Method and system for determining proportion of sludge curing agent

Abstract

The invention provides a sludge curing agent proportion determination method and system, and relates to the technical field of sludge curing treatment.The sludge curing agent proportion determination method comprises the following steps that a sludge sample is obtained, and physicochemical characteristics of the sludge sample are measured through a standard soil test method and a chemical analysis technology; based on the physicochemical characteristics of the sludge sample, performing Huggins equation fitting on the relationship between the fluidity of the sludge and the mixing amount of the curing agent, and determining the hydraulics action radius of the curing agent according to the fitting result; determining the mixing amount range of the curing agent based on the hydraulics action radius of the curing agent, and determining the unconfined compressive strength of the sludge curing sample after standard curing according to the determined mixing amount range to obtain a strength development curve; and determining engineering performance indexes of the sludge based on the strength development curve, and determining the optimal proportion of the sludge curing agent in combination with preset engineering requirements. According to the method, the accuracy, repeatability and engineering applicability of sludge curing ratio design are remarkably improved.

Description

A method and system for determining the proportion of sludge solidifying agent Technical Field

[0001] This invention relates to the field of sludge solidification treatment technology, and more specifically, to a method and system for determining the proportion of sludge solidification agent. Background Technology

[0002] Soil consolidation in silty areas is an important engineering technique for enhancing land use value. Among these techniques, fluidized bed solidification is widely used due to its ease of construction and low cost. Commonly used solidifying agents include inorganic materials such as quicklime, hydrated lime, cement, and fly ash, as well as various admixtures. These materials improve soil properties through mechanisms such as hydration reactions, ion exchange, and cementation.

[0003] Currently, traditional methods rely on empirical formulas and orthogonal experiments, lacking the exploration and correlation of microscopic physical properties of the sludge-solidifying agent system. This makes it impossible to establish a quantitative relationship between rheological behavior (flowability, viscosity) and microscopic mechanisms of action, leading to significant arbitrariness in formulation design, high trial-and-error costs, and poor repeatability. Furthermore, these methods are not well-suited for special sludge types with high water content or high organic matter content. Simultaneously, the determination of the solidifying agent dosage range lacks scientific support from microscopic parameters, relying heavily on engineering experience. This can easily result in overly wide dosage ranges leading to redundant experimental quantities, or unreasonable dosage ranges that miss the optimal formulation.

[0004] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for determining the proportion of sludge solidifying agent to solve the above-mentioned problems.

[0006] To solve the above problems, the specific technical solution adopted by the present invention is as follows: According to a first aspect of the present invention, a method for determining the proportion of sludge solidifying agent is provided, the method comprising the following steps: S1, obtaining sludge samples and determining the physicochemical properties of the sludge samples using standard geotechnical testing methods and chemical analysis techniques; S2, fitting the relationship between the fluidity of the sludge and the dosage of the solidifying agent using the Huggins equation based on the physicochemical properties of the sludge samples, and determining the hydraulic radius of action of the solidifying agent based on the fitting results; S3, determining the dosage range of the solidifying agent based on the hydraulic radius of action of the solidifying agent, and determining the unconfined compressive strength of the sludge solidified sample after standard curing according to the determined dosage range, thereby obtaining a strength development curve; S4, determining the engineering performance indicators of the sludge based on the strength development curve, and determining the optimal proportion of the sludge solidifying agent in combination with preset engineering requirements.

[0007] Preferably, the relationship between the fluidity of the sludge and the amount of solidifying agent is fitted using the Huggins equation based on the physicochemical properties of the sludge sample, and the hydraulic radius of the solidifying agent is determined based on the fitting results, including the following steps: S21, using a rotational viscometer at a preset shear rate, the apparent viscosity and fluidity of the sludge-solidifying agent mixed slurry with different amounts of solidifying agent prepared according to the physicochemical properties of the sludge sample are measured; S22, based on the generalized Huggins equation corrected for maximum bulk fraction, the apparent viscosity and fluidity are nonlinearly fitted to obtain the intrinsic viscosity; S23, based on the intrinsic viscosity, the hydraulic radius of the solidifying agent is calculated.

