DE-102025003999-A1 - INTELLIGENT SYSTEM FOR CONTROLLING SLUDGE DRIPPING AND THE DOSING OF DEWASHING CHEMICALS IN WASTEWATER TREATMENT PLANTS BASED ON BIG DATA ANALYSES

Abstract

The present application discloses an intelligent system for controlling sludge removal and the dosing of dewatering chemicals in wastewater treatment plants based on big data analytics. The system comprises a big data analytics and control platform that is connected via communication cables to each water quality and quantity monitoring device, each sludge pump, each sludge flow meter, each microwave sludge concentration meter, each dewatering device, each chemical feed pump, and each chemical flow meter. The big data analytics and control platform analyzes and calculates the data received from the communication cables and then performs control functions. By establishing a big data analytics and control platform that integrates the wastewater treatment plant's mechanics and data fusion, the present application enables precise dynamic control of the sludge removal concentration and the amount of chemicals added for sludge dewatering, starting from the overall system of sludge removal, dewatering, and chemical addition. This reduces the operating load of the dewatering device and chemical consumption, resulting in smart, low-carbon operating effects.

Inventors

• Erfinder wird später genannt

Assignees

• SHANGHAI ZEXI ENVIRONMENTAL PROTECTION ENGINEERING CO.,LTD.

Dates

Publication Date

20260513

Application Date

20251112

Priority Date

20241112

Claims (9)

