A sewage treatment facility carbon emission real-time accounting and intelligent early warning method and system

Abstract

The present application relates to sewage treatment technical field, especially sewage treatment facility carbon emission real-time accounting and intelligent early warning method and system, wherein the method comprises: obtaining water quality, biological metabolic activity, sludge form and gas phase emission data; water quality and metabolic activity data are aligned to determine the response delay characteristic quantity; according to this, the impact type is identified and the mutation characteristic value is determined; when the mutation characteristic value is greater than the preset value, the sliding window is constructed and the curve feature point is selected as the segmentation reference according to the impact type, the time series data is segmented and the metabolic contribution rate is determined; the accounting factor is corrected combined with the sludge form data, and the accounting value is output after the correlation operation with the working condition parameters; the accounting value is compared with the gas phase emission data to correct the next time accounting value, and the threshold value is warned when it is exceeded. The present application realizes dynamic accurate accounting and real-time feedback correction by introducing response delay characteristic quantity to identify impact type and differentially select segmentation reference.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a method and system for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities. Background Technology

[0002] As a significant source of carbon emissions, the wastewater treatment industry is receiving increasing attention for carbon emission accounting and management. Currently, carbon emission accounting for wastewater treatment facilities mainly relies on the emission factor method or static empirical models based on water quality indicators. This involves collecting operational data such as influent water quality parameters, aeration rates, and sludge concentrations, and then substituting these data into fixed formulas to calculate carbon emissions. However, in actual operation, wastewater treatment facilities frequently face fluctuations in influent water quality and shock loads, leading to dynamic responses in microbial metabolic activity. This results in traditional accounting methods, based on steady-state assumptions, significantly deviating from actual emission levels. Existing accounting methods struggle to differentiate the impact of different shock types on microbial metabolic pathways, cannot dynamically capture changes in the ratio of maintenance metabolism to anabolism oxygen consumption, and lack a real-time comparison and feedback mechanism with measured escaping gases. These shortcomings result in lagging and inaccurate calculations, failing to meet the needs of refined carbon management.

[0003] Chinese Patent Publication No. CN118625674B discloses a wastewater treatment control method, medium, and equipment based on carbon emission accounting, relating to the field of wastewater treatment control technology. The method includes: identifying features based on a wastewater monitoring dataset to generate characteristic information of the wastewater to be treated; generating a wastewater treatment control scheme; performing carbon emission accounting and wastewater treatment quality prediction to generate wastewater treatment prediction results; determining whether the expected wastewater treatment constraints are met; if not, constructing a wastewater treatment optimization constraint tree; optimizing and adjusting to generate the optimal wastewater treatment control scheme result; and treating the wastewater to be treated. This method solves the technical problems of existing wastewater treatment control systems that focus on improving treatment efficiency and water quality standards while neglecting carbon emission accounting and control, leading to increased energy consumption and carbon emissions, as well as low targeting and effectiveness of wastewater treatment control. It achieves synergistic effects of pollution reduction and carbon reduction, resulting in reduced energy consumption and carbon emissions, and improved targeting of wastewater treatment control.

[0004] Therefore, the existing technology has the following problems: The lack of microbial metabolic activity response delay characteristics to identify impact types and dynamically calculate them, as well as the absence of a real-time comparison and feedback mechanism between calculated values ​​and measured escaping gases, results in insufficient calculation accuracy and delayed response under different impact types, making it difficult to achieve dynamic and accurate calculation and early warning. Summary of the Invention

[0005] To address this, the present invention provides a method and system for real-time carbon emission accounting and intelligent early warning of wastewater treatment facilities. This method overcomes the problems in existing technologies, which do not introduce microbial metabolic activity response delay characteristics to identify impact types and dynamically calculate emissions, nor do they establish a real-time comparison and feedback mechanism between calculated values ​​and measured escaping gases. These problems result in insufficient calculation accuracy and delayed response under different impact types, making it difficult to achieve dynamic and accurate accounting and early warning.

[0006] To achieve the above objectives, the present invention provides a method for real-time carbon emission accounting and intelligent early warning of wastewater treatment facilities, comprising: Step S1: Obtain influent water quality data, biological metabolic activity time series data, sludge morphology data and gas phase emission data of the wastewater treatment facility; align the influent water quality data with the biological metabolic activity time series data, and determine the response delay characteristic of the biological metabolic activity time series data to changes in influent water quality. Step S2: Based on the influent water quality data and the response delay characteristic, identify the impact type of the current influent and determine the mutation characteristic value of the biological metabolic activity time series data; Step S3: In response to the mutation feature value being greater than the preset mutation feature value, a sliding time window is constructed. The selection method of the curve feature points of the biological metabolic activity time series data within the sliding time window is determined according to the shock type. The value corresponding to the curve feature points selected by the selection method is used as the segmentation benchmark to divide the time series data within the sliding time window into a falling segment and a flat segment. The metabolic contribution rate is determined according to the value corresponding to the segmentation benchmark and the total metabolic amount within the window. Step S4: Determine the carbon emission accounting factor based on the metabolic contribution rate and the correction of mass transfer efficiency using the sludge morphology data; output the carbon emission accounting value after correlation calculation with the real-time operating parameters; compare the carbon emission accounting value with the gas phase emission data; correct the accounting value for the next moment based on the comparison result; and trigger an early warning when the accounting value exceeds a preset threshold.

[0007] Further, in step S1, determining the response delay characteristic of the biological metabolic activity time-series data to changes in influent water quality includes: Step S11: Filter the influent water quality data and the biological metabolic activity time series data respectively; Step S12: Using the abrupt change time of the influent water quality data as the reference time, record the time difference between when the amplitude change rate of the biological metabolic activity time series data reaches the preset change rate and the reference time, and use it as a candidate delay feature quantity; Step S13: Perform a moving average on several candidate delay features and output the response delay features.