[0008] Preferably, the generalized Huggins equation based on the maximum packing fraction correction is used to nonlinearly fit the apparent viscosity and fluidity to obtain the intrinsic viscosity, including the following steps: S221, based on the physicochemical properties of the sludge sample and the amount of solidifying agent, calculate the total volume fraction of solid particles in the sludge-solidifying agent mixture under different solidifying agent dosages; and measure the viscosity of pure water at the experimental temperature as the solvent viscosity; S222, determine the initial estimate of the maximum packing fraction of the system; S223, using the solid volume fraction corresponding to the solidifying agent dosage as the independent variable, and the measured apparent viscosity of the sludge-solidifying agent mixture as the solvent viscosity; The ratio of viscosity to solvent viscosity is the dependent variable. Nonlinear least squares fitting is performed based on the modified generalized Huggins equation. The optimal parameter combination is determined by goodness-of-fit evaluation, and a preliminary numerical solution of intrinsic viscosity is output. S224: Correlation analysis is performed between the flowability values ​​of sludge-soldering agent mixtures with different curing agent dosages and the preliminary numerical solution of intrinsic viscosity. If abnormal data points deviating from the correlation trend are found, the data is regarded as outliers and removed. The fitting process in step S223 is repeated until a stable fitting result with physical consistency and the final intrinsic viscosity value are obtained.

[0009] Preferably, the initial estimate of the maximum packing fraction of the system is determined by: calculating the theoretical initial estimate of the maximum packing fraction based on the particle size distribution of the sludge and the particle size of the curing agent, according to the packing theory of multi-component particle systems; or by measuring the limiting fluidity of dense slurries containing only sludge and curing agent particles under different low water-cement ratios, and using back analysis to calculate the empirical initial estimate of the maximum packing fraction.

[0010] Preferably, a correlation analysis is performed between the flowability values ​​of sludge-solidifier mixtures with different solidifier dosages and the preliminary numerical solutions of intrinsic viscosity. If outlier data points deviating from the correlation trend are found, these data points are considered outliers and removed. The fitting process in step S223 is repeated until a stable fitting result with physical consistency and the final intrinsic viscosity value are obtained. This includes the following steps: S2241. Using the preliminary numerical solution of intrinsic viscosity as the abscissa and the flowability value as the ordinate, a least squares method is used to linearly fit the preliminary numerical solution of intrinsic viscosity and the flowability value in a double logarithmic coordinate system to obtain an empirical correlation equation describing the statistical correlation trend between the preliminary numerical solution of intrinsic viscosity and the flowability value; S2242. Based on... The empirical correlation equation is used to calculate the relative residual between each measured flowability value and the flowability value predicted by the empirical correlation equation based on the preliminary numerical solution of intrinsic viscosity; S2243, based on the relative residual, outlier data points are identified and removed according to statistical criteria to obtain a set of valid data points; S2244, the set of valid data points is used as new input data, and step S223 is repeated to obtain an updated numerical solution of intrinsic viscosity; S2245, it is determined whether the updated numerical solution of intrinsic viscosity meets the preset convergence condition; if it does, it is used as the final intrinsic viscosity value; if it does not, it is used as a new preliminary numerical solution, and the process returns to step S2241 for the next round of iterative optimization until the convergence condition is met.

[0011] Preferably, the determination of the curing agent dosage range based on the hydraulic action radius of the curing agent, and the determination of the unconfined compressive strength of the sludge solidified sample after standard curing according to the determined dosage range, to obtain the strength development curve, includes the following steps: S31, estimating the effective action distance based on the hydraulic action radius to determine the curing agent dosage range; S32, acquiring the unconfined compressive strength test data of the sludge solidified sample prepared within the curing agent dosage range according to a preset time period; S33, plotting the obtained unconfined compressive strength test data into a strength development curve according to a preset time period.

