Intelligent sludge discharge and dewatering dosing control system for sewage plant based on big data analysis

Abstract

The application discloses a sewage plant intelligent sludge discharge and dewatering and dosing control system based on big data analysis, which comprises a big data analysis control platform connected with each water quality and quantity monitoring facility, each sludge pump, each sludge flowmeter, each microwave method sludge concentration meter, the dewatering equipment, the dosing pump and the dosing flowmeter through communication cables; and the big data analysis control platform controls after analyzing and calculating the data obtained from the communication cables. The application establishes a big data analysis control platform of sewage plant mechanism and data fusion, starts from the whole of sludge discharge, dewatering and dosing, accurately and dynamically controls sludge concentration, sludge dewatering and dosing amount, reduces the operation load of the dewatering equipment and the dosing amount, and achieves the effect of intelligent low-carbon operation.

Description

Technical Field

[0001] This invention relates to the field of wastewater and sludge treatment technology, and in particular to an intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis. Background Technology

[0002] Wastewater treatment plants are important public welfare projects, and sludge from these plants is an inevitable byproduct of wastewater treatment. The transportation and treatment of sludge is a key focus of wastewater treatment plants. Generally, each sedimentation tank and advanced treatment unit in a wastewater treatment plant discharges or recycles a portion of the sludge, which is then transported to a thickening or storage tank, and subsequently pumped to dewatering or drying incineration equipment for volume reduction before being disposed of off-site.

[0003] Traditional sludge pumps operate intermittently, with start and stop times set according to schedule. This simple and crude operating method can easily lead to untimely sludge discharge, posing a risk of sludge spillage and affecting the effluent quality of wastewater treatment plants. Alternatively, excessive sludge discharge can result in excessively low solids content in the discharged sludge, increasing the operating load of the thickening and dewatering equipment, as well as increasing energy consumption, as well as the consumption of chemicals and labor.

[0004] Moreover, in order to ensure that the dewatered sludge meets the required moisture content, traditional wastewater treatment plants often add excessive amounts of flocculants, resulting in serious waste of resources and contradicting my country's dual-carbon policy.

[0005] Therefore, how to achieve intelligent and optimal sludge discharge and dewatering dosing from the overall perspective of the entire sludge discharge and dewatering dosing system, improve the efficiency and stability of sludge discharge and dewatering, and at the same time save energy, reduce consumption, and operate in a low-carbon manner has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of the above-mentioned deficiencies of the prior art, the present invention provides an intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis. The purpose is to achieve intelligent and optimal sludge discharge and dewatering dosing from the overall perspective of the entire sludge discharge and dewatering dosing system, thereby improving the efficiency and stability of sludge discharge and dewatering, while saving energy, reducing consumption, and operating in a low-carbon manner.

[0007] To achieve the above objectives, this invention discloses an intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis, including wastewater treatment facilities, microwave online monitoring of sludge concentration, online monitoring of wastewater quality and quantity, as well as sludge flow meters and dosing flow meters;

[0008] The wastewater treatment facility is connected to a sludge storage tank, a thickening tank or a conditioning tank via sludge pipes, and then connected to dewatering equipment.

[0009] The online monitoring of wastewater quality and quantity is a water quality and quantity monitoring facility installed in the pretreatment and advanced treatment sections of the wastewater treatment facility.

[0010] Between the pretreatment section and the deep treatment section is a biochemical reaction and sedimentation tank;

[0011] The deep treatment section, the biochemical reaction and sedimentation tank, and the pretreatment section are respectively connected to the sludge storage tank, the thickening tank, or the conditioning tank through the sludge pipe. The sludge pipe connected to the sludge storage tank is equipped with a sludge pump, a sludge flow meter, and a microwave sludge concentration meter.

[0012] The three microwave sludge concentration meters installed between the deep treatment section, the biochemical reaction and sedimentation tank, the pretreatment section and the sludge storage tank are for online monitoring of microwave sludge concentration.

[0013] The sludge pipe between the sludge storage tank and the dewatering equipment is sequentially equipped with the sludge pump, the sludge flow meter, and the microwave sludge concentration meter.

[0014] The dewatering equipment is connected to a dosing pump via a dosing pipe, and a microwave sludge concentration meter is installed at the sludge outlet.

[0015] The dosing pipe is equipped with the dosing flow meter;

[0016] It also includes a big data analysis and control platform that connects each of the water quality and quantity monitoring facilities, each of the sludge pumps, each of the sludge flow meters, each of the microwave sludge concentration meters, the dewatering equipment, the dosing pumps and the dosing flow meters via communication cables;

[0017] The big data analysis and control platform analyzes and calculates the data obtained from the communication cable before performing control, specifically as follows:

[0018] Step 1: The big data analysis and control platform acquires the data accumulated in the early stage;

[0019] The aforementioned preliminary data accumulation refers to historical data or data obtained through simulation using the mechanistic model formula 2, specifically including:

[0020] The parameters for influent / effluent flow rate, SS, pH / ORP, BOD, COD, NH3-N, TP, and TN of each part of the wastewater treatment facility are as follows:

[0021] The operating status, frequency, and power of the dosing pump, the dewatering equipment, and each of the sludge pumps.

