A system for treating leather degreasing wastewater

Abstract

This invention provides a leather degreasing wastewater treatment system, comprising the following functional modules: a data acquisition and preprocessing module, used to simultaneously acquire and preprocess characteristic water quality index data and operating condition data from the demulsification tank inlet; a load demand analysis module, which calculates the demulsification demand index and its rate of change based on water quality data to determine the load trend; a dynamic dosage calculation module, which calculates the dynamic dosage coefficient by comprehensively considering influent temperature, hydraulic retention time, and load trend to determine the target dosage of demulsifier; and an operation mode matching module, which determines the load status based on the demulsification demand index and dynamically matches energy-saving, standard, or enhanced operation modes based on the trend. This invention achieves precise demulsifier dosage and adaptive switching of operation modes by integrating multi-source parameters, solving the problems of lag response and reagent waste in traditional methods, improving demulsification efficiency and operational economy, and reducing the risk of exceeding emission standards.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater treatment technology, specifically a leather degreasing wastewater treatment system. Background Technology

[0002] Leather degreasing wastewater is one of the main wastewaters generated in the leather industry, characterized by high oil content, high surfactant concentration, and drastic pH fluctuations. If it enters the subsequent biological treatment system directly without effective demulsification treatment, it can easily lead to pipe blockage, inhibited sludge activity, and even system collapse. The demulsification process is a crucial step in the pretreatment of degreasing wastewater, and its effectiveness directly depends on the precise addition of demulsifier. Insufficient addition leads to incomplete oil-water separation, while excessive addition results in wasted reagents and the risk of secondary pollution. Therefore, achieving precise control of the demulsification process is of great significance for ensuring the stable operation of the subsequent biological treatment system and reducing treatment costs.

[0003] In the prior art, CN117368432A discloses an intelligent detection system for wastewater treatment. This system includes a wastewater reaction tank, a wastewater sampling module, a wastewater detection module, and a wastewater monitoring server module. The wastewater sampling module includes a water flow agitation device and a water sample collection device. The water sample collection device includes a wastewater sampling head and a sampling workbench. The water flow agitation device and the water sample collection device are electrically connected to the wastewater monitoring server module. The water sample collection device is connected to the wastewater detection module via a transmission pipeline. The wastewater detection module is electrically connected to the wastewater monitoring server module. The system is characterized by having the sampling workbench fixed to the outer edge of the wastewater reaction tank. The system includes the following steps: wastewater mixing, wastewater sampling, data purification, averaging, and anomaly alarm.

[0004] However, in the aforementioned existing technologies, the system relies on a single water quality indicator and human experience to add demulsifier, making it difficult to accurately respond to the coupled fluctuations of influent oil, surfactants, and pH value. This can easily lead to excessive waste of reagents or insufficient demulsification effect. The addition control method is mostly a fixed ratio or simple feedback adjustment, lacking comprehensive consideration of operating parameters such as influent temperature and hydraulic retention time. This results in a lag in system response when the load changes rapidly, and unstable demulsification efficiency. In addition, the system adopts a uniform operating mode and cannot adaptively adjust according to the actual load status. Energy consumption is high at low loads, and there is a risk of exceeding emission standards at high loads due to adjustment lag. Overall, the operation is extensive and lacks intelligence.

[0005] 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

[0006] The purpose of this invention is to provide a leather degreasing wastewater treatment system to solve the problems mentioned in the background art. This invention achieves accurate perception and trend prediction of system load by constructing a load demand analysis module, integrating characteristic water quality indicators to calculate the demulsification demand index and analyzing its rate of change; by using a dynamic dosage calculation module, it generates a dynamic dosage coefficient by comprehensively considering influent temperature, hydraulic retention time, and load change trends, achieving precise adaptation and dynamic optimization of demulsifier dosage; and by using an operation mode matching module, it automatically matches energy-saving, standard, or enhanced operation modes based on the demulsification demand index and load change trends, realizing a shift from extensive operation to refined intelligent control, improving demulsification efficiency and operational economy, and reducing reagent consumption and the risk of exceeding emission standards.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A leather degreasing wastewater treatment system includes the following functional modules:

[0009] Data acquisition and preprocessing module: Synchronously acquires characteristic water quality index data and operating condition data of the demulsification tank inlet at fixed time intervals. The characteristic water quality index data includes inlet oil concentration data, inlet surfactant concentration data, and inlet pH value. The operating condition data includes inlet temperature data and inlet flow rate data. The module preprocesses the acquired characteristic water quality index data and operating condition data, including data cleaning and data standardization.

