A method and system for optimizing parameters of a pyrite denitrification biofilter and a storage medium

Abstract

This invention provides a method, system, and storage medium for optimizing parameters of a pyrite denitrification biological filter, relating to the field of water treatment technology. The method mainly includes: acquiring and standardizing basic data to form a dataset; calculating the suspended solids removal rate under different operating conditions, monitoring changes in filter layer porosity, and constructing a dataset of filtration performance indicators; determining key parameters such as the retardation factor, and constructing a calculation model of pressure and water quality cycles varying with filter layer thickness, combined with preset critical conditions; simultaneously establishing the models to derive the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal operating cycle when water quality and pressure cycles are equal; and substituting actual operating parameters to output optimized operating parameters. This invention systematically solves the problems of subjective parameter settings and insufficient resource utilization in traditional methods, accurately locking the filter's operational equilibrium point, ensuring stable effluent quality, avoiding resource waste, adapting to different scenarios, eliminating the need for repeated experiments, significantly reducing commissioning costs, and improving the flexibility and efficiency of engineering applications.

Description

Technical Field

[0001] This invention mainly relates to the field of water treatment technology, specifically to a method, system, and storage medium for optimizing parameters of a pyrite denitrification biological filter. Background Technology

[0002] In the field of water treatment, pyrite denitrification biological filters have become one of the preferred processes for deep nitrogen removal from wastewater due to their advantages such as not requiring an external carbon source and producing less sludge. However, the current operation of these filters faces challenges in optimizing core parameters, which restricts their operational efficiency and stability.

[0003] Traditional filter beds rely heavily on empirically set key parameters such as filter bed thickness and operating cycle, lacking a quantitative correlation model with water quality cycle (pollutant penetration time) and pressure cycle (critical head loss). In practical applications, parameter mismatch often occurs: an excessively thin filter bed leads to rapid pollutant penetration and a shortened water quality cycle; an excessively thick filter bed causes excessively rapid head loss and a shortened pressure cycle. The two cannot reach the critical state simultaneously, resulting in a waste of filter media adsorption capacity or hydraulic resources.

[0004] Meanwhile, existing technologies lack a systematic parameter optimization method, basic data lacks standardized processing, the connection between filtration performance indicators and cycle models is not tight, and theoretical calculations deviate significantly from actual operating data, making it impossible to achieve synergistic optimization of "water quality compliance" and "hydraulic stability." These problems result in poor filter operation efficiency and high energy consumption, making it difficult to meet the high-efficiency and low-carbon operation requirements of the wastewater treatment industry. Therefore, a scientific parameter optimization method is urgently needed to fill this technological gap. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, system and storage medium for optimizing parameters of a pyrite denitrification biofilter, which addresses the shortcomings of the prior art.

[0006] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for optimizing parameters of a pyrite denitrification biological filter, comprising the following steps: We obtained several basic data points from the pyrite denitrification biofilter, and then standardized these data points to obtain a standardized basic dataset. Based on the standardized basic dataset, the suspended solids removal rate under different operating conditions is calculated, and the porosity of the filter layer changes with the operating time is monitored. A filtration performance index dataset is formed based on the suspended solids removal rate and porosity change data. Based on the standardized basic dataset and the filtration performance index dataset, the resistance factor, initial permeability and permeability decay coefficient are determined. Based on the resistance factor, initial permeability and permeability decay coefficient and combined with the preset critical condition parameters, calculation models for pressure cycle variation with filter layer thickness and water quality cycle variation with filter layer thickness are constructed respectively. By combining the calculation models of pressure cycle with filter layer thickness and water quality cycle with filter layer thickness, the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle when the water quality cycle and pressure cycle of the filter are equal are derived. Substitute the basic parameters from the actual operating scenario into the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle to output the optimized operating parameters of the pyrite denitrification biological filter.

[0007] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a parameter optimization system for a pyrite denitrification biological filter, comprising: The basic data standardization module is used to acquire multiple basic data from the pyrite denitrification biofilter, and to standardize these multiple basic data to obtain a standardized basic dataset. The filtration performance index construction module is used to calculate the suspended solids removal rate under different operating conditions based on the standardized basic dataset, and to monitor the porosity change data of the filter layer with the operating time, and to form a filtration performance index dataset based on the suspended solids removal rate and porosity change data. The cycle calculation model construction module is used to determine the resistance factor, initial permeability and permeability decay coefficient based on the standardized basic dataset and the filtration performance index dataset. Based on the resistance factor, initial permeability and permeability decay coefficient and combined with the preset critical condition parameters, it constructs calculation models for pressure cycle with filter layer thickness and water quality cycle with filter layer thickness, respectively. The optimal parameter expression derivation module is used to combine the calculation model of pressure cycle with filter layer thickness and the calculation model of water quality cycle with filter layer thickness to derive the expression of optimal critical filter layer thickness and the calculation relationship of optimal working cycle when the water quality cycle and pressure cycle of the filter are equal. The operation parameter optimization output module is used to substitute the basic parameters in the actual operation scenario into the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship, and output the optimized operation parameters of the pyrite denitrification biological filter.

[0008] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a parameter optimization system for a pyrite denitrification biofilter, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the parameter optimization method for a pyrite denitrification biofilter as described above.

[0009] Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the parameter optimization method for pyrite denitrification biofilters as described above.

