Hydrogen sulfide trapping control method based on industrial monitoring software

Abstract

The invention relates to the technical field of sewage treatment and industrial automation control, in particular to a hydrogen sulfide trapping control method based on industrial monitoring software, which comprises the following steps: a data acquisition and pretreatment step: acquiring indirect state parameters and direct monitoring parameters of a sewage treatment system through the industrial monitoring software; a soft measurement reconstruction and correction step: inputting the indirect state parameters into a soft measurement reconstruction model to calculate and predict the hydrogen sulfide concentration, and correcting the direct monitoring parameters to generate a corrected hydrogen sulfide concentration value; a toxicity index resolving step: resolving a toxicity inhibition index of the downstream biological treatment unit based on the historical medicament dosage and the real-time flow data; a multi-mode strategy decision-making step: based on the corrected concentration value and the toxicity inhibition index, selectively generating an emergency blocking, controlled overflow or critical steady state strategy and outputting a medicament pump control instruction; the invention overcomes the defect that the traditional control neglects the side effect of the medicament, and realizes the system ductile operation of maintaining environment-friendly compliance and protecting biological activity.

Description

Technical Field

[0001] This invention relates to the fields of wastewater treatment and industrial automation control technology, specifically a hydrogen sulfide capture and elimination control method based on industrial monitoring software. Background Technology

[0002] Hydrogen sulfide capture and elimination control refers to adjusting the dosage of deodorizing agents based on the operating status of the wastewater treatment system to remove odorous pollutants, primarily hydrogen sulfide gas produced by organic matter in wastewater under anaerobic conditions. Current hydrogen sulfide control methods include three types: manual feedback control based on manual sampling, proportional dosing control based on influent flow rate, and proportional-integral-derivative automatic control based on feedback from a single gas sensor. However, when using existing technologies for agent dosing control, the single gas sensor used for direct measurement is prone to toxic drift or failure under harsh environments, leading to distorted feedback signals. Traditional control logic, especially flow-based proportional dosing control and single-sensor feedback control, lacks quantitative assessment of the toxic side effects of the agent on downstream biological treatment units, making it highly susceptible to overdosing and causing biological system collapse, thus reducing the stability and safety of the entire process. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a hydrogen sulfide capture and control method based on industrial monitoring software. Specifically, the technical solution of this invention includes: Indirect status parameters and direct monitoring parameters of the wastewater treatment system are collected through the data interface of industrial monitoring software. The indirect state parameters are input into a preset soft-sensor reconstruction model, which is configured to characterize the mapping relationship between the indirect state parameters and the hydrogen sulfide concentration. The predicted hydrogen sulfide concentration at the current moment is calculated, and the direct monitoring parameters are corrected using the predicted hydrogen sulfide concentration to generate a corrected hydrogen sulfide concentration value. Based on the historical reagent dosage data and real-time flow data of the wastewater treatment system, the toxicity inhibition index of the downstream biological treatment unit is calculated. Based on the corrected hydrogen sulfide concentration value and the toxicity inhibition index, a chemical dosing control strategy for the wastewater treatment system is determined, and a corresponding chemical pump control command is output. The determination of the reagent dosing control strategy for the wastewater treatment system includes: In response to the modified hydrogen sulfide concentration value being higher than the preset hard emission limit value, an emergency blocking strategy is generated as the agent dosing control strategy; In response to the modified hydrogen sulfide concentration value being lower than or equal to the hard emission limit value, the toxicity inhibition index is compared with a preset biodegradation threshold. In response to the toxicity inhibition index being higher than the biological collapse threshold, a controlled spillover strategy is generated as the drug dosing control strategy. In response to the toxicity inhibition index being lower than or equal to the biological collapse threshold, a critical steady-state strategy is generated as the drug dosing control strategy.

[0004] Preferably, the step of correcting the direct monitoring parameter using the predicted hydrogen sulfide concentration to generate a corrected hydrogen sulfide concentration value includes: The absolute value of the difference between the direct monitoring parameter and the predicted hydrogen sulfide concentration is calculated as the ratio of the predicted hydrogen sulfide concentration to the deviation ratio. In response to the deviation ratio exceeding a preset sensor drift threshold, a toxic drift is determined to have occurred in the sensor, and the predicted hydrogen sulfide concentration is used as the corrected hydrogen sulfide concentration value; or... In response to the deviation ratio not exceeding the sensor drift threshold, a weighted average of the direct monitoring parameter and the predicted hydrogen sulfide concentration is calculated as the corrected hydrogen sulfide concentration value.

[0005] Preferably, the controlled spillover generation strategy serves as the drug dosing control strategy, comprising: The control target concentration is set as a first target value, wherein the first target value is higher than the preset daily operation target value and lower than the hard emission limit value; Based on the first target value and the corrected hydrogen sulfide concentration value, the first reagent dosage is calculated using a proportional-integral-derivative control algorithm. The dosage of the first drug is packaged into the drug pump control command.

[0006] Preferably, the critical steady-state generation strategy, as the drug dosing control strategy, includes: The control target concentration is set as a second target value, wherein the second target value is equal to the daily operation target value; Based on the second target value and the corrected hydrogen sulfide concentration value, the dosage of the second agent is calculated using a proportional-integral-derivative control algorithm. The dosage of the second agent is packaged into the agent pump control command.

[0007] Preferably, the indirect state parameters include: The influent pH value, oxidation-reduction potential (ORP) value, influent flow rate, and influent pipeline pressure value.

[0008] Preferably, the calculation of the toxicity inhibition index of the downstream biological treatment unit based on the historical reagent dosage data and real-time flow data of the wastewater treatment system includes: Obtain the cumulative dosage of the historical drug dosage data within a preset time window; Obtain the cumulative inflow rate of the real-time flow data within the preset time window; Calculate the ratio of the cumulative dosage to the cumulative influent flow rate to obtain the current residual concentration of the reagent; The toxicity inhibition index is obtained by mapping calculation based on the current drug residue concentration using a preset bioactivity decay function, wherein the bioactivity decay function defines a positive correlation between drug residue concentration and toxicity inhibition index.