[0010] Load demand analysis module: Based on the pretreated influent oil concentration data, influent surfactant concentration data, and influent pH value, calculate the demulsification demand index, calculate the rate of change of the demulsification demand index, and judge the system load change trend based on the rate of change of the demulsification demand index.

[0011] Dynamic dosage calculation module: Based on the system load change trend and the pretreated influent temperature and flow rate data, calculate the dynamic dosage coefficient of demulsifier, and calculate the target dosage of demulsifier based on the dynamic dosage coefficient of demulsifier;

[0012] Operating mode matching module: Based on the demulsification demand index, it determines the current load status of the system and matches the corresponding system operating mode based on the current load status and the system load change trend.

[0013] Furthermore, the method used for data cleaning of the collected characteristic water quality index data and operating condition data is as follows:

[0014] The data cleaning includes the detection of outliers and duplicate data, the handling of missing values, the use of statistical methods to identify outliers and duplicate data in the characteristic water quality index data and operating condition data, the deletion of outliers and duplicate data in the characteristic water quality index data and operating condition data, and the filling of missing values ​​in the characteristic water quality index data and operating condition data with the mean, median or mode of the same type of parameter in the characteristic water quality index data and operating condition data.

[0015] Furthermore, the data standardization process is as follows:

[0016] The min-max normalization method is used to scale the collected characteristic water quality index data and operating condition data to the [0,1] interval. The formula used is as follows:

[0017]

[0018] in, These are normalized characteristic water quality index data and operating condition data. This consists of the original characteristic water quality index data and operating condition data. The minimum value among the same type of parameters in the characteristic water quality index data and the operating condition data. It refers to the maximum value of the same type of parameter in the characteristic water quality index data and the operating condition data.

[0019] Furthermore, the formula used to calculate the demulsification demand index is as follows:

[0020]

[0021] in, for Delaying demand index at any given moment;

[0022] For preprocessed Data on the concentration of grease in the influent at any given time;

[0023] This is the preset baseline concentration of oil;

[0024] For preprocessed Data on the concentration of surfactant in the influent at any given time;

[0025] This refers to the preset surfactant baseline concentration data;

[0026] For preprocessed The pH value of the influent at any given time;

[0027] The preset baseline demulsification pH value;

[0028] This is the preset baseline demulsification pH value deviation range;

[0029] , and These are the preset weighting coefficients for oil concentration, surfactant concentration, and demulsification pH deviation, respectively, and they satisfy the following conditions: , > > .

[0030] Furthermore, the formula used to calculate the rate of change of the demulsification demand index is as follows:

[0031]

[0032] in, for The rate of change of the demulsification demand index at any given moment;

[0033] for Delaying demand index at any given moment;

[0034] This is a preset, fixed time interval.

[0035] Furthermore, the logic for determining the system load change trend based on the rate of change of the demulsification demand index is as follows:

[0036] When k consecutive fixed time intervals all satisfy > At that time, it was determined that the system load was in a rapid upward trend;

[0037] When k consecutive fixed time intervals all satisfy < At that time, it was determined that the system load was in a rapid downward trend;

[0038] When the rate of change of the demulsification demand index Failed to satisfy within k fixed time intervals > as well as < At that time, it was determined that the system load was in a stable trend;

[0039] in, The preset threshold for a rapid upward trend is used, and it satisfies... >0;

[0040] The preset rapid downward trend threshold is used, and it satisfies... <0;

[0041] k is a preset positive integer, and k≥5.