[0010] The beneficial effects of this invention are: through steps such as data standardization, performance index construction, model construction, parameter derivation, and optimized output, it systematically solves the problems of subjective parameter setting and insufficient resource utilization in traditional methods, accurately locks the operating balance point of the filter bed, ensures stable effluent water quality, avoids resource waste, adapts to different scenarios, eliminates the need for repeated experiments, significantly reduces debugging costs, and improves the flexibility and efficiency of engineering applications. Attached Figure Description

[0011] Figure 1 A flowchart of a method for optimizing parameters of a pyrite denitrification biofilter provided in an embodiment of the present invention; Figure 2 The denitrification biological filter operating curve provided in the embodiments of the present invention; Figure 3 A block diagram of the parameter optimization device for pyrite denitrification biofilter provided in an embodiment of the present invention. Detailed Implementation

[0012] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0013] Example 1: As Figure 1 As shown in the figure, this invention provides a method for optimizing parameters of a pyrite denitrification biofilter, comprising the following steps: S1. Obtain multiple basic data of the pyrite denitrification biofilter, and standardize the multiple basic data to obtain a standardized basic dataset; S2. Calculate the suspended solids removal rate under different operating conditions based on the standardized basic dataset, and monitor the porosity change data of the filter layer with the running time. Based on the suspended solids removal rate and porosity change data, form a filtration performance index dataset. S3. Based on the standardized basic dataset and the filtration performance index dataset, determine the blocking factor, initial permeability and permeability decay coefficient. Based on the blocking factor, initial permeability and permeability decay coefficient and combined with the preset critical condition parameters, construct calculation models for pressure cycle with filter layer thickness and water quality cycle with filter layer thickness, respectively. S4. Combine the calculation models of pressure cycle with filter layer thickness and water quality cycle with filter layer thickness to derive the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle when the water quality cycle and pressure cycle of the filter are equal. S5. Substitute the basic parameters from the actual operating scenario into the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle, and output the optimized operating parameters of the pyrite denitrification biological filter.

[0014] In the above embodiments, through steps such as data standardization, performance index construction, model construction, parameter derivation, and optimized output, the operating parameters of the pyrite denitrification biofilter are systematically optimized, avoiding the subjectivity and blindness of traditional parameter settings.

[0015] By combining pressure cycle and water quality cycle models, the balance point between water quality compliance and hydraulic stability is accurately identified, and the optimal critical filter layer thickness and optimal working cycle are derived. This ensures that the filter operates in the best state where the pollutant interception capacity and hydraulic transport capacity are simultaneously exhausted, thereby improving the stability of effluent water quality and avoiding the waste of filter media adsorption capacity or hydraulic resources.

[0016] Adaptable to different actual operating scenarios, customized optimization solutions can be output by substituting scenario-based basic parameters, eliminating the need to repeatedly conduct complex experiments, significantly reducing the time and manpower costs of filter parameter debugging, and improving the flexibility and efficiency of engineering applications.

[0017] Preferably, step S1 involves acquiring multiple basic data points from a pyrite denitrification biofilter, standardizing these data points to obtain a standardized basic dataset, including: S101. Based on parameter attributes, the basic data is divided into three categories: filter media parameters, raw water pollution index parameters, and reactor operating structure parameters, which are collected separately. The filter media parameters include pyrite filter media and its particle size gradient, filter media density, initial porosity, and pretreatment process parameters. The raw water pollution index parameters include the raw water target pollutant concentration and auxiliary water quality parameters. The reactor operating structure parameters include reactor structural dimensions, filter media filling height gradient, support layer specifications, and operating control parameters. S102. Outlier removal is performed on the collected basic data, and missing values ​​are supplemented by retesting the same batch of samples. Specifically, outlier detection and removal are performed on various types of basic data, and statistical criteria are used to screen valid data that conform to the data distribution pattern. For key parameters with missing values, they are supplemented by retesting the same batch of samples, and auxiliary parameters are supplemented by interpolation of data from adjacent measurement periods. For parameters that are measured repeatedly, their mean is calculated as the original value of the basic data. S103. According to the preset unified unit system, perform unit conversion on the basic data of different dimensions. Among them, the parameters of length, volume, mass concentration and density are unified to the preset standard unit of measurement. All numerical basic data are uniformly calibrated to the preset number of significant figures or decimal places to obtain each standardized basic data. S104. Classify and integrate the data according to parameter category, parameter name, standardized basic data, and structured format of standard units to form a standardized basic dataset.

[0018] In the above embodiments, basic data is collected according to parameter attributes, the data types and ranges are clearly defined, and the comprehensiveness and relevance of the basic data are ensured, providing reliable data support for subsequent calculations and model construction.

[0019] Preprocessing operations such as outlier removal and missing value filling eliminate the interference of data errors on subsequent analysis and improve the reliability of basic data; unifying units and precision standards avoids calculation deviations caused by inconsistent units and precision, ensuring data availability.

[0020] By integrating standardized basic data in a structured format, a well-organized and easy-to-use dataset is formed, simplifying the data extraction process in subsequent steps and improving the overall efficiency of the method.

[0021] Preferably, S2, the suspended solids removal rate under different operating conditions is calculated based on the standardized basic dataset, and the porosity change data of the filter layer with operating time is monitored. A filtration performance index dataset is formed based on the suspended solids removal rate and porosity change data, including: S201. Based on the filter media parameters and reactor operating structure parameters in the standardized basic dataset, set multiple sets of differentiated test conditions. The differentiated test conditions include at least combinations of different filter media types, different filter media particle size gradients, and different filter layer filling heights. S202. For each set of operating conditions, based on the initial concentration parameters of raw water suspended solids in the standardized basic dataset, the influent suspended solids concentration and effluent suspended solids concentration at multiple preset time intervals are measured; using a preset removal rate calculation formula, the influent and effluent suspended solids concentrations for the corresponding operating conditions are substituted to calculate the suspended solids removal rate at different operating time intervals for each set of operating conditions. The removal rate calculation formula is as follows: , in, for The rate of removal of suspended solids at any given time. The initial influent concentration of suspended solids. for The concentration of suspended solids in the effluent at any given time; S203. The porosity of the filter layer under each working condition is dynamically measured by the porosity monitoring method, and the porosity data of the filter layer at each time node is recorded to form a time series data sequence of the change of filter layer porosity with operating time. S204. Verify the validity of the calculated suspended solids removal rate data for each working condition and the monitored time-series change data of filter layer porosity. Integrate the two types of data into a structured format according to the working condition number, running time, suspended solids removal rate and filter layer porosity to form a filtration performance index dataset.