1. An intelligent system for controlling sludge removal and the dosing of dewatering chemicals in wastewater treatment plants based on big data analytics, characterized in that it comprises: a big data analytics and control platform (1) connected via communication cables (14) to each water quality and quantity monitoring device (3), each sludge pump (4), each sludge flow meter (5), each microwave sludge concentration meter (6), a dewatering device (8), a chemical feed pump (9), and a chemical flow meter (11); and a wastewater treatment plant (2), an online microwave sludge concentration monitor, and an online water quality and quantity monitor; wherein the wastewater treatment plant (2) is connected via a sludge line (12) to a sludge storage tank, thickening tank, or conditioning tank (7) before being connected to a dewatering device (8); the online water quality and quantity monitoring relates to the water quality and quantity monitoring devices (3) installed in a pretreatment section (2-1) and a further treatment section (2-3) of the wastewater treatment plant (2); a biological reaction and sedimentation tank (2-2) is arranged between the pretreatment section (2-1) and the further treatment section (2-3); the further treatment section (2-3), the biological reaction and sedimentation tank (2-2), and the pretreatment section (2-1) are each connected to the sludge storage tank, thickening tank, or conditioning tank (7) via a sludge line (12); a sludge pump (4), a sludge flow meter (5), and a microwave sludge concentration meter (6) are attached to each sludge line (12) of the sludge storage tank, thickening tank, or conditioning tank (7); the three microwave sludge concentration meters (6), which are installed between the downstream treatment section (2-3), the biological reaction and sedimentation tank (2-2), and the pretreatment section (2-1) as well as the sludge storage tank, thickening tank, or conditioning tank (7), which constitute online microwave sludge concentration monitoring; a sludge pump (4), a sludge flow meter (5), and a microwave sludge concentration meter (6) are successively attached to a sludge line (12) between the sludge storage tank, thickening tank, or conditioning tank (7) and the dewatering device (8); the dewatering device (8) is connected to a chemical feed pump (9) via a chemical feed line (13), and a microwave sludge concentration meter (6) is attached to the sludge outlet of the dewatering device (8); the chemical feed line (13) is equipped with a chemical flow meter (11); wherein the big data analysis and control platform (1) serves to analyze and calculate the data received from the communication cables (14) and subsequently perform control functions, specifically as follows: Step 1: Acquisition of the pre-accumulation of data by the big data analysis and control platform (1); wherein the pre-accumulation of data includes historical data or data obtained by simulation with mechanistic model formulas, and specifically includes: water quantities, SS, pH/ORP, BOD, COD, NH3 -N, TP, TN in the inlet and outlet of the various parts of the wastewater treatment plant (2); operating status, frequency, and power of the chemical feed pump (9), the dewatering device (8), and each sludge pump (4); and Instantaneous values of each microwave sludge concentration meter (6) and each sludge flow meter (5); Step 2: Build a baseline database and store the pre-accumulated data in the baseline database; Step 3: Search the baseline database to assess whether each data record is complete; if any data record has missing values, the next data record is searched; if any data record is complete, the following steps are performed for each complete data record; Step 4: Search the baseline database to perform a bias and conformity assessment for each complete data record; Step 4 is specifically performed as follows: Step 4.1: The assessment of whether a data record is biased involves checking whether the chemical addition data Q0 satisfies the following formula; if the chemical addition data Q0 does not satisfie the following formula, the data is biased; if the chemical addition data Q0 satisfies the following formula, the process continues; − x % ≤ Q 0 − Q y Q y × 100 % ≤ y % where Q y is the numerical value of the chemical addition quantity; x% and y% are valid assessment coefficients, with values between 10% and 50%; where the numerical value of the chemical addition quantity Q y is calculated using a mechanistic model formula as follows: Q y = k × ∫ t = 0 t n ( F t × D t ) D y × t n where: Q y : instantaneous flow rate of the chemical addition during the period t n , unit: m 3 /h; F t : instantaneous value of the sludge inflow to the dewatering device (8) at time t , unit: m 3 /h; D t : sludge concentration value at the inlet of the dewatering device (8) at time t , unit: %; ∫ t = 0 t n ( F t × D t ) : Sludge dry mass during period t n ; D y : Chemical concentration produced, unit: %; k : Chemical addition coefficient, defined as the ratio of sludge dry mass to chemical addition quantity, with a range of values between 3 and 5.0; Step 4.2: The assessment of whether a data set meets the compliance requirements is carried out as follows: if, in a data set at any sampling time, one or more values for SS, pH/ORP, BOD, COD, NH3-N, TP, TN do not meet the compliance requirements, the control strategy of the sludge pump (4) at the previous sampling time is not suitable; if the microwave sludge concentration meter (6) at the sludge outlet of the dewatering device (8) detects a non-compliant water content of the sludge in a data set at any sampling time, the control strategy for the chemical addition quantity at the previous sampling time is not suitable; and Step 4.3: Summarizing all datasets that have passed the distortion and conformance assessment; Step 5: Building a valid data database and storing all datasets that are complete and have passed the distortion and conformance assessment in the valid data database; Step 6: Performing data preprocessing and dimensionality reduction for all data in the valid data database; Step 7: Performing cluster analyses and association rule analyses; Step 8: Building a machine learning operational model through big data analytics and robotic learning methods to provide various control strategies for sludge removal and dewatering chemical addition that ensure compliance with effluent values, sludge removal, and dewatering dry solids content; Step 9: Perform a low-carbon assessment for all control strategies for sludge removal and dewatering chemical addition using a low-carbon assessment model, and determine the carbon emission values for each strategy and derive the optimal control strategy; and Step 10: Operate the optimal control strategy on the big data analytics and control platform (1).