[0009] Preferably, the emergency blocking strategy generated in response to the modified hydrogen sulfide concentration value being higher than a preset hard emission limit includes: Start a timer to monitor the duration for which the corrected hydrogen sulfide concentration value remains above the hard emission limit; In response to the duration exceeding a preset emergency response time threshold, a plant-wide shutdown and cutoff command is generated; In response to the fact that the duration does not exceed the emergency response time threshold, a maximum load dosing command corresponding to the maximum rated flow rate of the agent pump is generated.

[0010] Preferably, the method further includes: After executing the drug dosing control strategy, the corrected hydrogen sulfide concentration value at the next moment is obtained as feedback data; Calculate the control error between the feedback data and the target concentration; The control error, the indirect state parameters, and the direct monitoring parameters are stored in the training database for periodic updates to the soft measurement reconstruction model.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This method achieves deep integration of physical measurement and soft measurement, ensuring the authenticity and reliability of the data source of the control system; by reconstructing the model through soft measurement to predict the hydrogen sulfide concentration, and combining the deviation ratio and sensor drift threshold judgment, it can effectively identify the poisoning drift or failure of physical sensors; when the sensor is normal, the data smoothness is improved by weighted averaging, and when there is a fault, it forces a switch to the predicted value, solving the problem of control misjudgment caused by the distortion of a single sensor.

[0012] 2. This method introduces a control strategy based on the calculation of the toxicity inhibition index of the downstream biological treatment unit and the comparison between this index and the biological collapse threshold, to prevent system collapse caused by excessive chemical dosing. By calculating the toxicity inhibition index of the downstream biological treatment unit using historical chemical dosing and real-time flow rate, the impact of chemical residues on microorganisms is quantified and used as a control basis. This mechanism changes the limitation of traditional control logic that only focuses on gas concentration. By triggering strategy adjustment when the toxicity inhibition index is too high, it effectively avoids the decline in downstream activated sludge activity and damage to the biological system caused by excessive chemical dosing.

[0013] 3. This method constructs a hierarchical dynamic response strategy, achieving resilient operation that balances environmental compliance and bioactivity. By flexibly switching between emergency blocking, controlled spillover, and critical steady-state strategies, the system can dynamically adjust the control target based on the corrected concentration value and toxicity inhibition index. In particular, under the controlled spillover strategy, by raising the control target value to reduce reagent dosage, space is created for the recovery of the biological system without exceeding the hard emission limits, thus improving the overall operational resilience and economy. Attached Figure Description

[0014] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0015] 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 and accompanying drawings.

[0016] Example 1: Please see Figure 1 A method for controlling hydrogen sulfide capture and elimination based on industrial monitoring software, comprising: Indirect status parameters and direct monitoring parameters of the wastewater treatment system are collected through the data interface of industrial monitoring software. The indirect state parameters are input into a preset soft-sensor reconstruction model. The soft-sensor reconstruction model is configured to characterize the mapping relationship between the indirect state parameters and the hydrogen sulfide concentration. The predicted hydrogen sulfide concentration at the current moment is calculated, and the direct monitoring parameters are corrected using the predicted hydrogen sulfide concentration to generate the corrected hydrogen sulfide concentration value. Based on historical reagent dosage data and real-time flow data of the wastewater treatment system, the toxicity inhibition index of the downstream biological treatment unit is calculated; the real-time flow data specifically refers to the real-time influent flow rate at the inlet of the wastewater treatment system. Based on the corrected hydrogen sulfide concentration value and toxicity inhibition index, a chemical dosing control strategy for the wastewater treatment system is determined, and corresponding chemical pump control commands are output. Among these, the determination of chemical dosing control strategies for wastewater treatment systems includes: In response to the revised hydrogen sulfide concentration value being higher than the preset hard emission limit, an emergency blocking strategy is generated as a reagent dosing control strategy. In response to the modified hydrogen sulfide concentration being lower than or equal to the hard emission limit, the toxicity inhibition index is compared with the preset biodegradation threshold. In response to a toxicity inhibition index exceeding the biological collapse threshold, a controlled spillover strategy is generated as a drug dosing control strategy. In response to a toxicity inhibition index being lower than or equal to the biological collapse threshold, a critical steady-state strategy is generated as a drug dosing control strategy.

