A biochemical pond process mode judgment and parameter regulation operation decision system

Abstract

This invention relates to an operational decision-making system for determining the process mode and regulating parameters of a biological treatment pond, comprising: a parameter input module for acquiring operational parameters; a parameter processing module for acquiring first data based on the operational parameters; a process mode determination module for acquiring the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment pond under test and determining the process mode of the biological treatment pond under test; an internal carbon source denitrification relationship analysis module for acquiring all permutations and combinations of operational regulation parameters under the target internal carbon source denitrification rate when the process mode tends to undergo denitrification mainly based on enhanced internal carbon source denitrification and has optimization space; and a first scenario parameter regulation module for acquiring the suitable process mode for the biological treatment pond under test under the current water quality and the regulation range of the optimal permutation and combination of operational regulation parameters under the mode; this invention effectively improves the precision and intelligence level of wastewater treatment.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to an operational decision-making system for determining the process mode and adjusting parameters of a biological treatment tank. Background Technology

[0002] With the increasing stringency of wastewater treatment discharge standards in my country, designers typically incorporate measures such as adding packing material, setting up deoxygenation zones, setting up anoxic / aerobic variable zones, intermittent aeration, and increasing the size of anoxic areas into traditional AAO and AAOAO processes to achieve stable and efficient nitrogen removal. Theoretically, typical processes incorporating these new measures not only improve nitrogen and phosphorus removal efficiency in biological treatment tanks but also increase the probability of nitrogen removal modes such as internal carbon source denitrification and simultaneous nitrification-denitrification. Literature indicates that through one or more of the above measures and reasonable operational control strategies, multiple AAO or AAOAO water plants have exhibited varying degrees of internal carbon source denitrification and simultaneous nitrification-denitrification, achieving deep nitrogen removal while significantly reducing the amount of commercial carbon source added.

[0003] However, due to the uneven and generally low skill levels of water plant operators in my country, most operators are unable to effectively adjust the process operation mode and parameters based on the actual influent water quality and the actual design of the biological treatment tank. Instead, they blindly follow crude measures such as increasing aeration, increasing the reflux ratio, increasing sludge concentration, and increasing carbon source dosage, resulting in sludge aging, expansion, and imbalance of functional bacteria species. This prevents the inherent biological treatment tank from achieving its efficient denitrification level and leads to high operating costs.

[0004] To address issues such as inefficient operation, technicians have developed various intelligent management systems or platforms. However, most of the existing systems or platforms are intelligent operation management systems or systems designed for single-point problems in biological treatment ponds. These systems have greatly improved the management efficiency of chemical consumption, power consumption, and personnel, but they still cannot guide operators to adjust relevant process modes and specific parameters based on different water qualities and the actual design of the biological treatment pond.

[0005] Therefore, there is an urgent need for an operational decision-making system for judging the process mode and controlling the parameters of a biochemical pool. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides an operation decision system for judging the process mode and adjusting the parameters of a biological treatment tank. It solves the technical problems of high operating costs and inability to guide operators to adjust the relevant process mode and specific parameters according to different water qualities and the actual design of the biological treatment tank in the prior art.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0010] This invention provides an operational decision-making system for determining the process mode and adjusting parameters in a biological treatment tank, comprising:

[0011] The parameter input module is used to obtain the design parameters and operating parameters of the water plant that are involved in the operation decision of the biological treatment tank under test;

[0012] The design parameters include the civil engineering parameters of the biochemical tank to be tested; the operating parameters include: the operating mode, actual parameters, and target internal carbon source denitrification rate of the biochemical tank to be tested.

[0013] The parameter processing module is used to input the design parameters and operating parameters of the water plant participating in the operation decision of the biochemical pool under test into the pre-constructed parameter model to obtain the first data;

[0014] The process mode determination module is used to obtain the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test based on the actual parameters and the first data, and to determine the process mode of the biological treatment tank under test based on the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test.

[0015] The internal carbon source denitrification relationship analysis module is used to retrieve the target internal carbon source denitrification rate and civil engineering parameters when the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and there is room for optimization. The target internal carbon source denitrification rate and civil engineering parameters are then input into the pre-constructed third internal carbon source denitrification perfect relationship model to obtain all permutations and combinations of operating control parameters under the target internal carbon source denitrification rate.

[0016] The first scenario parameter control module is used to obtain the suitable process mode for the biological treatment tank under the current water quality and the control range of the optimal combination of operating control parameters under the current process mode and the operating control parameters under the target carbon source denitrification rate, based on the process mode of the biological treatment tank to be tested and all permutations and combinations of the operating control parameters.

[0017] Optionally, the operating mode of the biochemical test tank includes: AAO process and AAOAO process;

[0018] The civil engineering parameters include: anaerobic tank volume data, anoxic tank volume data, aerobic tank volume data, post-anoxic tank volume data, post-aerobic tank volume data, effective water depth data of the biological treatment tank, secondary sedimentation tank volume data, and effective water depth data of the secondary sedimentation tank.

[0019] The actual parameters include: daily average influent temperature data, daily treated water volume data, influent pH data, daily average influent chemical oxygen demand (COD) concentration data, daily average five-day biochemical oxygen demand (BOD) concentration data, daily average influent total nitrogen concentration data, daily average influent total phosphorus concentration data, mixed liquor suspended solids concentration data in the biological treatment tank, mixed liquor volatile suspended solids concentration data in the biological treatment tank, external recirculation ratio data, internal recirculation ratio data, dissolved oxygen at the end of the aerobic tank data, carbon source dosage data, sludge age data, and effluent from the anaerobic tank. Data on total nitrogen concentration in water, phosphate concentration at the end of the anaerobic tank, nitrate nitrogen concentration in the effluent from the anaerobic tank, total nitrogen concentration in the effluent from the anoxic tank, nitrate nitrogen concentration in the effluent from the anoxic tank, ammonia nitrogen concentration at the end of the anoxic tank, total nitrogen concentration in the effluent from the aerobic tank, nitrate nitrogen concentration in the effluent from the aerobic tank, total nitrogen concentration in the effluent from the post-anoxic tank, total nitrogen concentration in the effluent from the post-aerobic tank, total nitrogen concentration in the effluent from the secondary sedimentation tank, total nitrogen concentration in the externally returned sludge, and suspended solids concentration in the mixed liquor of the returned sludge.

