and day 4n and the compressive strength output by the target model based on the target parameter set is greater than or equal to the strength threshold, the step of testing the multiple test samples is executed.

[0011] Optionally, the silt has a moisture content ranging from 50% to 150%, a cement content ranging from 20% to 40%, and a slag-to-cement ratio ranging from 10% to 30%.

[0012] Optionally, the sludge moisture content in the sample parameter group is 50%, 100%, or 150%, the cement content is 20%, 30%, or 40%, and the slag replacement ratio of cement is 10%, 20%, or 30%.

[0013] Optionally, the strength threshold is 6% of the compressive strength of the target sample measured on day n and day 4n.

[0014] Optionally, the step of generating multiple test samples based on the multiple sample parameter sets includes: Based on any one of the multiple sample parameter groups, sludge, cement, and slag are sequentially added to a mixer and stirred to obtain a mixture. The mixture was placed in a mold and allowed to stand for 24 hours before being demolded to obtain the initial sample. The initial sample is placed in the curing room and cured for the target number of days to obtain the test sample corresponding to any sample parameter group.

[0015] Optionally, the curing temperature in the curing room is 18-20℃ and the relative humidity is ≥95%.

[0016] The beneficial effects of the technical solutions provided in this application include at least the following: A method for solidifying sludge is provided. This method obtains multiple sample parameter sets, including sludge moisture content, cement content, and the proportion of slag replacing cement. Multiple test samples are manufactured and tested based on these parameter sets to obtain the compressive strength of the test samples on day n and day 4n. Linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models are established based on these parameter sets, and the fitting coefficients of each model for the multiple parameter sets are obtained. This accurately characterizes the correlation between sludge moisture content, cement content, the proportion of slag replacing cement, and the compressive strength of the test samples, improving the accuracy of compressive strength prediction. The model with the largest fitting coefficient is determined as the target model. Response surface analysis is performed on the compressive strength of the test samples based on the target model to obtain multiple initial parameter sets where the sum of the compressive strengths of the test samples on day n and day 4n is greater than a specified value. A target parameter set is then determined from these initial parameter sets, achieving the beneficial effect of simultaneously satisfying the early strength and long-term stability of the solidified sludge.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0019] Figure 1 A flowchart illustrating a method for solidifying sludge, provided as an embodiment of this application; Figure 2 A flowchart of another sludge solidification treatment method provided in an embodiment of this application; Figure 3 A 3D response surface and contour plot of compressive strength on day 7 provided in this application embodiment; Figure 4 A 3D response surface and contour plot of compressive strength on day 28 provided for embodiments of this application.

[0020] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0022] Figure 1 A flowchart of a sludge solidification treatment method provided in this application embodiment is shown below. Figure 1 As shown, the specific steps of the solidification treatment method for this sludge are as follows: Step 101: Obtain multiple sample parameter groups. Each sample parameter group includes three parameters: sludge moisture content, cement content, and slag replacement ratio of cement. At least one parameter in any two sample parameter groups must be different.

[0023] Step 102: Create multiple test samples based on multiple sample parameter groups.

[0024] Step 103: Test multiple test samples to obtain the compressive strength of multiple test samples on day n and day 4n.

[0025] Step 104: Establish multiple initial models based on multiple test parameter sets, including linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models. Each test parameter set includes a sample parameter set and the compressive strength of test samples manufactured based on the sample parameter set on day n and day 4n. n is greater than or equal to 1.

[0026] The initial model is used to output the compressive strength of the test samples based on the sludge moisture content, cement content, and the proportion of slag replacing cement. This compressive strength is the predicted value of the compressive strength output by the initial model.

[0027] Step 105: Obtain the fitting coefficients of each of the linear model, second-order interactive model, quadratic polynomial model, and cubic polynomial model for multiple test parameter sets.

[0028] Step 106: Determine the initial model with the largest fitting coefficient as the target model.

[0029] Step 107: Perform response surface analysis on the compressive strength of the test sample based on the target model to obtain multiple initial parameter sets in which the sum of the compressive strength of the manufactured test sample on day n and day 4n is greater than a specified value. Each initial parameter set includes sludge moisture content, cement content, and the proportion of slag replacing cement.