[0017] In this embodiment, the method relies on industrial monitoring software deployed on a central control room server. This software communicates with field programmable logic controllers and various online instruments through an open platform communication unified architecture or the Modbus transmission control protocol. The system collects indirect status parameters and direct monitoring parameters of the wastewater treatment system in real time through the data interface of the industrial monitoring software. It should be noted that the agents mentioned in this method refer to chemical substances that can undergo neutralization, oxidation, or complexation reactions with hydrogen sulfide, including but not limited to sodium hypochlorite, hydrogen peroxide, potassium ferrate, alkaline solutions, or dedicated biological disinfectants. The control system adjusts the frequency of the agent pump to precisely regulate the dosage of the above-mentioned agents with different physicochemical properties to adapt to the deodorization requirements under different water quality conditions. The directly monitored parameters refer to the real-time concentration values ​​read by hydrogen sulfide gas sensors installed in the reaction tank or at the emission outlet. Because hydrogen sulfide gas is highly corrosive and toxic, this parameter is prone to drift or distortion during long-term operation. Indirect state parameters refer to physical quantity vectors that are not directly measured for hydrogen sulfide but are strongly related to its formation mechanism or physical transport process. ; In the specific configuration of this embodiment, in order to accurately reflect the dynamic characteristics of the wastewater treatment process and clarify the input scale of the model, vector... Specifically defined, it consists of four physical quantities: influent pH, oxidation-reduction potential (ORP), influent flow rate, and influent pipeline pressure. These parameters together determine the reaction environment and transport status of hydrogen sulfide generation. In particular, when using vectors Before inputting the model, in order to eliminate the influence of the differences in the dimensions of various physical quantities on the convergence speed of the neural network gradient descent, the system performs standard score standardization preprocessing: in, For vectors The first in One portion, The input values ​​are standardized. and These are the moving average and standard deviation of the physical quantity over the past 30 days of historical data; As a preset numerical stability constant, this embodiment takes... This is used to prevent the standard deviation from increasing when a physical quantity remains constant over a long period of time. A division-by-zero error occurs when the value is zero; statistical parameters and The system is set to automatically update based on historical data from the previous 30 days at 00:00 every day. This step ensures that pH values ​​of 0-14 and pipeline pressures of 0-500 kPa are calculated on the same numerical scale and can adapt to seasonal water quality baseline drift, avoiding numerical saturation. To address the potential poisoning drift issue in sensors, the system introduces a soft-sensor reconstruction model. This model is pre-trained using a deep learning algorithm, and its input is standardized data. The output is a predicted value for the current hydrogen sulfide concentration. The model is configured to characterize the nonlinear mapping relationship between indirect state parameters and hydrogen sulfide concentration. Specifically, the soft-sensor reconstruction model employs a Long Short-Term Memory (LSTM) network architecture to capture large lags and temporal correlation features in the wastewater treatment process. In other similar embodiments, the model can be replaced with gated recurrent units, spatiotemporal convolutional networks, or deep neural networks, depending on the computational power requirements, as long as they can characterize indirect state parameters. Compared with predicted hydrogen sulfide concentration The non-linear mapping relationship between them can be used.

[0018] The model structure consists of an input layer configured to receive a temporal tensor with dimensions (batch, 60, 4), where 60 represents the time step. This involves inputting a historical data sequence from the past 60 minutes. The selection of this time step is not based on general experience, but rather strictly matches the hydraulic retention time of the equalization tank in this wastewater treatment system, with a design value of 55 minutes. The aim is to ensure that the input sequence fully covers the physical transport and reaction cycle of wastewater from the inlet to the hydrogen sulfide generation point, enabling the LSTM network to capture long-range dependencies with clear physical causality. This is a key improvement that distinguishes this model from general time series prediction models; 4 represents a vector. The system consists of two hidden layers, each containing 64 LSTM units, activated by the hyperbolic tangent function; and a fully connected output layer that outputs a single-dimensional predicted concentration. ; It is worth noting that the direct output of the neural network Typically within the normalized domain, such as -1 to 1 or 0 to 1, in order to obtain a physically meaningful concentration value in ppm, inverse normalization must be performed; this embodiment specifies that during the model training phase, the target label... The historical hydrogen sulfide concentration, which has also been standardized, is used in the inference phase to calculate the final predicted concentration using the following formula. : in, and The mean and standard deviation of historical hydrogen sulfide concentration labels stored in the database are updated synchronously with the input statistical parameters to ensure a strict correspondence between the model output space and the physical space. The model training employs the backpropagation algorithm over time, utilizing historically cleaned sensor datasets for supervised learning. Specifically, to ensure the model's convergence and generalization ability, the training process uses an adaptive moment estimation optimizer with an initial learning rate set to 0.001, coupled with a learning rate decay strategy, which reduces the learning rate by 10% every 10 iterations. The loss function is the mean squared error, the batch size is set to 64, and the maximum number of iterations is 200. Meanwhile, to prevent overfitting, a random deactivation mechanism is introduced in the fully connected layer with a dropout rate of 0.2, and 20% of the data is allocated as a validation set. An early stopping mechanism is triggered if the validation set loss does not decrease within 15 consecutive epochs, thus ensuring the prediction accuracy of the pre-defined model. Predicted hydrogen sulfide concentration, sourced from model output, unit: ppm; : Soft measurement mapping function, sourced from a pre-defined LSTM model; : The standardized indirect state parameter vector; Based on this, utilize right Make corrections to generate the corrected hydrogen sulfide concentration value. This step is equivalent to constructing a shadow observer in the digital space to verify and patch blind spots in physical sensors; to avoid overdosing leading to the collapse of downstream biological processing units, the system introduces a toxicity inhibition index. This index, calculated based on historical reagent dosage data and real-time flow data of the wastewater treatment system, quantifies the potential kill risk of current residual reagents to activated sludge microorganisms, thereby constructing a biosafety boundary assessment mechanism for wastewater treatment systems; furthermore, based on and Determine the pesticide dosing control strategy and output pesticide pump control commands; This decision logic is essentially a finite state machine, containing the following state transitions: when At that time, among them, As a preset mandatory emission limit, this embodiment sets the emission limit according to the GB14554-93 Odor Pollutant Emission Standard. ppm, the system generates an emergency containment strategy designed to reduce pollutant concentrations as quickly as possible; when and At that time, among them, The preset biological collapse threshold, its specific value The definition and acquisition method will be detailed in subsequent embodiments. The system implements a controlled overflow strategy, actively allowing the hydrogen sulfide concentration to rise appropriately within a safe range in exchange for a reduction in the amount of reagent added. when and At that time, the system executes a critical steady-state strategy, with the goal of minimizing reagent costs and reducing chemical sludge production while meeting emission standards. Regarding the toxicity inhibition index mentioned in the embodiments and implementation methods To address the issue of frequent control strategy jumps caused by fluctuations around the threshold, this embodiment introduces a hysteresis comparator mechanism in its specific logic implementation. Specifically, although the theoretical threshold is set to... However, in engineering implementation, an asymmetric switching boundary is defined: only when At that time, a switch from critical steady state to controlled overflow is triggered; and if and only if Falling back to ,Right now Only under the following conditions is it permissible to switch back to the critical steady state from controlled overflow; this setting is... The dead zone width effectively filters out noise caused by computation. The small oscillation of the values ​​avoids frequent switching between two sets of significantly different proportional-integral-derivative parameters, thus ensuring the stability of the actuator and the continuity of the process.