[0012] Preferably, determining the engineering performance indicators of sludge based on the strength development curve and determining the optimal ratio of sludge solidifying agent in combination with preset engineering requirements includes the following steps: S41, for the strength development curve corresponding to each dosage in the dosage range, extract the measured value of unconfined compressive strength corresponding to the preset key curing age as the core performance data for evaluating the solidification effect; S42, compare the core performance data with the preset engineering performance threshold and screen out candidate dosage combinations that meet the engineering requirements; S43, based on the candidate dosage combinations and combined with economic indicators, determine the dosage that minimizes the cost as the optimal ratio of sludge solidifying agent.

[0013] According to a second aspect of the present invention, a system for determining the proportion of a sludge solidifying agent is provided. This system includes: a characteristic analysis module for acquiring sludge samples and determining their physicochemical properties using standard geotechnical testing methods and chemical analysis techniques; a hydraulic radius determination module for fitting the relationship between the sludge's fluidity and the solidifying agent dosage to the Huggins equation based on the sludge's physicochemical properties and determining the hydraulic radius of the solidifying agent based on the fitting result; a strength development curve calculation module for determining the dosage range of the solidifying agent based on its hydraulic radius, determining the unconfined compressive strength of the sludge solidified sample after standard curing according to the determined dosage range, and obtaining a strength development curve; and an optimal proportion determination module for determining the engineering performance indicators of the sludge based on the strength development curve and determining the optimal proportion of the sludge solidifying agent in conjunction with preset engineering requirements.

[0014] According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the programs to implement the steps of the above-described method.

[0015] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, wherein the steps of the above-described method are implemented when the computer program controls the device in which the computer-readable storage medium is located to execute during runtime.

[0016] The beneficial effects of this invention are as follows: 1. This invention organically couples the physicochemical properties, rheological behavior and mechanical properties of sludge to construct a scientific decision-making chain from the hydraulic action radius to the strength development curve. On the one hand, it uses the Huggins equation to fit the relationship between fluidity and dosage, avoiding the blindness of the traditional trial and error method. On the other hand, it determines the optimal ratio based on measured strength data and clear engineering requirements, taking into account both technical feasibility and economic rationality, and significantly improving the accuracy, repeatability and engineering applicability of sludge solidification ratio design.

[0017] 2. This invention achieves high-precision modeling of the rheological behavior of the sludge-solidifying agent system by using a generalized Huggins equation corrected for maximum packing fraction, combined with multi-dimensional rheological tests of rotational viscosity and fluidity, and integrating particle volume fraction calculation, initial values ​​of maximum packing fraction estimated by both theory and experience, as well as an automatic identification and iterative optimization mechanism for abnormal data based on statistical criteria. It scientifically deduces the key microscopic parameter of the hydraulic action radius of the solidifying agent, significantly improving the physical consistency and reliability of the fitting results. This lays a solid physical property foundation for the subsequent reasonable determination of the solidifying agent dosage range, effectively avoiding misjudgments caused by data noise or local anomalies in traditional empirical proportioning, and improving the scientificity, robustness, and engineering feasibility of the entire solidification proportioning design method. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 is a flowchart of a method for determining the proportion of sludge solidifying agent according to an embodiment of the present invention; Figure 2 is a principle block diagram of a system for determining the proportion of sludge solidifying agent according to an embodiment of the present invention; Figure 3 is a hardware structure block diagram of the host device in a method for determining the proportion of sludge solidifying agent according to an embodiment of the present invention.

[0019] In the diagram: 1. Characteristic analysis module; 2. Hydraulic action radius determination module; 3. Intensity development curve calculation module; 4. Optimal ratio determination module. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0021] The method embodiments provided in this application can be executed in a host device or similar computing device. Taking operation on a host device as an example, as shown in FIG3, the host device may include one or more (only one is shown in FIG3) processors (processors may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs) and storage for storing data. The host device may also include transmission devices for communication functions and input / output devices. Those skilled in the art will understand that the structure shown in FIG3 is merely illustrative and does not limit the structure of the host device. For example, the host device may include more or fewer components than shown in FIG3, or have a different configuration than shown in FIG3.