[0030] Step 108: Determine a target parameter set from multiple initial parameter sets.

[0031] Step 109: Solidify the sludge based on the target parameter set.

[0032] In summary, the sludge solidification method provided in this application obtains multiple sample parameter sets, including sludge moisture content, cement content, and the proportion of slag replacing cement. Multiple test samples are manufactured and tested based on these parameter sets to obtain the compressive strength of the test samples on day n and day 4n. Linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models are established based on these parameter sets, and the fitting coefficients of each model for the multiple parameter sets are obtained. These fitting coefficients characterize the correlation between sludge moisture content, cement content, the proportion of slag replacing cement, and the compressive strength of the test samples, improving the accuracy of the compressive strength prediction results. The initial model with the largest fitting coefficient is determined as the target model. Response surface analysis is performed on the compressive strength of the test samples based on the target model to obtain multiple initial parameter sets where the sum of the compressive strengths of the test samples on day n and day 4n is greater than a specified value. A target parameter set is then determined from these initial parameter sets, achieving the beneficial effect of simultaneously satisfying the early strength and long-term stability of the solidified sludge.

[0033] Figure 2 A flowchart of another sludge solidification treatment method provided in the embodiments of this application is shown below. Figure 2 As shown, the specific steps of the solidification treatment method for this sludge are as follows: Step 201: Obtain multiple sample parameter groups. Each sample parameter group includes three parameters: sludge moisture content, cement content, and slag replacement ratio of cement. At least one parameter in any two sample parameter groups must be different.

[0034] The slag-to-cement ratio refers to the percentage of slag mass to cement mass. That is, when slag replaces a portion of the cement, the proportion of slag mass relative to cement mass. In this embodiment, P·O 42.5 ordinary Portland cement is used. The chemical composition of the cement and slag is shown in Table 1 below: Table 1 Chemical composition of raw materials (%)

[0035] Since the moisture content of silt typically varies between 53.9% and 145%, this embodiment selects moisture contents with a lower limit of 50% and an upper limit of 150% for testing. The cement content varies from 20% to 40%, and the proportion of slag replacing cement varies from 10% to 30%. Within the above ranges, three values ​​are selected for each parameter, as shown in Table 2 below: Table 2: Experimental parameters and levels

[0036] Step 202: Create multiple test samples based on multiple sample parameter groups.

[0037] The specific manufacturing steps for the test samples are described in the following sub-steps: Sub-step 2021: Based on any one of the multiple sample parameter groups, put the sludge, cement and slag into the mixer in sequence and mix them to obtain a mixture.

[0038] In this embodiment, based on any one of multiple sample parameter groups, a forced mixer is used to mix the corresponding proportions of sludge, cement, and slag for 3 minutes. The mixing drum of the forced mixer is fixed, and the rotating blades inside the drum forcefully shear, compress, and tumble the materials, enabling uniform mixing of sludge, cement, and slag in a short time. Specific mixer specifications are not limited in this embodiment.

[0039] Sub-step 2022: After the mixture is placed into the mold and left to stand for 24 hours, it is demolded to obtain the initial sample.

[0040] In this embodiment, the mixture is loaded into a 100 mm × 100 mm × 100 mm triple mold, then vibrated on a vibrating table for 30 s, covered with plastic film and left to stand for 24 h before demolding.

[0041] Sub-step 2023: Place the initial sample in the curing room and cure it for the target number of days to obtain the test sample.

[0042] The temperature in the curing room is controlled at 20±2°C, and the humidity is ≥95%. The target number of days can be 4n days, where n can be greater than or equal to 1 and less than or equal to 10, for example, n=7.

[0043] Step 203: Test multiple test samples to obtain the compressive strength of multiple test samples on day n and day 4n.

[0044] A Box-Behnken (BBD) response surface methodology was used to generate 17 experimental schemes. This experiment employed a three-factor, three-level design, comprising 12 factorials and 5 centroids. The centroids, representing combinations where all factors are at their median levels, were used to estimate experimental error and verify model stability. The parameters and measured compressive strength data for each experimental scheme are shown in Table 3 below. The test item was unconfined compressive strength, and the curing ages of the test samples were day n and day 4n. In this embodiment, n is 7.

