Electroplating wastewater-based heavy metal monitoring analysis method and system

Abstract

The present application relates to heavy metal monitoring technical field, especially based on electroplating wastewater's heavy metal monitoring analysis method and system, through introducing hydraulic dilution compensation coefficient and flow deviation integral term, the heavy metal analyzer discrete detection data is combined with the continuous flow data of inlet water, the continuous estimation concentration of heavy metal in wastewater is obtained by real-time deduction, the concentration data aging risk component is calculated by using exponential decay model, the nonlinear decay law of data reliability with time is accurately characterized, the concept of collaborative margin and control target conflict energy is proposed by comparing the theoretical dosing flow instruction with the actual dosing flow, the dosing execution resistance component is calculated, according to the real-time running state of electroplating wastewater treatment device, the weight proportion of feedforward control and feedback control is dynamically adjusted, the adaptive optimization of device monitoring and control is realized, and the control effect and running stability of heavy metal treatment are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of heavy metal monitoring technology, and in particular to a method and system for monitoring and analyzing heavy metals based on electroplating wastewater. Background Technology

[0002] The electroplating industry is a crucial foundation of my country's high-end manufacturing sector and industrial support system. The wastewater generated during its production process contains large amounts of heavy metal ions such as nickel, chromium, and copper. These heavy metal pollutants are highly toxic and prone to accumulation; if discharged directly without effective treatment, they will cause severe and irreversible damage to aquatic environments and ecosystems. Chemical precipitation is currently the mainstream process for treating heavy metals in electroplating wastewater due to its mature technology, low cost, and wide applicability. The precision of chemical dosing control in this process significantly affects the removal efficiency of these heavy metals and plays a decisive role in the cost of reagents consumed by the treatment system.

[0003] Currently, electroplating wastewater treatment systems commonly employ online heavy metal analyzers to monitor influent heavy metal concentrations, combined with feedforward-feedback control algorithms to adjust chemical dosage. However, limited by detection principles and equipment costs, online heavy metal analyzers typically have sampling intervals as long as 15 minutes, resulting in discrete monitoring data that cannot monitor dynamic fluctuations in influent water quality and flow rate in real time. Furthermore, traditional control methods do not consider the impact of data aging on control accuracy and lack effective monitoring of malfunctions in the chemical dosing process. This leads to poor matching between chemical dosage and actual requirements, easily resulting in excessive heavy metal levels in the effluent and overdosing of chemicals, making it difficult to meet increasingly stringent environmental discharge requirements for industrial wastewater.

[0004] The shortcomings of existing technologies are as follows: Traditional feedforward control directly uses discrete measurements from heavy metal analyzers as the control basis, without considering the hydraulic dilution effect caused by fluctuations in influent flow rate. When the influent flow rate changes significantly within the analyzer's sampling interval, the actual concentration deviates significantly from the measured value, leading to a large error in the theoretical dosage calculation. As monitoring time progresses, the reliability of the analyzer's measurements gradually decreases, but existing technologies do not quantitatively assess the degree of data aging, always equating lagging discrete detection data with real-time continuous dynamic data, which can easily cause systematic control deviations under sudden changes in water quality. Existing technologies only focus on the generation of control commands, without real-time monitoring of the actual output flow of the dosing pump, making it impossible to detect execution failures such as pipeline blockage in a timely manner, resulting in a significant deviation between the actual dosage and the theoretical command. Traditional feedforward-feedback control uses a fixed weight allocation mechanism, which cannot adaptively and dynamically adjust the control weight ratio according to the system's operating status. When abnormal processes occur, such as distorted water quality detection data or obstructed dosing, the fixed weight strategy's over-reliance on feedforward control amplifies the error, while over-reliance on feedback control leads to a delayed control response, making it difficult to balance the speed and stability of control. Summary of the Invention

[0005] The main objective of this invention is to provide a method for monitoring and analyzing heavy metals based on electroplating wastewater, and further to provide a system for monitoring and analyzing heavy metals based on electroplating wastewater that can operate and implement the above method, effectively solving the problems mentioned in the background art.