[0022] The memory can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the exception handling method in this embodiment. The processor executes various functional applications and data processing by running the computer program stored in the memory, thus implementing the above-described method. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the host device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0023] Transmission devices are used to receive or send data over a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the host device. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0024] According to embodiments of the present invention, a method and system for determining the proportion of sludge solidifying agent are provided.

[0025] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. As shown in Figure 1, according to the first embodiment of the present invention, a method for determining the proportion of sludge solidification agent is provided. The method includes the following steps: S1, obtaining sludge samples and using standard geotechnical testing methods and chemical analysis techniques to determine the physicochemical properties of the sludge samples; it should be noted that representative original samples can be collected from multiple points in the preset target sludge site, homogenized, and then the key physicochemical properties of the sludge can be determined using standard geotechnical testing and chemical analysis techniques: physical properties mainly include natural moisture content, wet density and particle size distribution, liquid limit and plastic limit, etc.; chemical properties mainly include organic matter content, pH value, and soluble salts or specific pollutant components as needed.

[0026] S2. Based on the physicochemical properties of the sludge sample, the relationship between the sludge's fluidity and the amount of solidifying agent is fitted using the Huggins equation, and the hydraulic radius of the solidifying agent is determined based on the fitting results. As a preferred embodiment, fitting the relationship between the sludge's fluidity and the amount of solidifying agent using the Huggins equation based on the physicochemical properties of the sludge sample, and determining the hydraulic radius of the solidifying agent based on the fitting results, includes the following steps: S21. Using a rotational viscometer at a preset shear rate, the apparent viscosity and fluidity of sludge-solidifying agent mixed slurries with different solidifying agent dosages prepared in advance according to the physicochemical properties of the sludge sample are measured. Specifically, based on the basic physical properties of the sludge, mainly water content and density, and using the dry sludge mass as a benchmark, a series of sludge-solidifying agent mixed slurries are prepared according to a preset dosage gradient covering the potential application range, for example, from 4% to 20% by mass, at intervals of 2% or 4%. The rotational viscometer is used at a fixed shear rate that can simulate actual construction shear conditions, for example, 100s. -1 The apparent viscosity of each slurry after stabilization was measured; and the expansion diameter or slump of the slurry under its own weight was measured using a flowability meter as an auxiliary indicator to characterize its flowability.

[0027] S22. Based on the generalized Huggins equation modified by the maximum packing fraction, nonlinear fitting is performed on the apparent viscosity and fluidity to obtain the intrinsic viscosity. As a preferred embodiment, the nonlinear fitting of the apparent viscosity and fluidity based on the generalized Huggins equation modified by the maximum packing fraction to obtain the intrinsic viscosity includes the following steps: S221. Based on the physicochemical properties of the sludge sample and the amount of solidifying agent, the total volume fraction of solid particles in the sludge-solidifying agent mixture under different solidifying agent dosages is calculated; and the viscosity of pure water at the experimental temperature is measured as the solvent viscosity. Specifically, the total volume fraction of all solid particles in the slurry at each ratio can be calculated through material balance using the natural density of the sludge, particle density, and the preset amount of solidifying agent. This parameter is the core variable determining the consistency of the suspension. Simultaneously, the viscosity of pure water at the experimental ambient temperature is measured using a precision viscometer.

[0028] S222. Determine the initial estimate of the maximum packing fraction of the system; as a preferred embodiment, the method for determining the initial estimate of the maximum packing fraction of the system includes: based on the particle size distribution of the sludge and the particle size of the solidifying agent, calculations are performed according to the packing theory of multi-component particle systems to obtain the initial estimate of the theoretical maximum packing fraction; it should be noted that the particle size distribution curve of the original sludge is determined by techniques such as laser particle size analysis, and the typical particle size distribution or median diameter of the solidifying agent is obtained. Then, the entire solid system, such as sludge particles and solidifying agent particles, is considered as a multi-component particle mixture, and a classical packing theory model is applied, for example, the Andreasen & Andersen continuous particle size distribution model or its modified form is used for calculation. This continuous particle size distribution model is based on the optimized packing principle of "smaller particles filling the gaps between larger particles," and seeks the particle distribution state that maximizes the theoretical packing density by adjusting the proportions of each component in the mixture. Finally, the maximum solid volume fraction calculated under this state is the theoretical initial estimate.

