A method for monitoring the running state of a waste incineration plant leachate treatment equipment

Abstract

The application discloses a kind of waste incineration plant leachate treatment equipment operating condition supervision methods, it is related to leachate treatment equipment operating condition supervision technical field, specifically includes the following steps: real-time acquisition of each area in sedimentation tank suspended solids operating information, and after acquisition is analyzed, respectively generates each area suspended solids settling velocity coefficient and sedimentation tank processing efficiency index;The suspended solids settling velocity coefficient and sedimentation tank processing efficiency index of each area generated constructs settling evaluation model, generates the sedimentation effect evaluation coefficient of each area, and after generation is analyzed, the settling efficiency grade of each area in sedimentation tank is evaluated, and according to the evaluation result, each area is divided into high-efficiency area, normal-efficiency area and low-efficiency area.The application solves the problem of settling efficiency decline caused by suspended solids composition fluctuation, achieves real-time dynamic adjustment of operating parameters, and optimizes the technical effect of sedimentation tank processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of leachate treatment equipment operation status monitoring technology, specifically to a method for monitoring the operation status of leachate treatment equipment in a waste incineration plant. Background Technology

[0002] Leachate treatment equipment is used to treat leachate generated during landfill or incineration. Leachate is a highly concentrated wastewater containing large amounts of harmful substances and heavy metals; untreated discharge will cause serious environmental pollution. Leachate treatment equipment in waste incineration plants typically includes a leachate collection system, physical treatment equipment (such as filtration devices), chemical treatment equipment (such as dosing systems), biochemical treatment equipment (such as bioreactors), and membrane treatment systems (such as reverse osmosis membranes and ultrafiltration membranes). These devices remove suspended solids, heavy metals, organic pollutants, and dissolved salts from the leachate through multi-stage treatment, ultimately achieving compliant discharge or reuse. Monitoring the operation of leachate treatment equipment in waste incineration plants is essential because leachate has a complex and variable composition, and long-term high-load operation of equipment can easily lead to malfunctions or reduced efficiency. Failure to promptly detect and address equipment abnormalities may result in substandard treatment and secondary pollution. Therefore, real-time data monitoring, fault prediction, and dynamic adjustments can ensure efficient equipment operation and guarantee the stability and environmental compliance of leachate treatment.

[0003] Existing technologies for monitoring the operational status of leachate treatment equipment in waste incineration plants primarily utilize sensor networks and automated systems for comprehensive real-time monitoring. Firstly, during the leachate treatment process, sensors installed at key nodes collect operational data such as flow rate, pressure, temperature, pH value, and conductivity. This data is then transmitted to the central control system via a data transmission system. Subsequently, the central control system preprocesses the raw data, including noise reduction and missing data completion, to ensure accuracy and completeness. Next, a built-in algorithm model analyzes the equipment's operational status in real time, assessing its efficiency and health, and generating relevant health indices and efficiency coefficients. Based on these analyses, the system can predict potential equipment failures and implement proactive maintenance measures. Furthermore, by dynamically adjusting operating parameters (such as flow rate and chemical dosage), the system achieves optimal equipment performance. If an abnormality occurs or a failure is imminent, the system automatically generates an early warning and provides feedback to the operator through a visual interface, allowing for timely intervention and adjustments to ensure the stability of the leachate treatment process and compliance with emission standards.

[0004] The existing technology has the following shortcomings:

[0005] In the leachate treatment process of waste incineration plants, when the composition of suspended solids in the leachate fluctuates due to different influent sources or changes in waste composition, the particle size, density, and water quality conditions of the suspended solids change accordingly. This fluctuation directly affects the settling velocity of suspended solids in the sedimentation tank. However, existing sedimentation tank monitoring systems can only monitor the concentration of suspended solids and cannot accurately predict changes in the settling velocity of suspended solids through real-time data. The system lacks the ability to dynamically adjust the operating parameters of the sedimentation tank based on real-time fluctuations in the suspended solids composition. This leads to a significant reduction in the settling efficiency when high concentrations of suspended solids enter the sedimentation tank, preventing the suspended solids from settling sufficiently and increasing the load on subsequent treatment equipment. Furthermore, suspended solids that are not effectively removed may remain in subsequent treatment equipment, causing equipment blockage or accelerated wear, increasing the cost of equipment maintenance and replacement, and ultimately affecting the operational stability and efficiency of the entire leachate treatment system.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a method for monitoring the operation of leachate treatment equipment in waste incineration plants, in order to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring the operational status of leachate treatment equipment in a waste incineration plant, specifically comprising the following steps:

[0009] Determine the division scheme of the sedimentation tank, divide the sedimentation tank into several areas evenly, and establish a zoned monitoring framework for each area based on the suspended solids characteristics and leachate conditions in each area.

[0010] Real-time information on the movement of suspended solids in each area of ​​the sedimentation tank is acquired and analyzed to generate the settling velocity coefficient of suspended solids and the treatment efficiency index of the sedimentation tank for each area.

[0011] A sedimentation evaluation model was constructed for the suspended solids settling velocity coefficient and sedimentation tank treatment efficiency index of each region, and a sedimentation effect evaluation coefficient for each region was generated. After generation, the model was analyzed to evaluate the sedimentation efficiency level of each region in the sedimentation tank. Based on the evaluation results, each region was divided into high-efficiency region, normal-efficiency region and low-efficiency region.

[0012] Based on the division of each region, corresponding operational adjustment strategies are adopted for regions with different settlement efficiency levels.

[0013] The system continuously monitors the operational status of each area and the effectiveness of operational adjustment strategies. It provides real-time feedback on operational data from each area, dynamically optimizes operational parameters, and regularly updates the sedimentation assessment model to optimize the overall treatment efficiency of the sedimentation tank.