[0022] The instantaneous values ​​of each microwave sludge concentration meter and each sludge flow meter;

[0023] Step 2: Establish a basic database and store the previously accumulated data into the basic database;

[0024] Step 3: Search the basic database to determine whether each set of data is complete; if any set of data has missing values, search the next set of data; if any set of data is complete, perform the subsequent steps for each complete set of data.

[0025] Step 4: Search the basic database and perform distortion and compliance judgment on each complete set of data;

[0026] Step 5: Establish an effective database by storing all complete data from all groups that have passed the distortion and compliance checks into the effective database;

[0027] Step 6: Perform data preprocessing and dimensionality reduction on all the data in the effective database;

[0028] Step 7: Perform cluster analysis and association rule analysis;

[0029] Step 8: Establish a machine learning operation model through big data analysis and robot learning methods, and provide a variety of sludge discharge and dewatering chemical control strategies to meet the standards for effluent, sludge discharge and dewatering solids content.

[0030] Step 9: Using a low-carbon assessment model, conduct a low-carbon assessment of all the sludge discharge and dewatering dosing control strategies, provide the carbon emission value for each of the sludge discharge and dewatering dosing control strategies, and provide the optimal control strategy.

[0031] Step 10: Run the optimal control strategy on the big data analysis and control platform.

[0032] Preferably, each of the water quality and quantity monitoring facilities is installed in the corresponding pretreatment section or advanced treatment section via a wastewater sampling pipe;

[0033] The sludge pump, the dewatering equipment, the dosing pump, the sludge flow meter and the microwave sludge concentration meter installed on the sludge pipe between the sludge pump and the dewatering equipment, the sludge flow meter installed on the dosing pipe between the dosing pump and the dewatering equipment, and the microwave sludge concentration meter at the sludge outlet of the dewatering equipment constitute an intelligent sludge discharge and dewatering dosing control module.

[0034] Preferably, each of the sludge pumps and the dosing pumps is a variable frequency pump.

[0035] Each of the sludge pumps and the dosing pumps is a variable frequency pump, which makes it easier to accurately control the amount of sludge discharged and the amount of chemicals added.

[0036] Preferably, the big data analysis and control platform includes a hardware support layer, a data support layer, and an application service layer;

[0037] The hardware support layer provides the necessary support hardware for the big data analysis and control platform, including servers, storage devices, communication devices, computers, display devices, and instruments and meters for water quality and quantity, sludge concentration and flow rate, and chemical dosing flow rate.

[0038] The data support layer classifies and stores the collected data, and establishes corresponding models to process, analyze, calculate and evaluate the data, including a basic database, an effective database, a mechanism model, a low-carbon assessment model and a big data decision optimization and control model.

[0039] The application service layer provides wastewater treatment plant supervisors with data display and interaction capabilities, including display of calculation results, display of energy-saving effects, monitoring and management of various sludge dewatering and dosing equipment, and human-computer interaction.

[0040] Preferably, step 4 is as follows:

[0041] Step 4.1: Determining whether the data in each group is distorted means judging whether the dosage data Q0 satisfies the following formula; if it does not satisfy the formula, the data is distorted; if it does satisfy the formula, continue to the next step.

[0042]

[0043] Among them, Q y This refers to the dosage of the medicine.

[0044] Both x% and y% are valid judgment standard coefficients, with values ​​ranging from 10% to 50%.

[0045] Step 4.2, the specific determination of whether each group of data meets the standard is as follows:

[0046] If any one or more of the following parameters—SS, pH / ORP, BOD, COD, NH3-N, TP, and TN—fail to meet the standards in any set of data at any sampling time, then the control strategy for the sludge pump at the previous sampling time is not advisable.

[0047] If the microwave sludge concentration meter at the sludge outlet of the dewatering equipment detects that the sludge moisture content is below standard in any set of data at any of the sampling times, then the dosage control strategy at the previous sampling time is not advisable.

[0048] Step 4.3: Summarize the data from all groups that passed the distortion and compliance tests.

[0049] More preferably, the dosage value Q y The calculation is performed using a mechanistic model formula, as follows:

[0050]

[0051] Among them, Q y For tn Instantaneous flow rate of the drug during a given time period, in units of m³. 3 / h;

[0052] F t The instantaneous flow rate of the dewatering equipment at time t is given in m³. 3 / h;

[0053] D t The sludge concentration at time t is the sludge concentration at the inlet of the dewatering equipment, expressed in %;

[0054] For t n Dry sludge volume over a given period;

[0055] D y The concentration of the prepared reagent is expressed in %;

[0056] k is the dosing coefficient, which is the ratio of dry mud quantity to dosing quantity. The general empirical value is 3 to 5.0, and it can be adjusted.

[0057] Preferably, in step 6, a discrete standardization process is used to preprocess all the data in the effective database;

[0058] The random forest algorithm is used to perform dimensionality reduction on all the data in the effective database.

[0059] Preferably, in step 7, each data object in the effective database after data preprocessing and dimensionality reduction is divided into m clusters, and the sum of squares of all data points in each cluster to the corresponding cluster center is minimized.