[0029] Specifically, the Andreasen & Andersen continuous particle size distribution model is used for calculations, which refers to applying this classical particle packing theory to predict and optimize the maximum theoretical packing density of the sludge-solidifier mixture. Specifically, this continuous particle size distribution model is based on the core assumption that particles with an infinitely continuous particle size distribution can achieve the densest packing, and its standard expression is: P(D) = (D / D) max ) q In the formula, P(D) represents the cumulative volume fraction of particles smaller than D, and D max q is the maximum particle size in the mixture, and q is the distribution modulus (usually the optimal value is between 0.3 and 0.5, and for the densest packing, it is often taken as around 0.37).

[0030] The specific implementation steps are as follows: Integrate the measured particle size distribution of sludge with the particle size distribution of the solidifying agent to construct a complete particle size distribution dataset for the entire solid system. Using the aforementioned continuous particle size distribution model formula as the objective function, adjust the theoretical distribution parameters, mainly the q-value, to achieve the best fit between the target cumulative distribution curve and the measured cumulative distribution curve of the mixed particle system. After obtaining the optimal continuous distribution model, the solid volume fraction where the interparticle porosity is minimized can be calculated based on this ideal distribution; this value is the theoretical maximum packing fraction.

[0031] By measuring the limiting fluidity of dense slurries containing only sludge and solidifier particles under different low water-cement ratios, and using back analysis calculations, an initial estimate of the empirical maximum packing fraction was obtained.

[0032] It should be noted that the initial estimate of the empirical maximum bulk fraction is obtained by back-calculating from direct physical experimental phenomena, closely approximating the actual mixing and stress state. In practice, a series of dense slurries with extremely low water-cement ratios (i.e., water consumption only slightly higher than the minimum required to wet solid particles) need to be prepared. Their solid phase composition consists only of sludge and solidifying agent particles, without any water-reducing or dispersing additives. The flowability of these slurries is tested to plot the water-cement ratio-flowability relationship curve, and the criteria for limiting flowability are clearly defined, such as an extended diameter not exceeding 110% of the reference diameter or the slurry no longer being able to deform continuously. At this point, the solid particles in the slurry are considered to have reached the closest packing in a flowable state. Subsequently, by measuring the accurate solid mass and volume of the slurry at this limiting state, the corresponding critical solid volume fraction is calculated. This critical value is the initial estimate of the empirical maximum bulk fraction obtained through back-analysis based on the actual system.

[0033] S223. Using the solid volume fraction corresponding to the curing agent dosage as the independent variable and the ratio of the measured apparent viscosity to the solvent viscosity of the sludge-curing agent mixture as the dependent variable, perform nonlinear least squares fitting based on the modified generalized Huggins equation. Determine the optimal parameter combination through goodness-of-fit evaluation and output the preliminary numerical solution of the intrinsic viscosity. S224. Perform correlation analysis between the flowability values ​​of the sludge-curing agent mixture with different curing agent dosages and the preliminary numerical solution of the intrinsic viscosity. If abnormal data points deviating from the correlation trend are found, the data is regarded as an outlier and removed. Repeat the fitting process of step S223 until a stable fitting result with physical consistency and the final intrinsic viscosity value are obtained.