[0020] Optionally, the system further includes:

[0021] The traditional external carbon source denitrification operation analysis module is used to obtain actual relevant indicators based on the operating parameters when the process mode tends to be denitrification mainly caused by external carbon source denitrification. The actual relevant indicators are compared with theoretical relevant indicators to determine whether the denitrification capacity of the biological treatment tank under test is normal.

[0022] The second scenario parameter control module is used to obtain the suggested operating values ​​of various relevant indicators as the specific values ​​of the controllable parameters when the denitrification capacity of the biochemical tank under test is abnormal.

[0023] The relevant indicators include: nitrate removal rate in the anoxic tank, internal reflux ratio, external reflux ratio, denitrification rate, and external carbon source addition.

[0024] Optionally, in the traditional external carbon source operation analysis module, determining whether the denitrification capacity of the biochemical tank under test is normal includes:

[0025] When the actual value of any of the following values ​​is more than 20% lower than the theoretical value: either the denitrification rate or the amount of nitrate nitrogen removed in the anoxic tank, or the actual value of any of the following values ​​is more than 20% higher than the theoretical value: either the external reflux ratio, the internal reflux ratio, or the amount of external carbon source added, the evaluation result is considered abnormal; otherwise, the evaluation is considered normal.

[0026] Optionally, the process mode determination module includes:

[0027] The actual internal carbon source denitrification rate acquisition unit is used to input the actual parameters into the following formula to obtain the actual internal carbon source denitrification rate of the biological treatment tank under test:

[0028]

[0029] Where A is the actual internal carbon source denitrification rate, R is the external recirculation ratio, r is the internal recirculation ratio, TN1 is the total nitrogen concentration data of the anoxic tank effluent, TN2 is the total nitrogen concentration data of the aerobic tank effluent, TN3 is the total nitrogen concentration data of the post-aerobic tank effluent, TN4 is the total nitrogen concentration data of the secondary sedimentation tank effluent, TN5 is the total nitrogen concentration data of the externally recirculated sludge, and TN... 进水 This refers to the daily average total nitrogen concentration data for the influent;

[0030] The theoretical maximum internal carbon source denitrification rate acquisition unit is used to retrieve the design parameters, operating parameters and first data into the pre-constructed first internal carbon source denitrification general relationship model to obtain the theoretical maximum internal carbon source denitrification rate.

[0031] The judgment unit is used to determine the process mode of the biochemical pool to be tested based on the pre-constructed judgment rules.

[0032] Optionally, the judgment rule pre-constructed in the judgment unit is:

[0033] When both the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate are less than 20%, the denitrification process tends to be dominated by traditional external carbon source denitrification.

[0034] When the actual internal carbon source denitrification rate is higher than 20% and higher than the theoretical maximum internal carbon source denitrification rate, it tends to cause denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters are optimal.

[0035] When the theoretical maximum internal carbon source denitrification rate is higher than 20% and higher than the actual internal carbon source denitrification rate, it tends to undergo denitrification mainly based on enhanced internal carbon source denitrification and has room for optimization.

[0036] Optionally, the system further includes:

[0037] The model training module is used to train the general relationship model of first internal carbon source denitrification using the first training dataset to obtain the trained general relationship model of first internal carbon source denitrification; to train the trained general relationship model of first internal carbon source denitrification a second time using the second training dataset to obtain the specific relationship model of second internal carbon source denitrification; and to train the specific relationship model of second internal carbon source denitrification again using the third training dataset to obtain the complete relationship model of third internal carbon source denitrification.

[0038] The first training dataset consists of historical actual parameters of any water plant involved in the operation decision of the biological treatment pond under test, which are the actual internal carbon source denitrification rate over the past six months to one year.

[0039] The second training dataset consists of historical actual parameters of the water plant currently participating in the operation decision of the biological treatment tank under test for the past six months to one year, as well as the historical actual internal carbon source denitrification rate.

[0040] The third training dataset consists of the historical actual parameters and historical actual internal carbon source denitrification rates of all water plants that participated in the operation decision of the biological treatment tanks under test in the past two years, when they tended to undergo denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters were optimal.

[0041] The first internal carbon source denitrification general relationship model, the second internal carbon source denitrification specific relationship model, and the third internal carbon source denitrification perfect relationship model are deep learning models.

[0042] Optionally, when the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters are optimal, the current actual parameters are obtained, and the current actual parameters and the current actual internal carbon source denitrification rate are used as a set of data in the third training dataset to train the second internal carbon source denitrification specific relationship model.

[0043] Optionally, the first scene parameter control module includes:

[0044] The adjustable parameter sorting unit is used to filter all the possible combinations of operating control parameters under the target carbon source denitrification rate according to the adjustable parameter range, based on all permutations and combinations of operating control parameters under the target carbon source denitrification rate and the operating business scenario of the biochemical tank to be tested, to obtain the optimal permutation and combination.

[0045] All operating control parameters include: mixed liquor suspended solids concentration in the biological treatment tank, volatile suspended solids concentration in the mixed liquor in the biological treatment tank, external reflux ratio, internal reflux ratio, dissolved oxygen concentration, carbon source dosage, and sludge age;

[0046] The adjustable parameter range acquisition unit is used to input the optimal permutation and combination into a pre-set adjustable parameter range operation model to obtain the control range of the optimal permutation and combination of operation control parameters under the target carbon source denitrification rate.