[0060] The correlation between the data after the clustering analysis is mined using the Apriori algorithm, and a specific classification boundary is defined for each data after the clustering analysis. Then, the minimum support and confidence are analyzed using an association rule analysis algorithm.

[0061] Preferably, in step 8, an LSTM model or an RNN-LSTM model is used, and the number of hidden layer units, learning rate, and number of iterations are adjusted and optimized. The equipment operation status is tracked, and intelligent analysis and calculation are performed based on the real-time collected production data to provide multiple sludge discharge and dewatering dosing control strategies that meet the standards for effluent and dewatering rate, including the control commands of the sludge pump, the frequency control commands of the sludge pump, the control commands of the dosing pump, and the frequency control commands of the dosing pump.

[0062] Preferably, in step 9, the control strategy with the lowest comprehensive carbon emission value is selected as the optimal control strategy given by the expert decision optimization model.

[0063] Preferably, the low-carbon assessment model converts the power consumption, chemical consumption, and greenhouse gas emissions of all the sludge pumps, dewatering equipment, and dosing pumps into a comprehensive carbon emission value, and assesses the carbon emission value of various sludge discharge and dewatering dosing control strategies. The specific formula is as follows:

[0064]

[0065] Among them, CE Z The comprehensive carbon emission value of the dry basis of sludge treated per unit of time period t0 is expressed in kgCO2-eq / TDS.

[0066] p is the carbon emission value coefficient for electricity consumption, which is generally set to 1 and is adjustable.

[0067] q is the carbon emission coefficient for pharmaceutical consumption, which is generally set to 1 and is adjustable;

[0068] r is the greenhouse gas carbon emission coefficient, which is generally set to 1 and is adjustable.

[0069] The total power consumption of all the sludge pumps, the dewatering equipment, and the dosing pumps during time period t0 is expressed in kWh.

[0070] EF n The regional electricity emission factor is expressed in kgCO2-eq / kWh.

[0071] The dry basis weight of sludge treated in time period t0 is expressed in TDS.

[0072] The dosage of the j-th agent during time period t0 is expressed in m³. 3 ;

[0073] EF j Let J be the emission factor of the j-th reagent, in kgCO2-eq / m³. 3 ;

[0074] CES qt The carbon emission value is the value of greenhouse gas emissions. Greenhouse gases mainly include CO2, CH4, and N2O, expressed as kgCO2-eq. Since CO2, CH4, and N2O are difficult to measure, if the on-site carbon emission value requirements are not high, CES (Carbon Emissions Standard) may be used. qt It can be ignored, or the percentage of the dry basis of sludge can be taken according to experience.

[0075] The beneficial effects of this invention are:

[0076] This invention takes a holistic approach to the entire sludge removal and dewatering chemical dosing system, achieving intelligent and optimal sludge removal and dewatering chemical dosing, improving sludge removal and dewatering efficiency and stability, while simultaneously saving energy, reducing consumption, and operating in a low-carbon manner.

[0077] This invention establishes a big data analysis and control platform that integrates the mechanisms and data of wastewater treatment plants. Starting from the overall process of sludge discharge, dewatering, and chemical dosing, it precisely and dynamically controls the sludge discharge concentration and the amount of chemicals used for sludge dewatering, thereby reducing the operating load and dosage of dewatering equipment and achieving intelligent and low-carbon operation.

[0078] This invention establishes a mechanism model, a low-carbon assessment model, and a big data decision-making optimization control model, and achieves deep integration of mechanism and data models, making the intelligent low-carbon optimization control system for sludge discharge and dewatering dosing more intelligent and the control strategy more precise.

[0079] This invention innovatively proposes and establishes a low-carbon assessment model, implementing the dual-carbon policy down to the level of sludge discharge and dewatering chemical control, thereby achieving energy conservation and emission reduction from the ground up, and promoting low-carbon environmental protection.

[0080] This invention establishes a mechanism model for sludge discharge and dewatering dosing. In the early stages, relevant data can be accumulated through the operation of this model, and the model can also be used to determine whether the data is distorted or invalid. This solves the problem of lack of data analysis and data validity judgment in the early stages of big data decision optimization control models.

[0081] This invention employs microwave sludge concentration meters to measure sludge concentration online in real time at sludge discharge pipes, dewatering machine inlet pipes, and sludge outlet pipes. This method offers high detection accuracy and reliable data, providing reliable real-time data for big data analysis and control platforms, optimizing sludge discharge and return flow, saving energy and reducing consumption, and lowering the load on sludge treatment facilities.

[0082] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0083] Figure 1 This diagram illustrates the connection between a wastewater treatment facility, a dosing device, a sludge storage tank, a thickening tank or a conditioning tank, and a big data analysis and control platform in one embodiment of the present invention.

[0084] Figure 2 This diagram illustrates the structure of a big data analysis and control platform according to an embodiment of the present invention.

[0085] Figure 3 The diagram illustrates the workflow of a big data analysis and control platform according to an embodiment of the present invention.