[0045] Table 3. Compressive strength of silt under different factors and levels

[0046] Step 204: Establish multiple initial models based on multiple test parameter groups, including linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models.

[0047] Each test parameter group includes a sample parameter group and the compressive strength of the test sample manufactured based on the sample parameter group on day n and day 4n.

[0048] This initial model is used to output the compressive strength of the test sample based on the sludge moisture content, cement content, and the proportion of slag replacing cement. The output can be the predicted values ​​of the compressive strength of the test sample on day n and day 4n. For example, multiple initial models, including linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models, can be established based on multiple test parameter sets using professional experimental design and data analysis software. These initial models are all compressive strength models, which are key tools for evaluating the compressive strength capacity of materials or structures. Their core logic is to characterize the elasticity, plasticity, and failure characteristics of materials through stress-strain curves. In the embodiments of this application, different types of models are used to adapt to different interaction relationships between variables, which can comprehensively reflect the influence of single factors, interactions between factors, and higher-order terms on compressive strength, providing theoretical support for subsequent data fitting, significance analysis, and parameter optimization.

[0049] Step 205: Obtain the fitting coefficients of each of the linear model, second-order interactive model, quadratic polynomial model, and cubic polynomial model for multiple test parameter sets.

[0050] The coefficient of fit is used to characterize the degree to which the model fits the experimental data. The closer the coefficient of fit is to 1, the higher the degree of agreement between the model and the experimental data. After constructing the four types of models, the fitting effects and related evaluation indicators of each model are summarized and analyzed to obtain four evaluation indicators: continuity, goodness of fit, adjusted value, and predicted value. Continuity is used to judge whether the overall change pattern of the model conforms to physical laws; goodness of fit reflects the overall degree of fit of the model to the original experimental data; and adjusted value is the adjusted coefficient of determination R. 2 The adjusted value is the corrected fit index after removing the interference of the number of variables; the predicted value is used to evaluate the model's ability to predict unknown experimental results. All four indices can be used to evaluate the quality of the model. Among them, the adjusted value, which comprehensively considers both the number of model variables and the fit effect, provides a more objective and reliable evaluation result. Therefore, the adjusted value is used as the core criterion for model selection in this study, i.e., the fit coefficient of the test parameter group. The statistical results of the various evaluation indices for the four types of models are shown in Table 4 below: Table 4. Results of Compressive Strength Model Analysis

[0051] The data in Table 4 can be calculated using professional experimental design and data analysis software based on each model and the aforementioned test parameter groups.

[0052] As shown in Table 4 above, the cubic polynomial model may be overfitting because its goodness of fit cannot be calculated. Therefore, the quadratic polynomial model has a larger adjustment value and is the optimal model obtained in this screening. In other words, in this step, we can also obtain the goodness of fit and fit coefficients of each model (linear model, second-order interactive model, quadratic polynomial model, and cubic polynomial model) for multiple test parameter groups, remove overfitting initial models based on the goodness of fit, and then select the initial model with the largest fit coefficient from the remaining initial models.

[0053] Therefore, when the initial model with the largest fitting coefficient is a quadratic polynomial model, the quadratic regression equations for the compressive strength on day 7 and day 28 of the quadratic polynomial model include: y1=1.65875-0.005125x1+0.042751x2+0.04275x3-0.00018x1x2+0.000255x1x3+0.000375x2x3+0.000020x1 2 +0.0012x2 2 -0.002525x3 2 ; y2=2.91625-0.004675x1-0.067375x2+0.03075x3-0.00018x1x2+0.000265x1x3+0.000625x2x3+0.000017x1 2 +0.001325x2 2 -0.0024x3 2 ; Where y1 is the compressive strength on day n, y2 is the compressive strength on day 4n, x1 is the moisture content of the silt, x2 is the cement content, and x3 is the proportion of slag replacing cement.

[0054] Step 206: Perform variance analysis on the initial model with the largest fitting coefficient to obtain the first significance probability, the second significance probability of each parameter in the initial model with the largest fitting coefficient, the product of every two parameters in the silt moisture content, the cement content, and the proportion of slag replacing cement, the third significance probability of the product of every two parameters in the three parameters, and the fourth significance probability of the square of each parameter in the three parameters.