[0047] Optionally, in the first scenario parameter control module, the operating scenario of the biochemical pool to be tested is obtained based on the daily average temperature data in the actual parameters and the first data;

[0048] The first data includes: carbon-nitrogen ratio data, carbon-phosphorus ratio data, total nitrogen volumetric load data of the anoxic tank, total nitrogen sludge load data of the anoxic tank, actual hydraulic retention time data of the anaerobic tank, actual hydraulic retention time data of the anoxic tank, actual hydraulic retention time data of the aerobic tank, actual hydraulic retention time data of the post-anoxic tank, actual hydraulic retention time data of the post-aerobic tank, actual total hydraulic retention time data of the biological treatment tank, actual hydraulic retention time data of the secondary sedimentation tank, hydraulic load data of the secondary sedimentation tank, and solids load data of the secondary sedimentation tank.

[0049] (III) Beneficial Effects

[0050] The beneficial effects of this invention are as follows: The operational decision-making system for judging the process mode and regulating parameters of a biological treatment tank, as described in this invention, uses a process mode judgment module to determine the process mode of the biological treatment tank under test. At the same time, it uses a first internal carbon source denitrification general relationship model, a second internal carbon source denitrification specific relationship model, and a third internal carbon source denitrification perfect relationship model to obtain the optimal process operation mode and the control range of operation regulation parameters. This saves some carbon source while improving the denitrification efficiency of the biological treatment tank, effectively improving the level of refinement and intelligence in wastewater treatment. Attached Figure Description

[0051] Figure 1 This is a structural diagram of an operational decision-making system for determining the process mode and controlling parameters in a biochemical tank according to Embodiment 1 of the present invention;

[0052] Figure 2 A schematic diagram of the parameter input module;

[0053] Figure 3 This is a schematic diagram of the process mode judgment and analysis module. Detailed Implementation

[0054] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0055] AAO process: The AAO process, also known as the anaerobic-anoxic-aerobic process, includes three main stages:

[0056] In the anaerobic stage, where no molecular or combined oxygen is present, polyphosphate-accumulating bacteria release phosphorus and absorb easily degradable organic matter.

[0057] In the anoxic stage, denitrifying bacteria use organic carbon sources in the wastewater to reduce nitrate nitrogen in the return mixed liquor to nitrogen gas, thus achieving denitrification.

[0058] The aerobic stage provides sufficient oxygen to oxidize ammonia nitrogen into nitrate (nitrification process), while polyphosphate-accumulating bacteria take up excessive phosphorus for subsequent removal through the waste sludge system.

[0059] The AAOAO process adds an anoxic stage and an aerobic stage to the AAO process, forming a more complex five-stage treatment process.

[0060] This invention proposes an operational decision-making system for determining the process mode and adjusting parameters of a biological treatment tank. To address the technical problem in existing technologies where operators cannot effectively adjust the process operation mode and parameters based on the actual influent water quality and the actual design of the biological treatment tank, resulting in high operating costs and low denitrification efficiency, this invention uses a process mode determination module to identify the process mode of the biological treatment tank under test. Simultaneously, it employs a first internal carbon source denitrification general relationship model, a second internal carbon source denitrification specific relationship model, and a third internal carbon source denitrification perfect relationship model to obtain the optimal process operation mode and the control range of the operating parameters. This saves some carbon source while improving the denitrification efficiency of the biological treatment tank, effectively enhancing the refinement and intelligence of wastewater treatment.

[0061] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0062] Example 1

[0063] See Figure 1 An operational decision-making system for determining the process mode and adjusting parameters in a biochemical tank, according to an embodiment of the present invention, includes:

[0064] The parameter input module is used to obtain the design parameters and operating parameters of the water plant that are involved in the operation decision of the biological treatment tank under test;

[0065] The design parameters include the civil engineering parameters of the biochemical tank to be tested; the operating parameters include: the operating mode, actual parameters, and target internal carbon source denitrification rate of the biochemical tank to be tested.

[0066] The parameter processing module is used to input the design parameters and operating parameters of the water plant participating in the operation decision of the biochemical pool under test into a pre-constructed parameter processing model to obtain the first data;

[0067] The process mode determination module is used to obtain the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test based on the actual parameters and the first data, and to determine the process mode of the biological treatment tank under test based on the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test.

[0068] The internal carbon source denitrification relationship analysis module is used to retrieve the target internal carbon source denitrification rate and civil engineering parameters when the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and there is room for optimization. The target internal carbon source denitrification rate and civil engineering parameters are then input into the pre-constructed third internal carbon source denitrification perfect relationship model to obtain all permutations and combinations of operating control parameters under the target internal carbon source denitrification rate.

[0069] The first scenario parameter control module is used to obtain the suitable process mode for the biological treatment tank under the current water quality and the control range of the optimal combination of operating control parameters under the current process mode and the operating control parameters under the target carbon source denitrification rate, based on the process mode of the biological treatment tank to be tested and all permutations and combinations of the operating control parameters.

[0070] This embodiment provides an operational decision-making system for judging the process mode and controlling parameters of a biological treatment tank. It provides operators with the optimal range of control parameters when the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and there is room for optimization. This saves some carbon source while improving the denitrification efficiency of the biological treatment tank, resulting in good economic benefits. It also helps to improve the level of refined and intelligent operation of wastewater treatment plants.

[0071] Example 2

[0072] This embodiment of an operational decision-making system for determining the process mode and adjusting parameters in a biological treatment tank includes:

[0073] The parameter input module is used to obtain the design parameters and operating parameters of the water plant that are involved in the operation decision of the biological treatment tank under test;

[0074] like Figure 2 As shown, the design parameters in the parameter input module include the civil engineering parameters of the biological treatment tank to be tested; the operating parameters include the operating mode, actual parameters, and target internal carbon source denitrification rate of the biological treatment tank to be tested.

[0075] The target internal carbon source denitrification rate refers to the degree of internal carbon source denitrification that the user expects to occur in the biological treatment tank of this embodiment under the current water quality and operating conditions. This data is generally between 20% and 50%.

[0076] The parameter processing module is used to input the operating parameters of the water plant involved in the operation decision of the biochemical pool under test into the pre-constructed parameter processing model to obtain the first data;

[0077] The process mode determination module is used to obtain the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test based on the actual parameters and the first data, and to determine the process mode of the biological treatment tank under test based on the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test.