[0022] In the above embodiments, multiple sets of differentiated operating conditions are set based on standardized basic data, covering key influencing factors such as filter media type, particle size, and filter layer thickness, to ensure the representativeness of the test conditions and provide comprehensive sample support for filtration performance analysis.

[0023] By employing a clear formula for calculating suspended solids removal rate and combining concentration monitoring at multiple time points, we can accurately obtain filtration efficiency data under different operating conditions. We can also dynamically monitor changes in filter layer porosity and capture the evolution of filter layer structure over time. These two types of data together constitute a comprehensive filtration performance index system.

[0024] The data is validated and structured to form a standardized dataset of filtering performance indicators, providing accurate and complete input data for the subsequent calculation of key parameters such as blocking factor and penetration rate, thus ensuring the scientific nature of the subsequent model construction.

[0025] Preferably, S3, based on the standardized basic dataset and the filtration performance index dataset, the resistance factor, initial permeability, and permeability decay coefficient are determined. Based on the resistance factor, initial permeability, and permeability decay coefficient, and combined with preset critical condition parameters, calculation models for pressure cycle variation with filter layer thickness and water quality cycle variation with filter layer thickness are constructed respectively, including: S301. The resistance factor is calculated using a preset formula and based on the filter media density and initial suspended solids concentration parameters in the standardized basic dataset, as well as the filter layer porosity data in the filtration performance index dataset. The formula for calculating the resistance factor is as follows: , in, As a blocking factor, For filter media density, The adsorption coefficient of the filter media for suspended solids. The initial influent concentration of suspended solids. The porosity of the filter layer; S302. Based on the filter media parameters and reactor operating parameters in the standardized basic dataset, the initial permeability is determined by permeation test under the initial clean state of the filter bed. , Based on the data on head loss changes during continuous operation of the filter, the permeability attenuation coefficient is calculated using a dynamic calculation formula. The dynamic calculation formula for the attenuation coefficient is as follows: , in, for The permeability decay coefficient at time [time]. The initial permeability decay coefficient, The attenuation coefficient is a constant. Runtime; S303, based on initial penetration rate and permeability decay coefficient In conjunction with preset critical condition parameters, a calculation model for the pressure cycle as a function of filter layer thickness is constructed. The calculation model for the pressure cycle as a function of filter layer thickness is as follows: , in, The filter layer thickness is Pressure cycle during time, The viscosity coefficient of the fluid. For running traffic, For filter layer thickness, The critical pressure difference. This refers to the cross-sectional area of ​​the reactor. S304. Based on the calculated resistance factor R, combined with the suspended solids removal rate in the filtration performance index dataset... and filter layer porosity and runtime traffic in the standardized base dataset and filter layer thickness A calculation model for the variation of water quality cycle with filter layer thickness is constructed. The calculation model for the variation of water quality cycle with filter layer thickness is as follows: , in, The filter layer thickness is Water quality cycle at time, for The rate of removal of suspended solids at any given time.

[0026] In the above embodiments, based on the standardized basic dataset and the filtering performance index dataset, the blocking factor, initial penetration rate and penetration rate decay coefficient are accurately determined through clear calculation formulas, ensuring that the calculation logic of key parameters is rigorous and the results are reliable, laying the core foundation for model construction.

[0027] By combining preset critical condition parameters, calculation models for pressure cycle and water quality cycle as a function of filter layer thickness are constructed respectively, clearly quantifying the influence of filter layer thickness on the two types of cycles, filling the gap in traditional methods that lack a quantitative correlation model between filter layer thickness and operating cycle.

[0028] The model construction process fully integrates filter media characteristics, operating parameters, and filtration performance data to ensure that the model can truly reflect the actual operating mechanism of the filter bed, providing a practical theoretical tool for subsequent optimal parameter derivation.

[0029] Preferably, S4, combining the calculation models of pressure cycle with filter layer thickness and water quality cycle with filter layer thickness, derives the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle when the water quality cycle and pressure cycle of the filter bed are equal, including: S401. Based on the condition that the optimal operating state of the filter bed is that the water quality cycle and the pressure cycle are equal, the calculation models of the pressure cycle changing with the filter bed thickness and the water quality cycle changing with the filter bed thickness are combined to obtain the following equations: ; S402, based on filter layer thickness For the variable to be solved, the expression for the optimal critical filter layer thickness when the water quality cycle and pressure cycle of the filter bed are equal is derived. The expression for the optimal critical filter layer thickness is: , in, The optimal critical filter layer thickness is [value missing]. Initial penetration rate, This is the permeability decay coefficient. As a blocking factor, The porosity of the filter layer. for The rate of removal of suspended solids at any given time. The viscosity coefficient of the fluid. For running traffic, The critical pressure difference. This refers to the cross-sectional area of ​​the reactor. S403. The derived expression for the optimal critical filter layer thickness. Substituting these values ​​into the calculation models for pressure cycle variation with filter layer thickness and water quality cycle variation with filter layer thickness, respectively, we obtain the optimal working cycle calculation relationship: , in, To achieve the optimal work cycle, Take the filter layer thickness as Calculated value of pressure cycle at time, Take the filter layer thickness as The calculated value of water quality over a period of time.

[0030] In the above embodiments, the criterion for determining the optimal operating state of the filter is "the water quality cycle is equal to the pressure cycle". This accurately captures the balance between the filter's operating efficiency and hydraulic stability, providing a scientific basis for the derivation of optimal parameters.

[0031] By combining two types of periodic calculation models, the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle are derived, providing a clear mathematical basis for the optimization of filter parameters.

[0032] The optimal parameter expression and calculation relationship can be directly linked to the actual operating parameters. The optimal solution can be quickly calculated without complicated experimental iterations, which significantly improves the efficiency and accuracy of parameter optimization and provides a convenient technical tool for engineering applications.