[0008] Further, in step S2, the impact type of the current inflow is identified, including: If the rate of change of chemical oxygen demand concentration in the influent water quality data is greater than a first rate of change threshold, it is determined to be a carbon source shock. If the rate of change of ammonia nitrogen concentration in the influent water quality data is greater than the second rate of change threshold, it is determined to be a nitrogen source shock. If the response delay feature is greater than a delay threshold and the rate of decrease in the amplitude of the biological metabolic activity time series data is greater than a rate of decrease threshold, it is determined to be a toxicity inhibition shock.

[0009] Furthermore, in step S2, the mutation characteristic value of the biological metabolic activity time series data is the maximum value of the first derivative of the biological metabolic activity time series data within a preset time window.

[0010] Further, in step S3, determining the selection method for the curve feature points of the biological metabolic activity time-series data within the sliding time window includes: In response to the impact type being a carbon source impact, the zero-crossing point of the second derivative of the biological metabolic activity time series data within the sliding time window is used as the curve feature point. In response to the impact type being nitrogen source impact, the zero-crossing point of the second derivative of the biological metabolic activity time series data within the sliding time window is taken as the main curve feature point, and the intercept value when the slope of the tangent at the inflection point of the biological metabolic activity time series data curve returns to zero is taken as the backup curve feature point. In response to the impact type being a toxic inhibitory impact, the intercept value at the inflection point of the biological metabolic activity time-series data curve within the sliding time window, where the slope of the tangent line returns to zero, is used as the curve feature point.

[0011] Further, in step S3, the time series data within the sliding time window is divided into a falling segment and a smooth segment, including: Using the segmentation benchmark as the dividing point, the time series data within the sliding time window that is located before the segmentation benchmark and whose respiratory rate is continuously decreasing is divided into a decreasing segment, and the time series data that is located after the segmentation benchmark and whose respiratory rate fluctuation amplitude is less than the fluctuation threshold is divided into a smooth segment.

[0012] Further, in step S3, the metabolic contribution rate is determined, including: Using the respiratory rate value corresponding to the segmentation benchmark as the instantaneous value of ineffective oxygen, the ratio of the instantaneous value of ineffective oxygen to the total oxygen consumption within the sliding time window is determined, and the ratio is calibrated as the metabolic contribution rate.

[0013] Further, in step S4, the carbon emission accounting factor is determined, including: The basic carbon emission accounting factor is determined based on the difference between the metabolic contribution rate and the preset benchmark contribution rate. The oxygen mass transfer coefficient is determined based on the particle size distribution of sludge flocs in the sludge morphology data, and the mass transfer efficiency correction coefficient is determined based on the ratio of the oxygen mass transfer coefficient to the standard oxygen mass transfer coefficient. The basic carbon emission accounting factor is corrected using the mass transfer efficiency correction coefficient, and the carbon emission accounting factor is output.

[0014] Further, in step S4, the calculated value for the next time step is corrected based on the comparison result, including: The carbon emission calculation value is output after being correlated and calculated with the carbon emission accounting factor and real-time operating parameters, including: Based on the carbon emission accounting factor and the influent flow rate and aeration rate in the real-time operating parameters, combined with the sludge concentration in the real-time operating parameters, the carbon emission accounting value at the current moment is determined. The current carbon emission calculation value is compared with the gas phase emission data to obtain the deviation rate; The carbon emission accounting factor for the next time step is corrected using the aforementioned deviation rate to obtain the accounting value for the next time step.

[0015] A system for real-time carbon emission accounting and intelligent early warning methods applied to the aforementioned wastewater treatment facilities includes: The data acquisition module is used to acquire influent water quality data, biological metabolic activity time series data, sludge morphology data and gas phase emission data of the wastewater treatment facility. The data processing module is used to determine the response delay characteristics of the biological metabolic activity time series data to changes in influent water quality, as well as to identify the shock type and determine the mutation characteristic value; The dynamic segmentation module is used to construct a sliding time window, select curve feature points, segment time-series data, and determine the metabolic contribution rate. The calculation and early warning module is used to determine the carbon emission calculation factor, output the carbon emission calculation value, correct the calculation value for the next moment based on the comparison results, and trigger an early warning when the calculation value exceeds the preset threshold.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This embodiment removes data noise through two-stage filtering and retains the true trend characteristics of water quality changes and respiration response; by using the first time the respiration rate amplitude change rate reaches the threshold set based on the normal operation fluctuation range as the response judgment criterion, the response lag time of microbial metabolic activity to water quality shocks is objectively quantified; by repeatedly extracting and performing moving average output from multiple independent shock events, the estimation bias caused by single anomalies is effectively filtered out, so that the output response delay characteristic can stably characterize the inherent response characteristics of the facility under the current operating state.

[0017] Furthermore, this embodiment achieves rapid and accurate identification of carbon source shocks and nitrogen source shocks by comparing the rate of change of chemical oxygen demand concentration with a first rate of change threshold and the rate of change of ammonia nitrogen concentration with a second rate of change threshold, respectively, providing a basis for differentiated accounting under different organic load types; by comparing the response delay characteristic quantity with the delay threshold and combining the dual condition judgment of the rate of decrease in the amplitude of biological metabolic activity with the rate of decrease threshold, accurate identification of toxicity inhibition shocks is achieved, avoiding misjudgment of respiratory decline caused by depletion of normal substrates; at the same time, through the non-mutually exclusive judgment mechanism of multiple types of shocks, it is possible to identify composite shock scenarios.

[0018] Furthermore, this embodiment achieves precise positioning of the inflection points of the respiration rate curve under different impact morphologies by selecting curve feature points as segmentation benchmarks based on the differences in impact types. For carbon source impacts, the zero-crossing point of the second derivative is used to accurately capture the moment when the respiration rate transitions from a decrease to a stable state. For nitrogen source impacts, a dual-benchmark mechanism is adopted, with the zero-crossing point of the second derivative as the primary benchmark and the intercept of the inflection point tangent as a backup, to effectively address positioning deviations caused by zero-crossing point offsets or lags. For toxicity-inhibiting impacts, the intercept of the inflection point tangent is used to replace the missing zero-crossing point as the segmentation benchmark, ensuring effective segmentation even in extreme cases where there are no inflection points throughout the curve. Based on this, the time-series data is divided into a decreasing segment and a flat segment using the segmentation benchmark as the boundary, providing an accurate data interval division for calculating the metabolic contribution rate, thereby ensuring the accuracy of subsequent carbon emission accounting.