[0014] Preferably, the suspended solids movement information of each area in the sedimentation tank is acquired in real time and analyzed to generate the suspended solids settling velocity coefficient and sedimentation tank treatment efficiency index for each area. This specifically includes the following steps:

[0015] Real-time acquisition of suspended solids movement information in various areas of the sedimentation tank, followed by preprocessing.

[0016] Extract settling velocity and treatment efficiency information from the suspended solids operation information of each area in the pretreated sedimentation tank;

[0017] The sedimentation velocity and treatment efficiency information of suspended solids in each area of ​​the sedimentation tank were analyzed to generate the suspended solids sedimentation velocity coefficient and sedimentation tank treatment efficiency index for each area.

[0018] Preferably, the logic for obtaining the suspended solids settling velocity coefficient of each region is as follows:

[0019] Settling velocity information is extracted from the suspended solids operation data of various zones within the pretreated sedimentation tank. Specifically, this includes the suspended solids density, leachate density, leachate dynamic viscosity, average particle size of the suspended solids, and gravitational acceleration at different times within a given period in each zone of the sedimentation tank. These values ​​are then calibrated as follows: d i and g, This represents the suspended solids density in the i-th region of the sedimentation tank at time m within a certain time period. This represents the leachate density in the i-th region of the sedimentation tank at time m within a certain time period. d represents the dynamic viscosity of the leachate in the i-th region of the sedimentation tank at time m over a period of time. i Let m represent the average particle size of suspended solids in the i-th region of the sedimentation tank, g represent the gravitational acceleration of each region in the sedimentation tank, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., x, where k and x are both positive integers;

[0020] The specific formula for calculating the settling velocity coefficient of suspended matter in each region is as follows:

[0021]

[0022] In the formula, SPSVC i Let be the settling velocity coefficient of suspended matter in the i-th region.

[0023] Preferably, the logic for obtaining the sedimentation tank treatment efficiency index of each region is as follows:

[0024] The treatment efficiency information of suspended solids in each area of ​​the pretreated sedimentation tank is extracted, specifically including the suspended solids concentration and leachate flow rate of each area at different times within a certain period, and calibrated accordingly. and This represents the suspended solids concentration in the i-th region of the sedimentation tank at time m within a certain time period. Let represent the leachate flow rate of the i-th region in the sedimentation tank at time m within a certain time period, where i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., x, and k and x are both positive integers;

[0025] The specific formula for calculating the treatment efficiency index of sedimentation tanks in each region is as follows:

[0026]

[0027] In the formula, STTEI i Let be the sedimentation tank treatment efficiency index for the i-th region.

[0028] Preferably, a sedimentation evaluation model is constructed based on the suspended solids settling velocity coefficient and sedimentation tank treatment efficiency index of each generated region, generating sedimentation effect evaluation coefficients for each region. This specifically includes the following steps:

[0029] The settling velocity coefficients of suspended solids in various regions, the treatment efficiency index of sedimentation tanks, and the corresponding sedimentation effect evaluation coefficients generated over a period of time were collected and calibrated as follows: and x represents the number of several suspended solids settling velocity coefficients, sedimentation tank treatment efficiency indices and corresponding sedimentation effect evaluation coefficients generated in various regions over a period of time, n = 3, 4, 5, ..., y, where y is a positive integer, and the collected data over a period of time are used to form a historical dataset.

[0030] A multiple regression model was chosen as the settlement assessment model, and it was trained using historical datasets to determine the values ​​of the regression coefficients, based on the formula:

[0031]

[0032] In the formula, β0, β1, and β2 are regression coefficients;

[0033] By minimizing the error between the predicted and actual values, the regression coefficients are optimized, and the values ​​of the regression coefficients β0, β1, and β2 are finally determined.

[0034] Using the finalized regression coefficients, the suspended solids settling velocity coefficients (SPSVCs) for each region, generated in real time, are input into the constructed settling assessment model. i Sedimentation Tank Treatment Efficiency Index (STTEI) i Real-time generation of sedimentation effect evaluation coefficients (SPEC) for each region i .

[0035] Preferably, the precipitation effect evaluation coefficients (SPEC) for each generated region are used. i Compared with the pre-set sedimentation effect evaluation coefficient threshold range [SPEC] min SPEC max A comparison was conducted, and the settling efficiency level of each area in the sedimentation tank was evaluated based on the comparison results. Based on the evaluation results, each area was divided into high-efficiency, normal-efficiency, and low-efficiency areas. The specific comparison analysis and division are as follows:

[0036] If SPEC i <SPEC min If the settling efficiency level of this area in the sedimentation tank is low, then this area is classified as a low-efficiency area.

[0037] If SPEC min ≤SPEC i ≤SPEC max If the settling efficiency level of this area in the sedimentation tank is medium, then this area is classified as the normal efficiency area.

[0038] If SPEC i SPEC max If the sedimentation efficiency level of a certain area in the sedimentation tank is high, then that area is classified as a high-efficiency area.

[0039] Preferably, based on the division results of each region, corresponding operational adjustment strategies are adopted for regions with different settlement efficiency levels, specifically as follows:

[0040] For low-efficiency areas with a low settling efficiency level, the adjustment measures taken are to increase the residence time, enhance the stirring intensity, adjust the water flow rate, and monitor the settling effect after the adjustment in real time.

[0041] For normal efficiency areas with a settlement efficiency level of medium, the operational measures taken are to maintain the current operating parameter settings, perform only routine monitoring, and not make any additional adjustments.

[0042] For high-efficiency areas with a high settling efficiency level, the optimization measures adopted are to reduce the water flow rate, reduce the stirring intensity, and carry out routine monitoring.

[0043] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0044] 1. This invention acquires and analyzes real-time suspended solids (SLS) data from various areas, including SLS particle size, density, and leachate water quality conditions. The system can dynamically capture fluctuations in leachate composition, particularly under varying influent sources and waste composition. This high-precision data acquisition and analysis method allows the system to promptly obtain key parameters and generate SLS settling velocity coefficients and sedimentation tank treatment efficiency indices using a multiple regression model. This real-time data-based model accurately predicts changes in settling velocity, thus solving the prediction errors inherent in existing technologies that only monitor SLS concentration, significantly improving prediction accuracy.