[0033] Preferably, after obtaining the expression for the optimal critical filter layer thickness, the method further includes a step of correcting the parameters of the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship through multi-condition comparative test results, including: Based on the deviation calculation formula, the actual core operating data under each set of working conditions is compared with the parameters of the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship to obtain the deviation rate. The deviation calculation formula is as follows: , in, The deviation rate, This refers to the theoretically optimal critical filter layer thickness or the theoretically optimal working cycle. This refers to the actual critical filter layer thickness or the actual operating cycle. Set a preset deviation threshold; if the deviation rate under a certain set of working conditions... If the deviation exceeds the preset threshold, the corresponding related parameters in the optimal critical filter layer thickness expression are determined to need to be corrected. For the parameters that need to be corrected, the least squares method is used to fit and correct the parameters based on the deviation between theoretical and measured data of multiple working conditions, and the corrected parameter values ​​are obtained. Substitute the corrected parameter values ​​into the expression for the optimal critical filter layer thickness, recalculate the theoretical optimal critical filter layer thickness and optimal working cycle for each group of working conditions, and compare them again with the measured data until the deviation rate is reached. If the deviation is below the preset threshold, the parameters of the optimal critical filter layer thickness expression are corrected.

[0034] Specifically, (1) Based on the standardized basic dataset of S1, multiple sets of comparative working conditions are set up by selecting key variables that affect the operating efficiency of the filter bed. The key variables include at least the filter media type, filter media particle size gradient, and filter layer filling height. Each set of working conditions changes only a single key variable, while the other operating parameters remain the same to ensure the effectiveness and comparability of the experiment. (2) Conduct filter operation tests according to multiple sets of comparative working conditions designed, and collect core operating data under each set of working conditions simultaneously. The core operating data includes filter layer porosity, suspended solids removal rate, head loss and actual filter layer thickness and corresponding operating cycle when the critical operating state is reached (water quality is not up to standard or head loss exceeds the limit) at different operating times. (3) Compare the actual core operating data under each set of working conditions with the theoretical calculation data obtained by deriving the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship through S4, and calculate the deviation value between the two. The deviation value is calculated as follows: ,in, The deviation rate, These are theoretically calculated values ​​(theoretical optimal critical filter layer thickness or theoretical optimal working cycle). These are measured values ​​(actual critical filter layer thickness or actual operating cycle). (4) Set a preset deviation threshold. If the deviation rate under a certain working condition is... If the deviation exceeds a preset threshold, the corresponding parameters in the optimal critical filter layer thickness expression must be corrected; for the parameters that need to be corrected (including but not limited to the adsorption coefficient)... Initial permeability decay coefficient Attenuation coefficient constant Based on the deviation between theoretical and measured data of multiple working conditions, the least squares method is used to correct the parameter fitting and obtain the corrected parameter values. (5) Substitute the corrected parameter values ​​into the expression for the optimal critical filter layer thickness, recalculate the theoretical optimal critical filter layer thickness and optimal working cycle for each group of working conditions, and compare them with the measured data again until the deviation rate is reached. If the deviation is below the preset threshold, the parameters of the optimal critical filter layer thickness expression are corrected.

[0035] In the above embodiments, a multi-condition comparative test and deviation correction mechanism are introduced. By comparing and calibrating theoretical data with measured data, the key parameters in the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship are corrected, which effectively reduces the deviation between the theoretical value and the actual operating value of the model and improves the reliability and accuracy of the expression and calculation relationship.

[0036] The least squares method is used for parameter fitting correction to ensure the scientific and rigorous nature of the correction process. This allows the optimized parameter expression to more accurately reflect the actual operation of the filter bed and avoid poor optimization results caused by the mismatch between model parameters and actual operating conditions.

[0037] The revised expressions and calculation relationships can be repeatedly applied to parameter optimization of similar filters without the need to derive formulas for different operating conditions. This further enhances the versatility and engineering applicability of the method, providing stronger technical support for the long-term stable and efficient operation of filters.

[0038] The following examples illustrate the application of parameter optimization methods for pyrite denitrification biological filters.

[0039] I. Basic Data Acquisition and Preprocessing (a) Determination of test materials and raw water data Define the basic data related to the filter media: Select pyrite and quartz sand filter media from Yunfu, Guangdong. After crushing and screening, obtain two particle sizes of pyrite filter media (1.0-2.0mm and 1.2-2.4mm) and quartz sand filter media (1.0-2.0mm). Record the density of the filter media (pyrite filter media density). =5050kg / After pretreatment of the filter media, the porosity of the pyrite filter media was measured and recorded. (Initial porosity was determined experimentally, and subsequently dynamically monitored over time.)

[0040] Determining the core pollution indicators for raw water: Using the effluent from the secondary sedimentation tank of a wastewater treatment plant in Fangchenggang City, Guangxi Zhuang Autonomous Region as raw water, the initial influent concentration of suspended solids (SS) was determined. =20mg / L, which is used as the main reference pollutant indicator.

[0041] (II) Recording of operating parameters of the test apparatus Record the reactor structural parameters: the upflow fixed-bed bioreactor has an inner diameter of 11 cm and a total volume of 14.06 L. The bottom support layer consists of 8.0-16.0 mm pebbles. Record the filter media filling height (filter layer thickness) for different experimental groups. (Including gradient settings such as 240cm and 180cm).

[0042] Fixed operating key parameters: Set filtration rate V=6m / h, through Convert to traffic Maintain room temperature operation and record auxiliary parameters such as water bath temperature and light protection conditions to ensure consistency of experimental conditions.

[0043] II. Calculation of Core Filtration Performance Indicators (a) Calculation of suspended solids (SS) removal rate According to different operating conditions in the experimental design (different filter media types, filter media particle sizes, and filter layer thicknesses), samples were taken at specific time points to determine the SS concentration in the effluent at each time point. .

[0044] The SS removal rate is calculated using equation (1). : ,in, The initial influent concentration of SS is 20 mg / L. To measure the SS concentration during sampling, three parallel experiments were set up for each operating condition, and the average value was taken as the SS removal rate under that operating condition.

[0045] (II) Recording of dynamic monitoring data on filter layer porosity During continuous operation of the filter bed, the porosity changes of the pyrite filter media were monitored at set time intervals (0h, 10h, 20h, etc.), and the porosity corresponding to different operating times t was recorded. This generates a dataset showing how porosity changes over time.