[0019] Furthermore, this embodiment uses the respiratory rate value corresponding to the segmentation benchmark as the instantaneous value of ineffective oxygen, which objectively reflects the oxygen consumption level of microorganisms to maintain metabolism at the turning point. By calculating the ratio of the instantaneous value of ineffective oxygen to the total oxygen consumption within the window, the influence of the absolute value difference of total oxygen consumption under different operating conditions on the calculation results is eliminated, making the metabolic contribution rate a dimensionless relative indicator, which is convenient for comparison between different facilities and different operating conditions.

[0020] Furthermore, this embodiment establishes a dynamic verification mechanism for the calculation results by comparing the carbon emission calculation values ​​with the measured gas phase emission data in real time; by feedback correction of the calculation factors at the next moment based on the deviation rate, a closed-loop self-correction of the calculation process is realized, so that the calculation values ​​continuously approach the actual emission levels. In long-term operation, it can effectively suppress model drift and accumulated errors, and improve the continuous accuracy of the calculation. Attached Figure Description

[0021] Figure 1 This is a flowchart of the real-time carbon emission accounting and intelligent early warning method for wastewater treatment facilities in this embodiment; Figure 2 This is a flowchart illustrating the determination of response delay characteristic quantities in the real-time carbon emission accounting and intelligent early warning method for wastewater treatment facilities in this embodiment; Figure 3 This is a flowchart illustrating the process of identifying the impact type of the current influent in the real-time carbon emission accounting and intelligent early warning method for wastewater treatment facilities in this embodiment; Figure 4 This is a schematic diagram of the real-time carbon emission accounting and intelligent early warning system for wastewater treatment facilities in this embodiment. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] Please see Figure 1 As shown, it is a flowchart of the real-time carbon emission accounting and intelligent early warning method for wastewater treatment facilities in this embodiment.

[0025] This embodiment provides a method for real-time carbon emission accounting and intelligent early warning of wastewater treatment facilities, including: Step S1: Obtain influent water quality data, biological metabolic activity time series data, sludge morphology data and gas phase emission data of the wastewater treatment facility; align the influent water quality data with the biological metabolic activity time series data, and determine the response delay characteristic of the biological metabolic activity time series data to changes in influent water quality. Step S2: Based on the influent water quality data and the response delay characteristic, identify the impact type of the current influent and determine the mutation characteristic value of the biological metabolic activity time series data; Step S3: In response to the mutation feature value being greater than the preset mutation feature value, a sliding time window is constructed. The selection method of the curve feature points of the biological metabolic activity time series data within the sliding time window is determined according to the shock type. The value corresponding to the curve feature points selected by the selection method is used as the segmentation benchmark to divide the time series data within the sliding time window into a falling segment and a flat segment. The metabolic contribution rate is determined according to the value corresponding to the segmentation benchmark and the total metabolic amount within the window. Step S4: Determine the carbon emission accounting factor based on the metabolic contribution rate and the correction of mass transfer efficiency using the sludge morphology data; output the carbon emission accounting value after correlation calculation with the real-time operating parameters; compare the carbon emission accounting value with the gas phase emission data; correct the accounting value for the next moment based on the comparison result; and trigger an early warning when the accounting value exceeds a preset threshold.

[0026] This embodiment identifies shock types by introducing the response delay characteristic of biological metabolic activity to influent water quality, and selects curve feature points as segmentation benchmarks based on the differences in shock types, thereby achieving dynamic and accurate calibration of metabolic contribution rate. At the same time, it combines sludge morphology data to correct mass transfer efficiency, and corrects the next time-of-flight calculation value by comparing the calculated value with the measured gas phase emission data in real time, forming a closed-loop self-correcting calculation mechanism. This solves the problems of insufficient accuracy and response lag of traditional static calculation methods under different shock types, and realizes dynamic and accurate calculation and timely early warning of carbon emissions from wastewater treatment facilities.

[0027] In this embodiment, influent water quality data, biological metabolic activity time-series data, sludge morphology data, and gas phase emission data of the wastewater treatment facility are acquired. Influent water quality data is collected in real-time by online water quality monitoring instruments installed at the influent channel, including at least chemical oxygen demand (COD), ammonia nitrogen, total phosphorus, pH, and water temperature, with a sampling frequency of once per minute. Biological metabolic activity time-series data is acquired through online dissolved oxygen meters and online respiration rate monitors installed in the aerobic zone of the biological tank, continuously collecting the oxygen consumption rate or oxygen absorption rate of the mixed liquor at a frequency of seconds to form a continuous time series. Sludge morphology data is acquired through an online particle size analyzer installed at the outlet of the biological tank, including at least the average particle size of sludge flocs, particle size distribution range, and specific surface area. When an online particle size analyzer is unavailable, sludge samples can be collected periodically, analyzed in a laboratory, and manually entered as an alternative data source. Gas phase emission data is acquired through an online gas monitoring system installed at the sealed cover of the aerobic zone, including at least the real-time concentrations of carbon dioxide and methane, while simultaneously collecting gas flow rate data at this location for comparison and verification of subsequent calculations. All four types of data are aggregated using a unified time label through a data acquisition and monitoring system.

[0028] Please see Figure 2 As shown, it is a flowchart for determining the response delay characteristic quantity in the real-time carbon emission accounting and intelligent early warning method for wastewater treatment facilities in this embodiment.

[0029] Specifically, in step S1, determining the response delay characteristic of the biological metabolic activity time-series data to changes in influent water quality includes: Step S11: Filter the influent water quality data and the biological metabolic activity time series data respectively; Step S12: Using the abrupt change time of the influent water quality data as the reference time, record the time difference between when the amplitude change rate of the biological metabolic activity time series data reaches the preset change rate and the reference time, and use it as a candidate delay feature quantity; Step S13: Perform a moving average on several candidate delay features and output the response delay features.

