Method and device for configuring composite substrate for sewage treatment and server

Abstract

The application provides a configuration method and device of a composite substrate for sewage treatment and a server, relates to the technical field of water treatment material preparation, and comprises the following steps: performing central combination treatment on a ternary composite substrate for sewage treatment to generate a test formula set; obtaining test data corresponding to the test formula set, taking the removal rates of various substrates as response values, and obtaining target test data; performing regression analysis processing on the target test data in a quadratic polynomial regression model of the ternary composite substrate to obtain model prediction values, and performing model verification and update processing on the quadratic polynomial regression model by using the model prediction values and the target test data to determine a target quadratic polynomial regression model; and performing optimization processing on the proportioning of the ternary composite substrate by using the target quadratic polynomial regression model to determine a target mass proportioning of the ternary composite substrate. The application can significantly improve the pollutant removal rate of the collaborative pollutant removal of various pollutants in the sewage treatment process.

Description

Technical Field

[0001] This invention relates to the technical field of water treatment material preparation, and in particular to a method, apparatus and server for preparing a composite matrix for wastewater treatment. Background Technology

[0002] Developing efficient and economical water treatment technologies has become a key research focus in the field of environmental engineering. Currently, composite matrix materials are commonly used in water pollution treatment, as they can integrate the advantages of multiple single materials and show significant application potential in wastewater treatment and sediment improvement. However, related technologies suggest that existing composite matrix materials rely mainly on manual experience or simple literature reviews to determine the proportions during preparation, lacking systematic multi-objective optimization research. Therefore, it is difficult to guarantee the stability and consistency of product performance. In addition, the above-mentioned solutions also ignore the complex interactions of the components during actual synergistic work, resulting in low utilization efficiency of the matrix after proportioning, which in turn affects the removal rate of water pollutants. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, apparatus and server for preparing composite substrates for wastewater treatment, which can significantly improve the pollutant removal rate for the synergistic removal of multiple pollutants during wastewater treatment.

[0004] In a first aspect, embodiments of the present invention provide a method for configuring a composite matrix for wastewater treatment. The method includes: performing a central combination treatment on a ternary composite matrix for wastewater treatment to generate a set of experimental formulations, wherein the ternary composite matrix includes biochar, zeolite, and volcanic rock, and the set of experimental formulations includes composite matrices with different mass ratios; acquiring experimental data of wastewater treatment experiments corresponding to the set of experimental formulations, and using the removal rate corresponding to each matrix in the experimental data as a response value to obtain target experimental data; substituting the target experimental data into a quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain model prediction values, and using the model prediction values ​​and the target experimental data to perform model verification and update processing on the quadratic polynomial regression model to determine the target quadratic polynomial regression model; and optimizing the proportion of the ternary composite matrix through the target quadratic polynomial regression model to determine the target mass ratio of the ternary composite matrix.

[0005] In one embodiment, the step of generating a set of experimental formulations by performing central combination treatment on a ternary composite matrix for wastewater treatment includes: substituting the ternary composite matrix into a mathematical framework of central combination design; determining the corresponding coding value of the ternary composite matrix based on the number of factors of the ternary composite matrix and the preset coding level of the mathematical framework, wherein the preset coding level includes: factor points, axis points and center points; converting the coding value into actual value to determine the set of experimental formulations based on the actual value.

[0006] In one embodiment, after the step of using the removal rate corresponding to each matrix in the test data as the response value, the method includes: performing data analysis processing on the test data to determine the mean and standard deviation of the test; sending the mean and standard deviation to a preset coefficient of variation calculation model to obtain the coefficient of variation; and determining the test data as the target test data when the coefficient of variation is less than a preset coefficient of variation threshold.

[0007] In one embodiment, the step of substituting the target experimental data into a quadratic polynomial regression model of a ternary composite matrix for regression analysis to obtain the model prediction value includes: substituting the target experimental data into a quadratic polynomial regression model of a ternary composite matrix for regression analysis to determine the model coefficients corresponding to each constant term in the quadratic polynomial regression model; constructing a regression equation for the removal rate of the target water quality index based on the model coefficients, and using the regression equation for the removal rate of the target water quality index for model prediction to obtain the model prediction value, wherein the regression equation for the removal rate of the target water quality index includes: a regression equation for the total nitrogen removal rate, a regression equation for the chemical oxygen demand (COD) removal rate, and a regression equation for the total phosphorus removal rate.

[0008] In one implementation, before constructing the regression equation for the removal rate of the target water quality index based on the model coefficients, the method includes: calculating the coefficient of determination, adjusted coefficient of determination, F-validation value, and P-value in the quadratic polynomial regression model, and constructing the regression equation for the removal rate of the target water quality index based on the model coefficients when the calculation results of the coefficient of determination, adjusted coefficient of determination, F-validation value, and P-value are all verified.

[0009] In one implementation, the step of performing model validation and updating on a quadratic polynomial regression model using model predictions and target experimental data to determine the target quadratic polynomial regression model includes: determining a relative error value by performing error analysis on the model predictions and target experimental data; if the relative error value meets a preset relative error threshold, then the current quadratic polynomial regression model is determined as the target quadratic polynomial regression model; if the relative error value does not meet the preset relative error threshold, then the current quadratic polynomial regression model is subjected to closed-loop iteration processing.

[0010] In one embodiment, the step of optimizing the proportion of the ternary composite matrix using a target quadratic polynomial regression model to determine the target quality proportion of the ternary composite matrix includes: solving the target quadratic polynomial regression model based on constraints of total nitrogen removal rate, chemical oxygen demand removal rate, and total phosphorus removal rate, maximizing the expected function value within the range that satisfies the constraints, and obtaining the target quality proportion of the ternary composite matrix.

[0011] Secondly, embodiments of the present invention also provide a configuration device for a composite matrix for wastewater treatment. The device includes: a test formula generation module, which performs central combination processing on a ternary composite matrix for wastewater treatment to generate a set of test formulas, wherein the ternary composite matrix includes biochar, zeolite, and volcanic rock, and the set of test formulas includes composite matrices with different mass ratios; a data acquisition module, which acquires test data of wastewater treatment tests corresponding to the set of test formulas, and uses the removal rate corresponding to each matrix in the test data as the response value to obtain target test data; a model optimization module, which substitutes the target test data into a quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain model prediction values, and uses the model prediction values ​​and the target test data to perform model verification and update processing on the quadratic polynomial regression model to determine the target quadratic polynomial regression model; and a composite matrix ratio module, which optimizes the ratio of the ternary composite matrix through the target quadratic polynomial regression model to determine the target mass ratio of the ternary composite matrix.