[0045] 2. This invention establishes a settling effect evaluation model, which can classify different areas according to their varying settling effects and implement corresponding adjustment strategies for low-efficiency, normal-efficiency, and high-efficiency areas. The system can automatically adjust key operating parameters such as water flow rate, stirring intensity, and residence time based on real-time feedback, ensuring that each area remains in optimal operating condition. This flexible and automated operation significantly reduces the increased load on the equipment caused by incomplete settling of suspended solids, reduces the risk of clogging and wear on subsequent treatment equipment, extends equipment lifespan, and lowers maintenance costs.

[0046] 3. This invention further optimizes the overall treatment efficiency of the sedimentation tank by dynamically monitoring and periodically updating the sedimentation assessment model. It not only optimizes operating parameters in real time but also allows the system to adapt to future changes in leachate composition through historical data analysis and model self-updating. This closed-loop feedback and intelligent optimization-based approach ensures long-term stable system operation and can cope with complex and constantly changing treatment needs. In summary, this invention improves the stability, accuracy, and efficiency of leachate treatment, providing an intelligent and automated optimization solution for leachate treatment equipment in waste incineration plants. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0048] Figure 1 This is a flowchart illustrating a method for monitoring the operational status of leachate treatment equipment in a waste incineration plant, as described in this invention. Detailed Implementation

[0049] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0050] This invention provides, for example Figure 1 The method for monitoring the operational status of leachate treatment equipment in a waste incineration plant, as shown, specifically includes the following steps:

[0051] Determine the division scheme of the sedimentation tank, divide the sedimentation tank into several areas evenly, and establish a zoned monitoring framework for each area based on the suspended solids characteristics and leachate conditions in each area.

[0052] The sedimentation tank can be divided into several zones using a virtual model created by software. Numerical simulation technology can be used to model the fluid dynamics of the sedimentation tank, determining the water flow path, velocity distribution, and sediment deposition areas. Based on this data, the system can divide the sedimentation tank into several independent zones according to fluid flow characteristics. The division criteria can be based on the fluid movement pattern and the uniformity of suspended solids distribution to determine the size and boundaries of each zone. Simultaneously, the system can set a uniform division rule to ensure balanced water flow and leachate load in each zone, enabling precise control of the settling efficiency in each zone.

[0053] For each designated zone, a dynamic monitoring algorithm embedded in the software can set independent monitoring parameters for each zone based on the particle size and density of suspended solids, as well as leachate conditions (such as flow rate, temperature, and pH). These monitoring parameters are acquired in real time via sensor data and modeled and analyzed in the software to reflect the actual operating status of each zone. The implementation of the zoned monitoring framework involves establishing virtual monitoring nodes for each zone, collecting characteristic data within the zone, and dynamically analyzing the sedimentation behavior of each zone through algorithms to adjust its monitoring intensity and operating parameters. In this way, the system can run the monitoring process independently in each zone, ensuring that each zone maintains stable sedimentation effects under different fluctuations in suspended solids composition.

[0054] Dividing the sedimentation tank into multiple zones and establishing an independent monitoring framework for each zone aims to address the complex fluctuations in suspended solids composition in leachate more precisely, solving the problem of unpredictable changes in suspended solids settling velocity in existing technologies. Through this division and monitoring approach, the system can accurately identify the suspended solids characteristics and sedimentation tank operating status in different zones, adjusting operating parameters in real time to avoid overloading or reduced efficiency in any single zone. This method effectively improves the treatment flexibility of the sedimentation tank, preventing a significant drop in sedimentation efficiency when high concentrations of suspended solids enter. Simultaneously, refined monitoring enhances overall treatment stability, reducing equipment clogging and wear caused by insufficient suspended solids settling, and ensuring the efficient operation of the entire leachate treatment system.

[0055] Real-time information on the movement of suspended solids in each area of ​​the sedimentation tank is acquired and analyzed to generate the settling velocity coefficient of suspended solids and the treatment efficiency index of the sedimentation tank for each area.

[0056] In this embodiment, the real-time operation information of suspended solids in each area of ​​the sedimentation tank is acquired and analyzed to generate the settling velocity coefficient of suspended solids and the treatment efficiency index of the sedimentation tank for each area. The specific steps include:

[0057] Real-time acquisition of suspended solids movement information in various areas of the sedimentation tank, followed by preprocessing.

[0058] Information on suspended solids (SLS) movement in different zones of the sedimentation tank can be obtained through various types of sensors installed in each zone. These sensors monitor in real time the particle size, density, concentration of SLS, leachate flow rate, and water quality parameters (such as temperature and pH). This data is transmitted to the central control system via a wireless network or data acquisition system. Specifically, this involves using a high-precision laser particle size analyzer to measure the particle size, a flotation / sinking meter to measure the density, and a flow rate sensor to monitor the leachate flow rate in real time. Combined with automatic data acquisition and transmission, the system can acquire and integrate SLS movement information from all zones in a short time, ensuring accurate monitoring of the dynamics of SLS in each zone of the sedimentation tank.

[0059] Preprocessing is essential to ensure data accuracy and consistency, preventing noise, outliers, or missing data from affecting subsequent analysis. Preprocessing is necessary because real-time acquired data may contain errors caused by environmental interference, sensor malfunctions, or network transmission delays. Specific preprocessing steps include: first, noise filtering, using mean or median filtering to eliminate short-term noise; second, data smoothing to eliminate sudden outliers and ensure data continuity; and finally, data completion, using interpolation or historical data matching to fill in missing data. Through these preprocessing steps, the system ensures that the data used in subsequent analysis has high accuracy and stability, thereby improving the accuracy of calculating the settlement velocity coefficient and processing efficiency index.