[0019] Example 2: The predicted hydrogen sulfide concentration is used to correct the directly monitored parameters, generating corrected hydrogen sulfide concentration values, including: The absolute value of the difference between the directly monitored parameter and the predicted hydrogen sulfide concentration is calculated as the ratio of the predicted hydrogen sulfide concentration to the actual deviation ratio. If the deviation ratio exceeds a preset sensor drift threshold, the sensor is determined to have experienced toxic drift, and the predicted hydrogen sulfide concentration is used as the corrected hydrogen sulfide concentration value; or, if the deviation ratio does not exceed the sensor drift threshold, the weighted average of the direct monitoring parameters and the predicted hydrogen sulfide concentration is calculated as the corrected hydrogen sulfide concentration value.

[0020] This embodiment details the use of predicted hydrogen sulfide concentration. For directly monitored parameters The specific process of correction; in order to accurately identify whether the sensor has malfunctioned or drifted, the system calculates the deviation ratio. This ratio reflects the degree of deviation between physical measurements and soft measurement predictions, and is calculated using the following formula: in, The deviation ratio is calculated and its physical meaning is a reverse index of sensor confidence. The parameters are directly monitored and sourced from physical sensor readings, with units of ppm. The hydrogen sulfide concentration is predicted from the output of a soft-sensing model, in ppm. A minute constant, derived from a preset value; in this embodiment, it is taken as [value missing]. This is used to prevent the denominator from being zero; System execution decision logic: in response to ,in, The preset sensor drift threshold is used in this embodiment. The specific value is set at 0.35; this addresses the fundamental flaw in existing technologies that rely solely on the relative deviation ratio—namely, the potential for issues with the predicted value. Even with significant absolute deviations, such as 10 ppm, when the value is relatively large (e.g., 100 ppm), the calculated value will still be affected. It is still possible that the value is below the threshold, thus missing the fault detection. This embodiment has made key improvements to the judgment logic and introduced an absolute deviation circuit breaker mechanism. Specifically, the logic for determining whether a sensor has experienced poisoning drift is constructed as a dual-trigger condition: Absolute deviation fuse: Check Does it exceed the preset limit deviation? In this embodiment, the value is set to 5.0 ppm; if this value is exceeded, regardless of... The calculation results indicate that the system's forced judgment deviation ratio has exceeded the safety limit, which is logically equivalent to... This mechanism directly triggers drift detection, ensuring that large errors in high-concentration backgrounds can be captured in a timely manner. Relative deviation check and noise threshold: If the fuse is not triggered, check... Whether this is true; at the same time, in order to prevent, in a low concentration background, such as ppm, relative error due to sensor white noise. Abnormal amplification can trigger misjudgments. This embodiment introduces an absolute error threshold mechanism in this branch: that is, in this case, the necessary and sufficient condition for determining drift is: deviation ratio. And absolute deviation ,in, The minimum significant deviation is set to 0.5 ppm in this embodiment; In summary, when either the absolute deviation trigger condition is met or the relative deviation check condition is met and the noise threshold is exceeded, the system determines that the physical sensor is unreliable, possibly due to poisoning drift or hysteresis caused by high-concentration sulfate impact. In this case, to prevent erroneous data from misleading the control system, the soft sensor value is directly used as the correction value. Alternatively, if none of the above conditions are met, it indicates that the physical sensor is working properly and the deviation is within an acceptable range. In this case, to balance the real-time performance of physical detection and the stability of the soft measurement model, a weighted average method is used to generate a correction value. in, The corrected hydrogen sulfide concentration value is calculated and is expressed in ppm. The reliability weighting coefficient is a preset value, set to 0.7 in this embodiment. This value is not arbitrarily selected, but calculated based on the minimum variance estimation principle. Specifically, it is obtained by statistically analyzing the noise variance of the sensor after its most recent calibration. and the variance of the prediction residuals of the soft measurement model on the validation set. Determined according to the formula: In the field working conditions of this embodiment, the measurements were... ,correspond ppm accuracy ,correspond ppm accuracy, calculated In engineering practice, it is rounded down to 0.7; This coefficient establishes a fusion strategy within the grayscale range where the drift threshold is not triggered: the system primarily relies on physical measurements (70% weight) and secondarily on model predictions (30% weight); this design utilizes... By absorbing random noise and using the aforementioned composite drift judgment logic to handle severe sensor drift, random errors and systematic failures are logically distinguished, avoiding the logical closed-loop risk of a single weighted formula when the sensor fails, and ensuring the authenticity of the data source. This embodiment constructs a data firewall. Under the conditions of short influent pipe network and high flow rate in sewage treatment plants, in the event of false negatives due to low readings caused by high concentration of hydrogen sulfide poisoning of sensors, the soft measurement model based on reaction kinetics can keenly capture changes in influent parameters, thereby forcibly raising the correction concentration value, triggering system defense, avoiding environmental accidents caused by sensor distortion, and ensuring the authenticity and reliability of the control system's data source.

[0021] Example 3: Generate a controlled spillover strategy as a drug dosing control strategy, including: The control target concentration is set as the first target value, which is higher than the preset daily operation target value and lower than the hard emission limit value; Based on the first target value and the corrected hydrogen sulfide concentration value, the first reagent dosage is calculated using a proportional-integral-derivative control algorithm. The dosage of the first agent is packaged into a control command for the agent pump.