[0012] Thirdly, embodiments of the present invention also provide a server, including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects:

[0015] This invention provides a method, apparatus, and server for configuring a composite matrix for wastewater treatment. The method involves centrally combining the ternary composite matrix for wastewater treatment to generate a set of experimental formulations. Then, it acquires experimental data from wastewater treatment experiments corresponding to the set of formulations and uses the removal rate of each matrix component in the experimental data as the response value to obtain target experimental data. The target experimental data is then substituted into a quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain model prediction values. Using the model prediction values ​​and the target experimental data, the quadratic polynomial regression model is validated and updated to determine the target quadratic polynomial regression model. Finally, the proportions of the ternary composite matrix are optimized using the target quadratic polynomial regression model to determine the target mass ratio of the ternary composite matrix. This invention can significantly improve the pollutant removal rate for the synergistic removal of multiple pollutants during wastewater treatment.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of a composite substrate preparation system for wastewater treatment provided in an embodiment of the present invention;

[0020] Figure 2 A schematic flowchart illustrating a method for preparing a composite substrate for wastewater treatment according to an embodiment of the present invention;

[0021] Figure 3 A flowchart illustrating a closed-loop iterative method provided in an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of a device for preparing a composite substrate for wastewater treatment provided in an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Currently, with the increasingly severe water pollution problem, the development of efficient and economical water treatment technologies has become a research focus in the field of environmental engineering. Among them, sediment improvement technology, as one of the core measures for water ecological restoration, has received increasing attention, especially under complex sediment conditions, such as sandy soil, pebble bottom, newly excavated hard bottom, and hardened bottom. Sediment conditions directly limit the establishment and survival of submerged plants and directly affect the restoration effect of the aquatic ecosystem. Composite matrix materials, due to their ability to integrate the advantages of multiple single materials, have shown significant application potential in the fields of wastewater treatment and sediment improvement.

[0026] In terms of preparation technology, existing composite matrix preparation processes mainly include key steps such as raw material pretreatment and ingredient mixing. Raw material pretreatment typically includes basic operations such as crushing, sieving, and washing; some studies also involve simple activation and modification treatments. The ingredient mixing process mainly relies on manual experience or simple literature reviews to determine the proportions, lacking systematic multi-objective optimization studies. The mixing process mostly uses traditional equipment such as V-type mixers, double cone mixers, or horizontal ribbon mixers. Operators usually set the mixing time according to equipment instructions or experience, using visual uniformity as the standard for judging the mixing endpoint. Although this process mode is simple and easy to implement, it is difficult to guarantee the stability and consistency of product performance. For example, in the preparation method of bottom sediment conditioner for aquaculture, it is necessary to mix and granulate various powder and liquid materials to ensure the performance of the final product. From the perspective of materials science, existing composite matrices mainly adopt physical mixing methods, combining materials with different functional properties, and often only focus on the removal effect of single or two pollutants, with insufficient research on the synergistic removal mechanism of multiple pollutants such as COD, total nitrogen, and total phosphorus.

[0027] Existing technologies lack scientific rigor and systematic approach in optimizing formulations. Formulation schemes based on experience or simple literature combinations often overlook the complex interactions (including positive synergy and negative antagonism) of components during actual synergistic action. For example, a certain ratio might be beneficial for COD adsorption but could inhibit the activity of certain microorganisms, affecting nitrogen removal; or two materials might compete for adsorption sites. The failure to use multivariate statistical optimization methods to find the globally optimal ratio for a specific objective (such as simultaneously maximizing COD, TN, and TP removal rates) means that the potential of existing composite matrices is not fully explored, and their overall treatment efficiency is often lower than the theoretical optimum. This non-optimal formulation also leads to low material utilization efficiency, thus requiring larger amounts of matrix to achieve the desired treatment effect, increasing costs.

[0028] Based on this, the wastewater treatment composite matrix configuration method provided by this invention significantly improves the synergistic removal efficiency of the water treatment matrix for multiple pollutants through a scientific and systematic approach compared with the prior art: by using response surface methodology combined with central composite design, the proportions of each component and their interactions are comprehensively examined, an accurate mathematical model is established, and the globally optimal ratio scheme is determined, thereby significantly improving the pollutant removal efficiency, enhancing the effluent quality, simplifying the subsequent treatment process, ensuring the high efficiency and repeatability of the treatment effect, and improving the stability and reliability of the entire water treatment process.

[0029] Furthermore, this invention also saves resources and protects the environment through optimized proportioning and precise preparation techniques: by scientifically optimizing to find the most efficient proportioning scheme, the utilization efficiency of raw materials is improved, the matrix consumption per unit of treated water is reduced, the high-precision batching system and efficient mixing process significantly reduce the generation of waste and defective products, and reduce material loss. At the same time, the high-efficiency composite matrix prepared by this invention can more thoroughly remove pollutants from water, reduce the pollution load on downstream water bodies, and has significant environmental benefits.

[0030] Furthermore, this invention organically integrates advanced statistical optimization methods, precise automated control technology, and innovative mixing processes into the research and development and production of composite water treatment matrices. This surpasses the traditional experience-based and trial-and-error approach, providing a systematic, data-driven, and quantifiable solution. The integrated online monitoring and feedback system further enhances the technological advancement, enabling the system to perform real-time evaluation and self-adjustment optimization. This aligns with the development trend of intelligent and refined water treatment technologies and has the potential to lead industry development.

[0031] To facilitate understanding of this embodiment, a method for preparing a composite substrate for wastewater treatment disclosed in this invention will first be described in detail. This method is applied to the proportioning optimization model of the composite substrate preparation system for wastewater treatment. To facilitate understanding of the composite substrate preparation system for wastewater treatment, this invention provides a structural schematic diagram of the composite substrate preparation system for wastewater treatment, as shown below. Figure 1 As shown, the system consists of a ternary composite matrix silo (biochar silo, zeolite silo, volcanic rock silo), a twin-screw feeder, a belt conveyor, a mixing system, a PLC controller, a torque sensor, a speed sensor, an ultrasonic level sensor, a weight sensor, an inlet water quality sensor, an outlet water quality sensor, a data acquisition and processing unit, an intelligent analysis unit, and water pipes.