[0042] Furthermore, the formula used to calculate the dynamic dosage coefficient of the demulsifier is as follows:

[0043]

[0044] in, for Dynamic dosage coefficient of demulsifier at any given time;

[0045] This is the preset demulsification temperature baseline value;

[0046] For preprocessed Inlet water temperature data at any given time;

[0047] The design hydraulic retention time for the demulsification tank;

[0048] for The actual hydraulic residence time at any given time is determined based on the influent flow rate data, using the following formula: ,in, The effective volume of the demulsification pool, For preprocessed Real-time inflow rate data;

[0049] This is a trend-triggered indicator function, which confirms the trend of system load changes. The execution logic is as follows: when it is determined that the system load is in a rapid upward trend, When the system load is determined to be in a rapidly decreasing trend or a stable trend, ;

[0050] , and These are the preset correction coefficients for inlet water temperature, hydraulic residence time, and trend.

[0051] Furthermore, the formula used to calculate the target dosage of the demulsifier is as follows:

[0052]

[0053] in, for The target dosage of demulsifier at any given time;

[0054] This is the preset baseline dosage of demulsifier.

[0055] Furthermore, the logic for determining the current load status of the system is as follows:

[0056] when < At that time, the system is determined to be in a low-load state;

[0057] when ≤ < At that time, the system is determined to be under medium load.

[0058] when ≥ At that time, the system is determined to be under high load.

[0059] in, and The preset load level threshold is met. < .

[0060] Furthermore, the matching of the corresponding system operating mode is executed according to the following logic:

[0061] When the system is under low load, the energy-saving operation mode is selected:

[0062] When the system is under moderate load, it matches the standard operating mode:

[0063] When the system is under high load, an enhanced operating mode is applied.

[0064] Dynamic optimization is performed based on the matched system operation mode and combined with the system load change trend:

[0065] When the system is under low load and the system load is rapidly increasing, the system operation mode is adjusted from energy-saving operation mode to standard operation mode.

[0066] When the system is under medium load and the system load is rapidly increasing, the system operation mode is adjusted from the standard operation mode to the enhanced operation mode.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] This invention overcomes the limitations of traditional leather degreasing wastewater treatment systems that rely solely on single parameters or manual experience for demulsifier addition and cannot accurately respond to water quality fluctuations. By constructing a load demand analysis module that integrates multiple water quality characteristics such as influent grease concentration, surfactant concentration, and pH value, the system calculates the demulsification demand index and analyzes its rate of change in real time. Furthermore, by incorporating influent temperature, hydraulic retention time, and system load change trends into a dynamic dosage calculation module, the system generates a dynamic demulsifier dosage coefficient. This solves the problem of wasted reagents or insufficient demulsification effect when fixed-ratio dosing methods are used during operating condition fluctuations, achieving precise adaptation and dynamic optimization of demulsifier dosage. Moreover, the system classifies low, medium, and high load states based on the demulsification demand index and automatically matches energy-saving, standard, or enhanced operation modes according to load change trends. This reduces energy consumption when the load is stable and pre-adjusts when the load rises rapidly, promoting the transformation of leather degreasing wastewater treatment from extensive operation to refined and intelligent control, improving demulsification efficiency and operational economy, and reducing reagent consumption and the risk of exceeding emission standards. Attached Figure Description

[0069] Figure 1 A block diagram of a leather degreasing wastewater treatment system;

[0070] Figure 2 This is a schematic diagram of the operation process of a leather degreasing wastewater treatment system. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0072] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0073] Example:

[0074] Please see Figures 1-2 The present invention provides a technical solution:

[0075] A leather degreasing wastewater treatment system includes the following functional modules:

[0076] The data acquisition and preprocessing module synchronously collects characteristic water quality index data and operational condition data from the demulsification tank inlet at fixed time intervals. This aims to ensure consistency and correlation between the two types of data over time, providing an accurate basis for subsequent analysis of the intrinsic relationship between water quality changes and operational conditions. The characteristic water quality index data includes influent oil concentration data, influent surfactant concentration data, and influent pH value. These indicators reflect changes in organic matter and chemical properties in the wastewater. The operational condition data includes influent temperature data and influent flow rate data, used to characterize the actual operating conditions during system operation. The module preprocesses the collected characteristic water quality index data and operational condition data to eliminate noise and abnormal information, improving data quality. The preprocessing includes data cleaning and data standardization of the collected characteristic water quality index data and operational condition data.