[0078] The internal carbon source denitrification relationship analysis module is used when the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and there is room for optimization. It retrieves the target internal carbon source denitrification rate and civil engineering parameters, and inputs the target internal carbon source denitrification rate and civil engineering parameters into the pre-constructed third internal carbon source denitrification perfect relationship model to obtain all permutations and combinations of operating control parameters under the target internal carbon source denitrification rate.

[0079] The first scenario parameter control module is used to obtain the suitable process mode for the biological treatment tank under the current water quality and the control range of the optimal combination of operating control parameters under the current process mode and the operating control parameters under the target carbon source denitrification rate, based on the process mode of the biological treatment tank to be tested and all permutations and combinations of the operating control parameters.

[0080] The operating modes of the biochemical test tanks include: AAO process and AAOAO process;

[0081] The civil engineering parameters include: anaerobic tank volume data, anoxic tank volume data, aerobic tank volume data, post-anoxic tank volume data, post-aerobic tank volume data, effective water depth data of the biological treatment tank, secondary sedimentation tank volume data, and effective water depth data of the secondary sedimentation tank.

[0082] Furthermore, regarding the operation mode and civil engineering parameters of the biological treatment tank, the operation mode serves to confirm the existence of functionally variable areas in each tank, mainly including the AAO process and the selection of the AAOAO process; the civil engineering parameters include anaerobic tank volume data, anoxic tank volume data, aerobic tank volume data, post-anoxic tank volume data, post-aerobic tank volume data, effective water depth data of the biological treatment tank, secondary sedimentation tank volume data, and effective water depth data of the secondary sedimentation tank. For biological treatment tanks with pre-anoxic, deoxygenated, or facultative zones, this operation decision system includes the effective tank volume of the pre-anoxic zone in the anaerobic zone volume, the deoxygenated zone volume in the aerobic tank volume, and the facultative zone volume in the aerobic zone or post-anoxic zone volume according to the aerobic or anoxic state of the tank at that time.

[0083] In the specific implementation process, the actual parameters can be divided into influent-related parameters, core operating parameters of the biological treatment tank, and water quality parameters along the process. Influent-related parameters include daily average influent temperature data, daily treated water volume data, influent pH data, daily average influent chemical oxygen demand (COD) concentration data, daily average five-day COD concentration data, daily average total nitrogen concentration data, and daily average total phosphorus concentration data. Core operating parameters of the biological treatment tank include mixed liquor suspended solids concentration data, mixed liquor volatile suspended solids concentration data, external reflux ratio data, internal reflux ratio data, and aerobic tank terminal parameters. Dissolved oxygen data, carbon source dosage data, and sludge age data; water quality parameters along the process include total nitrogen concentration data of anaerobic tank effluent, phosphate concentration data at the end of anaerobic tank, nitrate nitrogen concentration data of anaerobic tank effluent, total nitrogen concentration data of anoxic tank effluent, nitrate nitrogen concentration data of anoxic tank effluent, ammonia nitrogen concentration data at the end of anoxic tank, total nitrogen concentration data of aerobic tank effluent, nitrate nitrogen concentration data of post-anoxic tank effluent, total nitrogen concentration data of post-aerobic tank effluent, total nitrogen concentration data of secondary sedimentation tank effluent, total nitrogen concentration data of externally returned sludge, and suspended solids concentration data of returned sludge mixed liquor.

[0084] Furthermore, in the parameter processing module, the design and operating parameters of the water plant involved in the operational decision-making of the biochemical pool under test are input into the pre-constructed parameter processing model to obtain the first data, including:

[0085] Input the operating parameters of the water plant involved in the operation decision of the biological treatment tank into the following formula to obtain the first data:

[0086] Carbon-to-nitrogen ratio = Daily average chemical oxygen demand concentration of influent / Daily average total nitrogen concentration of influent;

[0087] Carbon-to-phosphorus ratio = Daily average chemical oxygen demand concentration of influent / Daily average total phosphorus concentration of influent;

[0088] Total nitrogen volumetric loading of anoxic tank = (average daily total nitrogen concentration of influent - total nitrogen in effluent of secondary sedimentation tank) × daily treated water volume / (anoxic tank volume + post-anoxic tank volume) / 1000;

[0089] Anoxic tank sludge load = (daily average total nitrogen concentration of influent - total nitrogen in effluent of secondary sedimentation tank) × daily treated water volume / (anoxic tank volume + post-anoxic tank volume) / mixed liquor suspended solids concentration in biological treatment tank;

[0090] Actual hydraulic retention time in anaerobic tank = anaerobic tank volume / daily water treatment volume × 24;

[0091] Actual hydraulic retention time in the anoxic tank = anoxic tank volume / daily water treatment volume × 24;

[0092] Actual hydraulic retention time in the aerobic tank = aerobic tank volume / daily water treatment volume × 24;

[0093] Actual hydraulic retention time in the post-anoxic tank = Post-anoxic tank volume / Daily water treatment volume × 24;

[0094] Actual hydraulic retention time of the post-aerobic tank = tank volume of post-aerobic tank / daily water treatment volume × 24;

[0095] Actual total hydraulic retention time of the biological treatment tank = (Anaerobic tank volume + Anoxic tank volume + Aerobic tank volume + Post-anoxic tank volume + Post-aerobic tank volume) / Daily water treatment volume × 24;

[0096] Actual hydraulic retention time in the secondary sedimentation tank = Secondary sedimentation tank volume / Daily water treatment volume × 24;

[0097] Secondary sedimentation tank hydraulic load = Daily water treatment volume / 24 / Secondary sedimentation tank volume × Effective water depth of secondary sedimentation tank;

[0098] Secondary sedimentation tank solid load = daily treated water volume × (1 + external reflux ratio) × biological treatment tank mixed liquor suspended solids concentration / secondary sedimentation tank volume × secondary sedimentation tank effective water depth;

[0099] The first data specifically includes:

[0100] Data on carbon-nitrogen ratio, carbon-phosphorus ratio, total nitrogen volumetric loading in the anoxic tank, total nitrogen sludge loading in the anoxic tank, actual hydraulic retention time in the anaerobic tank, actual hydraulic retention time in the anoxic tank, actual hydraulic retention time in the aerobic tank, actual hydraulic retention time in the post-anoxic tank, actual hydraulic retention time in the post-aerobic tank, actual total hydraulic retention time in the biological treatment tank, actual hydraulic retention time in the secondary sedimentation tank, hydraulic loading data in the secondary sedimentation tank, and solids loading data in the secondary sedimentation tank.