[0006] The technical solution of the present invention is as follows:

[0007] Firstly, a method for monitoring and analyzing heavy metals in electroplating wastewater is proposed, which includes the following steps:

[0008] S1. The device continuously collects the influent flow rate, pH value, heavy metal nickel ion concentration, and actual dosing flow rate. The internal timer accumulates the data and dynamically generates the current concentration data aging time.

[0009] S2. Combine the concentration of heavy metal nickel ions with the influent flow rate, and deduce the continuous estimated concentration at the current moment in real time using the hydraulic integral formula. Substitute the aging time of the concentration data into the exponential decay model to calculate the aging risk component of the concentration data. Based on the continuous estimated concentration and the real-time influent flow rate, combined with the chemical equivalent constant, calculate the theoretical dosing flow rate command that meets the precipitation requirements.

[0010] S3. Subtract the theoretical dosing flow rate command from the actual dosing flow rate to obtain the flow deviation. Divide the absolute value of the flow deviation by the rated maximum flow rate of the dosing pump to obtain the dosing execution obstruction component.

[0011] S4. The aging risk component of the concentration data and the obstructed component of drug administration are nonlinearly combined to calculate the synergistic margin and the conflict energy of the control target, and the weights of the feedforward control and feedback control are dynamically adjusted according to the value of the conflict energy of the control target.

[0012] S5. Generate a feedforward control reference based on the theoretical dosing flow command, generate a feedback control fine-tuning amount based on the flow deviation, and perform weighted calculation on the feedforward control reference and the feedback control fine-tuning amount according to the updated feedforward control weight and feedback control weight to generate the final dosing pump frequency command, and output the final control electrical signal to the frequency converter.

[0013] A further improvement of the present invention is that, specifically, S1 involves: collecting the inlet flow rate of the device using an inlet flow meter at 1-second intervals. pH values ​​were collected using an online pH meter. The actual dosing flow rate is collected by the flow meter in the dosing pipeline. The concentration of nickel ions in the heavy metal was collected using a heavy metal analyzer. It updates every 900 seconds; the dosing pump control cycle is set. and the effective time constant of the heavy metal analyzer The concentration data aging time It represents the elapsed time since the last update of the heavy metal analyzer data.

[0014] A further improvement of the present invention is that step S2 includes the following specific steps:

[0015] S21, Based on the inlet water flow rate of the device With respect to the concentration of the heavy metal nickel ions Calculate the continuously estimated concentration , ;in, This is the hydraulic dilution compensation coefficient. The average flow rate over the past 900 seconds;

[0016] S22. Calculate the aging risk component of the concentration data. , ;

[0017] S23, Based on the continuous concentration estimation Calculate the theoretical dosing flow rate command , ; It is the chemical equivalent constant.

[0018] A further improvement of the present invention is that step S3 includes the following specific steps:

[0019] S31. Based on the theoretical dosing flow rate instruction With the actual dosing flow rate Calculate the flow deviation , ;

[0020] S32. Calculate the component of drug administration obstruction. , ;in, This is the rated maximum output flow rate of the dosing pump.

[0021] A further improvement of the present invention is that step S4 includes the following specific steps:

[0022] S41. Based on the concentration data, aging risk component With the drug administration obstructed component Perform nonlinear combinations and calculate the cooperative margin. , Further calculate the conflict energy of the control target. , ;in, The rate of change of the amount of drug administration that was hindered;

[0023] S42, when When the system is in a normal cooperative state, the feedforward control weights are set. The feedback control weight is 0.7. It is 0.3; when When a control conflict is detected, the feedforward control weights are set. The feedback control weight is 0.2. It is 0.8.