[0022] This embodiment details the execution logic of the controlled overflow strategy, which is the core means for the system to cope with dual asymmetric interference. When the system enters the controlled overflow mode, the control objective is no longer to pursue zero emissions, but to pursue survival; the system sets the control target concentration to the first target value. ,satisfy ,in, The preset daily operating target value, These are hard emission limits; specifically, in order to transform this inequality constraint into deterministic computer-executable control instructions, this embodiment defines... For safe backoff points based on hard limits: in, The preset controlled overflow safety factor is set to 0.85 in this embodiment, which is the set value. ppm; and the preset daily operating target value. Set to 0.5 ppm; this parameter setting ensures that... Within the specified range, the control system can minimize the dosage of the pesticide, thereby suppressing the toxicity index. The growth of these factors provides a valuable window of opportunity for biological systems to recover. This setting implies that the system actively relinquishes a portion of its safety margin, converting it into survival space for downstream biological systems; based on and the current corrected concentration The dosage of the first agent is calculated using a proportional-integral-derivative control algorithm. Given that the control system is a discrete digital system, the above calculations are performed using a position-based discrete proportional-integral-differential algorithm: Among them, sampling period The interval is set to 1 minute, which is synchronized with the sensor's refresh rate. To prevent historical cumulative interference from the integral term during switching between different control strategies, a lower limit for the integral summation in the formula is set. Defined as the starting moment when the current control strategy is activated, i.e., the integrator is automatically reset each time a strategy switch is initiated; At the same time, for differential terms The calculation, in the first time step of the policy switch. The system forcibly sets historical error. This makes the differential term output zero, to prevent the control target value from being affected. The differential impact caused by the sudden change damaged the actuator; The original control quantity is calculated using the proportional-integral-derivative algorithm; The error value at the current time is calculated and defined as follows: ; The parameters are proportional-integral-differential, sourced from a preset configuration. In the controlled overflow strategy of this embodiment, to avoid drastic fluctuations in drug dosage caused by adjustments to the target value, which could lead to secondary shocks to the biological system, the parameter configuration adopts a weak proportional, strong integral strategy, specifically set as follows: proportional coefficient L / h / ppm, integral coefficient L / h / (ppm·min), differential coefficient L / h / (ppm / min); The values ​​of the aforementioned proportional-integral-derivative control parameters are not arbitrarily set, but are obtained by tuning according to the Ziegler-Nichols closed-loop critical proportional gain method. The specific steps are as follows: During the system commissioning phase, the controller is placed in pure proportional mode, and the proportional gain is gradually increased until the system produces critical oscillations. The critical gain is then measured. and critical oscillation period After calculating the basic parameters based on the Ziegler-Nichols formula, and considering the control objective of rapidly suppressing the rise in the toxicity index in the controlled spillover strategy, the proportional gain was appropriately increased, resulting in the above-mentioned... Specific values; this disclosed tuning method enables those skilled in the art to obtain specific parameters applicable to their actual applications by reproducing the tuning process in different wastewater treatment systems; To ensure that the calculation results comply with the limitations of the physical actuator and to prevent negative values ​​from being added or exceeding the pump's range, the system... Physical constraint truncation was performed to obtain the final dosage of the first agent. : in, The rated maximum flow rate of the pharmaceutical pump is 500 L / h in this embodiment; The function is used to handle when If the value is negative, meaning the proportional-integral-derivative output is negative when the current concentration is better than the target value, the addition will be forcibly stopped by resetting to zero. because The error was increased, and the calculated error was... This will decrease, thus affecting the output dosage of the drug. Significantly reduced; the calculated The frequency or stroke signal is converted into a pump frequency signal and encapsulated as a pharmaceutical pump control command for issuance. Specifically, in order to map the calculated volumetric flow rate into a hardware-executable electrical signal, the system executes the following logic: when At that time, directly set the frequency command. To stop pumping and prevent the pump body from operating ineffectively at dead-zone frequencies; when At that time, perform a linear transformation: in, This is the frequency command issued to the inverter. This refers to the rated frequency of the pump motor, typically 50Hz. The dead zone frequency of the pump, which is the minimum frequency at which the diaphragm can be driven to move, is measured to be 12Hz in this embodiment; this step ensures that the control command can drive the physical pump to output the expected drug flow rate. This embodiment dynamically raises the control target, forcibly reducing the addition of oxidant without violating laws and regulations or exceeding the limits. This counterintuitive strategy effectively curbs the upward trend of the toxicity inhibition index and prevents secondary disasters caused by panic-induced overdosing, namely the collapse of biological systems. Thus, it opens up a third operational path between the environmental protection red line and the bottom line of biological survival.

[0023] Example 4: Generate critical steady-state strategies as drug dosing control strategies, including: Set the control target concentration as the second target value, where the second target value is equal to the daily operation target value; Based on the second target value and the corrected hydrogen sulfide concentration value, the dosage of the second agent is calculated using a proportional-integral-derivative control algorithm. The dosage of the second agent is packaged into a control command for the agent pump.

[0024] This embodiment details the execution logic of the critical steady-state strategy; when the system is in a low-risk state, it reverts to an operating mode that pursues the ultimate cost-effectiveness; the control target concentration is set to the second target value. ,in Equal to daily operating target value In this embodiment, it is explicitly set ppm; in this mode, It is usually set to a value close to the emission standard, which is called critical operation, in order to minimize the consumption of reagents. based on and The dosage of the second agent was calculated using a proportional-integral-differential algorithm. At this point, the proportional-integral-derivative parameters are configured with conservative values ​​that prioritize system stability over response speed, specifically set as follows: proportional coefficient L / h / ppm, with values ​​approximately in the controlled overflow strategy 40%, integral coefficient L / h / (ppm·min), differential coefficient L / h / (ppm / min), in this mode the derivative action is turned off to suppress high-frequency noise interference; this parameter combination can smooth out small concentration fluctuations and avoid shortening the equipment life due to frequent adjustment of the reagent pump frequency; The determination method for the above parameters differs from that in Example 3. Given that the critical steady-state strategy places greater emphasis on the system's anti-interference capability and economy, the Cohen-Kuhn tuning method or the Lambda tuning method based on the process response curve is adopted. Specifically, the static gain, time constant, and pure time delay of the object are obtained through open-loop step testing, and the parameters are calculated with maximizing the phase margin as the objective function, thereby obtaining the aforementioned smaller proportional coefficient. And integral coefficients; this model-based parameter optimization process ensures that the controller will not make unnecessary adjustments due to measurement noise under low load conditions; This embodiment ensures the system's economy under normal conditions and achieves the goal of energy conservation and consumption reduction. At the same time, since it is always maintained in a critical state, once the operating conditions change abruptly, the system can seamlessly switch to a rapid state switching mechanism to deal with the risks. While ensuring the minimum daily operating costs, it maintains the baseline response capability to environmental changes.