[0101] In the specific implementation process, the operation decision system of this embodiment also includes:

[0102] The traditional external carbon source denitrification operation analysis module is used when the process mode tends to be denitrification mainly by external carbon source. It obtains the actual relevant indicators based on the operating parameters, compares the actual relevant indicators with the theoretical relevant indicators, and judges whether the denitrification capacity of the biological treatment tank under test is normal.

[0103] The second scenario parameter control module is used to obtain the suggested operating values ​​of various relevant indicators as the specific values ​​of the controllable parameters when the denitrification capacity of the biochemical tank under test is abnormal.

[0104] Relevant indicators include: nitrate removal rate in the anoxic tank, internal reflux ratio, external reflux ratio, denitrification rate, and external carbon source addition.

[0105] In the traditional external carbon source denitrification operation analysis module, the specific relevant indicators obtained based on the operating parameters include:

[0106] Actual denitrification rate = (daily average total nitrogen concentration of influent - total nitrogen concentration of effluent from post-aerobic tank) × daily treatment volume × 24 / (volume of anoxic tank + volume of post-anoxic tank) / concentration of suspended solids in mixed liquor of biological tank;

[0107] Actual nitrate removal in the anoxic tank = ((1 + internal recirculation ratio) × nitrate nitrogen in the aerobic tank effluent + (1 + external recirculation ratio) × nitrate nitrogen in the anaerobic tank effluent - nitrate nitrogen in the anoxic tank effluent) / (1 + internal recirculation ratio + external recirculation ratio);

[0108] The actual values ​​of internal reflux ratio, external reflux ratio, and external carbon source dosage are all derived from the corresponding index values ​​in the actual parameters of the parameter input module.

[0109] Furthermore, the theoretically relevant indicators are obtained through a theoretical value calculation algorithm, as follows:

[0110] The theoretical value of denitrification rate = 0.06 × 1.08^(average daily influent temperature - 20°C) / 1.1;

[0111] Theoretical value of nitrate nitrogen removal in anoxic tank = Theoretical value of denitrification rate × Actual hydraulic retention time in anoxic tank × Concentration of suspended solids in mixed liquor in biological tank / 1000;

[0112] Theoretical value of external return ratio = concentration of suspended solids in mixed liquor of biological treatment tank / (concentration of suspended solids in mixed liquor of returned sludge - concentration of suspended solids in mixed liquor of biological treatment tank);

[0113] Theoretical value of internal recirculation ratio = (daily average total nitrogen concentration of influent - total nitrogen concentration of secondary sedimentation tank effluent) / daily average total nitrogen concentration of influent / (1 - (daily average total nitrogen concentration of influent - total nitrogen concentration of secondary sedimentation tank effluent) / daily average total nitrogen concentration of influent) - theoretical value of external recirculation ratio;

[0114] Theoretical value of external carbon source addition = (5 - carbon-nitrogen ratio) × (daily average total nitrogen concentration of influent - total nitrogen concentration of secondary sedimentation tank effluent) × daily treated water volume / 1.131 / 200000;

[0115] Specifically, whether the denitrification capacity is normal refers to the following: when the actual value of any of the above-mentioned relevant indicators, such as the denitrification rate and the amount of nitrate nitrogen removed in the anoxic tank, is more than 20% lower than the theoretical value, and the actual value of any of the external reflux ratio, internal reflux ratio, and external carbon source addition is more than 20% higher than the theoretical value, the assessment result is abnormal; otherwise, the assessment is normal.

[0116] When the evaluation result is abnormal, recommended operating values ​​for each relevant indicator are derived according to the calculation algorithm, and these recommended operating values ​​are used as the specific values ​​for the adjustable parameters. When the evaluation result is normal, no adjustment of the adjustable parameters is required.

[0117] In this embodiment, see Figure 3The process mode determination module includes:

[0118] The actual internal carbon source denitrification rate acquisition unit is used to input actual parameters into the following formula to obtain the actual internal carbon source denitrification rate of the biological treatment tank under test:

[0119]

[0120] Where A is the actual internal carbon source denitrification rate, R is the external recirculation ratio, r is the internal recirculation ratio, TN1 is the total nitrogen concentration data of the anoxic tank effluent, TN2 is the total nitrogen concentration data of the aerobic tank effluent, TN3 is the total nitrogen concentration data of the post-aerobic tank effluent, TN4 is the total nitrogen concentration data of the secondary sedimentation tank effluent, TN5 is the total nitrogen concentration data of the externally recirculated sludge, and TN... 进水 This refers to the daily average total nitrogen concentration data for the influent;

[0121] The theoretical maximum internal carbon source denitrification rate acquisition unit is used to retrieve the design parameters, operating parameters and first data into the pre-constructed first internal carbon source denitrification general relationship model to obtain the theoretical maximum internal carbon source denitrification rate.

[0122] The judgment unit is used to determine the process mode of the biochemical pool to be tested based on the pre-constructed judgment rules.

[0123] In the judgment unit, the pre-constructed judgment rules are as follows:

[0124] When both the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate are less than 20%, the denitrification process tends to be dominated by traditional external carbon source denitrification.

[0125] When the actual internal carbon source denitrification rate is higher than 20% and higher than the theoretical maximum internal carbon source denitrification rate, it tends to cause denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters are optimal.