[0060] Extract settling velocity and treatment efficiency information from the suspended solids operation information of each area in the pretreated sedimentation tank;

[0061] The settling velocity and treatment efficiency information from the suspended solids operation data of various zones within a pretreated sedimentation tank can be extracted using data segmentation and feature extraction algorithms. Specifically, the pretreated suspended solids operation data is first categorized using a feature selection algorithm. Data related to suspended solids particle size, density, flow rate, and leachate viscosity are extracted as settling velocity information, while data related to suspended solids removal rate, flow rate, residence time, and treatment load are extracted as treatment efficiency information. The system can set different data labels and categories, and use algorithms to identify and segment the information, ensuring accurate classification of relevant features from the data source. During the extraction process, the software system can dynamically identify feature patterns in various types of information, separating key data related to settling velocity and treatment efficiency for subsequent analysis and calculation. The algorithm also automatically handles overlapping parts between information during extraction, ensuring the accuracy and relevance of data segmentation.

[0062] The sedimentation velocity and treatment efficiency information of suspended solids in each area of ​​the sedimentation tank were analyzed to generate the suspended solids sedimentation velocity coefficient and sedimentation tank treatment efficiency index for each area.

[0063] In this embodiment, the logic for obtaining the suspended solids settling velocity coefficient in each region is as follows:

[0064] Settling velocity information is extracted from the suspended solids operation data of various zones within the pretreated sedimentation tank. Specifically, this includes the suspended solids density, leachate density, leachate dynamic viscosity, average particle size of the suspended solids, and gravitational acceleration at different times within a given period in each zone of the sedimentation tank. These values ​​are then calibrated as follows: d i and g, This represents the suspended solids density in the i-th region of the sedimentation tank at time m within a certain time period. This represents the leachate density in the i-th region of the sedimentation tank at time m within a certain time period. d represents the dynamic viscosity of the leachate in the i-th region of the sedimentation tank at time m over a period of time. i Let m represent the average particle size of suspended solids in the i-th region of the sedimentation tank, g represent the gravitational acceleration of each region in the sedimentation tank, i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., x, where k and x are both positive integers;

[0065] Extracting settling velocity information from various areas within the pretreated sedimentation tank can be accomplished using a data parsing algorithm within the software system. This algorithm extracts information based on multi-source data collected by sensors. Specifically, a sensor network monitors and records data such as suspended solids density, leachate density, leachate dynamic viscosity, average suspended solids particle size, and gravitational acceleration at different time points in various areas of the sedimentation tank in real time. After being collected by sensors, this data is transmitted to a central processing system. The system extracts data from multiple time points according to set time intervals to reflect the dynamic changes in suspended solids. For example, suspended solids density and leachate density can be collected using a flotation meter or densitometer, leachate dynamic viscosity can be obtained using a viscometer, suspended solids particle size can be monitored in real time using a laser particle size analyzer, while gravitational acceleration is a fixed value (9.81 m / s²). 2 The reason for obtaining data at different times over a period of time is that the settling behavior of suspended solids is dynamic. Changes at different points in time can reflect the fluctuation trends of density, viscosity and particle size of suspended solids during the settling process, thereby more accurately calculating the settling velocity and ensuring real-time optimization of treatment efficiency.

[0066] The specific formula for calculating the settling velocity coefficient of suspended matter in each region is as follows:

[0067]

[0068] In the formula, SPSVC i Let be the settling velocity coefficient of suspended matter in the i-th region.

[0069] This formula calculates the settling velocity coefficient of suspended solids by considering several key factors. First, the formula averages multiple samples taken at different times over a period of time to ensure that the settling velocity reflects the overall trend of dynamic changes. The square term of particle size (d) i ) 2 This indicates the significant impact of particle size on settling velocity; larger particles settle faster. The difference between the suspended solids density and the leachate density... This reflects the difference in buoyancy; the greater the density difference, the faster the settling velocity. Gravitational acceleration g is the natural driving force during the settling process, ensuring that the suspended matter receives a continuous settling force. Viscosity... This reflects the resistance of the fluid; the higher the viscosity, the slower the settling velocity. The denominator is multiplied by 18 because of the constant term in Stokes' Law, which corrects for units and adjusts the relationship between settling velocity and viscosity, ensuring that other variables in the formula are accurately reflected in the actual settling velocity calculation. Through this calculation process, the formula can comprehensively consider factors such as suspended particle size, density difference, and viscosity to accurately estimate the settling velocity in different regions.

[0070] The suspended solids settling velocity coefficient SPSVC in the i-th region i The magnitude of the settling velocity coefficient directly affects the assessment of the settling efficiency level of a region. A larger settling velocity coefficient indicates that suspended solids settle faster in that region, with a greater density difference between the suspended solids and leachate, larger particle size, and lower leachate viscosity. This signifies higher settling efficiency and the region can be assessed as a high-efficiency area. Conversely, a smaller settling velocity coefficient indicates slower settling and greater resistance, potentially preventing some suspended solids from settling completely. Therefore, the settling efficiency of this region is lower and it may be assessed as an inefficient area. Thus, the settling velocity coefficient is a crucial indicator for assessing the settling efficiency level of each region, directly reflecting the treatment effect and providing a basis for subsequent adjustments.