[0086] The system includes: 1. Big data analysis and control platform; 2. Wastewater treatment facilities; 2-1. Pretreatment section; 2-2. Biochemical reaction and sedimentation tank; 2-3. Advanced treatment section; 3. Water quality and quantity monitoring facilities; 4. Sludge pump; 5. Sludge flow meter; 6. Microwave sludge concentration meter; 7. Sludge storage tank, thickening tank or conditioning tank; 8. Dewatering equipment; 9. Dosing pump; 10. Intelligent sludge discharge and dewatering dosing control module; 11. Dosing flow meter; 12. Sludge pipe. ; 13. Dosing pipe; 14. Communication cable; 101. Server; 102. Storage device; 103. Communication equipment; 104. Computer; 105. Display device; 111. Basic database; 112. Effective database; 113. Mechanism model; 114. Low-carbon assessment model; 115. Big data decision-making optimization control model; 121. Calculation result display; 122. Energy saving effect display; 123. Monitoring and management; 124. Human-computer interaction. Detailed Implementation

[0087] Example

[0088] like Figure 1 and Figure 3 As shown, the intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis includes wastewater treatment facility 2, microwave sludge concentration online monitoring and wastewater quality and quantity online monitoring (these two features are highlighted in red below), as well as sludge flow meter 5 and dosing flow meter 11.

[0089] The wastewater treatment facility 2 is connected to the sludge storage tank, thickening tank or conditioning tank 7 through the sludge pipe 12, and then connected to the dewatering equipment 8.

[0090] The wastewater quality and quantity online monitoring system is the wastewater quality and quantity monitoring system 3 installed in the pretreatment section 2-1 and the advanced treatment section 2-3 of the wastewater treatment facility 2;

[0091] Between the pretreatment section 2-1 and the advanced treatment section 2-3 is the biochemical reaction and sedimentation tank 2-2;

[0092] The deep treatment section 2-3, the biochemical reaction and sedimentation tank 2-2, and the pretreatment section 2-1 are respectively connected to the sludge storage tank, the thickening tank or the conditioning tank 7 through the sludge pipe 12. The sludge pipe 12 connected to the sludge storage tank, the thickening tank or the conditioning tank 7 is equipped with a sludge pump 4, a sludge flow meter 5 and a microwave sludge concentration meter 6.

[0093] Three microwave sludge concentration meters 6 are installed between the deep treatment section 2-3, the biochemical reaction and sedimentation tank 2-2, the pretreatment section 2-1 and the sludge storage tank, thickening tank or conditioning tank 7 for online monitoring of microwave sludge concentration.

[0094] The sludge pipe 12 between the sludge storage tank, thickening tank or conditioning tank 7 and the dewatering equipment 8 is equipped with a sludge pump 4, a sludge flow meter 5 and a microwave sludge concentration meter 6 in sequence.

[0095] The dewatering equipment 8 is connected to the dosing pump 9 via the dosing pipe 13, and a microwave sludge concentration meter 6 is installed at the sludge outlet.

[0096] The dosing pipe 13 is equipped with a dosing flow meter 11;

[0097] It also includes a big data analysis and control platform 1 that connects each water quality and quantity monitoring facility 3, each sludge pump 4, each sludge flow meter 5, each microwave sludge concentration meter 6, dewatering equipment 8, dosing pump 9 and dosing flow meter 11 via communication cable 14;

[0098] The big data analysis and control platform 1 analyzes and calculates the data obtained from the communication cable 14 before performing control, specifically as follows:

[0099] Step 1: The big data analysis and control platform 1 acquires the data accumulated in the early stage;

[0100] The initial data accumulation consists of historical data or data obtained through simulation using the mechanistic model formula 2, specifically including:

[0101] The influent / effluent flow rate, SS, pH / ORP, BOD, COD, NH3-N, TP, and TN of each part of the wastewater treatment facility 2.

[0102] The operating status, frequency, and power of the dosing pump 9, the dewatering equipment 8, and each sludge pump 4.

[0103] Instantaneous values ​​for each microwave sludge concentration meter 6 and each sludge flow meter 5;

[0104] Step 2: Establish a basic database and store the previously accumulated data into the basic database;

[0105] Step 3: Search the basic database to determine if each set of data is complete; if any set of data has missing values, search the next set of data; if any set of data is complete, perform the subsequent steps for each complete set of data.

[0106] Step 4: Search the basic database and perform distortion and compliance checks on each complete set of data;

[0107] Step 5: Establish an effective database by storing all complete group data that have passed the distortion and compliance checks into the effective database;

[0108] Step 6: Perform data preprocessing and dimensionality reduction on all data within the valid database;

[0109] Step 7: Perform cluster analysis and association rule analysis;

[0110] Step 8: Establish a machine learning operation model through big data analysis and robot learning methods, and provide a variety of sludge discharge and dewatering chemical control strategies to meet the standards for effluent, sludge discharge and dewatering solids content.

[0111] Step 9: Using a low-carbon assessment model, conduct a low-carbon assessment of all sludge discharge and dewatering chemical dosing control strategies, provide the carbon emission value for each sludge discharge and dewatering chemical dosing control strategy, and provide the optimal control strategy.