[0055] From the quadratic regression equations of the compressive strength on day 7 and day 28, it can be seen that the 7-day and 28-day strengths of the solidified sludge have a quadratic relationship with the moisture content, cement content, and the proportion of slag replacing cement. Tables 5 and 6 analyze the variance and lack of fit of the above model and evaluate the significance of the model. When n is 7, the variance analysis data are shown in Tables 5 and 6 below: Table 5. Analysis of variance of 7-day compressive strength

[0056] Table 6. Analysis of variance of 28-day compressive strength

[0057] The first significance probability corresponds to the assumed value P>F for the overall model term, used to determine the overall significance of the model; the second significance probability corresponds to the assumed value P>F for the single-factor terms of sludge moisture content, cement content, and slag replacement ratio of cement; the third significance probability corresponds to the assumed value P>F for the pairwise parameter interaction terms; and the fourth significance probability corresponds to the assumed value P>F for the quadratic square terms of each parameter. The F value is the variance ratio, calculated by dividing the between-group variance by the within-group error variance, used to reflect the degree of influence of each factor. The values ​​in Tables 5 and 6 can be calculated based on each model using professional experimental design and data analysis software.

[0058] From Tables 5 and 6, it can be found that the compressive strength R 2 All values ​​were greater than 0.99, indicating that the difference between the response values ​​and the true values ​​was minimal, and the two values ​​were essentially in agreement. The significance of each factor in the model was also determined by combining the data in the table.

[0059] Step 207: Determine whether the first significance probability, the second significance probability, the third significance probability, and the fourth significance probability are all less than the target significance probability. If they are all less than the target significance probability, proceed to step 208. If they are not all less than the target significance probability, proceed to step 203.

[0060] In this embodiment, the target significance probability is set to 0.05 to determine whether the influence of the model and each parameter reaches a significant level. It can be determined whether the first, second, third, and fourth significance probabilities at 7 days and 28 days are all less than the target significance probability.

[0061] When the first, second, third, and fourth significance probabilities are all less than the target significance probability, it indicates that the difference between the response value and the true value is very small and they are essentially the same. Simultaneously verifying the significance of each factor in the model, the initial model with the largest fitting coefficient can be determined as the target model.

[0062] When the first, second, third, or fourth significance probabilities are greater than or equal to the target significance probability, it indicates that the significance of each factor in the model is poor, and we can then return to step 203 to retest each sample.

[0063] Step 208: Determine the initial model with the largest fitting coefficient as the target model.

[0064] In this embodiment, when the first significance probability, the second significance probability, the third significance probability, and the fourth significance probability are all less than the target significance probability, the initial model with the largest fitting coefficient can be determined as the target model.

[0065] Step 209: Perform response surface analysis on the compressive strength of the test sample based on the target model to obtain multiple initial parameter sets in which the sum of the compressive strength of the manufactured test sample on day n and day 4n is greater than a specified value. Each initial parameter set includes sludge moisture content, cement content, and the proportion of slag replacing cement.

[0066] After obtaining a reliable quadratic polynomial regression model and verifying its significance and fitting accuracy, a surface methodology analysis of compressive strength was conducted based on this target model. Surface method analyses of compressive strength on day n and day 4n were performed. Specifically, the surface methodology analysis of compressive strength on the seventh day after sludge solidification, combined with Table 5, revealed that moisture content and cement content significantly affected compressive strength, while the proportion of slag replacing cement had a highly significant impact. Furthermore, there was an interaction among the three factors, with the order of influence of each factor on compressive strength being x3>x1x3>x1>x1x2>x2>x2x3.