[0077] The method used for data cleaning of the collected characteristic water quality index data and operating condition data is as follows:

[0078] The data cleaning process includes outlier and duplicate data detection and missing value handling. For outlier and duplicate data detection, statistical methods are used to identify values ​​in characteristic water quality index data and operational condition data that significantly deviate from the normal data distribution range, do not conform to physical meaning or statistical regularity, and contain completely identical or logically duplicated data records. For identified outliers and duplicate data, the system removes them from the dataset to eliminate their interference with subsequent analysis and decision-making processes. Regarding missing value handling, for data missing due to sensor malfunction, signal interruption, or other reasons, the system employs imputation strategies to maintain data integrity. Specifically, for the same type of parameter, if the data exhibits a stable distribution, the mean of its historical data is used for imputation; if the data distribution is skewed or affected by extreme values, the median is used; if the parameter is a categorical variable or the data distribution has no obvious regularity, the mode is used. Through these methods, the imputed data is ensured to be statistically close to the true situation, maintaining the continuity and usability of the dataset and providing a reliable foundation for subsequent analysis.

[0079] The data standardization process is as follows:

[0080] The min-max normalization method is used to scale the collected characteristic water quality index data and operating condition data to the [0,1] interval. The formula used is as follows:

[0081]

[0082] in, These are normalized characteristic water quality index data and operating condition data. This consists of the original characteristic water quality index data and operating condition data. The minimum value among the same type of parameters in the characteristic water quality index data and the operating condition data. It refers to the maximum value of the same type of parameter in the characteristic water quality index data and the operating condition data.

[0083] Load demand analysis module: Based on the pretreated influent oil concentration data, influent surfactant concentration data, and influent pH value, calculate the demulsification demand index, calculate the rate of change of the demulsification demand index, and judge the system load change trend based on the rate of change of the demulsification demand index.

[0084] The formula used to calculate the demulsification demand index is as follows:

[0085]

[0086] in, for Delaying demand index at any given moment;

[0087] For preprocessed Data on the concentration of grease in the influent at any given time;

[0088] This is the preset baseline concentration of oil;

[0089] For preprocessed Data on the concentration of surfactant in the influent at any given time;

[0090] This refers to the preset surfactant baseline concentration data;

[0091] For preprocessed The pH value of the influent at any given time;

[0092] The preset baseline demulsification pH value;

[0093] This is the preset baseline demulsification pH value deviation range;

[0094] , and These are preset weighting coefficients for oil concentration, surfactant concentration, and demulsification pH deviation, respectively. These coefficients were calibrated by system commissioning engineers based on on-site commissioning results of the demulsification tank under different influent water quality conditions, and meet the following requirements: Since oil concentration is the primary factor determining the difficulty of demulsification, it is given the highest weight; surfactants affect emulsion stability, but their effect is less significant than oil concentration, so they are given the next highest weight; pH fluctuations, as an environmental disturbance factor affecting the demulsification process, are given the lowest weight. > > ;

[0095] The larger, The larger the value of the term, the more... An increase indicates that the higher the oil content in the feed water, the more stable the oil-water emulsion system, and the stronger the demulsification effect is required to destroy the oil droplet interface film. The larger, The larger the value of the term, the more... An increase indicates that a higher surfactant concentration enhances the stability of the emulsion, thus increasing the difficulty of demulsification. Deviation The larger, The larger the value of the term, the more... An increase in pH indicates that when the pH value deviates from the optimal demulsification range, the activity and stability of the demulsifier will change, which is not conducive to oil-water separation.

[0096] The formula used to calculate the rate of change of the demulsification demand index is as follows:

[0097]

[0098] in, for The rate of change of the demulsification demand index at any given moment;

[0099] for Delaying demand index at any given moment;

[0100] This is a preset, fixed time interval;

[0101] Rate of change of demulsification demand index It reflects the dynamic evolution characteristics of the system load per unit time.

[0102] The logic for determining the system load change trend based on the rate of change of the demulsification demand index is as follows:

[0103] When k consecutive fixed time intervals all satisfy > When the system load is at a certain level, it indicates that the system load is showing a continuous and significant upward trend. At this time, the concentration of oil and surfactant in the influent or the pH value fluctuates more intensely, and the system is about to face a high demulsification pressure. It is determined that the system load is in a rapid upward trend.

[0104] When k consecutive fixed time intervals all satisfy < When the system load shows a continuous slowdown during this period, the system load pressure gradually eases, indicating that the system load is in a rapid downward trend.