[0126] When the theoretical maximum internal carbon source denitrification rate is higher than 20% and higher than the actual internal carbon source denitrification rate, it tends to undergo denitrification mainly based on enhanced internal carbon source denitrification and has room for optimization.

[0127] The operational decision-making system in this embodiment also includes:

[0128] The model training module is used to train the general relationship model of first internal carbon source denitrification using the first training dataset to obtain the trained general relationship model of first internal carbon source denitrification; to train the trained general relationship model of first internal carbon source denitrification a second time using the second training dataset to obtain the specific relationship model of second internal carbon source denitrification; and to train the specific relationship model of second internal carbon source denitrification again using the third training dataset to obtain the complete relationship model of third internal carbon source denitrification.

[0129] The first training dataset consists of historical actual parameters of any water plant involved in the operation decision of the biological treatment tank under test for the past six months to one year, as well as the historical actual internal carbon source denitrification rate.

[0130] The second training dataset consists of historical actual parameters of the water plant currently involved in the operation decision of the biological treatment tank under test for the past six months to one year, as well as the historical actual internal carbon source denitrification rate.

[0131] The third training dataset consists of the historical actual parameters and historical actual internal carbon source denitrification rates of all water plants that participated in the operation decision of the biological treatment tanks under test in the past two years, when the plants tended to undergo denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters were optimal.

[0132] The general relationship model of the first internal carbon source denitrification, the specific relationship model of the second internal carbon source denitrification, and the perfect relationship model of the third internal carbon source denitrification are all deep learning models.

[0133] Specifically, the initial model is first trained using the first training dataset to obtain the first internal carbon source denitrification general relationship model. This step enables the model to understand the basic dynamics within a single wastewater treatment plant and establish basic prediction capabilities.

[0134] Next, the model that has been initially trained will be trained again using the second training dataset. The purpose is to allow the model to learn from the data of multiple sewage treatment plants, enhance its general applicability, and improve its adaptability to the differences between different water treatment plants.

[0135] Finally, the model was retrained using the third training dataset. This retraining was to ensure the comprehensiveness of the model and to fine-tune it, thereby obtaining a final comprehensive and high-precision model of the relationship between the third internal carbon source and denitrification.

[0136] Through the above three-stage training process and three models, it is possible to improve accuracy and operational efficiency while reducing data processing complexity.

[0137] Furthermore, when the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters are optimal, the current actual parameters are obtained, and the current actual parameters and the current actual internal carbon source denitrification rate are used as a set of data in the third training dataset to train the second internal carbon source denitrification specific relationship model.

[0138] In this embodiment, the first scene parameter control module includes:

[0139] The adjustable parameter sorting unit is used to reorder all the permutations and combinations of operating control parameters under the target carbon source denitrification rate according to the adjustable parameter range, based on all permutations and combinations of operating control parameters under the target carbon source denitrification rate and the operating business scenario of the biochemical tank to be tested, to obtain the optimal permutation and combination.

[0140] The operating control parameters include: the concentration of suspended solids in the mixed liquor of the biological treatment tank, the concentration of volatile suspended solids in the mixed liquor of the biological treatment tank, the external reflux ratio, the internal reflux ratio, the dissolved oxygen concentration, the carbon source dosage, and the sludge age;

[0141] The adjustable parameter range acquisition unit is used to input the optimal permutation and combination into the pre-set adjustable parameter range operation model to obtain the control range of the optimal permutation and combination of operation control parameters under the target carbon source denitrification rate.

[0142] In this embodiment, in the first scene parameter control module,

[0143] The operational scenarios of the biochemical pool under test are obtained based on the daily average temperature data and the first data in the actual parameters;

[0144] Specifically, the operational scenarios of the biochemical pool under test are obtained based on the daily average temperature data from the actual parameters and the carbon-nitrogen ratio data from the first data set. There are nine operational scenarios in total. The order of the operational control parameters under these nine scenarios refers to the order in which the parameters affecting the operational control parameters are adjusted in each operational scenario. The nine operational scenarios are detailed in Table 1.

[0145]

[0146] Furthermore, in the first scenario parameter control module, after classifying the business scenarios, the sorting results under the current business scenario are obtained, and all permutations and combinations of operating control parameters that meet the current water quality conditions and the user's expected target carbon source denitrification rate are calculated according to the sorting results and the corresponding gradients based on the pre-set adjustable parameter range calculation algorithm, so as to obtain the control range of operating control parameters and the optimal permutation and combination that meet the current water quality conditions and the user's expected target carbon source denitrification rate.

[0147] The corresponding control gradients for the operating control parameters are detailed in the table below:

[0148]

[0149] The operational decision-making system for determining the process mode and adjusting parameters of a biochemical pool in this embodiment also includes: an operational decision-making module, used to output relevant results;

[0150] The output will be any one of the following:

[0151] The process mode tends to be denitrification mainly by enhanced internal carbon source denitrification, and the current operating parameters are optimal and their corresponding current actual parameters are also optimal.

[0152] The process mode tends to achieve denitrification mainly through enhanced internal carbon source denitrification, and has optimization space and corresponding operating control parameters and the control range of optimal arrangement and combination.

[0153] The process mode tends to undergo denitrification mainly by traditional external carbon source denitrification and the denitrification capacity is abnormal. The recommended operating values ​​for the corresponding relevant indicators are as follows:

[0154] The process mode tends to involve denitrification mainly through traditional external carbon source denitrification, and the denitrification capacity is normal.

[0155] This embodiment presents an operational decision-making system for determining the process mode and adjusting parameters of a biological treatment tank. This system provides operators with the optimal process operation mode of the biological treatment unit and the operational parameters of controllable key indicators under different influent water quality conditions. It saves some carbon sources while improving the denitrification efficiency of the biological treatment tank, achieving good economic benefits and helping to improve the level of refined and intelligent operation of wastewater treatment plants.

[0156] Example 3

[0157] The operational decision-making system for determining the process mode and adjusting parameters of a biochemical pool in this embodiment is any one of the operational decision-making systems for determining the process mode and adjusting parameters of a biochemical pool in Embodiment 1 and Embodiment 2. It will not be described again here, but only the different situations in this invention are described.