[0032] The batching system in the wastewater treatment composite substrate preparation system employs a combination of high-precision weight sensing and intelligent control to achieve accurate proportioning of the ternary composite substrate. The system monitors the weight changes of each substrate material in real time using weight sensors. After signal conditioning, the weight signals are input to the PLC controller, which calculates the target feed amount for each substrate based on a proportioning optimization model. :

[0033]

[0034] in, The target feed amount (kg) Let be the proportioning coefficient of the i-th matrix. Total ingredient quantity (kg) Allowable deviation (%). For example, biochar. zeolite volcanic rock .

[0035] The PLC controller employs a PID closed-loop control algorithm to adjust the speed of the screw feeder based on real-time feedback from the weight sensor. :

[0036]

[0037] in, The rotational speed (r / min) of the screw feeder. For the deviation in feed rate, , , These are the proportional, integral, and differential coefficients, respectively.

[0038] In addition, the system is equipped with a material level detection device, which monitors the material height h in the silo in real time through an ultrasonic sensor. When the material level is lower than the warning value, a replenishment signal is triggered.

[0039] Each substrate is metered by a screw feeder and then conveyed to the mixing system by a belt conveyor. The conveyor belt speed v and the feed rate Q satisfy the following:

[0040]

[0041] Where v is the conveyor belt speed (m / s), Q is the feed rate (kg / s), and A is the cross-sectional area of ​​the conveyor belt (m / s). ), Bulk density of the material ( ).

[0042] Preferably, the stirring system of the present invention employs a three-stage variable-speed stirring process, combined with a double-propeller blade structure, to achieve efficient and uniform mixing of the composite matrix. The stirring process is divided into three stages:

[0043] The first stage is the high-speed premixing stage, with a stirring speed of [missing information]. 300-400 r / min, duration This stage lasts 3-5 minutes. The main purpose of this stage is to quickly break up the material and prevent clumping. (Impeller angle) Set to 45° to obtain a larger axial conveying force.

[0044] The second stage is the medium-speed homogenization stage, with the stirring speed... Reduce to 150-200 r / min, duration The time is 8-12 minutes. This stage focuses on achieving thorough mixing of the materials, and adjusting the blade angle. Adjust to 35° to enhance radial mixing. (Stirring power) satisfy:

[0045]

[0046] in, This is the stirring power coefficient. For material density, Rotational speed (r / s) The diameter of the agitator is in meters (m).

[0047] The third stage is the low-speed fine mixing stage, with the stirring speed... Further reduce to 80-100 r / min, duration The duration is 5-8 minutes. The purpose of this stage is to eliminate local non-uniformity and adjust the blade tilt angle. Increased to 55°, enhancing axial circulation flow. Mixing uniformity. Calculations based on sampling and testing:

[0048]

[0049] In the formula, The standard deviation of the sample. This is the average value of the samples. A concentration of ≤3% is considered a homogeneous mixture.

[0050] The mixing system adopts a double-helix blade structure, and its blade inclination angle is... The angle can be adjusted within the range of 30° to 60° to adapt to the needs of different mixing stages. The blades are made of wear-resistant alloy steel and the surface is treated with plasma spraying to improve wear resistance and service life.

[0051] Furthermore, the sensor system in the aforementioned composite matrix configuration system for wastewater treatment includes: an influent water quality sensor, an effluent water quality sensor, a data acquisition and processing unit, and an intelligent analysis unit. Through multi-parameter online monitoring technology, real-time analysis of COD removal rate, total nitrogen removal rate, and total phosphorus removal rate is achieved. The system consists of an influent and effluent water quality monitoring unit, a data acquisition and processing unit, and an intelligent analysis unit.

[0052] The influent and effluent water quality monitoring unit uses multi-parameter water quality sensors, which can simultaneously monitor COD, total nitrogen, total phosphorus, etc. The data acquisition and processing unit uses a high-performance processor with a sampling frequency of up to 100Hz.

[0053] The intelligent analysis unit analyzes water quality data in real time. COD removal rate. Calculated based on measured COD concentrations of influent and effluent:

[0054]

[0055] Total nitrogen removal rate Calculated based on the measured values ​​of total nitrogen concentration in the influent and effluent:

[0056]

[0057] Total phosphorus removal rate Calculated based on the measured values ​​of total phosphorus concentration in the influent and effluent:

[0058]

[0059] The inlet and outlet water sensors are connected by a DN50 stainless steel water pipe. The inner wall of the pipe is polished, with a surface roughness Ra≤0.4μm to reduce water flow resistance and contaminant adsorption. The calculation results from these measured data enable real-time evaluation and dynamic monitoring of the system performance.

[0060] In practical applications, water quality parameters can be set as follows: influent 350 mg / L, effluent 45 mg / L, influent total nitrogen 45 mg / L, effluent total nitrogen 12 mg / L, influent total phosphorus 8 mg / L, and effluent total phosphorus 0.8 mg / L. In addition, alternative indicators related to the concentration of target pollutants are monitored online using sensors that are easier to implement and have lower costs (such as turbidity, conductivity, pH, ORP, etc.).

[0061] based on Figure 1 The diagram shows a structural schematic of a composite substrate preparation system for wastewater treatment. This invention provides a detailed description of the preparation method for the composite substrate for wastewater treatment. (See attached image.) Figure 2 The diagram shows a process flow chart for preparing a composite substrate for wastewater treatment. This method mainly includes the following steps S202 to S206:

[0062] Step S202 involves centrally combining the ternary composite matrix for wastewater treatment to generate a set of experimental formulations. The ternary composite matrix includes biochar, zeolite, and volcanic rock, and the set of experimental formulations includes composite matrices with different mass ratios.

[0063] In one implementation, comprehensive collection and analysis of influent and effluent water quality parameters are first required. These parameters primarily include concentration data for key indicators such as COD, total nitrogen, and total phosphorus. Water samples are collected at both the influent and effluent ends to determine the concentration values ​​of each pollutant. The treatment efficiency is then calculated based on the concentration difference between the influent and effluent. This collection and analysis of water quality parameters provides a crucial data foundation for subsequent optimization of the formulation. Following this, Response Surface Methodology (RSM) is used to systematically optimize the formulation of the ternary composite matrix, using COD removal rate (…) as the primary metric. ), total nitrogen removal rate ( ) and total phosphorus removal rate ( As the response value, RSM is a statistical tool that uses a small number of experiments to fit a mathematical surface to quickly find the optimal formulation. You can think of all the matrix ratios as a mountain. RSM first drills 20 exploratory wells at the locations where the optimal ratio is most likely to be found, uses these data to fit a topographic map of the mountain, and then directly locates the optimal ratio.