[0025] Example 5: Indirect state parameters include: The influent pH value, oxidation-reduction potential (ORP) value, influent flow rate, and influent pipeline pressure value.

[0026] This embodiment details the indirect state parameters used for soft measurement reconstruction. The specific composition and physical meaning of the water; indirect state parameters include: influent pH value. Since the form of hydrogen sulfide in water is highly dependent on pH, the lower the pH, the higher the risk of molecular hydrogen sulfide escaping; redox potential (ORP) value... This parameter characterizes the redox environment of the water body. A sharp decrease indicates that the environment is suitable for the reproduction of sulfate-reducing bacteria, foreshadowing a surge in the kinetic potential for hydrogen sulfide generation; influent flow rate value It is used to determine hydraulic residence time and impact load; as well as the pressure value of the inlet pipe. Sudden pressure changes usually mean the start-up or shutdown of upstream pumping stations or drastic fluctuations in the hydraulic conditions of the pipeline network, often accompanied by the release of accumulated pollutants. In this embodiment, these parameters are selected to form a reaction fingerprint of hydrogen sulfide generation. Even if the direct sensor fails, the combined characteristics of these physicochemical parameters can accurately reflect the current hydrogen sulfide risk through the soft measurement model, ensuring the all-weather robustness of the system and solving the problem that single direct measurement is easily interfered with in complex water quality environments.

[0027] Example 6: Based on historical reagent dosage data and real-time flow data of the wastewater treatment system, the toxicity inhibition index of the downstream biological treatment unit is calculated, including: Obtain the cumulative dosage of historical pesticides within a preset time window; Acquire the cumulative inflow rate within a preset time window using real-time flow data; Calculate the ratio of cumulative reagent dosage to cumulative influent flow rate to obtain the current reagent residual concentration; Using a preset bioactivity decay function, a mapping calculation is performed based on the current drug residue concentration to obtain the toxicity inhibition index. The bioactivity decay function defines the positive correlation between drug residue concentration and the toxicity inhibition index.

[0028] This embodiment details the toxicity inhibition index. The calculation process, this index is the key link connecting the chemical deodorization unit and the biological treatment unit; the system sets a sliding time window. The length of the window Based on the hydraulic retention time setting of the downstream biological treatment unit, which is set to 4 hours in this embodiment, the evaluation scope covers the complete residual cycle of the reagent; within this window, the cumulative reagent dosage is obtained. and cumulative inflow rate The above cumulative quantities are calculated through discrete summation. To address the issue of dimensional consistency between engineering units and the time dimension, the calculation formula explicitly includes a unit conversion factor: in, The instantaneous flow rate of the pharmaceutical pump is L / h. The sampling interval is 1 minute; divide by 60 to convert minutes to hours. Similarly, for cumulative inflow rate If the inlet flow meter The unit of reading is The calculation formula is: Multiplying by 1000 is used to convert cubic meters to liters (L) to ensure that the units of the numerator and denominator are consistent in subsequent concentration calculations. To ensure that the calculation results meet the physical requirement of concentration in mg / L, the system introduces the mass concentration parameter of the active ingredient of the drug for dimensional conversion to calculate the current residual concentration of the drug. : in, Current residual concentration of the agent, obtained through calculation, in mg / L; The cumulative dosage of the pesticide is derived from historical data records and is explicitly defined as volume, in liters (L). The mass concentration of the active ingredient in the drug is obtained from the drug specification or laboratory determination, and the unit is mg / L. It is used as a conversion factor to convert the volumetric dosage into the mass of the active substance. The cumulative influent flow rate is derived from the flow meter integral and is explicitly defined as volumetric volume, with the unit being L; The preset numerical protection constant is 0.01L in this embodiment to prevent calculation errors caused by the denominator being zero under extreme flow interruption conditions; The reagent consumption coefficient, derived from a preset empirical constant, physically represents the proportion of reagent remaining after failing to participate in the deodorization reaction; it is a dimensionless number. Specifically... The method was determined by static beaker experiments, which involved adding a fixed amount of reagent to different batches of water samples, reacting for a fixed time (e.g., 15 minutes), measuring the residual oxidant concentration, and taking the average ratio as the coefficient. Using a preset bioactivity decay function Perform mapping calculations; given that symbols have been defined in the foregoing embodiments. To control errors, avoid symbol confusion, and clarify the technical meaning, the exponential function symbol is used here. Exponentiation of natural constants: in, : Toxicity inhibition index, derived from function mapping, dimensionless, with a value range of [0,1]; An exponential function with the natural constant as its base; : Toxicity response slope, derived from biological experimental determination, unit is L / mg, physical meaning is the sensitivity of microorganisms to the agent; : The median lethal concentration constant, derived from biological experimental determination, is expressed in mg / L; (This refers to the parameter...) and The parameters were obtained using an experiment to inhibit the specific oxygen consumption rate (SOUR) of activated sludge. To ensure that those skilled in the art can accurately reproduce the model parameters and achieve quantitative control, the specific experimental steps and regression solution process are as follows: Activated sludge mixture was collected from the end of the biological treatment tank at the wastewater treatment plant. After centrifugation to remove background matrix, it was resuspended in distilled water to adjust the suspended solids concentration to 3000 mg / L. Aeration was performed for 2 hours under constant temperature conditions to consume endogenous substrates. A reagent concentration gradient sequence was set, such as (0, 2, 4, 6, 8, 10) mg / L. Excess sodium acetate was added to each reactor as a substrate. The slope of dissolved oxygen change over time was recorded using a dissolved oxygen probe to calculate the specific oxygen consumption rate of each group. Next, calculate the relative inhibition rate. ,in, The specific oxygen consumption rate of activated sludge in the blank control group without added chemicals was used to construct the experimental dataset. ,in, Indicates the first The residual concentration gradient values ​​set in the group experiment, Indicates the first The relative inhibition rate of activated sludge corresponding to the group; Using scientific computing software, such as the curve fitting module in the SciPy library of Python, the Levenberg-Marquardt nonlinear least squares algorithm was employed, with minimization of the sum of squared residuals as the convergence criterion and a tolerance set to [value missing]. Fit the logistic function to the above dataset: in, The predicted inhibition rate output by the fitted function corresponds numerically to the toxicity inhibition index. ; Thus, the specific solution can be calculated. and Value; calculated using the above standard process, under typical operating conditions in this embodiment, the measured value is... L / mg, mg / L, to ensure that the model parameters conform to the characteristics of the biological community in the current wastewater treatment plant; Based on this, in order to clarify the biological collapse threshold of Example 1 The physical boundary, in this embodiment, is defined as the toxicity inhibition index value corresponding to when the specific oxygen consumption rate (SOUR) of activated sludge decreases to 75% of the baseline value, i.e., at a 25% inhibition rate; that is, it is directly set. This threshold represents a warning line for the function of biological systems, aiming to reserve 25% functional redundancy for biological systems to prevent the system from entering the irreversible decay zone. This embodiment quantifies invisible and intangible bioactivity into a specific numerical indicator. This allows the control system to predict the damage that the current dosing behavior will cause to the biological system in the next few hours, thus providing a quantitative basis for controlled spill decision-making and effectively avoiding the risk of downstream activated sludge poisoning caused by excessive use of chemical deodorizers.