2. Intelligent control system according to Claim 1 , wherein each water quality and quantity monitoring device (3) is installed via a wastewater sampling line at the corresponding pretreatment section (2-1) or downstream treatment section (2-3); the sludge pumps (4), the dewatering device (8), the chemical feed pumps (9), the sludge flow meter (5), and the microwave sludge concentration meter (6) on the sludge line (12) between the sludge pumps (4) and the dewatering device (8), the chemical flow meter (11) on the chemical feed line (13) between the chemical feed pumps (9) and the dewatering device direction (8), as well as the microwave sludge concentration measuring device (6) at the sludge outlet of the dewatering device (8) together form an intelligent control module (10) for sludge removal and dewatering chemical addition.
3. Intelligent control system according to Claim 1 , wherein each sludge pump (4) and the chemical supply pump (9) are each a pump with a frequency converter.
4. Intelligent control system according to Claim 1 , wherein the big data analytics and control platform (1) comprises a hardware support layer, a data support layer, and an application service layer; the hardware support layer provides the necessary hardware for the platform (1), including servers (101), storage device (102), communication device (103), computer (104), display device (105), and measuring instruments for water quality and quantity, sludge concentration and flow rate, and chemical addition flow rate; the data support layer classifies and stores the acquired data and creates appropriate models for data processing, analysis, calculation, and evaluation, including a fundamentals database (111), a valid data database (112), a mechanism model (113), a low-carbon assessment model (114), and a big data decision optimization and control model (115); the application service layer provides data visualization and interaction to the wastewater treatment plant supervisory staff, including a display of the calculation results (121), a display of the energy saving effects (122), monitoring management (123) of the sludge removal and dewatering chemical addition equipment, and a human-machine interface (124).
5. Intelligent control system according to Claim 1 , where in step 6 a discrete standardization method is used for data preprocessing of all data in the valid data database; a random forest algorithm is used for dimensional reduction of all data in the valid data database.
6. Intelligent control system according to Claim 1 , wherein in step 7, after completion of data preprocessing and dimensionality reduction, each data object in the valid data database is divided into m clusters, minimizing the sum of the squares of the distances of all data points in each cluster to the respective cluster center; the correlation between the data is determined after cluster analysis using the Apriori algorithm; a specific class boundary is defined for each data point after cluster analysis, and then minimal support and confidence are analyzed using association rule analysis algorithms.
7. Intelligent control system according to Claim 1 , wherein in step 8 an LSTM model or an RNN-LSTM model is used; an optimization of the hyperparameters, including: number of hidden layer units, learning rate, and number of iterations, is performed; the equipment operating state is monitored, and based on the intelligent analysis and calculation of the production data acquired in real time, a series of control strategies for sludge removal and dewatering chemical addition are calculated; strategies that include control commands for the sludge pump (4), frequency control commands for the sludge pump (4), control commands for the chemical feed pump (9), and frequency control commands for the chemical feed pump (9) and ensure compliance with the effluent values and the desired degree of dewatering are obtained.
8. Intelligent control system according to Claim 1 , wherein in step 9 the control strategy with the lowest integrated carbon emission value is selected as the optimal control strategy from the expert-supported decision optimization model.
9. Intelligent control system according to Claim 1 , wherein the low-carbon assessment model evaluates the carbon emissions of different control strategies for sludge removal and dewatering chemical addition by converting the power consumption of all sludge pumps (4), the dewatering device (8) and the chemical feed pumps (9), the chemical consumption and the greenhouse gas emissions into a comprehensive carbon emission value according to the following formula: C E Z = p × ∑ 1 n E t 0, i × E F n Q t 0, n + q × ∑ 1 m M t 0, j × E F j Q t 0, n + r × C E S q t Q t 0, n where: CE Z : the specific integrated carbon emission value per treated dry mass of sludge in the period t 0 , unit: kgCO 2 -eq/TDS; p : the carbon emission coefficient for electricity consumption, with a default value of 1; q : the carbon emission coefficient for chemical consumption, with a default value of 1; r : the carbon emission coefficient for greenhouse gases, with a default value of 1; ∑ 1 n E t 0, i : the total power consumption of all sludge pumps (4), the dewatering device tung (8), and the chemical supply pumps (9) during period t 0 , unit: kWh; EF n : the regional electricity emission factor, unit: kg CO 2 equivalent per kWh (kgCO 2 eq/kWh); Q t 0,n : the treated sludge dry matter in the period t 0 , unit: TDS; M t 0,j : the quantity of chemical type j used in period t 0 , unit: m 3 ; EF j : the emission factor for chemical type j , unit: kgCO 2 -eq/m 3 ; CES qt : the carbon dioxide equivalent of greenhouse gas emissions (greenhouse gases mainly include CO 2 , CH 4 , N 2 O ), unit: kgCO 2 -eq; since CO 2 , CH 4 , and N 2 O are difficult to measure directly, CES qt can be neglected or assumed to be a percentage of the sludge dry mass based on experience, provided that the requirements for the carbon emission balance on site are not very high.