[0064] Step S204: Obtain the test data of the wastewater treatment test corresponding to the test formula set, and use the removal rate of each matrix in the test data as the response value to obtain the target test data.

[0065] In one embodiment, the above-mentioned wastewater treatment composite matrix preparation system is used to sequentially execute the above-mentioned set of experimental formulations to conduct water treatment experiments with different ratios of ternary composite matrix. During the experiments, the consistency of experimental conditions is strictly controlled, including key parameters such as water temperature, pH value, and hydraulic retention time. For each experimental formulation, the mixing ratio of the ternary composite matrix is ​​first precisely prepared according to the designed coded values ​​to ensure mixing accuracy. Subsequently, the prepared composite matrix is ​​loaded into the experimental device for standardized water treatment experiments. During the experiments, influent and effluent samples are systematically collected and recorded, and water quality indicators such as chemical oxygen demand (COD), total nitrogen, and total phosphorus are measured to calculate the removal rate of each indicator. Through this standardized experimental procedure, a complete experimental dataset (i.e., the target experimental data) is obtained, thus laying a solid data foundation for subsequent response surface methodology analysis.

[0066] Step S206: Substitute the target experimental data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value. Then, use the model prediction value and the target experimental data to perform model validation and update processing on the quadratic polynomial regression model to determine the target quadratic polynomial regression model.

[0067] In one implementation, quadratic polynomial regression models for three response values—COD removal rate, total nitrogen removal rate, and total phosphorus removal rate—can be constructed based on experimental data from a central composite design. The experimental data are then subjected to multivariate quadratic regression analysis using these models to obtain the regression model coefficients and their significance levels, thereby validating the model.

[0068] Step S208: The ratio of the ternary composite matrix is ​​optimized by using a target quadratic polynomial regression model to determine the target quality ratio of the ternary composite matrix.

[0069] In one implementation, the target mass ratio needs to be sent to the wastewater treatment composite matrix preparation system so that the system can determine the target feed amount and prepare the matrix according to the target feed amount, and perform three-stage variable speed mixing to obtain a ternary composite matrix for wastewater treatment.

[0070] The method for preparing the composite substrate for wastewater treatment provided in this invention can efficiently remove chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) from water. Key technical points include: employing a response surface methodology (RSM) combined with central composite design (CCD) optimization method, using COD, TN, and TP removal rates as response values, to systematically determine the optimal mass ratio of the three components: biochar, zeolite, and volcanic rock. That is, the target mass ratio is then sent to the preparation system (including silos, sensors, PLC controllers, screw feeders, conveyors, and a mixing system with a specific mixing structure) in the above-mentioned wastewater treatment composite matrix preparation system. During system operation, the PLC controller calculates the target feed rate of each component based on the optimized ratio, and by reading real-time data from the sensors, it precisely controls the feeder speed using a PID closed-loop control algorithm. This achieves high-precision batching. Subsequently, the materials are conveyed to the mixing system, where a three-stage variable-speed mixing process (including high-speed premixing) is executed. Medium-speed homogenization Low-speed fine mixing (Stages) to ensure that the final ternary composite matrix is ​​extremely uniformly mixed. This invention combines scientifically optimized proportions with precise automated preparation for water purification in municipal sewage, industrial wastewater, and other fields, significantly improving the overall performance of water treatment matrices.

[0071] This invention also provides a specific implementation method for optimizing the matrix ratio, as shown in (1) to (6) below:

[0072] (1) Experimental Design (CCD): The ternary composite matrix can be substituted into the mathematical framework of the central composite design. Based on the number of factors of the ternary composite matrix and the preset coding level of the mathematical framework, the corresponding coding value of the ternary composite matrix is ​​determined. Finally, the coding value is converted into the actual value so as to determine the experimental formula set based on the actual value. The preset coding level includes: factor point, axis point and center point.

[0073] Specifically, a central composite design (CCD) scheme is used to construct the experimental design, with a ternary composite matrix as the independent variable, such as biochar ( ), zeolite ( ) and volcanic rocks ( Central composite design, a commonly used response surface methodology, consists of 2ᵏ factor points (k being the number of factors), 2k pivot points, and several central points. It effectively estimates the coefficients of a quadratic response surface model. To comprehensively study the influence of each factor on the response value, this experiment set five coding levels: -1.682, -1, 0, 1, and 1.682. The coding levels are dimensionless values ​​obtained by standardizing the actual factor levels. -1.682 and 1.682 represent the lowest and highest levels, respectively (rotational parameter α = 1.682), 0 represents the central point level, and -1 and 1 represent intermediate levels. Based on this design, 20 experimental formulations were generated, including 8 factor points, 6 pivot points, and 6 central points. The conversion relationship between the coding values ​​and the actual values ​​is as follows:

[0074]

[0075] in, For encoded values, This is the actual value. The actual value at the center point refers to the encoded value. When the value is 0, the independent variable The corresponding actual values, in this experiment, are the actual values ​​of the center point of the ternary composite matrix (biochar, zeolite, and volcanic rock). They are 0.650, 0.200, and 0.150 respectively; The step size represents the actual value change, indicating the step size when the encoded value changes. When the independent variable changes by 1 unit, The change in actual value. In this experiment, the step size of the change in the actual value of biochar. The step size for the actual value variation of zeolite and volcanic rock is 0.090. All values ​​were 0.060. Biochar, due to its high stability and large adsorption capacity, is often used in composite matrix formulations with a relatively high proportion, and its value range is relatively wide. Zeolite and volcanic rock, on the other hand, are usually used as functional fillers, accounting for a lower proportion in composite systems, and their physicochemical properties (such as pore structure and ion exchange capacity) determine that their optimal concentration is more concentrated. The settings need to be combined with its engineering practice background to ensure that the experimental design conforms to the actual application scenario.

