Water treatment agent adding control method and water treatment agent adding control system

Abstract

The invention relates to the technical field of water treatment, provides a chemical adding control scheme, and particularly relates to a chemical adding control method and system for a chemical for water treatment. Water quality data about to-be-treated water is obtained by predicting the water quality of inlet water, the water quality data is used as key parameters to obtain a corresponding agent feeding predicted amount, and a feedback control weight and a feedforward control weight are determined based on the predicted amount and changes of water body target data and water quality conditions in the agent feeding process; and combining the feedback control weight and the feed-forward control weight to obtain the dosing control quantity. According to the control method provided by the embodiment of the invention, the control quantity can be accurately determined based on the condition of the water quality and the overall environment change of the water treatment system, compared with a single control logic in the prior art, the accuracy of overall control is improved, and the problems of a complicated water treatment system and inaccurate environment control can be solved.

Description

Technical Field

[0001] This application relates to the field of water treatment technologies, and provides a chemical dosing control solution, specifically, a method and a control system for dosing chemicals used in water treatment. Background Art

[0002] Although automation control technologies have been popularized in the industrial field, there are few studies on the automation control of the chemical dosing process in sewage treatment plants. In the existing technologies, most of them focus on realizing chemical dosing by constructing empirical formulas. The low degree of automation control of chemical dosing results in waste of energy and also hinders the further upgrading of sewage treatment plants. Summary of the Invention

[0003] In view of the above technical problems, this application provides a method and a control system for dosing chemicals used in water treatment. By collecting multiple physical and chemical data of the influent water and determining the water quality information of the water to be treated, and combining the water quality information with the key indicators of the current treated water, the dosing amount of the chemicals is accurately controlled. To achieve the above purpose, the technical solutions adopted in the embodiments of the present invention are as follows:

[0004] In a first aspect, a method for controlling chemical dosing for water treatment is provided, which is applied to a water treatment system. The water treatment system includes at least one biological tank and a chemical dosing pump. The biological tank is connected to a water inlet pipe, and a plurality of sensors are arranged in the water inlet pipe and the biological tank. The plurality of sensors are used to obtain a plurality of physical and chemical data and target index data. The method includes: based on the obtained physical and chemical data of the water to be treated that is to enter the biological tank, and performing on-line water quality detection according to the physical and chemical data to obtain water quality data about the water to be treated; the physical and chemical data includes any one or more of conductivity, pH value, flow rate, temperature, ammonia content, and suspended solid content, and the water quality data includes any one or more of dissolved chemical oxygen demand, phosphate content, and carbon-nitrogen ratio; determining the predicted chemical dosing amount according to the plurality of water quality data and target index data; determining the initial frequency of the chemical dosing pump based on the predicted chemical dosing amount, and determining the error value between the real-time target index data and the set index data, determining the feedback frequency of the chemical dosing pump through the error value, determining the target frequency based on the initial frequency and the feedback frequency, and controlling the chemical dosing pump to dose chemicals based on the target frequency; the real-time target index data is obtained based on the gradient time.

[0005] In some specific implementation manners, weighted summation with corresponding weights is performed on the obtained conductivity, pH value, temperature, flow rate, and suspended solid content to obtain the dissolved chemical oxygen demand.

[0006] In some specific implementation manners, weighted summation with corresponding weights is performed on the obtained conductivity, pH value, temperature, flow rate, suspended solid content, ammonia content, and dissolved chemical oxygen demand to obtain the phosphate content.

[0007] In some specific implementation manners, weighted summation with corresponding weights is performed on the obtained conductivity, pH value, temperature, flow rate, suspended solid content, and phosphate content to obtain the carbon-nitrogen ratio.

[0008] In some specific implementation manners, determining the predicted chemical dosage based on multiple pieces of the water quality data and target index data includes: determining the weight values corresponding to the multiple pieces of water quality data according to the type of the target index data, updating the water quality data based on the weight values, and inputting the updated multiple pieces of water quality data and the target index data into a converged chemical prediction model for training to obtain the predicted chemical dosage.

[0009] In some specific implementation manners, the chemical prediction model is a feedforward neural network model constructed by an input layer, a hidden layer, and an output layer, and the weight values between the hidden layer and the output layer and the center width vector of the Gaussian basis function are optimized by a genetic algorithm, and the initial network structure and parameters are adjusted based on the optimized parameters, and training is performed on the adjusted chemical prediction model until convergence.