[0112] Step 10: Run the completed big data decision optimization control model, i.e. the optimal control strategy, on the big data analysis and control platform 1.

[0113] In this invention, the water quality and quantity monitoring facility 3, sludge pump 4, sludge flow meter 5, microwave sludge concentration meter 6, dewatering equipment 8, and dosing pump 9 are connected to the big data analysis and control platform 1 via communication cable 14. The big data analysis and control platform 1 analyzes and calculates the obtained data before controlling it to achieve the requirements of intelligent and low-carbon operation.

[0114] The big data analysis and control platform 1 achieves advanced control by integrating mechanistic and mathematical principles through data preprocessing, data dimensionality reduction, cluster analysis, association rule analysis, machine learning operation model construction and expert decision optimization model, while also incorporating mechanistic models and low-carbon assessment models.

[0115] In practical applications, the sludge discharge concentration is detected by a microwave sludge concentration meter 6 before being discharged into a sludge storage tank, thickening tank, or conditioning tank 7.

[0116] The operation of the dewatering equipment 8 and the dosing pump 9 is controlled based on the data collected by the microwave sludge concentration meter 6 and sludge flow meter 5 between the dewatering equipment 8 and the sludge storage tank, thickening tank or conditioning tank 7. The accurate dry basis of sludge treatment is calculated by the microwave sludge concentration meter 6 and sludge flow meter 5, which serves as an important basis for dewatering and dosing.

[0117] The big data analysis and control platform 1 discharges sludge as needed based on the sludge concentration collected by the microwave sludge concentration meter 6, which can reduce the load on subsequent sludge pumps 4, sludge storage tanks, thickening tanks or conditioning tanks 7 and dewatering equipment 8, and can extend the service life of these devices and reduce energy consumption.

[0118] A microwave sludge concentration meter 6, installed at the sludge outlet of the dewatering equipment 8, monitors in real time whether the solids content of the dewatered sludge meets the requirements and provides feedback to the big data analysis and control platform 1, forming a closed loop.

[0119] The sludge flow meter 5 installed in the dosing pipe 13 is used to monitor the dosing flow rate, so as to realize the precise control of the dosing amount of the dosing pump 9 by the big data analysis and control platform 1.

[0120] This invention continuously optimizes and achieves precise control of sludge discharge and dewatering dosing through basic mathematical models and analysis of historical operation and control data. By accurately monitoring sludge concentration in real time online, and controlling sludge discharge and dewatering dosing based on the amount of absolutely dry sludge (the product of concentration and flow rate), it can stabilize the moisture content of discharged and dewatered sludge, reduce the total amount of sludge discharged, return flow, treatment volume, dosage, energy consumption, and manual labor for sampling and testing, thereby saving energy, reducing pollution and carbon emissions.

[0121] In some embodiments, each water quality and quantity monitoring facility 3 is installed in the corresponding pretreatment section 2-1 or advanced treatment section 2-3 via a wastewater sampling pipe 31;

[0122] The sludge pump 4, dewatering equipment 8, dosing pump 9, sludge flow meter 5 and microwave sludge concentration meter 6 installed on sludge pipe 12 between sludge pump 4 and dewatering equipment 8, sludge flow meter 5 installed on dosing pipe 13 between dosing pump 9 and dewatering equipment 8, and microwave sludge concentration meter 6 at the sludge outlet of dewatering equipment 8 constitute an intelligent sludge discharge and dewatering dosing control module 10.

[0123] In some embodiments, each sludge pump 4 and dosing pump 9 is a variable frequency pump.

[0124] Each sludge pump 4 and dosing pump 9 is a variable frequency pump, which makes it easier to accurately control the amount of sludge discharged and the amount of chemicals added.

[0125] In practical applications, the starting sludge concentration D for each sludge pump 4 is... Q Concentration D when pump is stopped Z It can be set manually or calculated through a big data analysis and control platform.

[0126] Pump start-up sludge concentration D Q Concentration D when pump is stopped Z The specific formula varies depending on the scale and process of the wastewater treatment plant, and can also be based on the total amount of sludge designed for the plant area, with the sludge concentration at each discharge point calculated in reverse.

[0127] Pump start-up sludge concentration D Q For every 0.1% increase or decrease in TDS, the sludge pump control frequency increases or decreases by m accordingly. m is generally between 1Hz and 3Hz. The value of m can be set manually or calculated through the big data analysis and control platform 1.

[0128] like Figure 2 As shown, in some embodiments, the big data analysis and control platform 1 includes a hardware support layer, a data support layer, and an application service layer;

[0129] The hardware support layer provides the necessary supporting hardware for the big data analysis and control platform 1, including server 101, storage device 102, communication device 103, computer 104, display device 105, and instruments and meters including water quality and quantity, sludge concentration and flow rate and chemical dosing flow rate.

[0130] The data support layer classifies and stores the collected data, and establishes corresponding models to process, analyze, calculate and evaluate the data, including a basic database 111, an effective database 112, a mechanism model 113, a low-carbon assessment model 114 and a big data decision optimization and control model 115.