[0024] A further improvement of the present invention is that S5 includes the following specific content: generating a feedforward control reference based on the theoretical dosing flow command. Based on the flow deviation, a feedback control fine-tuning amount is generated. Synthesize the frequency command of the dosing pump , ;in, The flow-to-frequency conversion function is in the form of: a and b are calibration parameters; The proportional-integral-derivative adjustment function has the following discrete form: ;in, The flow deviation in the kth control cycle , This is the proportionality coefficient. The integral coefficient is... is the differential coefficient.

[0025] Secondly, a heavy metal monitoring and analysis system based on electroplating wastewater is proposed. The system includes: a signal acquisition module, a feature calculation module, a feature deviation calculation module, a dynamic adjustment module, and a control execution module.

[0026] The signal acquisition module is used to synchronously and continuously acquire the influent flow rate, pH value, heavy metal nickel ion concentration and actual dosing flow rate of the device, and accumulate the internal timer to dynamically generate the aging time of the current concentration data;

[0027] The feature calculation module is used to combine the concentration of heavy metal nickel ions with the influent flow rate, deduce the continuous estimated concentration at the current moment in real time using the hydraulic integral formula, substitute the aging time of the concentration data into the exponential decay model, calculate the aging risk component of the concentration data, and calculate the theoretical dosing flow rate command that meets the precipitation requirements based on the continuous estimated concentration, real-time influent flow rate, and chemical equivalent constant.

[0028] The characteristic deviation calculation module is used to subtract the theoretical dosing flow command from the actual dosing flow to obtain the flow deviation, and divide the absolute value of the flow deviation by the rated maximum flow of the dosing pump to obtain the dosing execution obstruction component.

[0029] The dynamic adjustment module is used to nonlinearly combine the aging risk component of the concentration data with the obstructed component of drug administration, calculate the synergy margin and the conflict energy of the control target, and dynamically adjust the weights of the feedforward control and feedback control according to the value of the conflict energy of the control target.

[0030] The control execution module is used to generate a feedforward control reference based on the theoretical dosing flow command, generate a feedback control fine-tuning amount based on the flow deviation, perform weighted calculation on the feedforward control reference and the feedback control fine-tuning amount according to the updated feedforward control weight and feedback control weight, generate the final dosing pump frequency command, and output the final control electrical signal to the frequency converter.

[0031] The technical effects of this invention are as follows:

[0032] A heavy metal monitoring and analysis method based on electroplating wastewater was constructed. Addressing issues such as fluctuations in influent flow rate, decay of detection data reliability, and adaptability of dosing control, a precise, stable, and adaptively adjustable heavy metal monitoring and control system was developed. By introducing a hydraulic dilution compensation coefficient and a flow deviation integral term, discrete detection data from the heavy metal analyzer was combined with continuous influent flow rate data to obtain a real-time estimate of the continuous heavy metal concentration in the wastewater. This effectively compensates for concentration calculation errors caused by influent flow rate fluctuations, improving dynamic monitoring accuracy. An exponential decay model was used to calculate the aging risk component of the concentration data, accurately characterizing the nonlinear decay law of data reliability over time, providing a quantitative basis for subsequent optimization and adjustment of control strategies. By comparing the theoretical dosing flow rate command with the actual dosing flow rate, the dosing execution obstruction component was calculated, enabling real-time monitoring and quantitative assessment of pipeline blockage failures and timely identification of abnormal equipment operation. The concepts of collaborative margin and control target conflict energy are proposed. The risk of concentration data aging and the risk of dosing obstruction are nonlinearly combined. Based on the real-time operating status of the electroplating wastewater treatment device, the weight ratio of feedforward control and feedback control is dynamically adjusted to achieve adaptive optimization of device monitoring and control, which significantly improves the control effect and operational stability of heavy metal treatment. Attached Figure Description

[0033] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0034] Figure 1 This is a schematic flowchart of the heavy metal monitoring and analysis method based on electroplating wastewater according to Embodiment 1 of the present invention;

[0035] Figure 2 This is a schematic diagram of the heavy metal monitoring and analysis system based on electroplating wastewater according to Embodiment 2 of the present invention. Detailed Implementation