[0030] In this embodiment, the influent water quality data and the biological metabolic activity time-series data are filtered separately. Because the raw data collected by online instruments contains sensor noise, signal jitter, and occasional spike interference, directly using it for delayed feature extraction would lead to misjudgment of abrupt changes and deviations in response point identification. This embodiment uses a combination of sliding median filtering and low-pass filtering to process the two types of data separately. First, median filtering is performed using multiple consecutive sampling points as a window to remove impulse noise. Then, a first-order low-pass filter smooths high-frequency fluctuations in the data, preserving the main trend characteristics of water quality changes and respiration responses. The filtered data is used for subsequent abrupt change detection and delayed feature extraction.

[0031] Using the abrupt change moment of the influent water quality data as the baseline moment, the time difference between when the amplitude change rate of the biological metabolic activity time-series data reaches a preset change rate and the baseline moment is recorded as a candidate delay feature. Specifically, the abrupt change detection is performed on the filtered influent water quality data using the sliding difference method: a sliding window of fixed length is set, and the water quality abrupt change threshold is determined based on the statistical value of the background fluctuation amplitude of the influent water quality data. The cumulative change of the water quality index within the window, i.e., represented by chemical oxygen demand, is calculated. When the change exceeds the set water quality abrupt change threshold, the start time of the window is determined as the influent abrupt change moment, and this moment is used as the baseline moment. Subsequently, starting from the baseline moment, the filtered respiration rate data is traversed backward on the time axis, and the amplitude change rate of the respiration rate relative to the baseline moment is calculated. When the change rate first reaches the preset change rate threshold, the current moment is recorded, and the time difference between the current moment and the baseline moment is calculated. This time difference is used as a candidate delay feature.

[0032] The preset rate of change threshold is determined based on the statistical fluctuation range of the respiration rate under normal operating conditions of the biological reactor, ensuring that the threshold can both eliminate background noise interference and promptly identify the true response. To eliminate the influence of a single random fluctuation, the above process is repeated for multiple consecutive independent impact events to obtain a series of candidate delay features. A moving average is performed on several candidate delay features to output the response delay feature. Specifically, the candidate delay features corresponding to the most recent impact events are used as a sliding window, and their arithmetic mean is calculated as the response delay feature output at the current moment. When the cumulative number of events is less than the window length, the average of the existing candidate values ​​is used as the output.

[0033] This embodiment uses two-stage filtering to remove data noise while retaining the true trend characteristics of water quality changes and respiration responses. By using the first time the rate of change of respiration rate amplitude reaches a threshold set based on the normal operating fluctuation range as the response judgment criterion, the response lag time of microbial metabolic activity to water quality shocks is objectively quantified. By repeatedly extracting and performing moving average output from multiple independent shock events, estimation bias caused by single anomalies is effectively filtered out, so that the output response delay characteristic can stably characterize the inherent response characteristics of the facility under the current operating state.

[0034] Please see Figure 3 As shown, Figure 3 This is a flowchart illustrating the process of identifying the impact type of the current influent in the real-time carbon emission accounting and intelligent early warning method for wastewater treatment facilities in this embodiment.

[0035] Specifically, in step S2, the impact type of the current inflow is identified, including: If the rate of change of chemical oxygen demand concentration in the influent water quality data is greater than a first rate of change threshold, it is determined to be a carbon source shock. If the rate of change of ammonia nitrogen concentration in the influent water quality data is greater than the second rate of change threshold, it is determined to be a nitrogen source shock. If the response delay feature is greater than a delay threshold and the rate of decrease in the amplitude of the biological metabolic activity time series data is greater than a rate of decrease threshold, it is determined to be a toxicity inhibition shock.

[0036] Specifically, in step S2, the mutation characteristic value of the biological metabolic activity time series data is the maximum value of the first derivative of the biological metabolic activity time series data within a preset time window.

[0037] In this embodiment, when identifying the type of shock, the rate of change of chemical oxygen demand (COD) concentration in the influent water quality data is monitored in real time. When the rate of change exceeds a preset first threshold, it is determined to be a carbon source shock. This first threshold is determined based on the statistical value of the COD concentration fluctuation range during normal operation. When the COD concentration rises rapidly above this threshold, it indicates a sudden increase in the organic load in the influent, and microorganisms will enter a metabolic state dominated by the rapid proliferation of heterotrophic bacteria. The rate of change of ammonia nitrogen concentration in the influent water quality data is also monitored in real time. When the rate of change exceeds a preset second threshold, it is determined to be a nitrogen source shock. This second threshold is determined based on the statistical value of the ammonia nitrogen concentration fluctuation range during normal operation. When the ammonia nitrogen concentration rises rapidly above this threshold, it indicates a sudden increase in the nitrogen load in the influent, and the metabolic activity of nitrifying bacteria is enhanced accordingly. Their oxygen consumption behavior has different kinetic characteristics from that of a carbon source shock.

[0038] When the response delay characteristic is greater than a preset delay threshold, and the rate of decrease in the amplitude of the biological metabolic activity time series data is greater than a preset rate of decrease threshold, it is determined to be a toxicity inhibition shock. The delay threshold is determined based on the average delay time between changes in influent water quality and the respiratory response under normal operating conditions. When the response delay characteristic significantly exceeds this average delay time, it indicates that microbial activity is inhibited. The rate of decrease threshold is determined based on the upper limit of the normal fluctuation rate of respiratory rate under normal operating conditions. When both conditions are met simultaneously, it indicates that microbial metabolism is inhibited by toxic substances, rather than a decrease in respiration caused by normal substrate depletion. The determination of the above three shock types is not mutually exclusive; when multiple conditions are met simultaneously, it is determined to be a composite shock. When determining the abrupt change characteristic value of the biological metabolic activity time series data, the first derivative of the filtered respiratory rate time series data is calculated to obtain the instantaneous rate of change at each moment. Then, the maximum value of this first derivative within a preset time window is extracted as the abrupt change characteristic value.