[0105] When the rate of change of the demulsification demand index Failed to satisfy within k fixed time intervals > as well as < When the system load fluctuates little during that period, the overall trend is stable, and the system load is in a relatively stable operating state, indicating that the system load is in a stable trend.

[0106] in, The preset threshold for a rapid upward trend is used, and it satisfies... >0;

[0107] The preset rapid downward trend threshold is used, and it satisfies... <0;

[0108] k is a preset positive integer, and k≥5.

[0109] Dynamic dosage calculation module: Based on the system load change trend and the pre-treated influent temperature and flow rate data, the module calculates the dynamic dosage coefficient of the demulsifier. The dynamic dosage coefficient is used to adjust the dosage of the demulsifier in real time to adapt to the treatment requirements under different working conditions. Based on the dynamic dosage coefficient, the module calculates the target dosage of the demulsifier to achieve precise control of the demulsification process, ensure stable demulsification effect and optimize the efficiency of agent use.

[0110] The formula used to calculate the dynamic dosage coefficient of the demulsifier is as follows:

[0111]

[0112] in, for Dynamic dosage coefficient of demulsifier at any given time;

[0113] This is the preset demulsification temperature baseline value;

[0114] For preprocessed Inlet water temperature data at any given time;

[0115] The design hydraulic retention time for the demulsification tank;

[0116] for The actual hydraulic residence time at any given time is determined based on the influent flow rate data, using the following formula: ,in, The effective volume of the demulsification pool, For preprocessed Real-time inflow rate data;

[0117] This is a trend-triggered indicator function. The trend-triggered indicator function dynamically generates a binary signal based on the system load change trend. The confirmation based on the system load change trend is as follows: when it is determined that the system load is in a rapid upward trend, it indicates that the wastewater treatment system is about to face high treatment pressure, and the treatment capacity needs to be increased in advance to avoid a decrease in the demulsification effect. When the system load is determined to be in a rapidly decreasing or stable trend, the load fluctuation is small or tends to ease, and no additional intervention is required. ;

[0118] , and These are preset inlet water temperature correction coefficient, hydraulic residence time correction coefficient, and trend correction coefficient. The inlet water temperature correction coefficient is calibrated based on the on-site debugging results of the demulsification effect under different inlet water temperature conditions. The hydraulic residence time correction coefficient is calibrated based on the on-site debugging results of the demulsification effect under different inlet water flow conditions. The trend correction coefficient is calibrated based on the on-site debugging results of the system response effect under simulated rapid load change conditions.

[0119] when < This indicates that the actual influent water temperature is below the optimal temperature range for the demulsification reaction. Lower temperatures lead to a decrease in the molecular motion rate of the demulsifier, an increase in the oil-water interfacial tension, and a slowdown in the demulsification reaction kinetics, thus weakening the demulsification effect. Therefore, the lower the temperature, the better. The larger the value of the term, the higher the dynamic dosage coefficient of the demulsifier. The value increases accordingly; when < This indicates that increased influent flow rate leads to a shorter actual residence time of wastewater in the demulsification tank, resulting in insufficient demulsification reaction time and an increased risk of incomplete oil-water separation. A shorter hydraulic residence time results in a less complete reaction, requiring a higher concentration of demulsifier to complete the demulsification process within a limited time. Therefore, a shorter actual hydraulic residence time... The larger the value of the term, the higher the dynamic dosage coefficient of the demulsifier. The value increases accordingly.

[0120] The formula used to calculate the target dosage of the demulsifier is as follows:

[0121]

[0122] in, for The target dosage of demulsifier at any given time;

[0123] This is the preset baseline dosage of demulsifier;

[0124] Demulsifiers are highly efficient water pollution control agents, belonging to the category of special agents for environmental pollution treatment. Their dosage needs to be dynamically optimized according to changes in water quality in order to improve treatment efficiency and reduce secondary pollution.

[0125] Operating mode matching module: Based on the demulsification demand index, it determines the current load status of the system. Based on the current load status and the system load change trend, it matches the corresponding system operating mode to ensure that the operating efficiency and processing effect can be optimized and balanced under different working conditions, and improve the system's adaptability to load changes.