[0034] As a preferred embodiment, a correlation analysis is performed on the flowability values ​​of sludge-solidifier mixtures with different solidifier dosages and the preliminary numerical solutions of intrinsic viscosity. If abnormal data points deviating from the correlation trend are found, the data is considered an outlier and removed. The fitting process in step S223 is repeated until a stable fitting result with physical consistency and the final intrinsic viscosity value are obtained. This includes the following steps: S2241, using the preliminary numerical solution of intrinsic viscosity as the abscissa and the flowability value as the ordinate, a least squares method is used to linearly fit the preliminary numerical solution of intrinsic viscosity and the flowability value in a double logarithmic coordinate system to obtain an empirical correlation equation describing the statistical correlation trend between the preliminary numerical solution of intrinsic viscosity and the flowability value; S2242, based on the empirical correlation equation, the relative residual between each measured flowability value and the flowability value predicted by the empirical correlation equation based on the preliminary numerical solution of intrinsic viscosity is calculated; S2243, based on the relative residual, outlier data points are identified and removed according to statistical criteria to obtain a set of valid data points. Specifically, data points with a relative residual greater than the mean of all residuals plus twice the standard deviation are automatically identified as outlier data points.

[0035] S2244. Take the set of valid data points as new input data, return and repeat step S223 to obtain the updated numerical solution of intrinsic viscosity; S2245. Determine whether the updated numerical solution of intrinsic viscosity meets the preset convergence condition; if it does, use it as the final intrinsic viscosity value; if it does not, use it as a new preliminary numerical solution and return to step S2241 for the next round of iterative optimization until the convergence condition is met.

[0036] S23. Calculate the hydrodynamic radius of the curing agent based on its intrinsic viscosity.

[0037] Specifically, based on colloid chemistry theory and the assumption of spherical particles of active hydration products in the sludge-solidifying agent system, the classical correlation between intrinsic viscosity and hydraulic radius of action is used for calculation. The correlation is [η] = 2πN a r_H 3 / 3, [η] is the final intrinsic viscosity value (unit: cm). 3 / g), N a Let Avogadro's constant be 6.022 × 10⁻⁶. 23 (mol-1), r_H is the hydraulic radius of action (unit: cm) of the active hydration products after the reaction of the solidifying agent and sludge; before calculation, the data units need to be unified and the intrinsic viscosity unit needs to be converted to cm. 3 / g. The hydrodynamic radius obtained by this formula can directly reflect the spatial size and dispersion characteristics of the active hydration products.

[0038] S3. Based on the hydraulic radius of action of the curing agent, determine the dosage range of the curing agent. According to the determined dosage range, measure the unconfined compressive strength of the sludge solidified sample after standard curing to obtain a strength development curve. As a preferred embodiment, determining the dosage range of the curing agent based on the hydraulic radius of action of the curing agent and measuring the unconfined compressive strength of the sludge solidified sample after standard curing to obtain a strength development curve includes the following steps: S31. Estimate the effective action distance based on the hydraulic radius of action to determine the dosage range of the curing agent; S32. Obtain the unconfined compressive strength test data of the sludge solidified sample prepared within the dosage range of the curing agent according to a preset time period; S33. Plot the obtained unconfined compressive strength test data into a strength development curve according to a preset time period.

[0039] S4. Determine the engineering performance indicators of sludge based on the strength development curve, and determine the optimal ratio of sludge solidification agent in combination with the preset engineering requirements.

[0040] As a preferred embodiment, determining the engineering performance indicators of sludge based on the strength development curve and determining the optimal ratio of sludge solidifying agent in combination with preset engineering requirements includes the following steps: S41, for the strength development curve corresponding to each dosage in the dosage range, extract the measured value of unconfined compressive strength corresponding to the preset key curing age as the core performance data for evaluating the solidification effect; S42, compare the core performance data with the preset engineering performance threshold and screen out candidate dosage combinations that meet the engineering requirements; S43, based on the candidate dosage combinations and in combination with economic indicators, determine the dosage that minimizes the cost as the optimal ratio of sludge solidifying agent.