[0046] III. Calculation of Key Parameters for the Periodic Model (a) Calculation of the retardation factor R Determine the adsorption coefficient Ks: Through experimental fitting or relevant literature reference, set the adsorption coefficient Ks of pyrite filter media (the basic parameter value is Ks=0.0012L / mg, and it is adjusted to 0.0018L / mg in subsequent optimization conditions).

[0047] Calculate the retardation factor R: ,in, For filter media density, The adsorption coefficient is . This represents the initial influent concentration of SS. For the filter media porosity, substitute the known parameters ( , (etc.) complete the calculation.

[0048] (II) Determination of Permeability-Related Parameters Record initial penetration rate In the initial stage of the experiment, with the filter layer clean, the initial filtration permeability was measured and recorded. (Basic parameter values) = Adjusted to the optimized operating conditions ).

[0049] Determine the permeability decay coefficient : Set the initial permeability decay coefficient (Basic parameter values) = Adjusted to the optimized operating conditions ), through formula Calculate different running times Corresponding congestion rate coefficient .

[0050] (III) Water Quality Cycle With pressure cycle calculate Water quality cycle Calculation: Combined formula ,Mode and Substitute the already calculated , Known , , Calculate different filter layer thicknesses using parameters such as... Corresponding water quality cycle The dynamic adsorption efficiency is expressed by formula: correction, The initial SS removal rate (i.e., the initial suspended solids removal rate).

[0051] Pressure cycle Calculation: Set critical pressure difference (Basic parameter values) (Adjusted to 35 kPa in optimized operating conditions), determine the fluid viscosity coefficient μ (take the corresponding standard value for the viscosity coefficient of pure water at room temperature), and combine it with formula (Running flow) and formula Substitute , , , , , Calculate different filter layer thicknesses using parameters such as... Corresponding pressure cycle .

[0052] IV. Model Fitting and Optimal Parameter Solution (I) Verification of the rationality of the periodic model Unit consistency check: Verify the pressure cycle formula (i.e., the calculation model for pressure cycle variation with filter bed thickness) and water quality cycle formula (i.e., the calculation model of water quality cycle with filter layer thickness) ensures that the units of each parameter are simplified to time (h) to verify the rationality of the equation.

[0053] Trend Consistency Verification: Analyze the pressure cycle based on the calculation results. With filter layer thickness The increasing decreasing trend and water quality cycle With filter layer thickness The increasing trend verifies the theoretical model. Figure 2 Consistency of the working curve.

[0054] (II) Determining the optimal filter layer thickness and working cycle Based on the actual operation of the pyrite denitrification biological filter, the following parameters are set: Table 1 shows the parameters corresponding to the actual operation of the pyrite denitrification biological filter.

[0055] Table 1 Simultaneous periodic equations: When When the filter reaches its optimal operating state, the pressure cycle formula and the water quality cycle formula are combined to obtain the equation. .

[0056] Solving for the optimal filter layer thickness : Through form Substitute the already calculated , , , , , The optimal critical filter layer thickness was calculated using parameters such as A. .

[0057] Determine the optimal work cycle: Substituting the values ​​into the pressure cycle formula or water quality cycle formula, the equilibrium point between the pressure cycle and the water quality cycle, i.e., the optimal working cycle, can be calculated. .

[0058] The current design contradiction is that the actual filter layer thickness (1.8 m) is slightly less than the calculated critical optimal thickness (1.84 m), which results in the water quality cycle being less than the pressure cycle. The hydraulic potential of the filter layer is not fully utilized, indicating that the denitrification biological filter can still be further optimized in future practical applications. In practical applications, in order to improve the operating efficiency of the filter, the system performance can be maintained in the following ways without significantly increasing the cost. (1) Optimize the backwashing frequency: The backwashing cycle can be set dynamically, for example, by setting the backwashing trigger threshold according to the pressure difference or effluent turbidity, instead of using a fixed time interval; strengthen the backwashing intensity and appropriately increase the backwashing flow rate to ensure that the filter media and pollutants in the depth of the filter are effectively separated. (2) Optimize the filter media gradation: A non-uniform gradation design concept can be adopted to build a layered structure of "coarse on top and fine on the bottom". Different particle sizes of pyrite filter media are used for complementary filling to enhance the interception of coarse particles, delay clogging, and improve the adsorption capacity of fine particles. (3) Dynamic monitoring and adaptive adjustment: Real-time monitoring of target pollutant concentration, and temporary increase of 5% to 10% of filter layer thickness according to seasonal water quality changes (such as the potential increase of suspended solids load during the flood season).

[0059] To further verify whether the model is consistent Figure 2 The trend of change in the filter bed is that the water quality cycle is greater than the pressure cycle. After adjusting the filter bed thickness, assuming that the actual filter bed thickness increases to L=2 m > L*=1.84 m, and the filter media particle size, filter media density, flow velocity, and suspended solids concentration in the influent and effluent are stable, the adsorption coefficient, permeability, attenuation coefficient, and critical pressure difference all change, and the values ​​are shown in Table 2: Table 2 (1) Water quality cycle ( )calculate: Blocking factor , , (2) Pressure cycle ( )calculate: , Based on the above results, it can be seen that when the thickness of the denitrification filter layer is 2 m and exceeds the critical thickness, the water quality cycle is greater than the pressure cycle. As the actual thickness increases, the difference between the two cycles shows a different state, which is consistent with the change law of the working curve of the pyrite denitrification biological filter.

[0060] V. Data Validation and Optimization (I) Comparison and verification of data under different working conditions Comparison of filter media types: The SS removal rate and pressure cycle of quartz sand and pyrite filter media under the same filter layer thickness (2.4m) and the same influent SS concentration (20mg / L) were calculated to verify the superiority of pyrite filter media.

[0061] Verification of the influence of filter media particle size: By comparing the SS removal rate and overflow point frequency of 1.0-2.0mm and 1.2-2.4mm pyrite filter media at the same filtration rate (6m / h) and the same filter layer thickness, the optimal filter media particle size was determined.