[0158] The operational decision-making system for determining the process mode and adjusting parameters in the biochemical pool according to this invention includes three scenarios, as detailed below:

[0159] (1) The process mode judgment module judges that the process mode tends to be denitrification with enhanced internal carbon source denitrification and the current operating parameters are optimal. At this time, the current actual parameters are obtained, and the current actual parameters and the current actual internal carbon source denitrification rate are used as a set of data in the third training dataset to train the second internal carbon source denitrification specific relationship model.

[0160] (2) The process mode judgment module judges that the process mode tends to be denitrification with enhanced internal carbon source denitrification and has optimization space. At this time, it enters the internal carbon source denitrification relationship analysis module to retrieve the target internal carbon source denitrification rate and civil engineering parameters, and inputs them into the pre-constructed third internal carbon source denitrification perfect relationship model to obtain all permutations and combinations of operating control parameters under the target internal carbon source denitrification rate. Then, it enters the first scenario parameter control module to obtain the control range of the optimal permutation and combination of operating control parameters under the target internal carbon source denitrification rate.

[0161] (3) The process mode judgment module judges that the process mode tends to be denitrification mainly by traditional external carbon source denitrification. At this time, it enters the traditional external carbon source denitrification operation analysis module to judge whether the denitrification capacity of the biological tank under test is normal. If it is not normal, it enters the second scenario parameter control module to obtain the operation suggestion value of each relevant indicator as the specific value of the controllable parameter; if it is normal, it outputs that the operation is normal.

[0162] This embodiment sets up three different treatment processes, which not only improves the level of refined management of the denitrification process in wastewater treatment, but also maximizes the use of existing resources by precisely controlling and optimizing operating parameters, thereby reducing costs and improving economic efficiency.

[0163] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0164] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0165] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "over," or "on top" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," or "beneath" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0166] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0167] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A biochemical pond process mode judgment and parameter regulation operation decision system, characterized in that, include: The parameter input module is used to obtain the design parameters and operating parameters of the water plant that are involved in the operation decision of the biochemical pool under test; The design parameters include the civil engineering parameters of the biochemical pool to be tested; The operating parameters include: the operating mode, actual parameters, and target internal carbon source denitrification rate of the biological treatment tank under test; The parameter processing module is used to input the design parameters and operating parameters of the water plant participating in the operation decision of the biochemical pool under test into the pre-constructed parameter model to obtain the first data; The process mode determination module is used to obtain the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test based on the actual parameters and the first data, and to determine the process mode of the biological treatment tank under test based on the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate of the biological treatment tank under test. The process mode determination module includes: The actual internal carbon source denitrification rate acquisition unit is used to input the actual parameters into the following formula to obtain the actual internal carbon source denitrification rate of the biological treatment tank under test: ； Where A is the actual internal carbon source denitrification rate, R is the external recirculation ratio, r is the internal recirculation ratio, TN1 is the total nitrogen concentration data of the anoxic tank effluent, TN2 is the total nitrogen concentration data of the aerobic tank effluent, TN3 is the total nitrogen concentration data of the post-aerobic tank effluent, TN4 is the total nitrogen concentration data of the secondary sedimentation tank effluent, TN5 is the total nitrogen concentration data of the externally recirculated sludge, and TN... 进水 This refers to the daily average total nitrogen concentration data for the influent; The theoretical maximum internal carbon source denitrification rate acquisition unit is used to retrieve the design parameters, operating parameters and first data into the pre-constructed first internal carbon source denitrification general relationship model to obtain the theoretical maximum internal carbon source denitrification rate. The internal carbon source denitrification relationship analysis module is used to retrieve the target internal carbon source denitrification rate and civil engineering parameters when the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and there is room for optimization. The target internal carbon source denitrification rate and civil engineering parameters are then input into the pre-constructed third internal carbon source denitrification perfect relationship model to obtain all permutations and combinations of operating control parameters under the target internal carbon source denitrification rate. The first scenario parameter control module is used to obtain the suitable process mode for the biological treatment tank under the current water quality and the control range of the optimal combination of operating control parameters under the current process mode and the operating control parameters under the target carbon source denitrification rate, based on the process mode of the biological treatment tank to be tested and all permutations and combinations of the operating control parameters.

2. The operational decision-making system for determining the process mode and adjusting parameters of the biochemical tank according to claim 1, characterized in that, The operating modes of the biochemical test tank include: AAO process and AAOAO process; The civil engineering parameters include: anaerobic tank volume data, anoxic tank volume data, aerobic tank volume data, post-anoxic tank volume data, post-aerobic tank volume data, effective water depth data of the biological treatment tank, secondary sedimentation tank volume data, and effective water depth data of the secondary sedimentation tank. The actual parameters include: daily average influent temperature data, daily treated water volume data, influent pH data, daily average influent chemical oxygen demand (COD) concentration data, daily average five-day biochemical oxygen demand (BOD) concentration data, daily average influent total nitrogen concentration data, daily average influent total phosphorus concentration data, mixed liquor suspended solids concentration data in the biological treatment tank, mixed liquor volatile suspended solids concentration data in the biological treatment tank, external recirculation ratio data, internal recirculation ratio data, dissolved oxygen at the end of the aerobic tank data, carbon source dosage data, sludge age data, and effluent from the anaerobic tank. Data on total nitrogen concentration in water, phosphate concentration at the end of the anaerobic tank, nitrate nitrogen concentration in the effluent from the anaerobic tank, total nitrogen concentration in the effluent from the anoxic tank, nitrate nitrogen concentration in the effluent from the anoxic tank, ammonia nitrogen concentration at the end of the anoxic tank, total nitrogen concentration in the effluent from the aerobic tank, nitrate nitrogen concentration in the effluent from the aerobic tank, total nitrogen concentration in the effluent from the post-anoxic tank, total nitrogen concentration in the effluent from the post-aerobic tank, total nitrogen concentration in the effluent from the secondary sedimentation tank, total nitrogen concentration in the externally returned sludge, and suspended solids concentration in the mixed liquor of the returned sludge.