[0039] This embodiment achieves rapid and accurate identification of carbon source shocks and nitrogen source shocks by comparing the rate of change of chemical oxygen demand concentration with a first rate of change threshold and the rate of change of ammonia nitrogen concentration with a second rate of change threshold, respectively, providing a basis for differentiated accounting under different organic load types; by comparing the response delay characteristic quantity with the delay threshold and combining the dual condition judgment of the rate of decrease of biological metabolic activity amplitude with the rate of decrease threshold, it achieves accurate identification of toxicity inhibition shocks and avoids misjudgment of respiratory decline caused by normal substrate depletion; at the same time, through the non-mutually exclusive judgment mechanism of multiple types of shocks, it can identify compound shock scenarios.

[0040] In this embodiment, the preset mutation characteristic value is determined based on the background fluctuation range of biological metabolic activity time-series data under normal operating conditions. Specifically, time-series data of respiration rate of the wastewater treatment facility are collected during periods of stable operation without shock events. The first derivative of the respiration rate time-series data during this period is calculated, and the maximum absolute value of the first derivative during this period is counted. This maximum value is used as the preset mutation characteristic value. When the real-time calculated mutation characteristic value exceeds this value, it indicates that the current change in respiration rate has significantly exceeded the normal operating fluctuation range.

[0041] In this embodiment, when the mutation characteristic value is greater than the preset mutation characteristic value, it indicates that the current impact severity has reached the condition for initiating dynamic segmentation calculation, at which point the construction of a sliding time window is triggered. Using the current moment as the window endpoint and a preset time length as the window width, a continuous historical time series data segment is extracted along the time axis, serving as the data interval to be segmented within the current sliding time window. The window width is determined based on the statistical characteristics of the impact response duration to ensure that the window can completely cover the entire process from the peak respiratory rate moment to the beginning of the plateau phase. Once the window is constructed, curve feature points are selected based on the impact type, and subsequent segmentation operations are performed. At the next sampling moment, the window slides forward one step along the time axis, discarding the oldest data within the window and incorporating the latest data, forming a new sliding time window used for determining the segmentation benchmark and calculating the metabolic contribution rate at the next moment.

[0042] Specifically, in step S3, determining the selection method for the curve feature points of the biological metabolic activity time-series data within the sliding time window includes: In response to the impact type being a carbon source impact, the zero-crossing point of the second derivative of the biological metabolic activity time series data within the sliding time window is used as the curve feature point. In response to the impact type being nitrogen source impact, the zero-crossing point of the second derivative of the biological metabolic activity time series data within the sliding time window is taken as the main curve feature point, and the intercept value when the slope of the tangent at the inflection point of the biological metabolic activity time series data curve returns to zero is taken as the backup curve feature point. In response to the impact type being a toxic inhibitory impact, the intercept value at the inflection point of the biological metabolic activity time-series data curve within the sliding time window, where the slope of the tangent line returns to zero, is used as the curve feature point.

[0043] In this embodiment, when the current shock type is determined to be a carbon source shock, the zero-crossing point of the second derivative of the biological metabolic activity time series data within the sliding time window is used as the curve feature point. Under a carbon source shock, the organic load in the influent increases sharply, and heterotrophic bacteria rapidly utilize easily degradable substrates for anabolism. The respiration rate shows a regular trend of first rising rapidly, reaching a peak, and then continuously decreasing. Its respiration rate curve has a regular shape, and the turning point between the decreasing segment and the flat segment is clear. The second derivative changes from a negative value to zero at this turning point and further turns to a positive value. The zero-crossing point is clear and stable. Directly using this zero-crossing point as the segmentation benchmark can accurately distinguish between the anabolism-dominated period and the maintenance metabolism-dominated period.

[0044] When the current shock type is determined to be nitrogen shock, the zero-crossing point of the second derivative of the biological metabolic activity time-series data within the sliding time window is used as the primary curve feature point, and the intercept value when the slope of the tangent at the inflection point of the biological metabolic activity time-series data curve reaches zero is used as the backup curve feature point. Under nitrogen shock, the sudden increase in influent ammonia nitrogen concentration leads to enhanced nitrifying bacteria metabolic activity, but the growth rate of nitrifying bacteria is relatively slow. The descending segment of its respiration rate curve is relatively gentle, and the gentle segment is lengthened. The zero-crossing point of the second derivative may shift or lag. Relying solely on the zero-crossing point may cause the segmentation benchmark to deviate from the true metabolic inflection point. Therefore, the zero-crossing point of the second derivative is used as the primary benchmark, and the intercept value when the slope of the tangent at the inflection point of the respiration rate curve reaches zero is calculated as the backup benchmark. When there is an abnormal jump in the continuity between the instantaneous value of the ineffective oxygen quantity corresponding to the primary benchmark and the respiration rate values ​​before and after, the system automatically switches to the backup benchmark to ensure the reliability and stability of the segmentation benchmark.

[0045] When the current shock type is determined to be a toxic inhibitory shock, the intercept value of the tangent slope at the inflection point of the biological metabolic activity time-series data curve within the sliding time window is used as the curve feature point. Under a toxic inhibitory shock, microbial activity is inhibited by toxic substances, and the respiration rate curve rapidly and continuously declines after the shock occurs, exhibiting an approximately exponential decay characteristic. The curve has no obvious inflection point throughout, and the zero-crossing point of the second derivative is often missing or appears at the end of the declining segment, making it unsuitable as a segmentation benchmark. In this case, the inflection point of the declining segment of the respiration rate curve is extracted, and the intercept value of the tangent slope at that inflection point is calculated as the segmentation benchmark. This intercept value can objectively reflect the boundary level of microbial metabolic activity transitioning from an inhibited state to a more stable state under toxic inhibition, avoiding the problem of being unable to segment due to the absence of a zero-crossing point.

[0046] Specifically, in step S3, the time series data within the sliding time window is divided into a falling segment and a smooth segment, including: Using the segmentation benchmark as the dividing point, the time series data within the sliding time window that is located before the segmentation benchmark and whose respiratory rate is continuously decreasing is divided into a decreasing segment, and the time series data that is located after the segmentation benchmark and whose respiratory rate fluctuation amplitude is less than the fluctuation threshold is divided into a smooth segment.