[0071] In this embodiment, the logic for obtaining the sedimentation tank treatment efficiency index of each region is as follows:

[0072] The treatment efficiency information of suspended solids in each area of ​​the pretreated sedimentation tank is extracted, specifically including the suspended solids concentration and leachate flow rate of each area at different times within a certain period, and calibrated accordingly. and This represents the suspended solids concentration in the i-th region of the sedimentation tank at time m within a certain time period. Let represent the leachate flow rate of the i-th region in the sedimentation tank at time m within a certain time period, where i = 1, 2, 3, ..., k, m = 1, 2, 3, ..., x, and k and x are both positive integers;

[0073] The treatment efficiency information of each area within the pretreated sedimentation tank can be extracted using data analysis algorithms and a sensor network within the software system. Specifically, this extraction is achieved by real-time monitoring of suspended solids concentration and leachate flow rate using suspended solids concentration sensors and flow meters deployed in each area. These sensors collect data at different time points over a period of time and transmit it to the central control system. Suspended solids concentration is obtained through optical sensors or turbidimeters, reflecting changes in suspended solids concentration; leachate flow rate is monitored by flow meters. The system software extracts data from multiple time points according to set time intervals to ensure that dynamically changing concentration and flow information is reflected. The reason for acquiring data at different times over a period of time is that leachate treatment efficiency fluctuates with time and flow rate, while suspended solids concentration gradually decreases. Therefore, by using data from multiple time points, the concentration change trend can be calculated, quantifying the suspended solids removal efficiency and treatment load of each area, thereby more accurately evaluating the overall treatment effect of the sedimentation tank.

[0074] The specific formula for calculating the treatment efficiency index of sedimentation tanks in each region is as follows:

[0075]

[0076] In the formula, STTEI i Let be the sedimentation tank treatment efficiency index for the i-th region.

[0077] This formula is used to calculate the treatment efficiency index of each zone in the sedimentation tank. By sampling and averaging data multiple times, it reflects the dynamic changes in the sedimentation tank's treatment capacity. The formula contains... Used to calculate the average value over a period of time, ensuring that the evaluation takes into account efficiency fluctuations over the entire time range; It is the difference in suspended solids concentration at different times, representing the amount of suspended solids removed in each time period, and reflecting the change in removal efficiency; Used to standardize concentration differences, ensuring that the treatment capacity of the area can be quantified; Taking leachate flow rate into account, a higher flow rate treats more suspended solids, thus correcting the relationship between treatment efficiency and flow rate. Overall, this calculation method, by combining changes in suspended solids concentration and flow rate, integrates data from different time points, making the treatment efficiency index more comprehensively reflect the dynamic treatment situation in each area, ensuring that the calculated efficiency is more representative and accurate.

[0078] The sedimentation tank treatment efficiency index (STTEI) of the i-th region iThe magnitude of the treatment efficiency index directly reflects the suspended solids removal effect in a given area, thus affecting the assessment of the area's settling efficiency level. A higher treatment efficiency index indicates a significant decrease in suspended solids concentration and a larger treatment flow rate within a specific time period, signifying higher treatment efficiency and classifying the area as a high-efficiency zone. Conversely, a lower treatment efficiency index indicates poor suspended solids removal and low treatment efficiency, potentially classifying the area as a low-efficiency zone. Therefore, the treatment efficiency index is a crucial basis for assessing the settling efficiency level of each area in a sedimentation tank. It quantifies the treatment effect of each area to determine its operational performance, providing effective data support for subsequent adjustments.

[0079] A sedimentation evaluation model was constructed for the suspended solids settling velocity coefficient and sedimentation tank treatment efficiency index of each region, and a sedimentation effect evaluation coefficient for each region was generated. After generation, the model was analyzed to evaluate the sedimentation efficiency level of each region in the sedimentation tank. Based on the evaluation results, each region was divided into high-efficiency region, normal-efficiency region and low-efficiency region.

[0080] In this embodiment, a sedimentation evaluation model is constructed based on the suspended solids settling velocity coefficient and sedimentation tank treatment efficiency index of each generated region, and a sedimentation effect evaluation coefficient for each region is generated. The specific steps include:

[0081] The settling velocity coefficients of suspended solids in various regions, the treatment efficiency index of sedimentation tanks, and the corresponding sedimentation effect evaluation coefficients generated over a period of time were collected and calibrated as follows: and x represents the number of several suspended solids settling velocity coefficients, sedimentation tank treatment efficiency indices and corresponding sedimentation effect evaluation coefficients generated in various regions over a period of time, n = 3, 4, 5, ..., y, where y is a positive integer, and the collected data over a period of time are used to form a historical dataset.

[0082] Collecting suspended solids settling velocity coefficients, sedimentation tank treatment efficiency indices, and corresponding sedimentation effect evaluation coefficients for various regions over a past period can be achieved through the system's database management and automatic data acquisition module. Specifically, the software system, using a real-time sensor network and monitoring algorithms, periodically stores the suspended solids settling velocity coefficients and sedimentation tank treatment efficiency indices generated in each region at different time points into the database. The system automatically archives this data for each time period, associates it with the sedimentation effect evaluation coefficients generated for each time period and region, and marks it chronologically. Through the database's periodic query function, the software can automatically retrieve all relevant data within a specific time range, ensuring that this historical data can be accurately retrieved for regression analysis, used to build sedimentation assessment models, or for subsequent optimization. This automated data storage and retrieval process ensures efficient and comprehensive historical data collection.

[0083] The reason why n is a positive integer greater than or equal to 3 is to ensure that the solution to the system of equations in the regression analysis can be uniquely determined. Specifically, there is only one objective equation, but we need to calculate three regression coefficients β0, β1, and β2. Therefore, at least three equations are needed to construct a solvable system of linear equations. If n is less than 3, the number of equations is insufficient, and the system of equations will not be able to find a unique solution for the regression coefficients. By setting n to be greater than or equal to 3, we can ensure that there are enough equations to solve the parameter estimation problem in the regression analysis, thereby accurately calculating the value of each regression coefficient and optimizing the settlement assessment model.