[0010] In some specific implementation manners, the gradient time is a time setting in which the sampling interval time gradually decreases, and real-time target index data of the water body is obtained based on the sampling interval time.

[0011] In some specific implementation manners, the initial frequency of the chemical dosing pump is determined based on the flow rate of the chemical dosing pump and the volume of the liquid in the chemical dosing pump, and the flow rate of the chemical dosing pump is determined based on the chemical concentration, the predicted chemical dosage, and the flow rate.

[0012] In some specific implementation manners, determining the target frequency based on the initial frequency and the feedback frequency includes: combining the initial frequency and the feedback frequency through a combined weight of the initial frequency and the feedback frequency; the combined weight is determined based on the change amount of the water quality data per unit time and the real-time target index data.

[0013] In a second aspect, a chemical dosing control system is provided, which is applied to a water treatment system. The water treatment system includes at least one biological pond and a chemical dosing pump. The biological pond is connected to a water inlet pipe, and a plurality of sensors are arranged in the water inlet pipe and the biological pond. The control system is electrically connected to the plurality of sensors and the chemical dosing pump, controls the plurality of sensors and the chemical dosing pump, and obtains a plurality of physical and chemical data and target index data collected by the plurality of sensors. The control system includes: a data processing device for performing on-line water quality detection according to the physical and chemical data to obtain water quality data of the water to be treated; a prediction device for determining a predicted chemical dosing amount according to the plurality of water quality data and target index data; a control output device for obtaining an initial frequency and a feedback frequency, determining a target frequency based on the initial frequency and the feedback frequency, and using the target frequency as a control output.

[0014] In the technical solution provided by the embodiment of the present application, water quality data of the water to be treated is obtained by predicting the water quality of the incoming water, and the water quality data is used as a key parameter to obtain a corresponding predicted chemical dosing amount. Based on this predicted amount and the changes in the target data of the water body and the changes in the water quality situation during the chemical dosing process, the feedback control weight and the feedforward control weight are determined, and the feedback control weight and the feedforward control weight are combined to obtain the chemical dosing control amount. The control method provided by the embodiment of the present application can accurately determine the control amount based on the water quality situation and the overall environment change of the water treatment system, increasing the overall control accuracy compared with the single control logic in the prior art, and can solve the problem of inaccurate control of the complex system and environment of water treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] The methods, systems, and / or programs in the drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, where the example numbers represent similar mechanisms in the various views of the drawings.

[0017] Figure 1 is a schematic structural diagram of the water treatment system provided by the embodiment of the present application.

[0018] Figure 2 is a schematic flowchart of the chemical dosing control method provided by the embodiment of the present application.

[0019] Figure 3It is a schematic structural diagram of the control system provided by an embodiment of the present application.

[0020] Figure 4 It is a schematic structural diagram of the terminal device provided by an embodiment of the present application. Detailed implementation manners

[0021] To better understand the above technical solutions, the technical solutions of the present application will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present application and the embodiments are detailed descriptions of the technical solutions of the present application, rather than limitations on the technical solutions of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0022] In the following detailed description, many specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to those skilled in the art that the present application may be practiced without these details. In other instances, well-known methods, procedures, systems, components, and / or circuits have been described at a relatively high level without detail in order to avoid unnecessarily obscuring aspects of the present application.

[0023] In the present application, a flowchart is used to illustrate the execution process performed by the system according to the embodiments of the present application. It should be clearly understood that the execution process of the flowchart may not be executed in sequence. On the contrary, these execution processes may be executed in reverse order or simultaneously. Additionally, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0024] Before further elaborating on the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention are described. The nouns and terms involved in the embodiments of the present invention are applicable to the following explanations.

[0025] (1) In response to, which is used to represent the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more of the executed operations may be real-time or may have a set delay; without special instructions, there is no limitation on the execution sequence of the multiple executed operations.

[0026] (2) Based on, which is used to represent the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more of the executed operations may be real-time or may have a set delay; without special instructions, there is no limitation on the execution sequence of the multiple executed operations.

[0027] An embodiment of the present application provides a chemical dosing control system applied to a water treatment system for precisely controlling the dosage of chemicals to be added during the water treatment process.