[0047] In this embodiment, the respiratory rate value corresponding to the determined curve feature point is used as the segmentation benchmark. Using this benchmark as the dividing point, the respiratory rate time series data is traversed forward along the time axis within a sliding time window. Continuous time series data where the respiratory rate shows a monotonically decreasing trend ending at the dividing point is divided into a decreasing segment. Conversely, the respiratory rate time series data is traversed backward along the time axis within the sliding time window. Continuous time series data where the respiratory rate fluctuation amplitude is less than a preset fluctuation threshold, starting from the dividing point, is divided into a smooth segment. The preset fluctuation threshold is determined based on the background fluctuation range of the respiratory rate under normal operating conditions and is used to distinguish the residual fluctuations of the smooth segment from those of the decreasing segment. When the respiratory rate fluctuates slightly after the segmentation benchmark but never exceeds the fluctuation threshold, it is determined to have entered a smooth metabolic state.

[0048] This embodiment achieves precise location of inflection points in respiration rate curves under different impact morphologies by selecting curve feature points as segmentation benchmarks based on the differences in impact types. For carbon source impacts, the zero-crossing point of the second derivative is used to accurately capture the transition moment when the respiration rate changes from decreasing to stabilizing. For nitrogen source impacts, a dual-benchmark mechanism is adopted, with the zero-crossing point of the second derivative as the primary benchmark and the intercept of the inflection point tangent as a backup, effectively addressing positioning deviations caused by zero-crossing point offsets or lags. For toxicity-inhibiting impacts, the intercept of the inflection point tangent replaces the missing zero-crossing point as the segmentation benchmark, ensuring effective segmentation even in extreme cases where the curve has no inflection points throughout. Based on this, the time-series data is divided into decreasing and flat segments using the segmentation benchmark as the boundary, providing an accurate data interval division for calculating the metabolic contribution rate, thereby ensuring the accuracy of subsequent carbon emission accounting.

[0049] Specifically, in step S3, the metabolic contribution rate is determined, including: Using the respiratory rate value corresponding to the segmentation benchmark as the instantaneous value of ineffective oxygen, the ratio of the instantaneous value of ineffective oxygen to the total oxygen consumption within the sliding time window is determined, and the ratio is calibrated as the metabolic contribution rate.

[0050] In this embodiment, the respiratory rate value corresponding to the determined segmentation benchmark is used as the instantaneous value of dead oxygen. This instantaneous value of dead oxygen represents the oxygen consumption rate level of microorganisms for maintaining metabolism rather than anabolism at the segmentation benchmark time. Then, the total area enclosed by the respiratory rate time series curve and the time axis within the sliding time window is calculated, and this total area is taken as the total oxygen consumption within the window. Finally, the ratio of the instantaneous value of dead oxygen to the total oxygen consumption within the window is calculated, and this ratio is calibrated as the metabolic contribution rate.

[0051] This embodiment uses the respiratory rate value corresponding to the segmentation benchmark as the instantaneous value of ineffective oxygen, which objectively reflects the oxygen consumption level of microorganisms to maintain metabolism at the turning point. By calculating the ratio of the instantaneous value of ineffective oxygen to the total oxygen consumption within the window, the influence of the absolute value difference of total oxygen consumption under different operating conditions on the calculation results is eliminated, making the metabolic contribution rate a dimensionless relative indicator, which is convenient for comparison between different facilities and different operating conditions.

[0052] Specifically, in step S4, the carbon emission accounting factor is determined, including: The basic carbon emission accounting factor is determined based on the difference between the metabolic contribution rate and the preset benchmark contribution rate. The oxygen mass transfer coefficient is determined based on the particle size distribution of sludge flocs in the sludge morphology data, and the mass transfer efficiency correction coefficient is determined based on the ratio of the oxygen mass transfer coefficient to the standard oxygen mass transfer coefficient. The basic carbon emission accounting factor is corrected using the mass transfer efficiency correction coefficient, and the carbon emission accounting factor is output.

[0053] In this embodiment, the basic carbon emission accounting factor is determined based on the difference between the metabolic contribution rate and the preset benchmark contribution rate. The preset benchmark contribution rate is determined based on the average metabolic contribution rate of the wastewater treatment facility under normal operating conditions and without shock events, representing the baseline maintenance metabolic level of the facility under steady-state operating conditions. The difference between the current metabolic contribution rate and the preset benchmark contribution rate is determined, and the basic carbon emission accounting factor is determined based on the sign and magnitude of this difference through a preset mapping relationship. When the metabolic contribution rate is higher than the benchmark value, it indicates an increase in the proportion of oxygen consumption for maintenance metabolism, and the corresponding carbon emission accounting factor is adjusted upward; when the metabolic contribution rate is lower than the benchmark value, it indicates an increase in the proportion of oxygen consumption for anabolism, and the corresponding carbon emission accounting factor is adjusted downward.

[0054] The oxygen mass transfer coefficient is determined based on the particle size distribution of sludge flocs in the sludge morphology data, and a mass transfer efficiency correction coefficient is determined based on the ratio of the oxygen mass transfer coefficient to the standard oxygen mass transfer coefficient. Specifically, the particle size distribution information of sludge flocs is extracted from the acquired sludge morphology data, and the oxygen mass transfer coefficient under the current sludge state is determined based on the particle size distribution characteristics through a preset oxygen mass transfer coefficient mapping relationship. The size of the sludge flocs directly affects the efficiency of oxygen transfer from the gas phase to the liquid phase and within the flocs: when the floc size is too large, the internal mass transfer resistance increases, and the oxygen mass transfer coefficient decreases; when the floc size is too small, the floc structure is loose, the settling performance deteriorates, and the mass transfer efficiency is also affected. The ratio of the current oxygen mass transfer coefficient to the standard oxygen mass transfer coefficient is used as the mass transfer efficiency correction coefficient. This ratio reflects the degree of influence of the current sludge morphology on the oxygen mass transfer efficiency relative to the baseline state, wherein the standard oxygen mass transfer coefficient is determined based on the sludge morphology parameters under design conditions. The basic carbon emission accounting factor is corrected using the mass transfer efficiency correction coefficient, and the carbon emission accounting factor is output. Specifically, based on the direction and degree of deviation between the oxygen mass transfer efficiency reflected by the mass transfer efficiency correction coefficient and the baseline state, the value of the basic carbon emission accounting factor is adjusted accordingly: when the mass transfer efficiency correction coefficient indicates that the oxygen mass transfer efficiency is better than the baseline state, the accounting factor is adjusted to increase; when the mass transfer efficiency correction coefficient indicates that the oxygen mass transfer efficiency is lower than the baseline state, the accounting factor is adjusted to decrease.