[0076] The specific experimental designs for the 20 groups are shown in Table 1 below:

[0077] Table 1:

[0078]

[0079] This central composite design scheme includes three types of experimental points: eight factor points located at the vertices of the cube, six axis points located on the coordinate axes, and six center points located at the center of the experimental design. Among them, factor points ( , , The ±1 value is mainly used to study the main effect of a single factor and the interaction between two factors; the axis point is located at ±1.682 from the center point, and each factor is taken at ±1.682 when other factors are at the 0 level, to estimate the nonlinear quadratic effect of the factor; the center point ( The experimental error and model fitness were evaluated through six repeated trials. The multiple repetitions at the center point are significant: repeated trials allow for the estimation of purely random errors, crucial for assessing the model's fitness and reliability; they also assess the stability and reproducibility of the experimental process; large differences in repeated trial results indicate potential instability. Furthermore, repeated trials at the center point can improve model accuracy, particularly in estimating the curvature of the quadratic response surface model, as detailed in Table 2 below.

[0080] Table 2

[0081]

[0082] (2) Experimental execution: After obtaining the results of the central composite design (CCD) experiment, it is also necessary to perform data analysis and processing on the experimental data to determine the mean and standard deviation of the experiment. Then, the mean and standard deviation are sent to the preset coefficient of variation calculation model to obtain the coefficient of variation. When the coefficient of variation is less than the preset coefficient of variation threshold, the experimental data is determined as the target experimental data. For details, please refer to (A) to (B) below:

[0083] (A) Calculate the average value of repeated trials at the center point ( ) and standard deviation ( ):

[0084]

[0085] in, The average value of repeated trials at the center point. For the first The response values ​​of the group experiment (COD removal rate, total nitrogen removal rate, or total phosphorus removal rate). The numbers 15 to 20 represent 6 center point trials.

[0086]

[0087] in, Standard deviation, The square of the deviation of each experimental value from the mean is 5, where 5 is the degree of freedom (number of repetitions minus 1).

[0088] (B) Calculate the coefficient of variation (CV) to assess the reproducibility of the experiment:

[0089]

[0090] Where CV is the coefficient of variation. Standard deviation, The value is the average. Generally, a CV of ≤5% is considered to indicate good reproducibility.

[0091] Such analysis can help evaluate the reliability of experimental methods and the quality of data. It has good rotation and orthogonality, and can obtain the maximum amount of information with the fewest number of experiments. It can effectively evaluate the linear effect, interaction effect and curvature effect of each factor.

[0092] In one implementation, the experimental results of the Central Composite Design (CCD) are shown in Table 3 below:

[0093] Table 3:

[0094]

[0095] In the above experimental results, the sum of the proportions of the ternary composite matrix (biochar, zeolite, and volcanic rock) is not strictly equal to 1. This is because this invention uses a standard central composite design (CCD) method to study the effect of changes in the content of each component on the treatment effect, rather than using a mixture experimental design. The focus of this invention is to examine the influence of changes in the content of each component on the water purification effect near the central ratio point (0.650:0.200:0.150). Although this design method causes the total component content to deviate from 1 at some experimental points, it can better reflect the effect of independent changes in the content of each component on the treatment effect.

[0096] (3) Regression analysis: The target experimental data can be substituted into the quadratic polynomial regression model of the ternary composite matrix for regression analysis, and the model coefficients corresponding to each constant term in the quadratic polynomial regression model can be determined. Then, the regression equation of the removal rate of the target water quality index is constructed based on the model coefficients, and the model prediction is performed using the regression equation of the removal rate of the target water quality index to obtain the model prediction value. The regression equation of the removal rate of the target water quality index includes: the regression equation of total nitrogen removal rate, the regression equation of chemical oxygen demand removal rate and the regression equation of total phosphorus removal rate.

[0097] In one implementation, the quadratic polynomial regression model is as follows:

[0098]

[0099] in, Represents the response value. For constant terms, The coefficient of the linear term, The coefficient of the quadratic term, This is the coefficient of the interaction term.

[0100] Furthermore, to evaluate the significance level and goodness of fit of the model, and to ensure that the model accurately reflects the influence of each process parameter on the treatment effect, the coefficient of determination in the quadratic polynomial regression model can be evaluated separately. ), Adjusted coefficient of determination ( The F-validation value and P-value were calculated, and when the calculation results of the coefficient of determination, adjusted coefficient of determination, F-validation value, and P-value were all verified, a regression equation for the removal rate of the target water quality index was constructed based on the model coefficients. See (A) to (D) below for details:

[0101] (A) The formula for calculating the coefficient of determination is as follows:

[0102]

[0103] in, Represents the sum of squared residuals. This represents the total sum of squared deviations. The coefficient of determination indicates the proportion of variation in the response variable that the model can explain; the closer its value is to 1, the better the model fit.

[0104] (B) Adjusted Coefficient of Determination: This method considers the number of independent variables in the model in addition to the coefficient of determination, allowing for a more accurate assessment of the model's goodness of fit, especially when there are many independent variables. The calculation formula is as follows:

[0105]

[0106] in, For sample size, This represents the number of independent variables in the model.

[0107] (C) The F-test value is used to test the overall significance of the regression model, and its calculation formula is as follows:

[0108]

[0109] in, To regress the mean square sum, For the sum of the root mean square of the residuals, This represents the regression sum of squares. The larger the F-value, the higher the overall significance of the model.

[0110] (D) The p-value is the probability value corresponding to the F-test value, used to determine the significance of the F-test. The smaller the p-value, the more significant the overall regression effect of the model. Generally, a p-value less than 0.05 is considered statistically significant.

[0111] In one implementation, by performing a multiple quadratic regression analysis on the experimental data, the present invention obtained the regression model coefficients and their significance levels for the COD removal rate, as shown in Table 4 below:

[0112] Table 4

[0113]

[0114] The table above shows that the model's coefficient of determination is 0.967, and the adjusted coefficient of determination is 0.952. The value is 42.80. The value is 0.0001. These statistical indicators all indicate that the model is significant and has excellent goodness of fit. Furthermore, the total nitrogen removal rate regression model further established in this invention is as follows:

[0115]

[0116] The regression model for COD removal rate is as follows:

[0117]

[0118] Meanwhile, the regression model expression for total phosphorus removal rate is:

[0119]

[0120] (4) Model validation: The relative error value can be determined by performing error analysis on the model prediction value and the target experimental data. If the relative error value meets the preset relative error threshold, the current quadratic polynomial regression model is determined as the target quadratic polynomial regression model. If the relative error value does not meet the preset relative error threshold, the current quadratic polynomial regression model is subjected to closed-loop iteration.