[0041] As shown in Figure 2, according to a second embodiment of the present invention, a system for determining the proportion of a sludge solidifying agent is provided. This system includes: a characteristic analysis module for acquiring sludge samples and determining their physicochemical properties using standard geotechnical testing methods and chemical analysis techniques; a hydraulic radius determination module for fitting the relationship between the sludge's fluidity and the solidifying agent dosage using the Huggins equation based on the sludge's physicochemical properties, and determining the hydraulic radius of the solidifying agent based on the fitting results; a strength development curve calculation module for determining the dosage range of the solidifying agent based on its hydraulic radius, and determining the unconfined compressive strength of the sludge solidified sample after standard curing according to the determined dosage range, thereby obtaining a strength development curve; and an optimal proportion determination module for determining the engineering performance indicators of the sludge based on the strength development curve, and determining the optimal proportion of the sludge solidifying agent in conjunction with preset engineering requirements.

[0042] According to a third embodiment of the present invention, an electronic device is provided, the electronic device comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the steps in any of the above method embodiments.

[0043] According to a fourth embodiment of the present invention, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the steps in any of the above method embodiments.

[0044] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the proportion of a sludge solidifying agent, characterized in that, The method includes the following steps: S1, obtaining silt samples and determining their physicochemical properties using standard geotechnical testing methods and chemical analysis techniques; S2, fitting the relationship between silt flowability and solidifier dosage using the Huggins equation based on the physicochemical properties of the silt samples, and determining the hydraulic radius of action of the solidifier based on the fitting results; S3, determining the dosage range of the solidifier based on its hydraulic radius of action, and determining the unconfined compressive strength of the silt solidified sample after standard curing according to the determined dosage range, obtaining a strength development curve; S4, determining the engineering performance indicators of the silt based on the strength development curve, and determining the optimal ratio of the silt solidifier in combination with the preset engineering requirements.

2. The method for determining the proportion of a sludge solidifying agent according to claim 1, characterized in that, The process of fitting the relationship between sludge flowability and solidifier dosage based on the physicochemical properties of sludge samples using the Huggins equation, and determining the hydraulic radius of the solidifier based on the fitting results, includes the following steps: S21, using a rotational viscometer at a preset shear rate, measuring the apparent viscosity and flowability of sludge-solidifier mixed slurries with different solidifier dosages prepared in advance based on the physicochemical properties of sludge samples; S22, performing nonlinear fitting on the apparent viscosity and flowability based on the generalized Huggins equation modified by the maximum packing fraction to obtain the intrinsic viscosity; S23, calculating the hydraulic radius of the solidifier based on the intrinsic viscosity.

3. The method for determining the proportion of a sludge solidifying agent according to claim 2, characterized in that, The generalized Huggins equation based on the maximum packing fraction correction is used to nonlinearly fit apparent viscosity and fluidity to obtain intrinsic viscosity, including the following steps: S221, based on the physicochemical properties of the sludge sample and the amount of solidifier, calculate the total volume fraction of solid particles in the sludge-solidifier mixture under different solidifier dosages; and measure the viscosity of pure water at the experimental temperature as the solvent viscosity; S222, determine the initial estimate of the maximum packing fraction of the system; S223, using the solid volume fraction corresponding to the solidifier dosage as the independent variable, and the measured apparent viscosity of the sludge-solidifier mixture as the solvent viscosity. The ratio of fluidity to solvent viscosity is the dependent variable. Nonlinear least squares fitting is performed based on the modified generalized Huggins equation. The optimal parameter combination is determined by the goodness-of-fit evaluation, and the preliminary numerical solution of intrinsic viscosity is output. S224: Correlation analysis is performed between the fluidity values ​​of sludge-soldering agent mixtures with different curing agent dosages and the preliminary numerical solution of intrinsic viscosity. If abnormal data points deviating from the correlation trend are found, the data is regarded as outliers and removed. The fitting process in step S223 is repeated until a stable fitting result with physical consistency and the final intrinsic viscosity value are obtained.

4. The method for determining the proportion of a sludge solidifying agent according to claim 3, characterized in that, The methods for determining the initial estimate of the maximum packing fraction of the system include: based on the particle size distribution of the sludge and the particle size of the curing agent, calculations are performed according to the packing theory of multi-component particle systems to obtain the initial estimate of the theoretical maximum packing fraction; by measuring the limiting fluidity of dense slurries containing only sludge and curing agent particles under different low water-cement ratios, and using back analysis calculations, an initial estimate of the empirical maximum packing fraction is obtained.