[0062] Verification of the effect of filter layer thickness: By comparing the SS removal rate and the time to reach the critical value of head loss under the same filtration rate and the same particle size of pyrite filter media with filter layer thickness of 240cm and 180cm, the relationship between filter layer thickness and filtration efficiency is verified.

[0063] (II) Model fit analysis and parameter adjustment Dynamic parameter correction: Introducing a dynamic adsorption efficiency equation and dynamic permeability decay equation Correcting different runtime conditions and This improves the fit between the model and actual operating data.

[0064] Boundary condition verification: Set filter layer thickness (greater than the optimal critical thickness) Adjust the adsorption coefficient Initial penetration rate Critical pressure difference Permeability attenuation coefficient Parameters, recalculate and This study verifies the pattern that "when the filter layer thickness is greater than the critical value, the water quality cycle is longer than the pressure cycle," further validating the reliability of the model.

[0065] To address the issue of uncontrolled water quality and pressure cycle balance in denitrifying biological filters, this study investigates the filtration performance of media filters for treating low-turbidity water under different construction and operating parameters. An optimization model for the filter bed in a pyrite denitrifying biological filter is constructed, and a parameter calculation method and optimization prediction trend for the filter under optimal operating conditions are proposed. The main conclusions are as follows: (1) Taking the effluent from the secondary sedimentation tank of a wastewater treatment plant in Fangchenggang City, Guangxi Zhuang Autonomous Region as the research object, the filtration performance of the filter bed under different types of filter media, filter media particle size, and filter layer thickness was investigated using a filter column test device. Analysis showed that all of the above factors affected the filtration performance of the pyrite filter bed, and the pyrite filter bed was superior to domestic graded quartz sand filters. The optimal operating conditions for the filter bed were a filter media particle size of 1.2-2.4 mm, a filter layer thickness of 1.8 m, and a suspended solids (SS) removal rate of 79%.

[0066] (2) In order to ensure the efficiency of the filter in treating pollutants, the concept of the optimal working cycle of the denitrifying biological filter was proposed, and the calculation formula of the optimal filter layer was derived. By combining it with the actual operating conditions of the filter, suitable operating parameters were determined: when pyrite is used as the filter material, the optimal critical filter layer thickness calculated by the model is 1.84 m and the working cycle is 26 h.

[0067] (3) The model suitable for the effective relationship between water quality cycle, pressure cycle and filter layer thickness is a linear model. Filter layer thickness is a significant factor affecting water quality cycle and pressure cycle. When the actual filter layer thickness is less than the optimal critical thickness, the pressure cycle is longer than the water quality cycle; conversely, the water quality cycle is longer than the pressure cycle.

[0068] Example 2: As Figure 3 As shown, this embodiment of the invention provides a parameter optimization system for a pyrite denitrification biofilter, comprising: The basic data standardization module is used to acquire multiple basic data from the pyrite denitrification biofilter, and to standardize these multiple basic data to obtain a standardized basic dataset. The filtration performance index construction module is used to calculate the suspended solids removal rate under different operating conditions based on the standardized basic dataset, and to monitor the porosity change data of the filter layer with the operating time, and to form a filtration performance index dataset based on the suspended solids removal rate and porosity change data. The cycle calculation model construction module is used to determine the resistance factor, initial permeability and permeability decay coefficient based on the standardized basic dataset and the filtration performance index dataset. Based on the resistance factor, initial permeability and permeability decay coefficient and combined with the preset critical condition parameters, it constructs calculation models for pressure cycle with filter layer thickness and water quality cycle with filter layer thickness, respectively. The optimal parameter expression derivation module is used to combine the calculation model of pressure cycle with filter layer thickness and the calculation model of water quality cycle with filter layer thickness to derive the expression of optimal critical filter layer thickness and the calculation relationship of optimal working cycle when the water quality cycle and pressure cycle of the filter are equal. The operation parameter optimization output module is used to substitute the basic parameters in the actual operation scenario into the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship, and output the optimized operation parameters of the pyrite denitrification biological filter.

[0069] Preferably, the suspended solids removal rate under different operating conditions is calculated based on the standardized basic dataset, and the porosity change data of the filter layer with operating time is monitored. A filtration performance index dataset is formed based on the suspended solids removal rate and porosity change data, including: Based on the filter media parameters and reactor operating structure parameters in the standardized basic dataset, multiple sets of differentiated test conditions are set. The differentiated test conditions include at least combinations of different filter media types, different filter media particle size gradients, and different filter layer filling heights. For each set of operating conditions, based on the initial concentration parameters of raw water suspended solids in the standardized basic dataset, the influent and effluent suspended solids concentrations at multiple preset time intervals are measured. Using a preset removal rate calculation formula, the influent and effluent suspended solids concentrations for the corresponding operating conditions are substituted to calculate the suspended solids removal rate at different operating time intervals for each set of operating conditions. The removal rate calculation formula is as follows: , in, for The rate of removal of suspended solids at any given time. The initial influent concentration of suspended solids. for The concentration of suspended solids in the effluent at any given time; The porosity of the filter layer under each working condition is dynamically measured by a porosity monitoring method. The porosity data of the filter layer at each time point is recorded to form a time series data sequence of the change of filter layer porosity with operating time. The validity of the calculated suspended solids removal rate data for each working condition and the monitored time-series change data of filter layer porosity are verified. The two types of data are linked and integrated according to the structured format of working condition number, running time, suspended solids removal rate and filter layer porosity to form a filtration performance index dataset.

[0070] Example 3: This embodiment of the invention provides a parameter optimization system for a pyrite denitrification biofilter, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the parameter optimization method for a pyrite denitrification biofilter as described above.

[0071] Example 4: This embodiment of the invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the parameter optimization method for pyrite denitrification biofilters as described above.