[0084] A multiple regression model was chosen as the settlement assessment model, and it was trained using historical datasets to determine the values ​​of the regression coefficients, based on the formula:

[0085]

[0086] In the formula, β0, β1, and β2 are regression coefficients;

[0087] Multiple regression models are used to predict a target variable (such as the sedimentation effect evaluation coefficient). ) and multiple independent variables (such as the settling velocity coefficient of suspended matter) And sedimentation tank treatment efficiency index Statistical methods for the relationship between ( ) are used. The reason for choosing a multiple regression model as the sedimentation assessment model is that the effectiveness of leachate treatment is influenced by multiple factors, such as the settling velocity of suspended solids and treatment efficiency, and these independent variables may have complex interactions with each other. Through the multiple regression model, these factors can be quantitatively integrated to analyze their overall impact on the sedimentation effect. The three regression coefficients β0, β1, and β2 in the model represent the constant term, respectively. and right The magnitude of the contribution. Specifically, β0 is a constant term, representing the value when... and The initial settling effect assessment value is 0; β1 represents the influence weight of the suspended solids settling velocity coefficient on the settling effect; β2 represents the influence weight of the sedimentation tank treatment efficiency index on the settling effect. Through training with historical data, the model can determine the optimal values ​​of these three coefficients, thereby accurately predicting the settling effect in each area.

[0088] By minimizing the error between the predicted and actual values, the regression coefficients are optimized, and the values ​​of the regression coefficients β0, β1, and β2 are finally determined.

[0089] Optimizing the regression coefficients by minimizing the error between predicted and actual values ​​is to ensure that the model can accurately predict the settlement effect assessment coefficients. This improves the accuracy of the regression model. The goal is to teach the model to find patterns in historical data. and and The optimal linear relationship between β0, β1, and β2 is determined to reduce prediction bias. This is typically achieved through optimization algorithms such as gradient descent or least squares in software. In practice, the software calculates the error between the model's predicted value (based on the initial values ​​of the current regression coefficients) and the actual historical data. It then iteratively adjusts the regression coefficients to gradually reduce the error, making the predicted value closer to the actual value. During each iteration, the software adjusts the regression coefficient values ​​based on the error until the error falls to an acceptable range or a preset convergence condition is met. Finally, the optimal values ​​of β0, β1, and β2 are determined, ensuring the most accurate prediction results from the model.

[0090] Using the finalized regression coefficients, the suspended solids settling velocity coefficients (SPSVCs) for each region, generated in real time, are input into the constructed settling assessment model. i Sedimentation Tank Treatment Efficiency Index (STTEI) i Real-time generation of sedimentation effect evaluation coefficients (SPEC) for each region i .

[0091] In this embodiment, the precipitation effect evaluation coefficients (SPEC) for each region are generated. i Compared with the pre-set sedimentation effect evaluation coefficient threshold range [SPEC] min SPEC max A comparison was conducted, and the settling efficiency level of each area in the sedimentation tank was evaluated based on the comparison results. Based on the evaluation results, each area was divided into high-efficiency, normal-efficiency, and low-efficiency areas. The specific comparison analysis and division are as follows:

[0092] If SPEC i <SPECmin If the settling efficiency level of this area in the sedimentation tank is low, then this area is classified as a low-efficiency area.

[0093] This situation indicates low settling efficiency and poor removal of suspended solids in the area. It suggests that suspended solids in this area have not settled sufficiently, resulting in a treatment efficiency far below expectations. Ineffectively removed suspended solids may increase the load on subsequent treatment equipment, and could even cause equipment blockage or accelerated wear. This impact can lead to overall instability in the leachate treatment system. Specific classification methods can be achieved through software analysis of the collected SPEC data. i With the preset SPEC min The thresholds are compared. If SPEC i Below SPEC min The system automatically marks the area as an inefficient area, triggers a warning, and prompts that the operating parameters of the area need to be adjusted, such as optimizing the stirring intensity or extending the residence time, in order to improve the settling effect.

[0094] If SPEC min ≤SPEC i ≤SPEC max If the settling efficiency level of this area in the sedimentation tank is medium, then this area is classified as the normal efficiency area.

[0095] This situation indicates that the settling efficiency in this area is within the normal range. This shows that the suspended solids treatment effect in this area is as expected, the treatment process is stable, and there are no obvious excessively high or low effects. At this point, the treatment efficiency is ideal, and the system can continue to maintain the current operating parameters to ensure balanced and efficient regional treatment capacity. The division method is based on the SPEC data obtained through the software system. i Values ​​are monitored in real time and compared dynamically. If SPEC i Values ​​are located in SPEC min and SPEC max In between, the system will automatically mark the area as a normal efficiency zone, requiring no additional adjustments. The system will periodically check this status to ensure it remains at a normal level and will only take further action if there are significant changes.

[0096] If SPEC i SPEC max If the sedimentation efficiency level of a certain area in the sedimentation tank is high, then that area is classified as a high-efficiency area.

[0097] This situation indicates high settling efficiency in the area, with excellent removal of suspended solids. It suggests that the treatment efficiency in this area is far above normal levels, with sufficient settling of suspended solids and superior treatment capacity. While seemingly advantageous, excessively high efficiency may indicate overutilization of resources or potentially excessive operating parameters, which could place unnecessary stress on the equipment. The division method is determined by software based on the SPEC. i The values ​​are continuously compared when SPEC i Exceeding SPEC max When this happens, the system will mark the area as a high-efficiency zone and prompt the operator whether it is necessary to adjust the operating parameters to avoid overuse of the equipment. For example, the water flow rate or the stirring intensity can be reduced to maintain the stability of the equipment and extend its service life.

[0098] The pre-defined threshold range for sedimentation effect evaluation coefficients can be determined through historical data analysis and model optimization. Specifically, the software system first collects a large amount of historical data, including the correlation between suspended solids settling velocity coefficients and sedimentation tank treatment efficiency indices for various regions at different time points and the actual treatment effects. Then, using this data to build a regression model or employing machine learning algorithms, the system automatically analyzes this data to determine the sedimentation effect evaluation coefficients (SPEC). i The distribution pattern. Based on this, the system can analyze the processing effect in each region to determine the SPEC. i The value distribution is divided into three parts: inefficient, normal, and efficient. SPEC for the inefficient region... min The threshold typically corresponds to the portion of the device's performance that is significantly below normal efficiency, while SPEC max This represents the upper limit of relatively high processing efficiency. In this way, the software can automatically generate reasonable threshold ranges to ensure that they reflect the actual processing capacity of the device and the operating performance of each area.