[0067] Figure 3 This application provides a 3D response surface and contour plot of compressive strength on day 7 as an embodiment of the present application. Figure 3 In the diagram, subplots 3a, 3b, and 3c represent the response surface plots of the interaction of three sets of factors. Each subplot has a different combination of variables, resulting in differences in the horizontal and vertical axes. Specifically: in subplot 3(a), the horizontal axis represents the sludge moisture content, and the vertical axis represents the cement content; in subplot 3b, the horizontal axis represents the sludge moisture content, and the vertical axis represents the proportion of slag replacing cement; in subplot 3c, the horizontal axis represents the cement content, and the vertical axis represents the proportion of slag replacing cement. The height of the surface in the diagram represents the compressive strength of the solidified sludge, which can intuitively reflect the influence and strength of the interaction between the two sets of variables on the strength. Figure 3It is evident that when the cement content exceeds 30%, the impact on the compressive strength of the silt is significant. Higher moisture content leads to a gradual decrease in silt strength. Conversely, lower moisture content and higher cement content result in higher silt compressive strength. These two factors interact with each other in affecting the compressive strength of the silt. Further investigation into the influence of slag replacement ratio and moisture content on compressive strength reveals that the compressive strength initially increases and then decreases with increasing slag replacement ratio, reaching its maximum value around 14%. Therefore, the slag replacement ratio should not be too high, as excessively high ratios can significantly reduce early-stage compressive strength. When cement content and slag replacement ratio interact, the graph shows that the influence of the slag replacement ratio is far greater than that of the cement content. The compressive strength of the silt primarily depends on the slag replacement ratio; when the slag replacement ratio exceeds 15%, the strength initially increases and then decreases with increasing cement content.

[0068] The compressive strength of the solidified sludge on day 28 was analyzed by the corresponding surface model. As shown in Table 5, the moisture content, cement content and the proportion of slag replacing cement all have significant effects on the concrete strength, and there is an interaction between the three factors. The order of influence of each factor on the compressive strength is x3>x1x3>x1>x1x2>x2>x2x3. Figure 4 This application provides a 3D response surface and contour plot of the compressive strength on day 28, as shown in Figure 4. Figures 4a, 4b, and 4c are also response surface plots of multiple factors interacting, with different combinations of variables and varying horizontal and vertical axis parameters in each subplot. Specifically: Figure 4a's horizontal axis represents the sludge moisture content, and its vertical axis represents the cement content; Figure 4b's horizontal axis represents the sludge moisture content, and its vertical axis represents the proportion of slag replacing cement; Figure 4c's horizontal axis represents the cement content, and its vertical axis represents the proportion of slag replacing cement. The surface height in the figures represents the compressive strength of the solidified sludge, clearly demonstrating the influence characteristics and degree of the coupled effects of the two sets of factors on the strength. Figure 4 It can be seen that when the moisture content is high, the cement content has little effect on the compressive strength. At low moisture content, the compressive strength of concrete gradually increases with the increase of cement content. Overall, the strength is higher when the cement content is high. Similarly, when considering both moisture content and the proportion of slag replacing cement, the compressive strength of concrete gradually decreases with changes in sludge moisture content and the proportion of slag replacing cement, but the proportion of slag replacing cement has a greater impact. Therefore, the proportion of slag must be clearly defined. In addition, the higher the cement content, the higher the compressive strength of the sludge. When the proportion of slag replacing cement is greater than 15%, the cement content decreases because the slag replaces most of the cement, resulting in a significant reduction in the hydration products generated and a decrease in the bonding effect on the sludge. However, when the slag replacement amount is appropriate, the pozzolanic reaction of the slag will generate more solidification, resulting in a better solidification effect of the sludge.

[0069] Step 210: Determine a target parameter set from multiple initial parameter sets.

[0070] The specific steps are as follows: Sub-step 2101: Determine the first average value of the compressive strength of the test samples manufactured from multiple initial parameter sets on day n, and the second average value of the compressive strength on day 4n.

[0071] In this embodiment of the application, the first average value is the average value of the 7-day compressive strength of the specimens corresponding to all initial parameter groups obtained by response surface optimization, and the second average value is the average value of the 28-day compressive strength of the specimens corresponding to all initial parameter groups.

[0072] Sub-step 2102: Remove the initial parameter groups from the multiple initial parameter groups where the compressive strength of the manufactured test sample on day n is less than the first average value, and the initial parameter groups where the compressive strength on day 4n is less than the second average value, to obtain the remaining initial parameter groups.

[0073] Sub-step 2103: When the number of remaining initial parameter groups is greater than 1, the parameter group with the largest sum of the compressive strength of the manufactured test sample on day n and the compressive strength on day 4n among the remaining multiple initial parameter groups is determined as the target parameter group.