[0028] Among them, generally, the sewage treatment process is generally divided into three levels. The primary treatment mainly uses physical methods to remove insoluble solid substances in sewage. By using methods such as sedimentation, filtration, flotation, and centrifugal separation, suspended solids or emulsified oils are removed through methods such as grille filtration, sedimentation and static settlement, and centrifugal separation principle. The secondary treatment commonly uses biological treatment methods. After the secondary treatment, some substances such as nitrogen, phosphorus, hardly degradable soluble compounds, and pathogens remain. The sewage after secondary treatment does not meet the higher discharge standards. Direct discharge may pollute rivers with weak dilution capacity and cannot be used for the replenishment of tap water and industrial water either. The tertiary treatment is to further remove the pollutants that were not treated in the secondary treatment, such as hardly degradable organic pollutants, inorganic pollutants, nitrogen, phosphorus, etc. On the basis of the secondary treatment, chemical methods, physicochemical methods, etc. are further used to further treat the pollutants that were not treated. The tertiary treatment consumes a large amount but can make full use of water resources. There may still be harmful substances in the sewage after tertiary treatment, such as harmful bacteria, etc. The harmful bacteria can be killed by methods such as ultraviolet disinfection and ozone disinfection to avoid polluting the clean water source with the discharged sewage.

[0029] For the water treatment system of the embodiment of the present application, it is the secondary or tertiary process of the above treatment. For the specific details of this water treatment system, please refer to Figure 1 , the water treatment system 100 includes at least one biological pool, where the biological pool is used to implement the above secondary or tertiary treatment process. The secondary biological pool is preferably selected as the target biological pool of this embodiment. Of course, in other embodiments, the water treatment can be other multi-level structures, and the selection of the biological pool can also be any level of the biological pool that requires chemical dosing treatment in other multi-structures.

[0030] A chemical dosing pump 110 is correspondingly configured in this biological pool for chemical dosing treatment to the biological pool. And the biological pool is connected to an external water source through a water inlet pipe, and the external water source is led into the biological pool through the water inlet pipe. The chemical dosing treatment of the chemical dosing pump is a key link in the water treatment process. The content of the added medicine determines the water treatment effect; when the content of the added medicine is small, the requirements for the water treatment result cannot be achieved. When the content of the added medicine is too much, it will not only cause waste of the medicine, but also cause biological and chemical changes in the original water body, thus reducing the water treatment effect. Therefore, how to achieve precise control of the medicine addition amount is a key technology in the current water treatment technology.

[0031] In the prior art, generally, the dosage of chemicals is adjusted manually depending on the experience of technicians to control the water quality. Technicians take samples, observe the water quality index data of the raw water after adding chemicals, and combine the past dosage and personal experience to determine the appropriate dosage; they can also conduct statistical analysis on historical production data, formulate a comparison table of water quality and chemical consumption, and determine the dosage in combination with the actual situation. This method highly relies on experienced technicians for judgment and control, thus resulting in high labor costs and easy waste of chemicals. With the improvement of the automation level, currently, in some water treatment scenarios, automated control methods are also adopted to achieve precise chemical dosing. Generally, in the automated chemical dosing methods in the prior art, a dynamic adjustment process is mainly adopted, real-time detection of specific sampling points, and comparison of the detected actual value with the preset target value to determine the deviation between the two. Based on this deviation, the corresponding control output is calculated to achieve precise adjustment of the chemical dosage. For this method, after the chemicals are added, the water body needs to undergo a relatively complex treatment process and the corresponding lag time is long. When the water quality or flow rate of the water body changes significantly, this control method cannot adjust the chemical dosage in a timely manner; although this method solves the problem of inaccurate control of chemical dosage by manual experience, its control accuracy still needs to be improved in actual use.

[0032] In view of the above technical background, to solve the problem of inaccurate control of chemical dosage in the prior art, in the embodiment of the present application, a chemical dosing control system 120 is further configured in the above water treatment system to precisely control the dosage. Specifically, for the added chemical dosing control system, a plurality of sensors 130 are respectively arranged in the water inlet pipe and the biological pool in the water treatment system to respectively obtain a plurality of physical and chemical data and target index data, and transmit the above data to the chemical dosing control system as the input source of the chemical dosing control system. And by configuring a chemical dosing control method in the chemical dosing control system, the real-time chemical dosage in the current water treatment system and the corresponding frequency of the chemical dosing pump are determined, and this frequency is sent to the chemical dosing pump to achieve chemical dosing control.