[0131] The application service layer provides data display and interaction for wastewater treatment plant supervisors, including display of calculation results 121, display of energy-saving effects 122, monitoring and management of various sludge dewatering and dosing equipment 123, and human-computer interaction 124.

[0132] In some embodiments, step 4 is specifically as follows:

[0133] Step 4.1: Determining whether each set of data is distorted means judging whether the dosage data Q0 satisfies the following formula; if it does not, the data is distorted; if it does, continue to the next step.

[0134]

[0135] Among them, Q y This refers to the dosage of the medicine.

[0136] Both x% and y% are valid judgment standard coefficients, with values ​​ranging from 10% to 50%.

[0137] Step 4.2, the specific judgment for whether each set of data meets the standard is as follows:

[0138] If any one or more of the following parameters in a set of data at any sampling time is not up to standard: SS, pH / ORP, BOD, COD, NH3-N, TP, TN, then the control strategy of sludge pump 4 at the previous sampling time is not advisable.

[0139] If the microwave sludge concentration meter 6 at the sludge outlet of the dewatering equipment 8 detects that the sludge moisture content is not up to standard in any set of data at any sampling time, then the dosage control strategy at the previous sampling time is not advisable.

[0140] Step 4.3: Summarize all group data that passed the distortion and compliance tests.

[0141] In practical applications, the interval between two adjacent sampling times can be set according to requirements.

[0142] In some embodiments, the dosage value Q y The calculation is performed using a mechanistic model formula, as follows:

[0143]

[0144] Among them, Q y For t n Instantaneous flow rate of the drug during a given time period, in units of m³. 3 / h;

[0145] F t The instantaneous sludge flow rate at time t is the inlet flow rate of the dewatering equipment (unit: m³ / s). 3 / h;

[0146] D t The sludge concentration at time t is the inlet sludge concentration of the dewatering equipment at time 8, in %;

[0147] For t n Dry sludge volume over a given period;

[0148] D y The concentration of the prepared reagent is expressed in %;

[0149] k is the dosing coefficient, which is the ratio of dry mud quantity to dosing quantity. The general empirical value is 3 to 5.0, and it can be adjusted.

[0150] In some embodiments, in step 6, a discrete normalization process is used to preprocess all data in the effective database;

[0151] The random forest algorithm is used to perform dimensionality reduction on all data in the effective database.

[0152] In practical applications, using discrete standardization to preprocess all data in the effective database can eliminate the impact of differences in the dimensions and value ranges of various production parameters.

[0153] Random forest algorithm is a commonly used big data modeling method for dimensionality reduction.

[0154] In some embodiments, in step 7, each data object in the effective database that has completed data preprocessing and dimensionality reduction is divided into m clusters, and the sum of squares of all data points in each cluster to the cluster center is minimized.

[0155] The correlation between the data after clustering analysis is mined using the Apriori algorithm. A specific classification boundary is defined for each data point after clustering analysis. Then, the minimum support and confidence are analyzed using an association rule analysis algorithm.

[0156] In some embodiments, in step 8, an LSTM model or an RNN-LSTM model is used, and the number of hidden layer units, learning rate, and number of iterations are adjusted and optimized. The equipment operation status is tracked, and intelligent analysis and calculation are performed based on the real-time collected production data to provide a variety of sludge discharge and dewatering dosing control strategies that meet the standards for effluent and dewatering rate, including control commands for sludge pump 4, frequency control commands for sludge pump 4, control commands for dosing pump 9, and frequency control commands for dosing pump 9.

[0157] In some embodiments, in step 9, the control strategy with the lowest overall carbon emission value is selected as the optimal control strategy given by the expert decision optimization model.

[0158] In some embodiments, the low-carbon assessment model converts the power consumption, chemical consumption, and greenhouse gas emissions of all sludge pumps 4, dewatering equipment 8, and dosing pumps 9 into a comprehensive carbon emission value, and assesses the carbon emission value of various sludge discharge and dewatering dosing control strategies. The specific formula is as follows:

[0159]

[0160] Among them, CE Z The comprehensive carbon emission value of the dry basis of sludge treated per unit of time period t0 is expressed in kgCO2-eq / TDS.

[0161] p is the carbon emission value coefficient for electricity consumption, which is generally set to 1 and is adjustable.

[0162] q is the carbon emission coefficient for pharmaceutical consumption, which is generally set to 1 and is adjustable;

[0163] r is the greenhouse gas carbon emission coefficient, which is generally set to 1 and is adjustable.

[0164] The total power consumption of all sludge pumps 4, dewatering equipment 8, and dosing pumps 9 during time period t0 is expressed in kWh.

[0165] EF n The regional electricity emission factor is expressed in kgCO2-eq / kWh.

[0166] The dry basis weight of sludge treated in time period t0 is expressed in TDS.

[0167] The dosage of the j-th agent during time period t0 is expressed in m³. 3 ;

[0168] EF j Let J be the emission factor of the j-th reagent, in kgCO2-eq / m³. 3 ;

[0169] CESqt The carbon emission value is the value of greenhouse gas emissions. Greenhouse gases mainly include CO2, CH4, and N2O, expressed as kgCO2-eq. Since CO2, CH4, and N2O are difficult to measure, if the on-site carbon emission value requirements are not high, CES (Carbon Emissions Standard) may be used. qt It can be ignored, or the percentage of the dry basis of sludge can be taken according to experience.