[0055] Specifically, in step S4, the calculated value for the next time step is corrected based on the comparison result, including: The carbon emission calculation value is output after being correlated and calculated with the carbon emission accounting factor and real-time operating parameters, including: Based on the carbon emission accounting factor and the influent flow rate and aeration rate in the real-time operating parameters, combined with the sludge concentration in the real-time operating parameters, the carbon emission accounting at the current moment is determined. The current carbon emission calculation value is compared with the gas phase emission data to obtain the deviation rate; The carbon emission accounting factor for the next time step is corrected using the aforementioned deviation rate to obtain the accounting value for the next time step.

[0056] In this embodiment, the determined carbon emission accounting factor is correlated with real-time operating parameters to output the carbon emission accounting value at the current moment. Specifically, the carbon emission accounting factor is correlated with the influent flow rate, aeration rate, and sludge concentration in the real-time operating parameters. The influent flow rate reflects the current treatment water load of the facility, the aeration rate reflects the oxygen supply intensity of the system, and the sludge concentration reflects the current active biomass level of the system. These three factors, together with the accounting factor, participate in the correlation calculation to output the carbon emission accounting value at the current moment. The carbon emission accounting value at the current moment is compared with the acquired gas phase emission data. This gas phase emission data represents the actual level of greenhouse gas emissions emitted from the aerobic zone. The deviation between the carbon emission accounting value and the gas phase emission data is calculated to obtain the deviation rate. If the accounting value is higher than the measured emission value, it indicates that the accounting result is too large and needs to be adjusted downward; if the accounting value is lower than the measured emission value, it indicates that the accounting result is too small and needs to be adjusted upward.

[0057] The carbon emission calculation factor for the next time step is corrected using the aforementioned deviation rate. Specifically, based on the direction and magnitude of the deviation rate, the carbon emission calculation factors involved in the calculation for the next time step are adjusted accordingly: when the deviation rate indicates a large calculation value, the calculation factor for the next time step is adjusted to decrease; when the deviation rate indicates a small calculation value, the calculation factor for the next time step is adjusted to increase; when the deviation rate is within a preset allowable range, the calculation factor remains unchanged. The corrected calculation factor is used for the carbon emission calculation calculation for the next time step.

[0058] This embodiment establishes a dynamic verification mechanism for the calculation results by comparing the carbon emission calculation values ​​with the measured gas phase emission data in real time. By correcting the calculation factors at the next moment based on the deviation rate, a closed-loop self-correction of the calculation process is achieved, making the calculation values ​​continuously approach the actual emission levels. In long-term operation, it can effectively suppress model drift and accumulated errors, and improve the continuous accuracy of the calculation.

[0059] In this embodiment, the determination of the adjustment range in each of the above adjustment steps is based on a preset mapping relationship. This mapping relationship uses the baseline state under normal operating conditions as the reference zero point to establish the correspondence between the deviation of each influencing factor and the adjustment range of the accounting factor. Specifically, three independent mapping relationships are established for the deviation of metabolic contribution rate, the deviation of mass transfer efficiency, and the deviation of escaping gas comparison: For the deviation of metabolic contribution rate, the statistical mean of metabolic contribution rate under normal operating conditions is used as the baseline zero point. The difference between the current metabolic contribution rate and the baseline value is divided into several deviation intervals. Each deviation interval corresponds to the adjustment step size of a basic carbon emission accounting factor. The larger the deviation, the larger the adjustment step size. The smaller the deviation, the smaller the adjustment step size. For mass transfer efficiency deviation, the oxygen mass transfer coefficient corresponding to the sludge morphology under normal operating conditions is used as the baseline zero point. The deviation of the current oxygen mass transfer coefficient from the baseline value is divided into several deviation intervals, and each deviation interval corresponds to the adjustment step size of a mass transfer efficiency correction coefficient. For escaping gas comparison deviation, the complete match between the calculated value and the measured escaping gas concentration is used as the baseline zero point. The deviation rate is divided into several deviation intervals, and each deviation interval corresponds to the correction step size of the calculation factor at the next moment. The correction step size increases with the deviation rate and decreases with the deviation rate. When the deviation rate falls within the allowable error range, the correction step size is reset to zero. The above mapping relationship is pre-set in the system in the form of a data table or piecewise function. The system automatically looks up the table or calls the function to determine the corresponding adjustment range based on the deviation value calculated in real time.

[0060] Please see Figure 4 As shown, it is a schematic diagram of the real-time carbon emission accounting and intelligent early warning system for wastewater treatment facilities in this embodiment.

[0061] Specifically, a system for real-time carbon emission accounting and intelligent early warning methods applied to the aforementioned wastewater treatment facility includes: The data acquisition module is used to acquire influent water quality data, biological metabolic activity time series data, sludge morphology data and gas phase emission data of the wastewater treatment facility. The data processing module is used to determine the response delay characteristics of the biological metabolic activity time series data to changes in influent water quality, as well as to identify the shock type and determine the mutation characteristic value; The dynamic segmentation module is used to construct a sliding time window, select curve feature points, segment time-series data, and determine the metabolic contribution rate. The calculation and early warning module is used to determine the carbon emission calculation factor, output the carbon emission calculation value, correct the calculation value for the next moment based on the comparison results, and trigger an early warning when the calculation value exceeds the preset threshold.