[0072] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0076] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing parameters of a pyrite denitrification biological filter, characterized in that, Includes the following steps: We obtained several basic data points from the pyrite denitrification biofilter, and then standardized these data points to obtain a standardized basic dataset. Based on the standardized basic dataset, the suspended solids removal rate under different operating conditions is calculated, and the porosity of the filter layer changes with the operating time is monitored. A filtration performance index dataset is formed based on the suspended solids removal rate and porosity change data. Based on the standardized basic dataset and the filtration performance index dataset, the resistance factor, initial permeability and permeability decay coefficient are determined. Based on the resistance factor, initial permeability and permeability decay coefficient and combined with the preset critical condition parameters, calculation models for pressure cycle variation with filter layer thickness and water quality cycle variation with filter layer thickness are constructed respectively. By combining the calculation models of pressure cycle with filter layer thickness and water quality cycle with filter layer thickness, the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle when the water quality cycle and pressure cycle of the filter are equal are derived. Substitute the basic parameters from the actual operating scenario into the expression for the optimal critical filter layer thickness and the calculation relationship for the optimal working cycle to output the optimized operating parameters of the pyrite denitrification biological filter.

2. The method for optimizing parameters of a pyrite denitrification biological filter according to claim 1, characterized in that, Several basic data points from a pyrite denitrification biofilter were obtained, and these data points were standardized to obtain a standardized basic dataset, including: Based on parameter attributes, the basic data are divided into three categories: filter media parameters, raw water pollution index parameters, and reactor operation and structural parameters, which are collected separately. Among them, the filter media parameters include pyrite filter media and filter media particle size gradient, filter media density, initial porosity, and pretreatment process parameters; the raw water pollution index parameters include the raw water target pollutant concentration and auxiliary water quality parameters; and the reactor operation and structural parameters include reactor structural dimensions, filter media filling height gradient, support layer specifications, and operation control parameters. Outlier removal was performed on multiple collected basic data sets, and missing values ​​in the basic data sets were supplemented by retesting the same batch of samples. According to the preset unified unit system, the units of the basic data of different dimensions are converted. Among them, the parameters of length, volume, mass concentration and density are unified to the preset standard units of measurement. All numerical basic data are uniformly calibrated to the preset number of significant figures or decimal places to obtain the various standardized basic data. The standardized basic dataset is formed by classifying and integrating data according to parameter category, parameter name, standardized basic data, and structured format of standard units.

3. The method for optimizing parameters of a pyrite denitrification biological filter according to claim 1, characterized in that, Based on the standardized basic dataset, the suspended solids removal rate under different operating conditions is calculated, and the porosity change data of the filter layer with operating time is monitored. Based on the suspended solids removal rate and porosity change data, a filtration performance index dataset is formed, including: Based on the filter media parameters and reactor operating structure parameters in the standardized basic dataset, multiple sets of differentiated test conditions are set. The differentiated test conditions include at least combinations of different filter media types, different filter media particle size gradients, and different filter layer filling heights. For each set of operating conditions, based on the initial concentration parameters of raw water suspended solids in the standardized basic dataset, the influent and effluent suspended solids concentrations at multiple preset time intervals are measured. Using a preset removal rate calculation formula, the influent and effluent suspended solids concentrations for the corresponding operating conditions are substituted to calculate the suspended solids removal rate at different operating time intervals for each set of operating conditions. The removal rate calculation formula is as follows: ， in, for The rate of removal of suspended solids at any given time. The initial influent concentration of suspended solids. for The concentration of suspended solids in the effluent at any given time; The porosity of the filter layer under each working condition is dynamically measured by a porosity monitoring method. The porosity data of the filter layer at each time point is recorded to form a time series data sequence of the change of filter layer porosity with operating time. The validity of the calculated suspended solids removal rate data for each working condition and the monitored time-series change data of filter layer porosity are verified. The two types of data are linked and integrated according to the structured format of working condition number, running time, suspended solids removal rate and filter layer porosity to form a filtration performance index dataset.

4. The method for optimizing parameters of a pyrite denitrification biological filter according to claim 1, characterized in that, Based on the standardized basic dataset and filtration performance index dataset, the resistance factor, initial permeability, and permeability decay coefficient are determined. Based on the resistance factor, initial permeability, and permeability decay coefficient, and combined with preset critical condition parameters, calculation models for pressure cycle variations with filter layer thickness and water quality cycle variations with filter layer thickness are constructed, including: The resistance factor is calculated using a preset formula based on the filter media density and initial suspended solids concentration parameters in the standardized basic dataset, as well as the filter layer porosity data in the filtration performance index dataset. The formula for calculating the resistance factor is as follows: ， in, As a blocking factor, For filter media density, The adsorption coefficient of the filter media for suspended solids. The initial influent concentration of suspended solids. The porosity of the filter layer; Based on the filter media parameters and reactor operating parameters in the standardized basic dataset, the initial permeability was determined through permeation tests under the initial clean state of the filter bed. , Based on the data on head loss changes during continuous operation of the filter, the permeability attenuation coefficient is calculated using a dynamic calculation formula. The dynamic calculation formula for the attenuation coefficient is as follows: ， in, for The permeability decay coefficient at time [time]. The initial permeability decay coefficient, The attenuation coefficient is a constant. Runtime; Based on initial penetration rate and permeability decay coefficient In conjunction with preset critical condition parameters, a calculation model for the pressure cycle as a function of filter layer thickness is constructed. The calculation model for the pressure cycle as a function of filter layer thickness is as follows: ， in, The filter layer thickness is Pressure cycle during time, The viscosity coefficient of the fluid. For running traffic, For filter layer thickness, The critical pressure difference. This refers to the cross-sectional area of ​​the reactor. Based on the calculated resistance factor R, combined with the suspended solids removal rate in the filtration performance index dataset... and filter layer porosity and runtime traffic in the standardized base dataset and filter layer thickness A calculation model for the variation of water quality cycle with filter layer thickness is constructed. The calculation model for the variation of water quality cycle with filter layer thickness is as follows: ， in, The filter layer thickness is Water quality cycle at time, for The rate of removal of suspended solids at any given time.