[0036] Example 1: This example constructs a heavy metal monitoring and analysis method based on electroplating wastewater. Addressing issues such as fluctuations in influent flow rate, decay of detection data reliability, and adaptability of dosing control, a precise, stable, and adaptively adjustable heavy metal monitoring and control system is built. By introducing a hydraulic dilution compensation coefficient and a flow deviation integral term, discrete detection data from the heavy metal analyzer is combined with continuous influent flow rate data to obtain a real-time estimated concentration of heavy metals in the wastewater. This effectively compensates for concentration calculation errors caused by influent flow rate fluctuations, improving dynamic monitoring accuracy. An exponential decay model is used to calculate the aging risk component of the concentration data, accurately characterizing the nonlinear decay law of data reliability over time, providing a quantitative basis for subsequent optimization and adjustment of control strategies. By comparing the theoretical dosing flow command with the actual dosing flow, the dosing execution obstruction component is calculated, enabling real-time monitoring and quantitative evaluation of pipeline blockage failures and timely identification of abnormal equipment operation. The concepts of collaborative margin and control target conflict energy are proposed. The risk of concentration data aging and the risk of dosing obstruction are nonlinearly combined. Based on the real-time operating status of the electroplating wastewater treatment device, the weight ratio of feedforward control and feedback control is dynamically adjusted to achieve adaptive optimization of device monitoring and control, which significantly improves the control effect and operational stability of heavy metal treatment.

[0037] Methods for monitoring and analyzing heavy metals in electroplating wastewater, such as Figure 1 As shown, the specific steps include the following:

[0038] S1. The device continuously collects the influent flow rate, pH value, heavy metal nickel ion concentration, and actual dosing flow rate. The internal timer accumulates the data and dynamically generates the current concentration data aging time.

[0039] In this embodiment, the specific content of S1 is: collecting the inlet flow rate of the device through the inlet flow meter at a time interval of 1 second. pH values ​​were collected using an online pH meter. The actual dosing flow rate is collected by the flow meter in the dosing pipeline. The concentration of nickel ions in the heavy metal was collected using a heavy metal analyzer. It updates every 900 seconds; the dosing pump control cycle is set. and the effective time constant of the heavy metal analyzer The concentration data aging time It represents the elapsed time since the last update of the heavy metal analyzer data.

[0040] In this embodiment, the unit of influent flow rate is liters per second (L / s), which comes from the influent flow meter and is updated every 1 second. The pH value is a dimensionless value, which comes from the online pH meter and is updated every 1 second. The unit of heavy metal nickel ion concentration is milligrams per liter (mg / L), which comes from the heavy metal analyzer and is updated every 900 seconds. The unit of actual dosing flow rate is liters per second (L / s), which comes from the dosing pipeline flow meter and is updated every 1 second. The unit of concentration data aging time is seconds (s), which represents the time elapsed since the last analyzer data refresh. The dosing pump control cycle is set to 1 second, and the effective time constant of the heavy metal analyzer is set to 900 seconds.

[0041] S2. Combine the concentration of heavy metal nickel ions with the influent flow rate, and deduce the continuous estimated concentration at the current moment in real time using the hydraulic integral formula. Substitute the aging time of the concentration data into the exponential decay model to calculate the aging risk component of the concentration data. Based on the continuous estimated concentration and the real-time influent flow rate, combined with the chemical equivalent constant, calculate the theoretical dosing flow rate command that meets the precipitation requirements.

[0042] In this embodiment, step S2 includes the following specific steps:

[0043] S21, Based on the inlet water flow rate of the device With respect to the concentration of the heavy metal nickel ions Calculate the continuously estimated concentration , ;in, This is the hydraulic dilution compensation coefficient. The average flow rate over the past 900 seconds;

[0044] S22. Calculate the aging risk component of the concentration data. , ;

[0045] S23, Based on the continuous concentration estimation Calculate the theoretical dosing flow rate command , ; It is the chemical equivalent constant.