3. The biochemical pond process mode judgment and parameter regulation operation decision system according to claim 1, characterized in that, The system also includes: The traditional external carbon source denitrification operation analysis module is used to obtain actual relevant indicators based on the operating parameters when the process mode tends to be denitrification mainly caused by external carbon source denitrification. The actual relevant indicators are compared with theoretical relevant indicators to determine whether the denitrification capacity of the biological treatment tank under test is normal. The second scenario parameter control module is used to obtain the suggested operating values ​​of various relevant indicators as the specific values ​​of the controllable parameters when the denitrification capacity of the biochemical tank under test is abnormal. The relevant indicators include: nitrate removal rate in the anoxic tank, internal reflux ratio, external reflux ratio, denitrification rate, and external carbon source addition.

4. The biochemical pond process mode judgment and parameter regulation operation decision system according to claim 3, characterized in that, In the traditional external carbon source operation analysis module, determining whether the denitrification capacity of the tested biochemical tank is normal includes: When the actual value of any of the following values ​​is more than 20% lower than the theoretical value: either the denitrification rate or the amount of nitrate nitrogen removed in the anoxic tank, or the actual value of any of the following values ​​is more than 20% higher than the theoretical value: either the external reflux ratio, the internal reflux ratio, or the amount of external carbon source added, the evaluation result is considered abnormal; otherwise, the evaluation is considered normal.

5. The biochemical pond process mode judgment and parameter regulation operation decision system according to claim 1, characterized in that, The process mode determination module also includes: The judgment unit is used to determine the process mode of the biochemical pool to be tested based on the pre-constructed judgment rules.

6. The biochemical pond process mode judgment and parameter regulation operation decision system according to claim 5, characterized in that, The judgment rule pre-constructed in the judgment unit is as follows: When both the actual internal carbon source denitrification rate and the theoretical maximum internal carbon source denitrification rate are less than 20%, the denitrification process tends to be dominated by traditional external carbon source denitrification. When the actual internal carbon source denitrification rate is higher than 20% and higher than the theoretical maximum internal carbon source denitrification rate, it tends to cause denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters are optimal. When the theoretical maximum internal carbon source denitrification rate is higher than 20% and higher than the actual internal carbon source denitrification rate, it tends to undergo denitrification mainly based on enhanced internal carbon source denitrification and has room for optimization.

7. The biochemical pond process mode judgment and parameter regulation operation decision system according to claim 5, characterized in that, The system also includes: The model training module is used to train the general relationship model of first internal carbon source denitrification using the first training dataset to obtain the trained general relationship model of first internal carbon source denitrification; to train the trained general relationship model of first internal carbon source denitrification a second time using the second training dataset to obtain the specific relationship model of second internal carbon source denitrification; and to train the specific relationship model of second internal carbon source denitrification again using the third training dataset to obtain the complete relationship model of third internal carbon source denitrification. The first training dataset consists of historical actual parameters of any water plant involved in the operation decision of the biological treatment pond under test, which are the actual internal carbon source denitrification rate over the past six months to one year. The second training dataset consists of historical actual parameters of the water plant currently participating in the operation decision of the biological treatment tank under test for the past six months to one year, as well as the historical actual internal carbon source denitrification rate. The third training dataset consists of the historical actual parameters and historical actual internal carbon source denitrification rates of all water plants that participated in the operation decision of the biological treatment tanks under test in the past two years, when they tended to undergo denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters were optimal. The first internal carbon source denitrification general relationship model, the second internal carbon source denitrification specific relationship model, and the third internal carbon source denitrification perfect relationship model are deep learning models.

8. The operational decision-making system for determining the process mode and controlling parameters of a biological treatment tank according to claim 7, characterized in that, When the process mode tends to undergo denitrification mainly by enhancing internal carbon source denitrification and the current operating parameters are optimal, the current actual parameters are obtained, and the current actual parameters and the current actual internal carbon source denitrification rate are used as a set of data in the third training dataset to train the second internal carbon source denitrification specific relationship model.

9. The operational decision-making system for determining the process mode and controlling parameters of a biological treatment tank according to claim 2, characterized in that, The first scene parameter control module includes: The adjustable parameter sorting unit is used to filter all the possible combinations of operating control parameters under the target carbon source denitrification rate according to the adjustable parameter range, based on all permutations and combinations of operating control parameters under the target carbon source denitrification rate and the operating business scenario of the biochemical tank to be tested, to obtain the optimal permutation and combination. All operating control parameters include: mixed liquor suspended solids concentration in the biological treatment tank, volatile suspended solids concentration in the mixed liquor in the biological treatment tank, external reflux ratio, internal reflux ratio, dissolved oxygen concentration, carbon source dosage, and sludge age; The adjustable parameter range acquisition unit is used to input the optimal permutation and combination into a pre-set adjustable parameter range operation model to obtain the control range of the optimal permutation and combination of operation control parameters under the target carbon source denitrification rate.

10. The operational decision-making system for determining the process mode and controlling parameters of a biological treatment tank according to claim 9, characterized in that, In the first scenario parameter control module, the operating scenario of the biochemical pool to be tested is obtained based on the daily average temperature data and the first data in the actual parameters; The first data includes: carbon-nitrogen ratio data, carbon-phosphorus ratio data, total nitrogen volumetric load data of the anoxic tank, total nitrogen sludge load data of the anoxic tank, actual hydraulic retention time data of the anaerobic tank, actual hydraulic retention time data of the anoxic tank, actual hydraulic retention time data of the aerobic tank, actual hydraulic retention time data of the post-anoxic tank, actual hydraulic retention time data of the post-aerobic tank, actual total hydraulic retention time data of the biological treatment tank, actual hydraulic retention time data of the secondary sedimentation tank, hydraulic load data of the secondary sedimentation tank, and solids load data of the secondary sedimentation tank.

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