5. The method for determining the proportion of a sludge solidifying agent according to claim 3, characterized in that, The correlation analysis was performed between the flowability values ​​and the intrinsic viscosity of the slurry mixture with different curing agent dosages; If outlier data points deviating from the correlation trend are found, these data points are considered outliers and removed. The fitting process in step S223 is repeated until a stable fitting result with physical consistency and the final intrinsic viscosity value are obtained. This includes the following steps: S2241. Using the preliminary numerical solution of intrinsic viscosity as the abscissa and the flowability value as the ordinate, a least squares method is used to linearly fit the preliminary numerical solution of intrinsic viscosity and the flowability value on a double logarithmic coordinate system to obtain an empirical correlation equation describing the statistical correlation trend between the preliminary numerical solution of intrinsic viscosity and the flowability value; S2242. Based on the empirical correlation equation, the correlation between each measured flowability value and the empirical correlation equation is calculated. S2243: Based on the relative residuals between the flowability values ​​predicted by the preliminary numerical solution of intrinsic viscosity, outlier data points are identified and removed according to statistical criteria based on the relative residuals to obtain a set of valid data points; S2244: The set of valid data points is used as new input data, and step S223 is repeated to obtain an updated numerical solution of intrinsic viscosity; S2245: It is determined whether the updated numerical solution of intrinsic viscosity meets the preset convergence condition; if it does, it is used as the final intrinsic viscosity value; if it does not, it is used as a new preliminary numerical solution, and the process returns to step S2241 for the next round of iterative optimization until the convergence condition is met.

6. The method for determining the proportion of a sludge solidifying agent according to claim 1, characterized in that, The process of determining the dosage range of the curing agent based on its hydraulic action radius, and measuring the unconfined compressive strength of the sludge solidified sample after standard curing according to the determined dosage range, to obtain the strength development curve, includes the following steps: S31, estimating the effective action distance based on the hydraulic action radius to determine the dosage range of the curing agent; S32, acquiring the unconfined compressive strength test data of the sludge solidified sample prepared within the curing agent dosage range according to a preset time period; S33, plotting the obtained unconfined compressive strength test data into a strength development curve according to a preset time period.

7. The method for determining the proportion of a sludge solidifying agent according to claim 1, characterized in that, The process of determining the engineering performance indicators of sludge based on the strength development curve and determining the optimal ratio of sludge solidifying agent in combination with preset engineering requirements includes the following steps: S41, for the strength development curve corresponding to each dosage in the dosage range, extract the measured value of unconfined compressive strength corresponding to the preset key curing age as the core performance data for evaluating the solidification effect; S42, compare the core performance data with the preset engineering performance threshold and screen out candidate dosage combinations that meet the engineering requirements; S43, based on the candidate dosage combinations and combined with economic indicators, determine the dosage that minimizes the cost as the optimal ratio of sludge solidifying agent.

8. A system for determining the proportion of a sludge solidifying agent, used to implement the method for determining the proportion of a sludge solidifying agent as described in any one of claims 1-7, characterized in that, The system includes: a characteristic analysis module for acquiring silt samples and determining their physicochemical properties using standard geotechnical testing methods and chemical analysis techniques; a hydraulic radius determination module for fitting the relationship between silt flowability and solidifier dosage using the Huggins equation based on the physicochemical properties of the silt samples, and determining the hydraulic radius of the solidifier based on the fitting results; a strength development curve calculation module for determining the dosage range of the solidifier based on its hydraulic radius, and determining the unconfined compressive strength of the silt solidified sample after standard curing according to the determined dosage range, thus obtaining the strength development curve; and an optimal ratio determination module for determining the engineering performance indicators of the silt based on the strength development curve, and determining the optimal ratio of the silt solidifier in combination with preset engineering requirements.

9. An electronic device, characterized in that, The electronic device includes: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the steps of the method according to any one of claims 1 to 7 are implemented when the computer program controls the device containing the computer-readable storage medium to execute during runtime.

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