[0170] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A smart sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis, characterized in that: The big data analysis and control platform (1) includes each water quality and quantity monitoring facility (3), each sludge pump (4), each sludge flow meter (5), each microwave sludge concentration meter (6), dewatering equipment (8), dosing pump (9) and dosing flow meter (11) connected by communication cables (14); It also includes wastewater treatment facilities (2), microwave sludge concentration online monitoring, wastewater quality and quantity online monitoring, as well as sludge flow meter (5) and dosing flow meter (11); The wastewater treatment facility (2) is connected to a sludge storage tank, a thickening tank or a conditioning tank (7) via a sludge pipe (12), and then connected to a dewatering device (8). The online monitoring of wastewater quality and quantity is a water quality and quantity monitoring facility (3) installed in the pretreatment section (2-1) and the advanced treatment section (2-3) of the wastewater treatment facility (2); Between the pretreatment section (2-1) and the advanced treatment section (2-3) is a biochemical reaction and sedimentation tank (2-2); The deep treatment section (2-3), the biochemical reaction and sedimentation tank (2-2), and the pretreatment section (2-1) are respectively connected to the sludge storage tank, thickening tank, or conditioning tank (7) through the sludge pipe (12). The sludge pipe (12) connected to the sludge storage tank, thickening tank, or conditioning tank (7) is equipped with a sludge pump (4), a sludge flow meter (5), and a microwave sludge concentration meter (6). The three microwave sludge concentration meters (6) set between the deep treatment section (2-3), the biochemical reaction and sedimentation tank (2-2), the pretreatment section (2-1) and the sludge storage tank, thickening tank or conditioning tank (7) are used for online monitoring of microwave sludge concentration. The sludge pipe (12) between the sludge storage tank, thickening tank or conditioning tank (7) and the dewatering equipment (8) is sequentially provided with The sludge pump (4), the sludge flow meter (5), and the microwave sludge concentration meter (6); The dewatering equipment (8) is connected to the dosing pump (9) via a dosing pipe (13), and the microwave sludge concentration meter (6) is installed at the sludge outlet; The dosing pipe (13) is equipped with the dosing flow meter (11); The big data analysis and control platform (1) analyzes and calculates the data obtained from the communication cable (14) before controlling it, specifically as follows: Step 1: The big data analysis and control platform (1) acquires the accumulated data from the previous stage; The aforementioned preliminary data accumulation refers to data obtained through historical data simulation, specifically including: The influent / effluent flow rate, SS, pH / ORP, BOD, COD, NH3-N, TP, TN of each part of the wastewater treatment facility (2), the operating status, frequency, and power of the dosing pump (9), the dewatering equipment (8) and each of the sludge pumps (4), and the instantaneous values ​​of each microwave sludge concentration meter (6) and each of the sludge flow meters (5). Step 2: Establish a basic database and store the previously accumulated data into the basic database; Step 3: Search the basic database to determine whether each set of data is complete; if any set of data has missing values, search the next set of data; if any set of data is complete, perform the subsequent steps for each complete set of data. Step 4: Search the basic database and perform distortion and compliance judgment on each complete set of data; Step 4 is as follows: Step 4.1: Determining whether the data in each group is distorted means judging whether the dosage data Q0 satisfies the following formula; if it does not satisfy the formula, the data is distorted; if it does satisfy the formula, continue to the next step. Among them, Q y This refers to the dosage of the medicine. Both x% and y% are valid judgment standard coefficients, with values ​​ranging from 10% to 50%. The dosage value Qy is calculated using a mechanistic model formula, as follows: Among them, Q y For t n Instantaneous flow rate and dosage at any given time, in units of m³. 3 / h; F t The instantaneous sludge flow rate of the dewatering equipment (8) at time t is given in m³. 3 / h; D t The sludge concentration at time t is the sludge concentration of the dewatering equipment (8) at that time, in %; For t n Dry sludge volume over a given period; D y The concentration of the prepared reagent is expressed in %; k is the dosing coefficient, which is the ratio of dry mud quantity to dosing quantity, and the value ranges from 3 to 5.

0. Step 4.2, the specific determination of whether each group of data meets the standard is as follows: If any one or more of the following parameters in a set of data at any sampling time is not up to standard: SS, pH / ORP, BOD, COD, NH3-N, TP, TN, then the control strategy of the sludge pump (4) at the previous sampling time is not advisable. If the microwave sludge concentration meter (6) at the sludge outlet of the dewatering device (8) detects that the sludge moisture content is not up to standard in any set of data at any of the sampling times, then the dosage control strategy at the previous sampling time is not advisable. Step 4.3: Summarize the data from all groups that passed the distortion and compliance tests. Step 5: Establish an effective database by storing all complete data from all groups that have passed the distortion and compliance checks into the effective database; Step 6: Perform data preprocessing and dimensionality reduction on all the data in the effective database; Step 7: Perform cluster analysis and association rule analysis; Step 8: Establish a machine learning operation model through big data analysis and robot learning methods, and provide a variety of sludge discharge and dewatering chemical control strategies to meet the standards for effluent, sludge discharge and dewatering solids content. Step 9: Using a low-carbon assessment model, conduct a low-carbon assessment of all the sludge discharge and dewatering dosing control strategies, provide the carbon emission value for each of the sludge discharge and dewatering dosing control strategies, and provide the optimal control strategy. Step 10: Run the optimal control strategy on the big data analysis and control platform (1).

2. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, Each of the aforementioned water quality and quantity monitoring facilities (3) is installed in the corresponding pretreatment section (2-1) or advanced treatment section (2-3) via a wastewater sampling pipe; The sludge pump (4), the dewatering equipment (8), the dosing pump (9), the sludge flow meter (5) and the microwave sludge concentration meter (6) installed on the sludge pipe (12) between the sludge pump (4) and the dewatering equipment (8), the sludge flow meter (5) installed on the dosing pipe (13) between the dosing pump (9) and the dewatering equipment (8), and the microwave sludge concentration meter (6) at the sludge outlet of the dewatering equipment (8) constitute an intelligent sludge discharge and dewatering dosing control module (10).

3. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, Each of the sludge pump (4) and the dosing pump (9) is a variable frequency pump; Each of the sludge pumps (4) and the dosing pumps (9) is a variable frequency pump, which makes it easier to accurately control the amount of sludge discharged and the amount of chemicals added.

4. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, The big data analysis and control platform (1) includes a hardware support layer, a data support layer and an application service layer; The hardware support layer provides the necessary support hardware for the big data analysis and control platform (1), including a server (101), storage device (102), communication device (103), computer (104), display device (105), and instruments including water quality and quantity, sludge concentration and flow rate and chemical dosing flow rate; The data support layer classifies and stores the collected data, and establishes corresponding models to process, analyze, calculate and evaluate the data, including a basic database (111), an effective database (112), a mechanism model (113), a low-carbon assessment model (114), and a big data decision optimization control model (115). The application service layer provides data display and interaction for wastewater treatment plant supervisors, including display of calculation results (121), display of energy-saving effects (122), monitoring and management of each sludge dewatering and dosing equipment (123), and human-computer interaction (124).

5. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, In step 6, discrete standardization is used to preprocess all the data in the effective database. The random forest algorithm is used to perform dimensionality reduction on all the data in the effective database.

6. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, In step 7, each data object in the effective database after data preprocessing and dimensionality reduction is divided into m clusters, and the sum of squares of all data points in each cluster to the corresponding cluster center is minimized. The correlation between the data after the clustering analysis is mined using the Apriori algorithm, and a specific classification boundary is defined for each data after the clustering analysis. Then, the minimum support and confidence are analyzed using an association rule analysis algorithm.

7. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, In step 8, an LSTM model or an RNN-LSTM model is used, and the number of hidden layer units, learning rate, and number of iterations are adjusted and optimized. The equipment operation status is tracked, and intelligent analysis and calculation are performed based on the real-time collected production data to provide a variety of sludge discharge and dewatering dosing control strategies that meet the standards for effluent and dewatering rate. These strategies include the control commands of the sludge pump (4), the frequency control commands of the sludge pump (4), the control commands of the dosing pump (9), and the frequency control commands of the dosing pump (9).

8. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, In step 9, the control strategy with the lowest overall carbon emission value is selected as the optimal control strategy given by the expert decision optimization model.

9. The intelligent sludge discharge and dewatering dosing control system for wastewater treatment plants based on big data analysis as described in claim 1, characterized in that, The low-carbon assessment model converts the power consumption, chemical consumption, and greenhouse gas emissions of all the sludge pumps (4), the dewatering equipment (8), and the dosing pumps (9) into a comprehensive carbon emission value, and assesses the carbon emission value of various sludge discharge and dewatering dosing control strategies. The specific formula is as follows: Among them, CE Z The comprehensive carbon emission value of the dry basis of sludge treated per unit of time period t0 is expressed in kgCO2-eq / TDS. p is the carbon emission value coefficient for electricity consumption, with a default value of 1; q is the carbon emission coefficient for pharmaceutical consumption, with a default value of 1; r is the greenhouse gas carbon emission coefficient, with a default value of 1; The total power consumption of all the sludge pumps (4), the dewatering equipment (8), and the dosing pumps (9) during the t0 period is expressed in kWh. EF n The regional electricity emission factor is expressed in kgCO2-eq / kWh. Q t0,n The dry basis weight of sludge treated in time period t0 is expressed in TDS. M t0,j The dosage of the j-th agent during time period t0 is expressed in m³. 3 ; EF j Let J be the emission factor of the j-th reagent, in kgCO2-eq / m³. 3 ; CESqt represents the carbon emissions value of greenhouse gases. Greenhouse gases mainly include CO2, CH4, and N2O, expressed as kgCO2-eq. Since CO2, CH4, and N2O are difficult to measure, CESqt can be ignored if on-site carbon emission requirements are not high. The percentage of the dry sludge weight is taken based on empirical values.

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