[0074] Sub-step 2104: When the number of remaining initial parameter sets is equal to 1, determine the remaining initial parameter set as the target parameter set.

[0075] Step 211: Create target samples based on the target parameter set.

[0076] The target parameters can be: moisture content of 54.3%, cement content of 39.5%, and slag replacing cement ratio of 16.1%.

[0077] Step 212: Measure the compressive strength of the target sample on day n and day 4n.

[0078] Step 213: Determine whether the difference between the measured compressive strength of the target sample on day n and day 4n and the compressive strength output by the target model based on the target parameter group is less than the strength threshold. If it is less than the threshold, proceed to step 214; if it is not less than the threshold, proceed to step 203.

[0079] If the difference between the measured compressive strength of the target sample on day n and day 4n and the compressive strength output by the target model based on the target parameter set is less than the strength threshold, it indicates that the target parameter set is accurate and appropriate; otherwise, it indicates that the target parameter set is inaccurate.

[0080] The strength threshold is 6% of the measured compressive strength of the target sample on day n and day 4n. In this embodiment, to verify the accuracy of the data and for ease of use, the moisture content is set to 54.3%, the cement content to 39.5%, and the slag replacement ratio to cement to 16.1%. The error comparison between the predicted and actual values ​​of the final response surface is shown in Table 7.

[0081] Table 7 Error between predicted and actual values ​​of response surface optimization

[0082] As shown in Table 7 above, the difference between the compressive strength of the target sample on day 7 and day 28 and the predicted value of the target model is less than 6%. The target parameter set is determined to be a moisture content of 54.3%, a cement content of 39.5%, and a slag replacement ratio of 16.1%.

[0083] Step 214: Solidify the sludge based on the target parameter set.

[0084] After the target parameter set has been verified to be accurate and appropriate, the cement dosage in the target parameter set is adjusted according to the type of mixing equipment, mixing time, and actual moisture content of the sludge at the construction site, resulting in the on-site construction parameter set. The sludge is then solidified based on this on-site construction parameter set. This solidification process is divided into multiple construction sections, which are carried out sequentially. After the solidification of each construction section is completed, on-site verification and model correction steps are also included, as detailed below: Select at least three verification points in the cured construction section, take core samples from each verification point, process the core samples into standard specimens as field verification samples, and cure them under actual field environmental conditions for 7 days and 28 days. Test the compressive strength of the field verification samples on the 7th and 28th days to obtain the measured strength values ​​in the field.

[0085] Calculate the deviation rate δ for each valid verification point, δ = |Measured strength value - Predicted value| / Predicted value × 100%. Where the predicted value is the predicted strength value output by the target model based on the on-site construction parameter set for that construction section. Obtain the average deviation rate δ for the three verification points. 平均 , and the δ 平均 This serves as a representative deviation rate for the construction section.

[0086] When δ 平均 If the deviation is less than the first deviation threshold, which is within the normal experimental error range, the target model remains unchanged without correction, and the original model continues to be used for prediction of subsequent construction sections. The first deviation threshold is 5%.

[0087] When δ 平均When the deviation is greater than or equal to the first deviation threshold and less than the second deviation threshold, the deviation exceeds the random error range, indicating that the target model has a systematic translational bias, but the relationship between variables is still correct. The second deviation threshold is 10%. Based on the direction of the deviation, the constant term (i.e., the intercept term: 1.65875 in the y1 equation and 2.91625 in the y2 equation) of the quadratic regression equation of the target model in step 206 above is corrected by a correction amount of δ. 平均 ×0.5, meaning that when the measured value is higher than the predicted value, the constant term δ will be increased. 平均 ×0.5, when the measured value is lower than the predicted value, the constant term δ is reduced. 平均 ×0.5, after the constant term is corrected, all coefficients of the linear term, the coefficients of the interaction term (the coefficients of the interaction term refer to the coefficients of the terms that are multiplied by different coefficients, such as the coefficients of x1x2) and the coefficients of the quadratic term remain unchanged, and the corrected target model is obtained.