[0121] In one implementation, the validation experiment aims to evaluate the predictive ability of the mathematical model constructed using response surface methodology for COD, total nitrogen, and total phosphorus removal rates. By comparing the relative errors between the model's predicted values ​​and the experimentally measured values, the reliability of the model and its applicability within the experimental range, i.e., the range of ternary composite matrix ratios, can be evaluated. If the validation experiment results are satisfactory, it indicates that the model can provide a basis for subsequent optimization, predict pollutant removal rates under different ratios, and ultimately guide the preparation of high-efficiency water treatment matrices. To illustrate the validation results more specifically, it is necessary to analyze the actual measured values ​​and model predicted values ​​of the validation experiment and calculate the relative error. When the relative error of most validation points is within an acceptable range, the model can be considered successfully validated. To comprehensively verify the accuracy and reliability of the model, this invention employs the Box-Behnken experimental design method, designing 15 sets of experimental points including boundary points and center points to fully evaluate the model's predictive ability throughout the entire experimental range. Relative Error The calculation formula is as follows:

[0122]

[0123] in, These are the model's predicted values. These are experimentally measured values. After completing the verification experiment, this invention compared and analyzed the experimentally measured values ​​with the model predicted values, and calculated the relative error. To quantitatively evaluate the predictive accuracy and reliability of the model.

[0124] In one implementation, based on constraints of total nitrogen removal rate, chemical oxygen demand (COD) removal rate, and total phosphorus removal rate, a target quadratic polynomial regression model can be solved. The desired function value is maximized within the constraints to obtain the target mass ratio of the ternary composite matrix. In other words, while meeting the COD removal rate requirements... Total nitrogen removal rate and total phosphorus removal rate Under the constraints, and combined with the results of response surface methodology and verification test data, the optimal mass ratio of the ternary composite matrix was finally determined.

[0125] To verify the accuracy and reliability of the above model, this invention designed a series of verification experiments, including 12 boundary points and 3 center points. The specific experimental design scheme is shown in Table 5 below:

[0126] Table 5

[0127]

[0128] The specific results of the verification experiment are shown in Table 6 below:

[0129] Table 6

[0130]

[0131] (5) Response Surface Visualization: To more intuitively understand the influence of various factors on the removal rate, this invention uses response surface analysis for visualization. By drawing a two-dimensional contour plot, the interactive influence of any two factors on the removal rate at a fixed level of a third factor is clearly shown. At the same time, a three-dimensional response surface plot is used to present the overall trend of the response value changing with each factor. This visualization method helps to intuitively grasp the region where the optimal ratio is located. The comprehensive analysis based on the contour plot and the response surface plot shows that when the proportion of biochar is at a high level and the proportions of zeolite and volcanic rock are at a medium level, the system exhibits the best pollutant removal effect. This visualization analysis result provides an important theoretical basis for subsequent optimization of the ratio scheme.

[0132] Based on response surface methodology (RSM) results, this invention sets maximizing overall removal efficiency as the optimization objective, while considering constraints such as COD removal rate not less than 85%, total nitrogen removal rate not less than 80%, and total phosphorus removal rate not less than 80%. The optimal ternary composite matrix ratio was obtained through numerical optimization. In this embodiment, the optimal ratios of biochar, zeolite, and volcanic rock are 72.0%, 18.0%, and 10.0%, respectively. Under these ratios, the model predicts COD, total nitrogen, and total phosphorus removal rates of 91.5%, 85.2%, and 88.7%, respectively, with an expected value of 0.93. This optimization result not only meets the minimum removal rate requirements for each indicator but also demonstrates significant advantages in overall treatment performance.

[0133] (6) Closed-loop iteration: See Figure 3The flowchart illustrates a closed-loop iterative method. To comprehensively evaluate the feasibility and expected effects of the optimization results and apply them to actual system operation, this invention integrates result evaluation, system application, and performance monitoring. First, the actual feasibility of the optimal ratio is evaluated, its performance improvement relative to the current ratio is calculated, and the expected improvement effects of various indicators are analyzed in depth, ultimately generating a detailed evaluation report. Subsequently, the optimized ratio scheme is practically applied to the actual system. Specific operations include transmitting the optimal ratio parameters to the system dashboard, updating the feeder's ratio settings, preparing the formula for the next batch of production, and finally executing the ratio adjustment to realize the application of the ratio scheme. After the system applies the new ratio scheme, the system's operating status is continuously monitored, water quality treatment data under the new ratio is collected, and the deviation between the actual removal rate and the predicted value is calculated. This comprehensively evaluates the optimization effect and determines whether a next round of optimization is needed, thus achieving performance monitoring and feedback data output. Response surface optimization itself is a closed-loop iterative process. Through continuous data collection, analysis, and optimization, the treatment efficiency of the ternary composite matrix can be continuously improved. Each round of optimization is based on the actual operating data of the previous round, ensuring that the system performance gradually approaches the optimal level. This data-driven optimization method can effectively adapt to changes in water quality and dynamic adjustments in system operating status, thereby ensuring the stability and reliability of the treatment effect.

[0134] In summary, the present invention has the following advantages:

[0135] (1) By integrating online monitoring and closed-loop feedback, a closed-loop system integrating real-time online water quality monitoring, data processing and analysis, and application of ratio optimization results was constructed to verify the optimization effect and realize continuous performance monitoring and dynamic adjustment of the system. The system monitors key indicators such as COD, TN, and TP of influent and effluent in real time and calculates the actual removal rate in real time. This provides rapid verification of the formulation effect. Simultaneously, the system compares and analyzes the actual performance data obtained from online monitoring with the predicted values ​​of the RSM model, calculating the relative error. This mechanism evaluates the optimization effect and feeds the evaluation results back to the ingredient batching system in real time, thus forming a complete closed-loop optimization mechanism that supports the system to continuously iterate and optimize based on actual operating results.