[0033] It should be noted that sewage treatment is a complex process involving multiple aspects, including physical, biological, and chemical reactions. This complexity is mainly reflected in several aspects: First, these reactions usually exhibit non-linear characteristics, making it difficult to establish accurate models. Second, there are many uncertain factors, such as fluctuations in influent water quality and changes in the operating status of treatment equipment, which increase the challenge of establishing accurate models. Third, the operating status of the sewage treatment system changes over time, including fluctuations in influent water quality and aging maintenance of treatment equipment, which makes it impossible for static models to accurately describe the dynamic behavior of the system. In addition, there is a certain time lag in the sewage treatment process, making real-time monitoring and control more difficult.

[0034] In summary, for the control model of the control system provided in this embodiment, in order to achieve precise chemical dosing control, it is necessary to use the influent water quality data as an important input index, and also use the changes in the water body during the process as key disturbance factors in the overall control process.

[0035] Therefore, the sensors in the inlet pipe and the biological pool in this embodiment are set differently and the collected data is also different. Among them, the sensors in the inlet pipe are used to collect water body data related to the water quality of the influent, and the sensors in the biological pool are used to collect water body data related to the markers indicating whether the discharge meets the standard in the biological pool. It can be understood that in this embodiment, it is necessary to determine the water quality information of the water body before dosing and determine the dosing amount according to the water quality information, and based on the dosing process, obtain the water body data in the biological pool to judge whether the discharge standard is met, and based on this water body data and the water quality information, perform a linear combination to obtain the adjustment amount of the dosing amount and perform the adjustment.

[0036] In another implementable manner, for the specific process of the chemical dosing control method provided in this embodiment, reference can be made to Figure 2 , including the following steps:

[0037] Step S21. Based on the obtained physical and chemical data of the water to be treated to enter the biological pool, and perform on-line water quality detection according to the physical and chemical data to obtain the water quality data of the water to be treated.

[0038] Generally, in the automatic control process, by collecting the key parameters of the current environment, and determining the compensation amount based on the difference between the key parameters and the key parameters corresponding to the control target, and determining the change of the control amount based on the compensation amount. In a single environment, because the disturbance factors are low and the changes in the system are linear or approaching linear, the changes in the system can be inferred based on the changes in the key parameters. However, in the water treatment scenario, the influence of the changes in the key parameters in the water body is composed of multiple factors, and the changes in the influencing factors cannot be inferred. If the control parameters of the system are inferred solely based on the changes in the key parameters, problems such as inaccurate system control and high latency will occur.

[0039] Therefore, in this embodiment, in order to improve the accuracy of dosing control, it is necessary to obtain the water quality information of the current water body to be processed and introduce the water quality information into the subsequent reasoning process, which improves the accuracy of obtaining control parameters.

[0040] Specifically, the physicochemical data includes any one or more of conductivity, pH value, flow rate, temperature, ammonia content, and suspended solid content; the water quality data includes any one or more of dissolved chemical oxygen demand, phosphate content, and carbon-nitrogen ratio.

[0041] Among them, the on-line measurement of the chemical oxygen demand (COD) of the water body is very important. However, in the actual operation process, the neglect of the on-line detection of the dissolved chemical oxygen demand (SCOD) of the water body results in the failure to fully utilize the COD in the raw water, thus affecting the accuracy of subsequent chemical dosing. The phosphate water quality index in the influent water body is an important index to be monitored during the operation process. Reasonably and accurately estimating the concentration of influent phosphate not only helps the manager to timely understand the influent water quality situation, but also contributes to the evaluation of the biodegradability of sewage. The influent carbon-nitrogen ratio (C / N) is a key parameter affecting chemical dosing in the sewage treatment denitrification process, which directly affects the dosing strategy through multiple dimensions such as microbial metabolic requirements, denitrification efficiency, and economy; for different influent sources, the carbon-nitrogen ratio in the water body is different, so it is necessary to detect the carbon-nitrogen ratio in the influent water body to determine the specific content in the water body.

[0042] In the prior art, for the detection methods of the above water quality data, if the on-line analysis method is adopted, it is necessary to perform artificial sampling - filtration - chemical analysis on the water body, or use a TP on-line analyzer. However, the above process generally takes 15 - 30 minutes. If the detection methods in the prior art are adopted, it is difficult to meet the requirements of real-time monitoring of SCOD, phosphate content, and carbon-nitrogen ratio in the influent water body.