[0046] In this embodiment, the concentration of heavy metal nickel ions is combined with the continuously fluctuating inlet water flow rate of the device. The continuously estimated concentration at the current moment is derived using a hydraulic integral formula. In the expression for the continuously estimated concentration, the dimension of the hydraulic dilution compensation coefficient is milligrams per liter squared (mg / L²), preferably ranging from 0.001 to 0.01. The specific value is determined based on the effective volume of the reaction tank and the hydraulic residence time of the inlet pipe. The average flow rate over the past 900 seconds is calculated by averaging the flow rate over the past 900 seconds. The design principle of this formula is that the sampling interval of heavy metal analyzers is relatively long, and they cannot reflect the concentration changes caused by fluctuations in the influent flow rate in real time. By introducing an integral term for the flow rate deviation, the deviation between the actual concentration and the analyzer measurement value caused by the instantaneous increase or decrease in the influent flow rate can be compensated, thereby improving the real-time performance of concentration estimation.

[0047] In this embodiment, the aging time of the concentration data is substituted into the exponential decay model to calculate the aging risk component of the concentration data. In the expression for the aging risk component of the concentration data, The formula is based on the principle that the reliability of analyzer measurements gradually decreases over time. The exponential decay model accurately characterizes the nonlinear process of data aging. At this point, the aging risk component of the concentration data is approximately 0.632, indicating that the reliability of the data has decreased to 36.8% of the initial value.

[0048] In this embodiment, based on the continuously estimated concentration and influent flow rate, combined with the stoichiometric constant, the theoretical dosing flow rate instruction that meets the precipitation requirements is calculated. In the calculation formula of the theoretical dosing flow rate instruction, the stoichiometric constant, in milligrams per liter (mg / L), characterizes the mass of nickel ions that can be removed per unit volume of reagent, and is determined by the reagent concentration, stoichiometric ratio, and excess coefficient.

[0049] S3. Subtract the theoretical dosing flow rate command from the actual dosing flow rate to obtain the flow deviation. Divide the absolute value of the flow deviation by the rated maximum flow rate of the dosing pump to obtain the dosing execution obstruction component.

[0050] In this embodiment, step S3 includes the following specific steps:

[0051] S31. Based on the theoretical dosing flow rate instruction With the actual dosing flow rate Calculate the flow deviation , ;

[0052] S32. Calculate the component of drug administration obstruction. , ;in, This is the rated maximum output flow rate of the dosing pump.

[0053] In this embodiment, the theoretical dosing flow rate command is subtracted from the actual dosing flow rate to obtain the flow deviation. This deviation reflects the difference between the actual output of the dosing pump and the theoretical requirement, caused by pipeline blockage. Dividing the absolute value of the deviation by the rated maximum flow rate of the dosing pump yields the dosing execution obstruction component. The rated maximum output flow rate of the dosing pump is measured in liters per second (L / s) and is determined based on the equipment nameplate parameters. This formula normalizes the flow deviation into a dimensionless value, facilitating subsequent combined calculations with the concentration data aging risk component.

[0054] S4. Nonlinearly combine the aging risk component of the concentration data with the obstructed component of drug administration, calculate the synergistic margin and the conflict energy of the control target, and dynamically adjust the weights of feedforward control and feedback control according to the value of the conflict energy of the control target.

[0055] In this embodiment, step S4 includes the following specific steps:

[0056] S41. Based on the concentration data, aging risk component With the drug administration obstructed component Perform nonlinear combinations and calculate the cooperative margin. , Further calculate the conflict energy of the control target. , ;in, The rate of change of the amount of drug administration that was hindered;

[0057] S42, when When the system is in a normal cooperative state, the feedforward control weights are set. The feedback control weight is 0.7. It is 0.3; when When a control conflict is detected, the feedforward control weights are set. The feedback control weight is 0.2. It is 0.8.