Description

Technical field The present application relates to the technical field of wastewater and sludge treatment, in particular a used intelligent system for controlling sludge removal and dosing of dewatering chemicals in sewage treatment plants based on big data analyses. State of the art Wastewater treatment plants are essential public utilities. The sludge produced during wastewater treatment is an unavoidable byproduct. The transport and treatment of this sludge constitute a key process step in wastewater treatment plants. Generally, the sludge is removed or returned from the various settling tanks and subsequent treatment stages, transported to thickening or storage tanks, and then pumped to dewatering or drying/incineration plants, where its volume is reduced and it is finally sent for external disposal. Conventional sludge removal pumps operate intermittently, controlled by timers. This operating method is simple but crude and can easily lead to problems: Failure to remove sludge in a timely manner risks carrying sludge away ("sludge breakthrough"), which worsens the effluent quality of the wastewater treatment plant. Conversely, excessive sludge removal results in a low dry matter content of the removed sludge, increasing the operating load on the thickening and dewatering plants and thus driving up energy consumption, chemical consumption, and personnel costs. Furthermore, conventional plants commonly add excess flocculants to achieve the desired dry matter content of the dewatered sludge. This leads to significant resource waste and contradicts China's "dual carbon" policy (peak carbon load and carbon neutrality). Therefore, a technical challenge that needs to be addressed is to achieve intelligent optimization of the processes involved in sludge removal and dewatering chemical dosing, starting with the overall system. The goal is to increase the efficiency and stability of sludge removal and dewatering while simultaneously saving energy, reducing consumption, and enabling low-carbon operation. Content of the present application In light of the aforementioned shortcomings of the prior art, the present application offers an intelligent control system for sludge removal and dewatering chemical addition in wastewater treatment plants based on big data analytics. The objective of this application is to achieve intelligent optimization of sludge removal and dewatering chemical addition within the overall system, thereby increasing the efficiency and stability of sludge removal and dewatering while simultaneously saving energy, reducing consumption, and enabling low-carbon operation. To achieve the above objective, the present application discloses an intelligent system for controlling sludge removal and the dosing of dewatering chemicals in wastewater treatment plants based on big data analytics, comprising: a wastewater treatment plant, an online microwave sludge concentration monitoring system, an online water quality and quantity monitoring system, as well as a sludge flow meter and a chemical flow meter; wherein the wastewater treatment plant is connected via a sludge line to a sludge storage tank, thickening tank, or conditioning tank before being connected to a dewatering device; The online water quality and quantity monitoring refers to the water quality and quantity monitoring equipment installed in a pretreatment section and a further treatment section of the wastewater treatment plant; A biological reaction and sedimentation tank is arranged between the pretreatment section and the subsequent treatment section; The downstream treatment section, the biological reaction and sedimentation tank, and the pretreatment section are each connected to the sludge storage tank, thickening tank, or conditioning tank via a sludge line; a sludge pump, a sludge flow meter, and a microwave sludge concentration meter are attached to each sludge line of the sludge storage tank, thickening tank, or conditioning tank; the three microwave sludge concentration measuring devices, which are located between the further treatment section, the biological reaction and sedimentation tank, and the pretreatment section as well as the sludge storage tank, thickening containers, or conditioning tanks are installed that provide online microwave sludge concentration monitoring; A sludge pump, a sludge flow meter, and a microwave sludge concentration meter are successively installed on a sludge line between the sludge storage tank, thickening tank, or conditioning tank and the dewatering device; the dewatering device is connected to a chemical supply pump via a chemical addition line, and a microwave sludge concentration meter is attached to the sludge outlet of the dewatering device; The chemical supply line is equipped with a chemical flow meter. Furthermore, the system includes a big data analysis and control platform that is connected via communication cables to each water quality and quantity monitoring device, each sludge pump,