[0136] (2) A ternary composite matrix ratio optimization method based on response surface methodology (RSM) and central composite design (CCD) was used to systematically design experiments and mathematically model the mass ratio of biochar, zeolite, and volcanic rock to scientifically determine the optimal ratio. Applying RSM and CCD to the ratio optimization of ternary composite matrices breaks through the limitations of traditional trial-and-error methods or single-factor experiments. By establishing a scientific experimental design and statistical analysis system, this method can efficiently and accurately identify the complex interactions between components and determine the globally optimal ratio with the goal of maximizing the removal rates of COD, total nitrogen, and total phosphorus simultaneously, significantly improving the comprehensive treatment efficiency of the composite matrix.

[0137] (3) High-precision intelligent batching and three-stage variable speed uniform mixing system: The present invention developed an automated batching and mixing system that integrates high-precision weight sensing, PLC intelligent control and special mixing process for the precise preparation of optimized ternary composite matrix. The system uses a weight sensor and combines the PLC's PID closed-loop control algorithm to achieve precise ratio control based on the target feed rate by precisely adjusting the feeder speed.

[0138] Regarding the preparation method of the composite substrate for wastewater treatment provided in the foregoing embodiments, this embodiment of the invention provides a preparation device for the composite substrate for wastewater treatment, see [link to previous embodiment]. Figure 4 The diagram shows a structural schematic of a composite substrate preparation device for wastewater treatment, which includes the following parts:

[0139] The experimental formula generation module 402 performs central combination treatment on the ternary composite matrix for wastewater treatment to generate an experimental formula set, wherein the ternary composite matrix includes: biochar, zeolite and volcanic rock, and the experimental formula set includes: composite matrix with different mass ratios;

[0140] The data acquisition module 404 acquires the test data of the wastewater treatment test corresponding to the test formula set, and uses the removal rate of each matrix in the test data as the response value to obtain the target test data;

[0141] The model optimization module 406 substitutes the target experimental data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value. Then, using the model prediction value and the target experimental data, the quadratic polynomial regression model is validated and updated to determine the target quadratic polynomial regression model.

[0142] The composite matrix ratio module 408 optimizes the ratio of the ternary composite matrix using a target quadratic polynomial regression model to determine the target quality ratio of the ternary composite matrix.

[0143] The wastewater treatment composite matrix configuration device provided in this application embodiment can significantly improve the pollutant removal rate for the synergistic removal of multiple pollutants during wastewater treatment.

[0144] In one embodiment, during the step of performing central combination treatment on a ternary composite matrix for wastewater treatment to generate a set of experimental formulations, the experimental formulation generation module 402 is further configured to: substitute the ternary composite matrix into the mathematical framework of the central combination design; determine the corresponding coding value of the ternary composite matrix based on the number of factors of the ternary composite matrix and the preset coding level of the mathematical framework, wherein the preset coding level includes: factor points, axis points, and center points; convert the coding value into actual value to determine the set of experimental formulations based on the actual value.

[0145] In one embodiment, after performing the step of using the removal rate corresponding to each matrix in the test data as the response value, the data acquisition module 404 is further configured to: perform data analysis and processing on the test data to determine the average value and standard deviation of the test; send the average value and standard deviation to a preset coefficient of variation calculation model to obtain the coefficient of variation, and determine the test data as the target test data when the coefficient of variation is less than the preset coefficient of variation threshold.

[0146] In one embodiment, when performing regression analysis by substituting the target experimental data into a quadratic polynomial regression model of the ternary composite matrix to obtain the model prediction value, the model optimization module 406 is further configured to: substitut the target experimental data into the quadratic polynomial regression model of the ternary composite matrix to determine the model coefficients corresponding to each constant term in the quadratic polynomial regression model; construct a regression equation for the removal rate of the target water quality index based on the model coefficients, and use the regression equation for the removal rate of the target water quality index to perform model prediction processing to obtain the model prediction value, wherein the regression equation for the removal rate of the target water quality index includes: a regression equation for the total nitrogen removal rate, a regression equation for the chemical oxygen demand removal rate, and a regression equation for the total phosphorus removal rate.

[0147] In one embodiment, before proceeding to the step of constructing a regression equation for the removal rate of the target water quality index based on the model coefficients, the model optimization module 406 is further configured to: calculate the coefficient of determination, adjusted coefficient of determination, F-validation value, and P-value in the quadratic polynomial regression model, and when the calculation results of the coefficient of determination, adjusted coefficient of determination, F-validation value, and P-value are all verified, construct a regression equation for the removal rate of the target water quality index based on the model coefficients.

[0148] In one embodiment, when performing the step of using model predictions and target experimental data to perform model validation and update processing on the quadratic polynomial regression model to determine the target quadratic polynomial regression model, the model optimization module 406 is further configured to: determine the relative error value by performing error analysis processing on the model predictions and target experimental data; if the relative error value meets the preset relative error threshold, then the current quadratic polynomial regression model is determined as the target quadratic polynomial regression model; if the relative error value does not meet the preset relative error threshold, then the current quadratic polynomial regression model is subjected to closed-loop iteration processing.

[0149] In one embodiment, when performing the step of optimizing the proportion of the ternary composite matrix using a target quadratic polynomial regression model to determine the target mass proportion of the ternary composite matrix, the composite matrix proportioning module 408 is further used to: solve the target quadratic polynomial regression model based on the constraints of total nitrogen removal rate, chemical oxygen demand removal rate and total phosphorus removal rate, maximize the expected function value within the range of satisfying the constraints, and obtain the target mass proportion of the ternary composite matrix.

[0150] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0151] This invention provides a server, specifically, the server includes a processor and a storage device; the storage device stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0152] Figure 5 This is a schematic diagram of the structure of a server provided in an embodiment of the present invention. The server 100 includes: a processor 50, a memory 51, a bus 52 and a communication interface 53. The processor 50, the communication interface 53 and the memory 51 are connected through the bus 52. The processor 50 is used to execute executable modules, such as computer programs, stored in the memory 51.

[0153] The memory 51 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 53 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0154] Bus 52 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0155] The memory 51 is used to store programs. After receiving an execution instruction, the processor 50 executes the programs. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0156] Processor 50 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 50 or by instructions in software form. Processor 50 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 51. The processor 50 reads the information in memory 51 and, in conjunction with its hardware, completes the steps of the above method.