[0058] In this embodiment, the coordination margin is first calculated. This parameter characterizes the probability that the water quality data is fresh and the pipeline is unobstructed, and its value ranges from 0 to 1. When the coordination margin is close to 1, it indicates that the operating state is ideal, and both feedforward control and feedback control can play an effective role. Then, the conflict energy of the control target is calculated. In the expression, The rate of change of the obstructed component in chemical dosing is calculated by dividing the difference between the obstructed component at the current moment and the previous moment by the dosing pump control cycle. This parameter characterizes the degree to which water quality data is outdated and pipeline blockage is occurring simultaneously and worsening. Introducing the rate of change term can help predict the development trend of pipeline blockage in advance and avoid further escalation of control conflicts. Dynamically adjust the feedforward control weights and feedback control weights ,when When the system is in a normal cooperative state, the water quality data is highly reliable and the dosing pipeline is operating normally. Therefore, the normal settings are maintained, and the feedforward control weights are set. The feedback control weight is 0.7. With a weighting of 0.3, this weighting scheme primarily uses feedforward control based on water quality prediction, supplemented by feedback control based on flow deviation, enabling rapid response to changes in influent water quality and flow rate. When When the system is in a control conflict state, it indicates that the water quality data is severely distorted and the dosing pipeline is severely blocked. Continuing to rely on feedforward control will lead to either excessive or insufficient dosage of chemicals. Therefore, the control weights are adjusted to the feedforward control weights. The feedback control weight is 0.2. With a weight of 0.8, this weight allocation can effectively avoid control errors caused by data distortion.

[0059] S5. Generate a feedforward control reference based on the theoretical dosing flow command, generate a feedback control fine-tuning amount based on the flow deviation, and perform weighted calculation on the feedforward control reference and the feedback control fine-tuning amount according to the updated feedforward control weight and feedback control weight to generate the final dosing pump frequency command, and output the final control electrical signal to the frequency converter.

[0060] In this embodiment, step S5 includes the following specific steps: generating a feedforward control reference based on the theoretical dosing flow rate command. Based on the flow deviation, a feedback control fine-tuning amount is generated. Synthesize the frequency command of the dosing pump , ;in, The flow-to-frequency conversion function is in the form of: a and b are calibration parameters; The proportional-integral-derivative adjustment function has the following discrete form: ;in, The flow deviation in the kth control cycle , This is the proportionality coefficient. The integral coefficient is... is the differential coefficient.

[0061] In this embodiment, the feedforward control reference and the feedback control fine-tuning amount are synthesized according to the latest weights to generate the final dosing pump frequency command. The unit is Hertz (Hz), where the flow-frequency conversion function is determined based on the flow-frequency characteristic curve of the dosing pump, and is usually a linear function in the form of... Where 'a' has dimensions in Hertz per liter per second (Hz / (L / s)) and 'b' has dimensions in Hertz (Hz), obtained through calibration experiments of the dosing pump. In the proportional-integral-derivative (PID) control function expression, the proportional coefficient has dimensions in Hertz per liter per second (Hz / (L / s)), preferably ranging from 0.5 to 2; the integral coefficient has dimensions in Hertz per liter (Hz / L), preferably ranging from 0.1 to 1; and the derivative coefficient has dimensions in Hertz per second squared (Hz・s² / L), preferably ranging from 0.01 to 0.1. Specific values ​​are determined experimentally based on the dynamic response characteristics of the dosing process. The final electrical signal is output to the frequency converter, causing the dosing pump to operate at the new frequency, changing the actual amount of sodium hydroxide pumped into the reaction tank, thereby altering the actual chemical precipitation rate of the water.