[0029] Example 7: In response to a revised hydrogen sulfide concentration exceeding a preset hard emission limit, an emergency containment strategy is generated, including: Start a timer to monitor the duration for which the revised hydrogen sulfide concentration remains above the hard emission limit; In response to an emergency response time threshold being exceeded for an extended period, a plant-wide shutdown and cutoff command is generated. If the duration does not exceed the emergency response time threshold, a maximum load dosing command corresponding to the maximum rated flow rate of the chemical pump is generated.

[0030] This embodiment details the execution logic of the emergency blocking strategy, which is the system's way of preventing touch. The last line of defense at the emission limit boundary; when At that time, the system starts a millisecond-level timer to record in real time the duration for which the corrected hydrogen sulfide concentration value remains above the hard emission limit. ; Implement a tiered response: In response to ,in, The preset emergency response time threshold is set to 15 minutes in this embodiment. This value is determined based on 50% of the hydraulic retention time of 30 minutes in the upstream regulating pool, which is intended to reserve sufficient time for physical cutoff execution. When the system determines that it is an instantaneous impact, it generates a maximum load dosing command corresponding to the maximum rated flow of the chemical pump, attempting to suppress the pollution peak by using excessive chemical. In response to This indicates that simply relying on chemical treatment is no longer sufficient to control the situation, and an environmental accident is imminent. The system immediately generates a plant-wide shutdown and interception command, links the inlet valves to close, cuts off the sewage from entering, and sends an alarm signal to the park management center. This embodiment establishes a time-based hierarchical defense mechanism, which not only avoids erroneous production stoppages caused by momentary sensor glitches, but also ensures that when real and continuous exceedances occur, the pollution source can be decisively cut off to prevent the situation from escalating into a legal environmental accident, demonstrating the system's risk management capabilities under extreme operating conditions.

[0031] Example 8: The method also includes: After implementing the reagent dosing control strategy, the corrected hydrogen sulfide concentration value at the next moment is obtained as feedback data; Calculate the control error between the feedback data and the target concentration; Control errors, indirect state parameters, and direct monitoring parameters are stored in the training database for periodic updates to the soft measurement reconstruction model.