[0043] In this embodiment, the determination of the above water quality data is achieved by establishing a detection model through multiple linear regression and realizing on-line detection based on this detection model.

[0044] Specifically, for the detection of SCOD, the final result is obtained by weighted summation of the corresponding weights for the collected conductivity, pH value, temperature, flow rate, and suspended solid content. Among them, the weights corresponding to the above physicochemical data are obtained by linear regression during the training of the detection model. In one implementation, for the training method, multiple sets of data of the above physicochemical data are obtained, the multiple sets of data are classified into training samples and calibration samples, multivariate linear regression analysis is used and the results are fitted to obtain a multivariate linear regression model corresponding to SCOD, which is expressed based on the following formula: SCOD = flow rate * 0.0271 + pH * 6.4424 + conductivity * 0.0091 + temperature * 1.7274 + suspended solid content * 0.1339 - 95.603.

[0045] Similarly, for the results of phosphate content and carbon-nitrogen ratio, the corresponding detection models are also constructed based on the method of multivariate linear regression, and the weight values corresponding to the physicochemical data are configured in this detection model. However, the difference is that the physicochemical data between phosphate content and carbon-nitrogen ratio and with SCOD are different. Among them, the physicochemical data input into the multivariate linear regression detection model for phosphate content includes conductivity, pH value, temperature, flow rate, suspended solid content, and ammonia content; and, in the detection of phosphate content, the dissolved chemical oxygen demand also needs to be used as a reference variable. For the multivariate linear regression detection model of phosphate, it is expressed based on the following formula: PO 3- 4 == 10.1583 - 0.0094 * SCOD + 0.0003 * flow rate - 2.2928 * pH + 0.0027 * conductivity + 0.1888 * temperature - 0.0028 * suspended solid content + 0.0593 * ammonia content. For the carbon-nitrogen ratio, the physicochemical data input into the two multivariate linear regression detection models includes conductivity, pH value, temperature, flow rate, and suspended solid content. And, in the detection of carbon-nitrogen ratio content, phosphate also needs to be used as a reference variable. For the multi-source linear regression detection model of carbon-nitrogen ratio, it is expressed based on the following formula: C / N = 12.4843 + 0.0007 * flow rate + 7.7775 * pH - 0.0048 * conductivity + 0.0314 * suspended solid content - 0.4178 * temperature - 11.0004 * PO 3- 4.

[0046] Step S22. Determine the predicted dosage of the medicament according to the multiple water quality data and the target index data.

[0047] In this embodiment, a plurality of water quality data are obtained in step S21. However, in the water treatment scenario, the treatment processes and objectives are different for different treatment stages, and the impacts of different types of data in the water quality are also different, as are the requirements for the drugs input. Therefore, it is necessary to optimize the water quality data according to different water treatment requirements to obtain the final predicted dosage of the medicament.

[0048] Specifically, in this embodiment, it is necessary to determine the weight values corresponding to the plurality of water quality data according to the types of the target index data, update the water quality data according to the weight values, and use the updated plurality of water quality data and the target index data as inputs to determine the predicted dosage of the medicament.

[0049] In one implementation manner, the determination of the predicted dosage of the medicament is obtained by using a medicament prediction model. Among them, the medicament prediction model is a feedforward neural network model constructed by an input layer, a hidden layer, and an output layer. Regarding the processing process of the feedforward neural network model, the updated dissolved chemical oxygen demand, phosphate content, carbon-nitrogen ratio, and target index data are transmitted to the input layer, and the above data are normalized in the input layer, the features are scaled to the interval [0, 1], and a series of radial basis functions mapped from the input layer to the hidden layer are obtained through the Gaussian kernel function as the dimension activation function to obtain non-linear features, and the non-linear features are transformed through the weights in the network to obtain output features. Among them, the output feature is the predicted value of the medicament dosage, that is, the predicted dosage of the medicament.

[0050] Among them, the accuracy of the prediction result of this neural network model is achieved by optimizing the key parameters of the model. In order to improve the prediction accuracy of this neural network model in this embodiment, a genetic algorithm is used as the optimization method to implement it.