[0126] The logic for determining the current load status of the system is as follows:

[0127] when < When the pH value is close to the baseline level, it indicates that the influent water quality has a low demand for demulsification treatment, the concentration of oil and surfactant is relatively low, the pH value is close to the baseline level, the demulsification process is relatively easy, and the system is judged to be in a low load state.

[0128] when ≤ < When the time is right, it indicates that the demulsification demand under the combined effect of various water quality indicators of the influent is within the normal range, the treatment difficulty is moderate, and the system is judged to be in a medium load state.

[0129] when ≥ When the concentration of oil or surfactant in the influent is high or the pH value deviates significantly from the baseline value, the demulsification treatment pressure increases significantly, indicating that the system is under high load.

[0130] in, and The preset load level threshold is met. < .

[0131] The matching system operating mode is executed according to the following logic:

[0132] When the system is under low load, an energy-saving operation mode is activated. This mode primarily aims to reduce energy consumption and reagent dosage, maintaining operating parameters at basic levels. While ensuring basic treatment effectiveness, it minimizes operating costs to achieve economical and energy-efficient operation.

[0133] When the system is under medium load, it operates in standard mode. In this mode, the system runs stably according to conventional design parameters, with a balance between processing efficiency and energy consumption. This mode is suitable for most operating conditions, ensuring stable and satisfactory demulsification results and good economic efficiency.

[0134] When the system is under high load, it is matched with an enhanced operation mode. In this mode, the system increases its operating intensity and simultaneously adds adsorption materials such as high-efficiency activated carbon to enhance the removal capacity of high-concentration pollutants, prioritize the safety of effluent water quality, cope with the impact of high-concentration pollutants, ensure stable and reliable demulsification effect, and avoid the risk of exceeding the emission standards.

[0135] Dynamic optimization is performed based on the matched system operation mode and combined with the system load change trend:

[0136] When the system is under low load and the system load is rapidly increasing, the system operation mode is adjusted from energy-saving operation mode to standard operation mode to cope with the initial change of load from low to high. While maintaining a certain processing capacity, it avoids a sudden increase in subsequent processing pressure due to the continuous increase in load, and provides a buffer space for the smooth transition of the system.

[0137] When the system is under medium load and the system load is rapidly increasing, the system operation mode is adjusted from the standard operation mode to the enhanced operation mode in order to enhance the processing capacity in advance, adapt to the processing pressure brought by the continuous increase in load, and prevent the demulsification effect from decreasing due to response lag.

[0138] 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.

[0139] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. 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 by 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.

[0140] 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; 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, depending on actual needs.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A leather degreasing wastewater treatment system, characterized in that, Includes the following functional modules: Data acquisition and preprocessing module: Synchronously acquires characteristic water quality index data and operating condition data of the demulsification tank inlet at fixed time intervals. The characteristic water quality index data includes inlet oil concentration data, inlet surfactant concentration data, and inlet pH value. The operating condition data includes inlet temperature data and inlet flow rate data. The module preprocesses the acquired characteristic water quality index data and operating condition data, including data cleaning and data standardization. Load demand analysis module: Based on the pretreated influent oil concentration data, influent surfactant concentration data, and influent pH value, calculate the demulsification demand index, calculate the rate of change of the demulsification demand index, and judge the system load change trend based on the rate of change of the demulsification demand index. Dynamic dosage calculation module: Based on the system load change trend and the pretreated influent temperature and flow rate data, calculate the dynamic dosage coefficient of demulsifier, and calculate the target dosage of demulsifier based on the dynamic dosage coefficient of demulsifier; Operating mode matching module: Based on the demulsification demand index, it determines the current load status of the system and matches the corresponding system operating mode based on the current load status and the system load change trend.

2. The leather degreasing wastewater treatment system according to claim 1, characterized in that: The method used for data cleaning of the collected characteristic water quality index data and operating condition data is as follows: The data cleaning includes the detection of outliers and duplicate data, the handling of missing values, the use of statistical methods to identify outliers and duplicate data in the characteristic water quality index data and operating condition data, the deletion of outliers and duplicate data in the characteristic water quality index data and operating condition data, and the filling of missing values ​​in the characteristic water quality index data and operating condition data with the mean, median or mode of the same type of parameter in the characteristic water quality index data and operating condition data.