[0032] This embodiment details the system's adaptive update mechanism; the method includes: during the current execution time... After implementing the control strategy, wait until the next sampling time. Obtain the corrected hydrogen sulfide concentration value. As feedback data; calculate feedback data and time. Control error between target concentrations ;Will ,time Indirect state parameters and directly monitored parameters Store in the training database; every preset period, such as every 24 hours, or when the cumulative mean absolute error exceeds 5%, the soft measurement reconstruction model is retrained or fine-tuned using the new data in the training database. To strictly comply with the limitation of using control error for updates in the embodiment and to address the issue of label data reliability during model training, this embodiment constructs an error-weighted loss function for incremental learning. The specific steps are as follows: Label filtering and truth injection: For each sample in the database Check its deviation ratio ;like If no sensor drift occurs, then the directly monitored parameters are selected. As training labels ;like The system triggers truth value injection logic: It queries the laboratory information management system to retrieve the information at time... Is there any manually sampled hydrogen sulfide analysis data within the past few minutes? If it exists Then the label will be forcibly set to The sample is retained; if it does not exist, the sample is considered to have a missing label and is removed from the training set. This mechanism ensures that when the model itself makes a prediction error, the sample is retained. When the size increases while the sensor is actually correct, or both are wrong, the system can correct long-term biases in the model using highly reliable laboratory data, preventing learning deadlock. Error-weighted loss calculation: To make the model focus more on poor control performance, i.e., control error. For prediction accuracy over a longer period, the weighted mean square error is used as the loss function for backpropagation. in, The total number of samples in the current training batch, i.e., the batch size defined above, is used in this embodiment. ; For the model's predicted output, The error concern factor is set to 2.0 in this embodiment. The absolute value of the control error is stored for the example; the formula introduces weights. This forces the LSTM network to prioritize correcting prediction biases at operating points that cause control system errors during gradient descent. The specific update process adopts an incremental learning mode, using the Adam optimizer to update the weights of the LSTM network with a small learning rate, such as 0.001, thereby minimizing the aforementioned weighted loss value and enabling the model to adapt to seasonal water quality changes. This embodiment enables the system to adapt to seasonal changes in the influent characteristics of wastewater treatment plants or model mismatch caused by process aging through closed-loop feedback and periodic updates, maintaining the long-term stability of control accuracy and ensuring the prediction accuracy of the soft measurement model throughout its entire life cycle.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling the capture and elimination of hydrogen sulfide based on industrial monitoring software, characterized in that, include: Indirect status parameters and direct monitoring parameters of the wastewater treatment system are collected through the data interface of industrial monitoring software. The indirect state parameters are input into a preset soft-sensor reconstruction model, which is configured to characterize the mapping relationship between the indirect state parameters and the hydrogen sulfide concentration. The predicted hydrogen sulfide concentration at the current moment is calculated, and the direct monitoring parameters are corrected using the predicted hydrogen sulfide concentration to generate a corrected hydrogen sulfide concentration value. Based on the historical reagent dosage data and real-time flow data of the wastewater treatment system, the toxicity inhibition index of the downstream biological treatment unit is calculated. Based on the corrected hydrogen sulfide concentration value and the toxicity inhibition index, a chemical dosing control strategy for the wastewater treatment system is determined, and a corresponding chemical pump control command is output. The determination of the reagent dosing control strategy for the wastewater treatment system includes: In response to the modified hydrogen sulfide concentration value being higher than the preset hard emission limit, an emergency blocking strategy is generated; In response to the modified hydrogen sulfide concentration being lower than or equal to the hard emission limit, the toxicity inhibition index is compared with a preset biodegradation threshold: in response to the toxicity inhibition index being higher than the biodegradation threshold, a controlled spillover strategy is generated; in response to the toxicity inhibition index being lower than or equal to the biodegradation threshold, a critical steady-state strategy is generated.

2. The method according to claim 1, characterized in that, The step of correcting the direct monitoring parameters using the predicted hydrogen sulfide concentration to generate a corrected hydrogen sulfide concentration value includes: The absolute value of the difference between the direct monitoring parameter and the predicted hydrogen sulfide concentration is calculated as the ratio of the predicted hydrogen sulfide concentration to the deviation ratio. In response to the deviation ratio exceeding a preset sensor drift threshold, a toxic drift is determined to have occurred in the sensor, and the predicted hydrogen sulfide concentration is used as the corrected hydrogen sulfide concentration value; or... In response to the deviation ratio not exceeding the sensor drift threshold, a weighted average of the direct monitoring parameter and the predicted hydrogen sulfide concentration is calculated as the corrected hydrogen sulfide concentration value.

3. The method according to claim 1, characterized in that, The controlled overflow generation strategy serves as the drug dosing control strategy, including: The control target concentration is set as a first target value, wherein the first target value is higher than the preset daily operation target value and lower than the hard emission limit value; Based on the first target value and the corrected hydrogen sulfide concentration value, the first reagent dosage is calculated using a proportional-integral-derivative control algorithm. The dosage of the first drug is packaged into the drug pump control command.

4. The method according to claim 1, characterized in that, The critical steady-state generation strategy, as the drug dosing control strategy, includes: The control target concentration is set as a second target value, wherein the second target value is equal to the daily operation target value; Based on the second target value and the corrected hydrogen sulfide concentration value, the dosage of the second agent is calculated using a proportional-integral-derivative control algorithm. The dosage of the second agent is packaged into the agent pump control command.

5. The method according to claim 1, characterized in that, The indirect state parameters include: The influent pH value, oxidation-reduction potential (ORP) value, influent flow rate, and influent pipeline pressure value.

6. The method according to claim 1, characterized in that, The calculation of the toxicity inhibition index of the downstream biological treatment unit based on the historical reagent dosage data and real-time flow data of the wastewater treatment system includes: Obtain the cumulative dosage of the historical drug dosage data within a preset time window; Obtain the cumulative inflow rate of the real-time flow data within the preset time window; Calculate the ratio of the cumulative dosage to the cumulative influent flow rate to obtain the current residual concentration of the reagent; The toxicity inhibition index is obtained by mapping calculation based on the current drug residue concentration using a preset bioactivity decay function, wherein the bioactivity decay function defines a positive correlation between drug residue concentration and toxicity inhibition index.

7. The method according to claim 1, characterized in that, The response to the modified hydrogen sulfide concentration value being higher than a preset hard emission limit value generates an emergency blocking strategy, including: Start a timer to monitor the duration for which the corrected hydrogen sulfide concentration value remains above the hard emission limit; In response to the duration exceeding a preset emergency response time threshold, a plant-wide shutdown and cutoff command is generated; In response to the fact that the duration does not exceed the emergency response time threshold, a maximum load dosing command corresponding to the maximum rated flow rate of the agent pump is generated.

8. The method according to claim 1, characterized in that, The method further includes: After executing the drug dosing control strategy, the corrected hydrogen sulfide concentration value at the next moment is obtained as feedback data; Calculate the control error between the feedback data and the target concentration; The control error, the indirect state parameters, and the direct monitoring parameters are stored in the training database for periodic updates to the soft measurement reconstruction model.

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