[0051] Specifically, the two parameters of the initial Gaussian function width vector and the weight value between the hidden layer and the output layer are chromosomally transformed into corresponding binary mixed codes through binary coding, and the XOR operation is performed on the transformed binary verticals to obtain new numerical values to generate special binary codes. The fitness of the generated special binary codes is calculated based on the fitness function, and it is based on whether the fitness meets the population requirements or the iteration upper limit; if the population requirements or the iteration upper limit are met, the current parameters are the optimal parameters, and if the above conditions are not met, the roulette wheel method is used to retain the individual corresponding to the parameter with the highest fitness. The selected individuals are crossed to generate new parameter combinations, and the width vector of the Gaussian function and the weight value between the hidden layer and the output layer are set with random probabilities to amplify the new parameter combinations. The amplified new parameter combinations are iterated until the above termination conditions are met and the optimal parameter combination is output as the target parameter, and the neural network model is optimized based on this target parameter.

[0052] Among them, the genetic algorithm is used to generate new parameter combinations by parameter crossover for individuals. Specifically, after the preliminary selection operation by the roulette wheel method, the parent generation starts to generate new offspring through initial adaptive crossover and mutation. Among them, the adaptive crossover probability and mutation probability will change continuously with the fitness of the offspring in the population, obtaining the initial crossover probability, mutation probability, and new crossover probability of the offspring. The two mutant offspring individuals are obtained by randomly selecting two parents from the optimized parent population for crossover selection and mutation optimization selection. The fitness of the mutant offspring chromosome is compared with that of the parent chromosome. If the fitness of the offspring is better than that of the parent, the offspring and the parent are replaced and incorporated into the population P to form a new population for iterative evolution. The two offspring chromosomes are obtained through crossover and mutation. Each individual in the population can be used as an independent computing unit for parallel computing optimization during the crossover and mutation processes, realizing multi-objective parallel optimization, thereby shortening the search process and obtaining the optimal selected individual.

[0053] Step S23. Based on the predicted chemical dosage, determine the initial frequency of the chemical dosing pump, and determine the error value between the real-time target index data and the set index data. Determine the feedback frequency of the chemical dosing pump through the error value, determine the target frequency based on the initial frequency and the feedback frequency, and control the chemical dosing pump to dose based on the target frequency.

[0054] In this embodiment, the real-time target index data is obtained based on the gradient time, where the gradient time is set for the time when the sampling interval time gradually decreases.

[0055] Specifically, the sampling interval refers to the time interval for the sensor to transmit data back. And it should be noted that the setting of the gradient time is set during a complete water treatment process. A complete water treatment process refers to that the volume of the water body currently being treated is the water storage volume of the biological pond, that is, the volume of the water body treated can be determined by the effluent flow rate and time with the start of the first water inlet as a node; or the start of dosing can be used as a node to determine the volume of the water body treated during the dosing process. The reason for such a setting is that with the change of the dosage and time of the chemical input, the change of the target index data in the water body intensifies. Based on the intensified change of the target index data, the sampling frequency should be increased to ensure the accuracy of control.

[0056] In this embodiment, a method combining a feedforward controller and a feedback controller is used for chemical dosing control.

[0057] Specifically, the input of the feed-forward controller is the predicted chemical dosage obtained in step S22, and it determines the initial frequency of the chemical dosing pump based on the predicted chemical dosage. The input of the feedback controller is the error value between the real-time target index data and the set index data collected based on the gradient time, and it determines the feedback frequency of the chemical dosing pump according to this error value. Finally, the initial frequency and the feedback frequency are linearly combined to obtain the target frequency, and the chemical dosing pump is controlled based on this target frequency for chemical feeding.

[0058] Among them, the initial frequency of the chemical dosing pump is determined based on the flow rate of the chemical dosing pump and the liquid volume of the chemical dosing pump, and it is calculated based on the following formula: , where is the flow rate of the chemical dosing pump, and C is the volume of the chemical per stroke; among them, the flow rate of the chemical dosing pump is determined based on the chemical concentration, the predicted chemical dosage, and the flow rate, and it is calculated based on the following formula: ; where y is the predicted chemical dosage, is the flow rate of the influent water body, and D is the chemical concentration.

[0059] In an implementable manner, the input of the feedback controller can be the error value between the real-time target index data and the set index data. Its controller can be constructed based on a PID controller. The controller updates the proportional coefficient, integral time, and differential time based on the error value to obtain the corresponding control variable, and this control variable is the feedback frequency of the chemical dosing pump. Among them, the set index data refers to the data that meets the drainage requirements.