3. The leather degreasing wastewater treatment system according to claim 2, characterized in that: The data standardization process is as follows: The min-max normalization method is used to scale the collected characteristic water quality index data and operating condition data to the [0,1] interval. The formula used is as follows: in, These are normalized characteristic water quality index data and operating condition data. This consists of the original characteristic water quality index data and operating condition data. The minimum value among the same type of parameters in the characteristic water quality index data and the operating condition data. It refers to the maximum value of the same type of parameter in the characteristic water quality index data and the operating condition data.

4. The leather degreasing wastewater treatment system according to claim 1, characterized in that: The formula used to calculate the demulsification demand index is as follows: in, for Delaying demand index at any given moment; For preprocessed Data on the concentration of grease in the influent at any given time; This is the preset baseline concentration of oil; For preprocessed Data on the concentration of surfactant in the influent at any given time; This refers to the preset surfactant baseline concentration data; For preprocessed The pH value of the influent at any given time; The preset baseline demulsification pH value; This is the preset baseline demulsification pH value deviation range; , and These are the preset weighting coefficients for oil concentration, surfactant concentration, and demulsification pH deviation, respectively, and they satisfy the following conditions: , > > .

5. The leather degreasing wastewater treatment system according to claim 4, characterized in that: The formula used to calculate the rate of change of the demulsification demand index is as follows: in, for The rate of change of the demulsification demand index at any given moment; for Delaying demand index at any given moment; This is a preset, fixed time interval.

6. The leather degreasing wastewater treatment system according to claim 5, characterized in that: The logic for determining the system load change trend based on the rate of change of the demulsification demand index is as follows: When k consecutive fixed time intervals all satisfy > At that time, it was determined that the system load was in a rapid upward trend; When k consecutive fixed time intervals all satisfy < At that time, it was determined that the system load was in a rapid downward trend; When the rate of change of the demulsification demand index Failed to satisfy within k fixed time intervals > as well as < At that time, it was determined that the system load was in a stable trend; in, The preset threshold for a rapid upward trend is used, and it satisfies... >0; The preset rapid downward trend threshold is used, and it satisfies... <0; k is a preset positive integer, and k≥5.

7. The leather degreasing wastewater treatment system according to claim 1, characterized in that: The formula used to calculate the dynamic dosage coefficient of the demulsifier is as follows: in, for Dynamic dosage coefficient of demulsifier at any given time; This is the preset demulsification temperature baseline value; For preprocessed Inlet water temperature data at any given time; The design hydraulic retention time for the demulsification tank; for The actual hydraulic residence time at any given time is determined based on the influent flow rate data, using the following formula: ,in, The effective volume of the demulsification pool, For preprocessed Real-time inflow rate data; This is a trend-triggered indicator function, which confirms the trend of system load changes. The execution logic is as follows: when it is determined that the system load is in a rapid upward trend, When the system load is determined to be in a rapidly decreasing trend or a stable trend, ; , and These are the preset correction coefficients for inlet water temperature, hydraulic residence time, and trend.

8. The leather degreasing wastewater treatment system according to claim 7, characterized in that: The formula used to calculate the target dosage of the demulsifier is as follows: in, for The target dosage of demulsifier at any given time; This is the preset baseline dosage of demulsifier.

9. A leather degreasing wastewater treatment system according to claim 1, characterized in that: The logic for determining the current load status of the system is as follows: when < At that time, the system is determined to be in a low-load state; when ≤ < At that time, the system is determined to be under medium load. when ≥ At that time, the system is determined to be under high load. in, and The preset load level threshold is met. < .

10. A leather degreasing wastewater treatment system according to claim 9, characterized in that: The matching system operating mode is executed according to the following logic: When the system is under low load, the energy-saving operation mode is selected: When the system is under moderate load, it matches the standard operating mode: When the system is under high load, an enhanced operating mode is applied. Dynamic optimization is performed based on the matched system operation mode and combined with the system load change trend: When the system is under low load and the system load is rapidly increasing, the system operation mode is adjusted from energy-saving operation mode to standard operation mode. When the system is under medium load and the system load is rapidly increasing, the system operation mode is adjusted from the standard operation mode to the enhanced operation mode.

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