[0099] Based on the division of each region, corresponding operational adjustment strategies are adopted for regions with different settlement efficiency levels.

[0100] In this embodiment, based on the division results of each region, corresponding operational adjustment strategies are adopted for regions with different settlement efficiency levels, specifically as follows:

[0101] For low-efficiency areas with a low settling efficiency level, the adjustment measures taken are to increase the residence time, enhance the stirring intensity, adjust the water flow rate, and monitor the settling effect after the adjustment in real time.

[0102] Measures for low-efficiency areas: increasing residence time, enhancing stirring intensity, and adjusting water flow rate can be implemented through automated control systems. Specifically, software systems monitor the settling effect evaluation coefficient (SPEC) of low-efficiency areas in real time.i The system will automatically trigger an adjustment command if the leachate falls below a preset threshold. It will increase the leachate's residence time by adjusting the water pump flow rate, and simultaneously enhance the stirring intensity based on data feedback to promote uniform distribution and faster settling of suspended solids. This is done to improve the settling effect of suspended solids by extending the material's residence time in the sedimentation tank and strengthening mixing, thus avoiding residual suspended solids that increase the processing load and improving the overall treatment efficiency of the area.

[0103] For normal efficiency areas with a settlement efficiency level of medium, the operational measures taken are to maintain the current operating parameter settings, perform only routine monitoring, and not make any additional adjustments.

[0104] For measures in the normal efficiency range: maintain the current operating parameter settings and perform only routine monitoring, which can be achieved through the software system's stability control module. Specifically, when the system monitors the settling effect evaluation coefficient in the normal efficiency range in real time, if it is within the preset normal range, the existing water flow rate, stirring intensity, and residence time remain unchanged. The system will continuously collect operating data for this range and periodically evaluate efficiency changes. This is to ensure that operating parameters under normal operating conditions are not frequently adjusted, avoiding unnecessary system disturbances, while ensuring that the range remains within the ideal treatment efficiency range through routine monitoring.

[0105] For high-efficiency areas with high settling efficiency, the optimization measures adopted are to reduce the water flow rate and reduce the stirring intensity to prevent the equipment from running excessively, while routine monitoring is carried out to ensure the continuous and stable treatment effect.

[0106] For high-efficiency areas, measures such as reducing water flow rate and decreasing stirring intensity can be achieved through automated adjustment of equipment parameters via software systems. Specifically, the software system detects the settling effect evaluation coefficient (SPEC) for that area. i Higher than the set SPEC max When the system reaches a certain value, the pump flow rate will be automatically reduced to decrease the water flow rate and the intensity of the agitation device, in order to prevent excessive resource consumption or equipment over-operation. This is because excessively high treatment efficiency may indicate an imbalance in resource utilization or excessive equipment load; adjusting operating parameters can extend equipment lifespan while ensuring that the sedimentation tank's treatment efficiency operates stably within an appropriate range.

[0107] The system continuously monitors the operational status of each area and the effectiveness of operational adjustment strategies. It provides real-time feedback on operational data from each area, dynamically optimizes operational parameters, and regularly updates the sedimentation assessment model to optimize the overall treatment efficiency of the sedimentation tank.

[0108] Continuous monitoring of the operational status of each area and the effectiveness of operational adjustment strategies can be achieved through a real-time data acquisition module and feedback control system integrated into the software system. Specifically, a sensor network within the sedimentation tank monitors key operational data in each area in real time, including suspended solids concentration, leachate flow rate, agitation intensity, and residence time. The software system automatically uploads data from these sensors at various time points to a central processing system. This system analyzes the data and feeds the results back to the control module. This ensures that operational adjustment strategies for each area are continuously tracked, and anomalies or areas requiring further optimization are promptly identified, thereby effectively improving the overall operational stability of the sedimentation tank.

[0109] Through the system's real-time feedback mechanism, the operational data of each area is periodically compared with preset standards or thresholds. When it is detected that the operational adjustment strategy in a certain area has not achieved the expected results, the system will automatically trigger further optimization commands. Specifically, the software system dynamically adjusts operating parameters based on real-time data fluctuations and historical data trends. For example, it automatically adjusts the water flow rate or stirring intensity to ensure that the operating parameters are always kept within the optimal range. Simultaneously, the system continues to monitor the adjusted results through a feedback loop, forming a closed-loop control process. This dynamic optimization method is designed to continuously fine-tune the process, adapting to potential fluctuations in each area and ensuring processing efficiency and equipment stability.

[0110] Regular updates to the sedimentation assessment model are achieved through the software's data model module. The software periodically inputs collected historical operational data, optimization strategies, and adjusted effects into the assessment model, using this data for retraining and optimization. The system uses regression analysis of historical data or machine learning algorithms to progressively optimize regression coefficients and algorithm parameters in the model, ensuring that the model more accurately reflects the actual operating status of each area of ​​the sedimentation tank. This is done because, during leachate treatment, the system's operating status changes over time and with variations in leachate composition. Regular model updates allow the system to adapt to these changes, preventing the model from becoming outdated and leading to improper adjustments of operating parameters, ultimately ensuring long-term optimization of overall treatment efficiency and efficient system operation.