[0088] When δ 平均 When the deviation is greater than or equal to the second deviation threshold, the deviation is too large, indicating that the relationship between the model variables has undergone a substantial change, and adjusting the constant term alone is insufficient for correction. In this case, the constant term and all linear coefficients of the quadratic regression equation of the target model are simultaneously corrected, with the constant term correction amount being δ. 平均 ×0.5, the correction amount for the coefficients of each linear term is δ 平均 ×0.3×fx, where fx is the ratio of the f-value corresponding to the current linear term to the sum of the f-values ​​of all linear terms (for example, the proportion of the f-value of a certain factor to the sum of the f-values ​​of all linear terms; the larger the proportion, the larger the coefficient correction of that factor. For example, in Table 5, the f-value of x1 is 43.24, the f-value of x2 is 26.95, and the f-value of x3 is 393.7. The sum of the f-values ​​of the three linear terms is 43.24 + 26.95 + 393.7 = 463.89. Therefore, the proportion of x1 is 43.24 / 463.89 ≈ 9.3%, the proportion of x2 is 26.95 / 463.89 ≈ 5.8%, and the proportion of x3 is 393.7 / 463.89 ≈ 84.9%). Specifically, when revising the 7-day compressive strength equation, the f-values ​​from the 7-day compressive strength variance analysis in Table 5 are used, and when revising the 28-day compressive strength equation, the f-values ​​from the 28-day compressive strength variance analysis in Table 6 are used.

[0089] The subsequent curing construction of the current construction section shall be carried out according to the revised model. At the same time, return to steps 202 and 203, add at least 6 sets of supplementary test samples, merge the supplementary test data with the original 17 sets of data and re-perform model regression to obtain the recalibrated target model.

[0090] The revised or recalibrated target model is used for the mix design and curing construction of the next construction section. After each construction section is cured, the above verification and revision steps are repeated until all construction sections are cured.

[0091] In summary, the sludge solidification method provided in this application obtains multiple sample parameter sets, including sludge moisture content, cement content, and the proportion of slag replacing cement. Multiple test samples are manufactured and tested based on these parameter sets to obtain the compressive strength of the test samples on day n and day 4n. Linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models are established based on these parameter sets, and the fitting coefficients of each model for the multiple parameter sets are obtained. This accurately characterizes the correlation between sludge moisture content, cement content, the proportion of slag replacing cement, and the compressive strength of the test samples, improving the accuracy of the compressive strength prediction results. The model with the largest fitting coefficient is determined as the target model. Response surface analysis is performed on the compressive strength of the test samples based on the target model to obtain multiple initial parameter sets where the sum of the compressive strengths of the test samples on day n and day 4n is greater than a specified value. A target parameter set is then determined from these initial parameter sets, achieving the beneficial effect of simultaneously satisfying the early strength and long-term stability of the solidified sludge.

[0092] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for solidifying sludge, characterized in that, The method includes: Multiple sample parameter groups are obtained, each sample parameter group includes three parameters, namely, sludge moisture content, cement content and slag replacement ratio of cement, and at least one parameter in any two sample parameter groups is different. Multiple test samples are manufactured based on the aforementioned multiple sample parameter sets; The multiple test samples are tested to obtain the compressive strength of the multiple test samples on day n and day 4n, where n is greater than or equal to 1; Multiple initial models are established based on multiple test parameter sets, including linear models, second-order interactive models, quadratic polynomial models, and cubic polynomial models. Each test parameter set includes a sample parameter set and the compressive strength of the test sample manufactured based on the sample parameter set on day n and day 4n. The initial model is used to output the compressive strength of the test sample based on the sludge moisture content, cement content, and the proportion of slag replacing cement. Obtain the fitting coefficients of each of the linear model, second-order interactive model, quadratic polynomial model, and cubic polynomial model for the multiple test parameter sets; The initial model with the largest fitting coefficient is determined as the target model; Based on the target model, the compressive strength of the test sample is analyzed by response surface analysis to obtain multiple initial parameter sets in which the sum of the compressive strength of the manufactured test sample on day n and day 4n is greater than a specified value. Each initial parameter set includes sludge moisture content, cement content, and slag replacement ratio of cement. Determine a target parameter set from the plurality of initial parameter sets; The sludge is solidified based on the target parameter set.