5. The method for optimizing parameters of a pyrite denitrification biological filter according to claim 4, characterized in that, By combining the calculation models of pressure cycle with filter bed thickness and water quality cycle with filter bed thickness, the optimal critical filter bed thickness expression and the optimal working cycle calculation relationship when the water quality cycle and pressure cycle of the filter bed are equal are derived, including: Based on the criterion that the optimal operating state of the filter bed is that the water quality cycle and the pressure cycle are equal, and by combining the calculation models of the pressure cycle and the water quality cycle with the filter bed thickness, we obtain the following simultaneous equations: ； With filter layer thickness For the variable to be solved, the expression for the optimal critical filter layer thickness when the water quality cycle and pressure cycle of the filter bed are equal is derived. The expression for the optimal critical filter layer thickness is: ， in, The optimal critical filter layer thickness is [value missing]. Initial penetration rate, This is the permeability decay coefficient. As a blocking factor, The porosity of the filter layer. for The rate of removal of suspended solids at any given time. The viscosity coefficient of the fluid. For running traffic, The critical pressure difference. This refers to the cross-sectional area of ​​the reactor. The derived expression for the optimal critical filter layer thickness Substituting these values ​​into the calculation models for pressure cycle variation with filter layer thickness and water quality cycle variation with filter layer thickness, respectively, we obtain the optimal working cycle calculation relationship: ， in, To achieve the optimal work cycle, Take the filter layer thickness as Calculated value of pressure cycle at time, Take the filter layer thickness as The calculated value of water quality over a period of time.

6. The method for optimizing parameters of a pyrite denitrification biological filter according to claim 5, characterized in that, After obtaining the expression for the optimal critical filter layer thickness, the process also includes steps to correct the parameters of the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship through multi-condition comparative test results, including: Based on the deviation calculation formula, the actual core operating data under each set of working conditions is compared with the parameters of the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship to obtain the deviation rate. The deviation calculation formula is as follows: ， in, The deviation rate, This refers to the theoretically optimal critical filter layer thickness or the theoretically optimal working cycle. This refers to the actual critical filter layer thickness or the actual operating cycle. Set a preset deviation threshold; if the deviation rate under a certain set of working conditions... If the deviation exceeds the preset threshold, the corresponding related parameters in the optimal critical filter layer thickness expression are determined to need to be corrected. For the parameters that need to be corrected, the least squares method is used to fit and correct the parameters based on the deviation between theoretical and measured data of multiple working conditions, and the corrected parameter values ​​are obtained. Substitute the corrected parameter values ​​into the expression for the optimal critical filter layer thickness, recalculate the theoretical optimal critical filter layer thickness and optimal working cycle for each group of working conditions, and compare them again with the measured data until the deviation rate is reached. If the deviation is below the preset threshold, the parameters of the optimal critical filter layer thickness expression are corrected.

7. A parameter optimization system for a pyrite denitrification biological filter, characterized in that, include: The basic data standardization module is used to acquire multiple basic data from the pyrite denitrification biofilter, and to standardize these multiple basic data to obtain a standardized basic dataset. The filtration performance index construction module is used to calculate the suspended solids removal rate under different operating conditions based on the standardized basic dataset, and to monitor the porosity change data of the filter layer with the operating time, and to form a filtration performance index dataset based on the suspended solids removal rate and porosity change data. The cycle calculation model construction module is used to determine the resistance factor, initial permeability and permeability decay coefficient based on the standardized basic dataset and the filtration performance index dataset. Based on the resistance factor, initial permeability and permeability decay coefficient and combined with the preset critical condition parameters, it constructs calculation models for pressure cycle with filter layer thickness and water quality cycle with filter layer thickness, respectively. The optimal parameter expression derivation module is used to combine the calculation model of pressure cycle with filter layer thickness and the calculation model of water quality cycle with filter layer thickness to derive the expression of optimal critical filter layer thickness and the calculation relationship of optimal working cycle when the water quality cycle and pressure cycle of the filter are equal. The operation parameter optimization output module is used to substitute the basic parameters in the actual operation scenario into the optimal critical filter layer thickness expression and the optimal working cycle calculation relationship, and output the optimized operation parameters of the pyrite denitrification biological filter.

8. The parameter optimization system for pyrite denitrification biological filter according to claim 7, characterized in that, Based on the standardized basic dataset, the suspended solids removal rate under different operating conditions is calculated, and the porosity change data of the filter layer with operating time is monitored. Based on the suspended solids removal rate and porosity change data, a filtration performance index dataset is formed, including: Based on the filter media parameters and reactor operating structure parameters in the standardized basic dataset, multiple sets of differentiated test conditions are set. The differentiated test conditions include at least combinations of different filter media types, different filter media particle size gradients, and different filter layer filling heights. For each set of operating conditions, based on the initial concentration parameters of raw water suspended solids in the standardized basic dataset, the influent and effluent suspended solids concentrations at multiple preset time intervals are measured. Using a preset removal rate calculation formula, the influent and effluent suspended solids concentrations for the corresponding operating conditions are substituted to calculate the suspended solids removal rate at different operating time intervals for each set of operating conditions. The removal rate calculation formula is as follows: ， in, for The rate of removal of suspended solids at any given time. The initial influent concentration of suspended solids. for The concentration of suspended solids in the effluent at any given time; The porosity of the filter layer under each working condition is dynamically measured by a porosity monitoring method. The porosity data of the filter layer at each time point is recorded to form a time series data sequence of the change of filter layer porosity with operating time. The validity of the calculated suspended solids removal rate data for each working condition and the monitored time-series change data of filter layer porosity are verified. The two types of data are linked and integrated according to the structured format of working condition number, running time, suspended solids removal rate and filter layer porosity to form a filtration performance index dataset.

9. A parameter optimization system for a pyrite denitrification biological filter, characterized in that, The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the parameter optimization method for pyrite denitrification biofilters as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the parameter optimization method for pyrite denitrification biofilter as described in any one of claims 1 to 6.

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