[0062] Example 2: This example proposes a heavy metal monitoring and analysis system based on electroplating wastewater, such as... Figure 2 As shown, it includes: a signal acquisition module, a feature calculation module, a feature deviation calculation module, a dynamic adjustment module, and a control execution module;

[0063] The signal acquisition module is used to synchronously and continuously acquire the influent flow rate, pH value, heavy metal nickel ion concentration and actual dosing flow rate of the device, and accumulate the internal timer to dynamically generate the aging time of the current concentration data;

[0064] The feature calculation module is used to combine the concentration of heavy metal nickel ions with the influent flow rate, deduce the continuous estimated concentration at the current moment in real time using the hydraulic integral formula, substitute the aging time of the concentration data into the exponential decay model, calculate the aging risk component of the concentration data, and calculate the theoretical dosing flow rate command that meets the precipitation requirements based on the continuous estimated concentration, real-time influent flow rate, and chemical equivalent constant.

[0065] The characteristic deviation calculation module is used to subtract the theoretical dosing flow command from the actual dosing flow to obtain the flow deviation, and divide the absolute value of the flow deviation by the rated maximum flow of the dosing pump to obtain the dosing execution obstruction component.

[0066] The dynamic adjustment module is used to nonlinearly combine the aging risk component of the concentration data with the obstructed component of drug administration, calculate the synergy margin and the conflict energy of the control target, and dynamically adjust the weights of the feedforward control and feedback control according to the value of the conflict energy of the control target.

[0067] The control execution module is used to generate a feedforward control reference based on the theoretical dosing flow command, generate a feedback control fine-tuning amount based on the flow deviation, perform weighted calculation on the feedforward control reference and the feedback control fine-tuning amount according to the updated feedforward control weight and feedback control weight, generate the final dosing pump frequency command, and output the final control electrical signal to the frequency converter.

[0068] The parameters and steps for implementing the corresponding functions of each unit module in the heavy metal monitoring and analysis system based on electroplating wastewater of the present invention described above can be referred to the parameters and steps in the embodiment of the heavy metal monitoring and analysis method based on electroplating wastewater in Example 1 above.

[0069] Example 3: This example provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-described method for monitoring and analyzing heavy metals based on electroplating wastewater by calling the computer program stored in the memory.

[0070] The electronic device can vary considerably depending on its configuration and performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the heavy metal monitoring and analysis method based on electroplating wastewater provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0071] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0072] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0073] This invention is described with reference to flowchart illustrations and block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and block diagrams, as well as combinations of blocks in the flowchart illustrations and block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for monitoring and analyzing heavy metals in electroplating wastewater, characterized in that: The specific steps include the following: S1. The device continuously collects the influent flow rate, pH value, heavy metal nickel ion concentration and actual dosing flow rate, and accumulates the data using an internal timer to dynamically generate the current concentration data aging time. S2. Combine the concentration of heavy metal nickel ions with the influent flow rate, and deduce the continuous estimated concentration at the current moment in real time using the hydraulic integral formula. Substitute the aging time of the concentration data into the exponential decay model to calculate the aging risk component of the concentration data. Based on the continuously estimated concentration and real-time influent flow rate, combined with the chemical equivalent constant, the theoretical dosing flow rate command that meets the sedimentation requirements is calculated. S3. Subtract the theoretical dosing flow rate command from the actual dosing flow rate to obtain the flow deviation. Divide the absolute value of the flow deviation by the rated maximum flow rate of the dosing pump to obtain the dosing execution obstruction component. S4. The aging risk component of the concentration data and the obstructed component of drug administration are nonlinearly combined to calculate the synergistic margin and the conflict energy of the control target, and the weights of the feedforward control and feedback control are dynamically adjusted according to the value of the conflict energy of the control target. S5. Generate a feedforward control reference based on the theoretical dosing flow command, generate a feedback control fine-tuning amount based on the flow deviation, and perform weighted calculation on the feedforward control reference and the feedback control fine-tuning amount according to the updated feedforward control weight and feedback control weight to generate the final dosing pump frequency command, and output the final control electrical signal to the frequency converter. S2 includes the following specific steps: S21, Based on the inlet water flow rate of the device With respect to the concentration of the heavy metal nickel ions Calculate the continuously estimated concentration ;in, This is the hydraulic dilution compensation coefficient. The average flow rate over the past 900 seconds. Ageing time for concentration data; S22. Calculate the aging risk component of the concentration data. ; S23, Based on the continuous concentration estimation Calculate the theoretical dosing flow rate command It is the chemical equivalent constant; S4 includes the following specific steps: S41. Based on the concentration data, aging risk component With the drug administration obstructed component Perform nonlinear combinations and calculate the cooperative margin. Further calculate the conflict energy of the control target. ;in, The rate of change of the amount of drug administration that was hindered. The set dosing pump control cycle; S42, when When the system is in a normal cooperative state, the feedforward control weights are set. The feedback control weight is 0.