2. The method according to claim 1, characterized in that, The step of determining the initial model with the largest fitting coefficient as the target model includes: An analysis of variance was performed on the initial model with the largest fitting coefficient to obtain the first significance probability of the initial model with the largest fitting coefficient, the second significance probability of each parameter among the silt moisture content, the cement content and the proportion of slag replacing cement, the third significance probability of the product of every two parameters among the three parameters, and the fourth significance probability of the square of each parameter among the three parameters. Determine whether the first significance probability, the second significance probability, the third significance probability, and the fourth significance probability are all less than the target significance probability; When the first significance probability, the second significance probability, the third significance probability, and the fourth significance probability are all less than the target significance probability, the initial model with the largest fitting coefficient is determined as the target model; When the first significance probability, the second significance probability, the third significance probability, or the fourth significance probability is greater than or equal to the target significance probability, the step of testing the plurality of test samples is performed.

3. The method according to claim 1, characterized in that, When the initial model with the largest fitting coefficient is the quadratic polynomial model, the quadratic regression equations for the compressive strength on day n and day 28 of the quadratic polynomial model include: y1=1.65875-0.005125x1+0.042751x2+0.04275x3-0.00018x1x2+0.000255x1x3+0.000375x2x3+0.000020x1 2 +0.0012x2 2 -0.002525x3 2 ; y2=2.91625-0.004675x1-0.067375x2+0.03075x3-0.00018x1x2+0.000265x1x3+0.000625x2x3+0.000017x1 2 +0.001325x2 2 -0.0024x3 2 ; Where y1 is the compressive strength on day n, y2 is the compressive strength on day 4n, x1 is the moisture content of the silt, x2 is the cement content, and x3 is the proportion of slag replacing cement.

4. The method according to claim 1, characterized in that, Determining a target parameter set from the plurality of initial parameter sets includes: Determine the first average compressive strength of the test samples manufactured from the plurality of initial parameter sets on day n, and the second average compressive strength on day 4n; Remove the initial parameter groups from the multiple initial parameter groups where the compressive strength of the manufactured test sample on day n is less than the first average value, and the initial parameter groups where the compressive strength on day 4n is less than the second average value, to obtain the remaining initial parameter groups. When the number of remaining initial parameter sets is greater than 1, the parameter set with the largest sum of the compressive strength of the manufactured test sample on day n and the compressive strength on day 4n among the remaining multiple initial parameter sets is determined as the target parameter set; When the number of remaining initial parameter sets is equal to 1, the remaining initial parameter set is determined as the target parameter set.

5. The method according to claim 1, characterized in that, The solidification of sludge based on the target parameter set includes: Generate target samples based on the target parameter set; The compressive strength of the target sample was measured on day n and day 4n. When the difference between the measured compressive strength of the target sample on day n and day 4n and the compressive strength output by the target model based on the target parameter set is less than the strength threshold, the sludge is solidified based on the target parameter set. When the difference between the measured compressive strength of the target sample on day n and day 4n and the compressive strength output by the target model based on the target parameter set is greater than or equal to the strength threshold, the step of testing the multiple test samples is executed.

6. The method according to claim 1, characterized in that, The silt has a moisture content ranging from 50% to 150%, a cement content ranging from 20% to 40%, and a slag-to-cement ratio ranging from 10% to 30%.

7. The method according to claim 1, characterized in that, The sample parameter group has a sludge moisture content of 50%, 100%, or 150%, a cement content of 20%, 30%, or 40%, and a slag replacement ratio of 10%, 20%, or 30%.

8. The method according to claim 5, characterized in that, The strength threshold is 6% of the compressive strength of the target sample measured on day n and day 4n.

9. The method according to claim 1, characterized in that, The process of generating multiple test samples based on the multiple sample parameter sets includes: Based on any one of the multiple sample parameter groups, sludge, cement, and slag are sequentially added to a mixer and stirred to obtain a mixture. The mixture was placed in a mold and allowed to stand for 24 hours before being demolded to obtain the initial sample. The initial sample is placed in the curing room and cured for the target number of days to obtain the test sample corresponding to any sample parameter group.

10. The method according to claim 9, characterized in that, The curing temperature in the curing room is 18-20℃ and the relative humidity is ≥95%.

Images