[0111] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0112] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0113] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0114] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0116] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0118] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring the operational status of leachate treatment equipment in a waste incineration plant, characterized in that, Specifically, the following steps are included: Determine the division scheme of the sedimentation tank, divide the sedimentation tank into several areas evenly, and establish a zoned monitoring framework for each area based on the suspended solids characteristics and leachate conditions in each area. Real-time information on the movement of suspended solids in each area of ​​the sedimentation tank is acquired and analyzed to generate the settling velocity coefficient of suspended solids and the treatment efficiency index of the sedimentation tank for each area. Specifically, the following steps are included: Real-time acquisition of suspended solids movement information in various areas of the sedimentation tank, followed by preprocessing. Extract settling velocity and treatment efficiency information from the suspended solids operation information of each area in the pretreated sedimentation tank; The settling velocity and treatment efficiency information of suspended solids in each area of ​​the sedimentation tank were analyzed to generate the settling velocity coefficient and sedimentation tank treatment efficiency index for each area. The logic for obtaining the suspended solids settling velocity coefficients for each region is as follows: Settling velocity information is extracted from the suspended solids operation data of various zones within the pretreated sedimentation tank. Specifically, this includes the suspended solids density, leachate density, leachate dynamic viscosity, average particle size of the suspended solids, and gravitational acceleration at different times within a given period in each zone of the sedimentation tank. These values ​​are then calibrated as follows: , , , and , Indicates the first in the sedimentation tank A region within a certain period of time The density of suspended matter at any given time, Indicates the first in the sedimentation tank A region within a certain period of time The density of the leachate at that time. Indicates the first in the sedimentation tank A region within a certain period of time The dynamic viscosity of the leachate at any given time. Indicates the first in the sedimentation tank The average particle size of suspended matter in each region This represents the gravitational acceleration in different areas within the sedimentation tank. , , and All are positive integers; The specific formula for calculating the settling velocity coefficient of suspended matter in each region is as follows: In the formula, For the first The settling velocity coefficient of suspended matter in each region; The logic for obtaining the sedimentation tank treatment efficiency index of each region is as follows: The treatment efficiency information of suspended solids in each area of ​​the pretreated sedimentation tank is extracted, specifically including the suspended solids concentration and leachate flow rate of each area at different times within a certain period of time, and calibrated accordingly. and , Indicates the first in the sedimentation tank A region within a certain period of time The concentration of suspended matter at any given time. Indicates the first in the sedimentation tank A region within a certain period of time The flow rate of leachate at any given time. , , and All are positive integers; The specific formula for calculating the treatment efficiency index of sedimentation tanks in each region is as follows: In the formula, For the first The sedimentation tank treatment efficiency index of each region; A sedimentation evaluation model was constructed for the suspended solids settling velocity coefficient and sedimentation tank treatment efficiency index of each region, and a sedimentation effect evaluation coefficient for each region was generated. After generation, the model was analyzed to evaluate the sedimentation efficiency level of each region in the sedimentation tank. Based on the evaluation results, each region was divided into high-efficiency region, normal-efficiency region and low-efficiency region. Based on the division of each region, corresponding operational adjustment strategies are adopted for regions with different settlement efficiency levels. The system continuously monitors the operational status of each area and the effectiveness of operational adjustment strategies. It provides real-time feedback on operational data from each area, dynamically optimizes operational parameters, and regularly updates the sedimentation assessment model to optimize the overall treatment efficiency of the sedimentation tank.

2. The method for monitoring the operation status of leachate treatment equipment in a waste incineration plant according to claim 1, characterized in that, A sedimentation evaluation model is constructed based on the suspended solids settling velocity coefficient and sedimentation tank treatment efficiency index of each generated region, and an evaluation coefficient for the sedimentation effect of each region is generated. The specific steps include: The settling velocity coefficients of suspended solids in various regions, the treatment efficiency index of sedimentation tanks, and the corresponding sedimentation effect evaluation coefficients generated over a period of time were collected and calibrated as follows: , and , This indicates the number of the settling velocity coefficient, sedimentation tank treatment efficiency index, and corresponding sedimentation effect evaluation coefficient for several suspended solids generated in various areas over a past period. , The value is a positive integer, and the collected data over a period of time will be used to form a historical dataset; A multiple regression model was chosen as the settlement assessment model, and it was trained using historical datasets to determine the values ​​of the regression coefficients, based on the formula: In the formula, , and These are the regression coefficients; By minimizing the error between the predicted and actual values, the regression coefficients are optimized and finally determined. , and The value of ; Using the finalized regression coefficients, the suspended solids settling velocity coefficients for each region, generated in real time, are input into the constructed settling assessment model. And sedimentation tank treatment efficiency index Real-time generation of sedimentation effect evaluation coefficients for each region .

3. The method for monitoring the operational status of leachate treatment equipment in a waste incineration plant according to claim 2, characterized in that, The sedimentation effect evaluation coefficients for each generated region. Compared with the pre-set sedimentation effect evaluation coefficient threshold range A comparison was conducted, and the settling efficiency level of each area within the sedimentation tank was evaluated based on the comparison results. Based on the evaluation results, each area was divided into high-efficiency, normal-efficiency, and low-efficiency areas. The specific comparison analysis and division are as follows: like If the settling efficiency level of this area in the sedimentation tank is low, then this area is classified as a low-efficiency area. like If the settling efficiency level of this area in the sedimentation tank is medium, then this area is classified as the normal efficiency area. like If the sedimentation efficiency level of a certain area in the sedimentation tank is high, then that area is classified as a high-efficiency area.

4. The method for monitoring the operation status of leachate treatment equipment in a waste incineration plant according to claim 3, characterized in that, Based on the division of each region, corresponding operational adjustment strategies are adopted for regions with different settlement efficiency levels, specifically as follows: For low-efficiency areas with a low settling efficiency level, the adjustment measures taken are to increase the residence time, enhance the stirring intensity, adjust the water flow rate, and monitor the settling effect after the adjustment in real time. For normal efficiency areas with a settlement efficiency level of medium, the operational measures taken are to maintain the current operating parameter settings, perform only routine monitoring, and not make any additional adjustments. For high-efficiency areas with a high settling efficiency level, the optimization measures adopted are to reduce the water flow rate, reduce the stirring intensity, and carry out routine monitoring.

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