7. It is 0.3; when When a control conflict is detected, the feedforward control weights are set. The feedback control weight is 0.

2. It is 0.8; S5 includes the following specific content: generating a feedforward control reference based on the theoretical dosing flow command. Based on the flow deviation, a feedback control fine-tuning amount is generated. Synthesize the frequency command of the dosing pump ;in, The flow-to-frequency conversion function is in the form of: For calibration parameters; The proportional-integral-derivative adjustment function has the following discrete form: ;in, The flow deviation in the kth control cycle This is the proportionality coefficient. The integral coefficient is... is the differential coefficient.

2. The method for monitoring and analyzing heavy metals in electroplating wastewater according to claim 1, characterized in that: The specific content of S1 is as follows: the inlet flow rate of the device is collected using an inlet flow meter at a time interval of 1 second. pH values ​​were collected using an online pH meter. The actual dosing flow rate is collected by the flow meter in the dosing pipeline. The concentration of nickel ions in the heavy metal was collected using a heavy metal analyzer. It updates every 900 seconds; the dosing pump control cycle is set. and the effective time constant of the heavy metal analyzer The concentration data aging time It represents the elapsed time since the last update of the heavy metal analyzer data.

3. The method for monitoring and analyzing heavy metals in electroplating wastewater according to claim 2, characterized in that: S3 includes the following specific steps: S31. Based on the theoretical dosing flow rate instruction With the actual dosing flow rate Calculate the flow deviation ; S32. Calculate the component of drug administration obstruction. ;in, This is the rated maximum output flow rate of the dosing pump.

4. A heavy metal monitoring and analysis system based on electroplating wastewater, implemented according to any one of claims 1-3, characterized in that, The system includes: a signal acquisition module, a feature calculation module, a feature deviation calculation module, a dynamic adjustment module, and a control execution module; The signal acquisition module is used to synchronously and continuously acquire the influent flow rate, pH value, heavy metal nickel ion concentration and actual dosing flow rate of the device, and accumulate the internal timer to dynamically generate the aging time of the current concentration data; The feature calculation module is used to combine the concentration of heavy metal nickel ions with the influent flow rate, deduce the continuous estimated concentration at the current moment in real time using the hydraulic integral formula, substitute the aging time of the concentration data into the exponential decay model, calculate the aging risk component of the concentration data, and calculate the theoretical dosing flow rate command that meets the precipitation requirements based on the continuous estimated concentration, real-time influent flow rate, and chemical equivalent constant. The characteristic deviation calculation module is used to subtract the theoretical dosing flow command from the actual dosing flow to obtain the flow deviation, and divide the absolute value of the flow deviation by the rated maximum flow of the dosing pump to obtain the dosing execution obstruction component. The dynamic adjustment module is used to nonlinearly combine the aging risk component of the concentration data with the obstructed component of drug administration, calculate the synergy margin and the conflict energy of the control target, and dynamically adjust the weights of the feedforward control and feedback control according to the value of the conflict energy of the control target. The control execution module is used to generate a feedforward control reference based on the theoretical dosing flow command, generate a feedback control fine-tuning amount based on the flow deviation, perform weighted calculation on the feedforward control reference and the feedback control fine-tuning amount according to the updated feedforward control weight and feedback control weight, generate the final dosing pump frequency command, and output the final control electrical signal to the frequency converter.

Images