[0062] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for real-time carbon emission accounting and intelligent early warning of wastewater treatment facilities, characterized in that, include: Step S1: Obtain influent water quality data, biological metabolic activity time series data, sludge morphology data and gas phase emission data of the wastewater treatment facility; align the influent water quality data with the biological metabolic activity time series data, and determine the response delay characteristic of the biological metabolic activity time series data to changes in influent water quality. Step S2: Based on the influent water quality data and the response delay characteristic, identify the impact type of the current influent and determine the mutation characteristic value of the biological metabolic activity time series data; Step S3: In response to the mutation feature value being greater than the preset mutation feature value, a sliding time window is constructed. The selection method of the curve feature points of the biological metabolic activity time series data within the sliding time window is determined according to the shock type. The value corresponding to the curve feature points selected by the selection method is used as the segmentation benchmark to divide the time series data within the sliding time window into a falling segment and a flat segment. The metabolic contribution rate is determined according to the value corresponding to the segmentation benchmark and the total metabolic amount within the window. Step S4: Determine the carbon emission accounting factor based on the metabolic contribution rate and the correction of mass transfer efficiency by the sludge morphology data, and output the carbon emission accounting value after associating the carbon emission accounting factor with the real-time operating parameters. The carbon emission calculation value is compared with the gas phase emission data, the calculation value for the next moment is corrected according to the comparison result, and an early warning is triggered when the calculation value exceeds a preset threshold.

2. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 1, characterized in that, In step S1, the response delay characteristic of the biological metabolic activity time-series data to changes in influent water quality is determined, including: Step S11: Filter the influent water quality data and the biological metabolic activity time series data respectively; Step S12: Using the abrupt change time of the influent water quality data as the reference time, record the time difference between when the amplitude change rate of the biological metabolic activity time series data reaches the preset change rate and the reference time, and use it as a candidate delay feature quantity; Step S13: Perform a moving average on several candidate delay features and output the response delay features.

3. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 1, characterized in that, In step S2, the impact type of the current inflow is identified, including: If the rate of change of chemical oxygen demand concentration in the influent water quality data is greater than a first rate of change threshold, it is determined to be a carbon source shock. If the rate of change of ammonia nitrogen concentration in the influent water quality data is greater than the second rate of change threshold, it is determined to be a nitrogen source shock. If the response delay feature is greater than a delay threshold and the rate of decrease in the amplitude of the biological metabolic activity time series data is greater than a rate of decrease threshold, it is determined to be a toxicity inhibition shock.

4. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 1, characterized in that, In step S2, the mutation characteristic value of the biological metabolic activity time series data is the maximum value of the first derivative of the biological metabolic activity time series data within a preset time window.

5. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 1, characterized in that, In step S3, determining the selection method for the curve feature points of the biological metabolic activity time-series data within the sliding time window includes: In response to the impact type being a carbon source impact, the zero-crossing point of the second derivative of the biological metabolic activity time series data within the sliding time window is used as the curve feature point. In response to the impact type being nitrogen source impact, the zero-crossing point of the second derivative of the biological metabolic activity time series data within the sliding time window is taken as the main curve feature point, and the intercept value when the slope of the tangent at the inflection point of the biological metabolic activity time series data curve returns to zero is taken as the backup curve feature point. In response to the impact type being a toxic inhibitory impact, the intercept value at the inflection point of the biological metabolic activity time-series data curve within the sliding time window, where the slope of the tangent line returns to zero, is used as the curve feature point.

6. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 5, characterized in that, In step S3, the time series data within the sliding time window is divided into a falling segment and a level segment, including: Using the segmentation benchmark as the dividing point, the time series data within the sliding time window that is located before the segmentation benchmark and whose respiratory rate is continuously decreasing is divided into a decreasing segment, and the time series data that is located after the segmentation benchmark and whose respiratory rate fluctuation amplitude is less than the fluctuation threshold is divided into a smooth segment.

7. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 1, characterized in that, In step S3, the metabolic contribution rate is determined, including: Using the respiratory rate value corresponding to the segmentation benchmark as the instantaneous value of ineffective oxygen, the ratio of the instantaneous value of ineffective oxygen to the total oxygen consumption within the sliding time window is determined, and the ratio is calibrated as the metabolic contribution rate.

8. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 1, characterized in that, In step S4, the carbon emission accounting factor is determined, including: The basic carbon emission accounting factor is determined based on the difference between the metabolic contribution rate and the preset benchmark contribution rate. The oxygen mass transfer coefficient is determined based on the particle size distribution of sludge flocs in the sludge morphology data, and the mass transfer efficiency correction coefficient is determined based on the ratio of the oxygen mass transfer coefficient to the standard oxygen mass transfer coefficient. The basic carbon emission accounting factor is corrected using the mass transfer efficiency correction coefficient, and the carbon emission accounting factor is output.

9. The method for real-time carbon emission calculation and intelligent early warning of wastewater treatment facilities according to claim 1, characterized in that, In step S4, the calculated value for the next time step is corrected based on the comparison results, including: The carbon emission calculation value is output after being correlated and calculated with the carbon emission accounting factor and real-time operating parameters, including: Based on the carbon emission accounting factor and the influent flow rate and aeration rate in the real-time operating parameters, combined with the sludge concentration in the real-time operating parameters, the carbon emission accounting value at the current moment is determined. The current carbon emission calculation value is compared with the gas phase emission data to obtain the deviation rate; The carbon emission accounting factor for the next time step is corrected using the aforementioned deviation rate to obtain the accounting value for the next time step.

10. A system for real-time carbon emission accounting and intelligent early warning method of wastewater treatment facilities according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire influent water quality data, biological metabolic activity time series data, sludge morphology data and gas phase emission data of the wastewater treatment facility. The data processing module is used to determine the response delay characteristics of the biological metabolic activity time series data to changes in influent water quality, as well as to identify the shock type and determine the mutation characteristic value; The dynamic segmentation module is used to construct a sliding time window, select curve feature points, segment time-series data, and determine the metabolic contribution rate. The calculation and early warning module is used to determine the carbon emission calculation factor, output the carbon emission calculation value, correct the calculation value for the next moment based on the comparison results, and trigger an early warning when the calculation value exceeds the preset threshold.

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