[0157] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0158] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0159] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for configuring a composite substrate for sewage treatment, characterized by, The method includes: A central combination treatment was carried out on a ternary composite matrix for wastewater treatment to generate a set of experimental formulations. The ternary composite matrix includes biochar, zeolite and volcanic rock, and the set of experimental formulations includes composite matrices with different mass ratios. Obtain the test data of the wastewater treatment test corresponding to the set of test formulations, and use the removal rate of each matrix in the test data as the response value to obtain the target test data; The target experimental data is substituted into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value. The model prediction value and the target experimental data are then used to perform model validation and update processing on the quadratic polynomial regression model to determine the target quadratic polynomial regression model. The proportions of the ternary composite matrix are optimized using the target quadratic polynomial regression model to determine the target quality proportions of the ternary composite matrix. The step of generating an experimental formulation set by centrally combining the ternary composite matrix for wastewater treatment includes: substituting the ternary composite matrix into the mathematical framework of the central combination design; determining the corresponding coding value of the ternary composite matrix based on the number of factors of the ternary composite matrix and the preset coding level of the mathematical framework, wherein the preset coding level includes: factor points, axis points, and center points; converting the coding value into actual values ​​to determine the experimental formulation set based on the actual values; The step of substituting the target experimental data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain the model prediction value includes: substituting the target experimental data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to determine the model coefficients corresponding to each constant term in the quadratic polynomial regression model; constructing a regression equation for the removal rate of the target water quality index based on the model coefficients, and using the regression equation for the removal rate of the target water quality index for model prediction to obtain the model prediction value, wherein the regression equation for the removal rate of the target water quality index includes: a regression equation for the total nitrogen removal rate, a regression equation for the chemical oxygen demand (COD) removal rate, and a regression equation for the total phosphorus removal rate; Before constructing the regression equation for the removal rate of the target water quality index based on the model coefficients, the process includes: calculating the coefficient of determination, adjusted coefficient of determination, F-validation value, and P-value in the quadratic polynomial regression model, and constructing the regression equation for the removal rate of the target water quality index based on the model coefficients when the calculation results of the coefficient of determination, the adjusted coefficient of determination, the F-validation value, and the P-value are all verified. The quadratic polynomial regression model is as follows: wherein, represents the response value, is a constant term, is a coefficient of the first order term, is a coefficient of the second order term, is a coefficient of the interaction term.

2. The method for disposing the composite substrate for sewage treatment according to claim 1, characterized by, After the step of using the removal rate corresponding to each matrix in the experimental data as the response value, the following is included: The experimental data were analyzed and processed to determine the mean and standard deviation of the experiment. The average value and the standard deviation are sent to a preset coefficient of variation calculation model to obtain a coefficient of variation, and the test data are determined as the target test data when the coefficient of variation is less than a preset coefficient of variation threshold.

3. The method for disposing the composite substrate for sewage treatment according to claim 1, characterized by, The model verification and update processing is performed on the quadratic polynomial regression model by using the model predicted value and the target test data, and the target quadratic polynomial regression model is determined. The relative error value is determined by performing error analysis processing on the model predicted value and the target test data. If the relative error value meets a preset relative error threshold, the current quadratic polynomial regression model is determined as the target quadratic polynomial regression model. If the relative error value does not meet the preset relative error threshold, the current quadratic polynomial regression model is subjected to closed-loop iteration processing.

4. The method for disposing the composite substrate for sewage treatment according to claim 1, characterized by, The target mass ratio of the ternary composite substrate is determined by optimizing the ratio of the ternary composite substrate by using the target quadratic polynomial regression model. The target mass ratio of the ternary composite substrate is obtained by maximizing the expected function value within the range meeting the constraint conditions of total nitrogen removal rate, chemical oxygen demand removal rate and total phosphorus removal rate.

5. A device for preparing a composite substrate for wastewater treatment, characterized in that, The device comprises: An experimental formula generation module performs central composite processing on a ternary composite substrate for sewage treatment to generate an experimental formula set, wherein the ternary composite substrate comprises biochar, zeolite and volcanic rock, and the experimental formula set comprises composite substrates with different mass ratios. A data acquisition module acquires test data of sewage treatment tests corresponding to the experimental formula set, and obtains target test data by taking the removal rates of each substrate in the test data as response values. A model optimization module performs regression analysis processing on the target test data in a quadratic polynomial regression model of the ternary composite substrate to obtain a model predicted value, and performs model verification and update processing on the quadratic polynomial regression model by using the model predicted value and the target test data to determine a target quadratic polynomial regression model. A composite substrate ratio module optimizes the ratio of the ternary composite substrate by using the target quadratic polynomial regression model to determine the target mass ratio of the ternary composite substrate. The step of performing central composite processing on the ternary composite substrate for sewage treatment to generate an experimental formula set comprises: substituting the ternary composite substrate into a mathematical framework of central composite design, determining the coding value corresponding to the ternary composite substrate according to the number of factors of the ternary composite substrate and a preset coding level of the mathematical framework, wherein the preset coding level comprises factor points, axis points and center points; and converting the coding value into an actual value to determine the experimental formula set according to the actual value. The step of substituting the target test data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to obtain a model predicted value comprises: substituting the target test data into the quadratic polynomial regression model of the ternary composite matrix for regression analysis to determine model coefficients corresponding to constant terms in the quadratic polynomial regression model; constructing a regression equation of a target water quality index removal rate according to the model coefficients, and performing model prediction processing by using the regression equation of the target water quality index removal rate to obtain the model predicted value, wherein the regression equation of the target water quality index removal rate comprises: a total nitrogen removal rate regression equation, a chemical oxygen demand removal rate regression equation, and a total phosphorus removal rate regression equation; Before the step of constructing a regression equation of a target water quality index removal rate according to the model coefficients, the step comprises: respectively calculating a determination coefficient, an adjusted determination coefficient, an F verification value, and a P value in the quadratic polynomial regression model, and when the calculation results of the determination coefficient, the adjusted determination coefficient, the F verification value, and the P value are all verified, constructing the regression equation of the target water quality index removal rate according to the model coefficients; The quadratic polynomial regression model is as follows: wherein, represents the response value, is a constant term, is a coefficient of the first order term, is a coefficient of the second order term, is a coefficient of the interaction term.

6. A server, characterized by The computer readable storage medium stores computer executable instructions, and the computer executable instructions, when called and executed by the processor, cause the processor to implement the method in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions, and the computer executable instructions, when called and executed by the processor, cause the processor to implement the method in any one of claims 1 to 4.

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