[0060] It should be noted that for the overall chemical dosing system, the disturbances caused by different water qualities are also different, and the reaction time of its treatment process is also different. It takes a certain time delay from the generation of the disturbance to the formation of an obvious deviation, which makes it difficult for the feedback controller to respond quickly. Therefore, in this embodiment, the feed-forward control and the feedback control are combined, and the weight of the feedback control is adjusted according to the changes in water quality to quickly respond to the interference brought by the water quality changes. Specifically, when the water quality changes significantly, the weight of the feedback control is reduced, and the weight of the feed-forward control is increased; when the water quality is relatively stable, the feed-forward weight is reduced and the feedback weight is increased to ensure more stable effluent water quality.

[0061] Among them, the linear combination of the feed-forward control and the feedback control is expressed based on the following formula: ; where w is the target frequency, a is the weight coefficient, v is the feed-forward output frequency, and u is the feedback output frequency.

[0062] In an implementable manner, the weight coefficient is adjusted according to the change of the water quality data, and its adjustment method is expressed based on the following formula: , where and Dissolved chemical oxygen demand at the current moment and the previous moment, respectively, and Phosphate content at the current moment and the previous moment, respectively, and Carbon-nitrogen ratio at the current moment and the previous moment, respectively. k, l, and m are adjustment coefficients, which are taken as 0.05, 0.02, and 0.03 respectively in this embodiment.

[0063] An embodiment of the present application provides a method for controlling the dosing of water treatment agents. By predicting the water quality of the incoming water, water quality data regarding the water to be treated is obtained, and the water quality data is used as a key parameter to obtain a corresponding predicted dosing amount of the agent. Based on this predicted amount and the changes in the target data of the water body and the changes in the water quality situation during the dosing process, the feedback control weight and the feedforward control weight are determined, and the feedback control weight and the feedforward control weight are combined to obtain the dosing control amount. The control method provided by the embodiment of the present application can accurately determine the control amount based on the water quality situation and the overall environment change of the water treatment system, increasing the overall control accuracy compared with the single control logic in the prior art, and can solve the problem of inaccurate control of the complex system and environment of water treatment.

[0064] In another implementable manner, referring to Figure 3 , for the dosing control system 120 in this embodiment, it includes:

[0065] A data processing device 121, configured to perform on-line water quality detection according to the physical and chemical data to obtain water quality data regarding the water to be treated;

[0066] A prediction device 122, configured to determine the predicted dosing amount of the agent according to a plurality of the water quality data and target index data;

[0067] A control output device 123, configured to obtain an initial frequency and a feedback frequency, and determine a target frequency based on the initial frequency and the feedback frequency, and use the target frequency as the control output.

[0068] Referring to Figure 4, the above method can also be integrated into the provided terminal device 40. Since the device may have relatively large differences due to configuration or performance, it may include one or more processors 401 and a memory 402. One or more application programs or data may be stored in the memory 402. Among them, the memory 402 may be short-term storage or persistent storage. The application programs stored in the memory 402 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the terminal device. Further, the processor 401 may be set to communicate with the memory 402 to execute a series of computer-executable instructions in the memory 402 on the terminal device. The terminal device may also include one or more power supplies 403, one or more wired / wireless network interfaces 404, one or more input / output interfaces 405, one or more keyboards 406, etc.

[0069] In a specific embodiment, the terminal device includes a memory and one or more programs, where one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the terminal device, and is configured to be executed by one or more processors. The one or more programs include the following computer-executable instructions:

[0070] Based on the obtained physical and chemical data of the water to be treated entering the biological pond, and performing on-line water quality detection according to the physical and chemical data to obtain water quality data about the water to be treated;

[0071] Determine the predicted dosage of the medicament according to the multiple water quality data and the target index data;

[0072] Based on the predicted dosage of the medicament, determine the initial frequency of the chemical dosing pump, and determine the error value between the real-time target index data and the set index data. Determine the feedback frequency of the chemical dosing pump through the error value, determine the target frequency based on the initial frequency and the feedback frequency, and control the chemical dosing pump to add the medicament based on the target frequency.

[0073] The following is a specific introduction to the components of the processor:

[0074] Among them, in this embodiment, the processor is an application specific integrated circuit (ASIC), or is one or more integrated circuits configured to implement the embodiments of the present application. For example: one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0075] Optionally, the processor can execute various functions by running or executing software programs stored in the memory and calling data stored in the memory, such as executing the above Figure 2 shown method.

[0076] In a specific implementation, as an embodiment, the processor may include one or more microprocessors.

[0077] Among them, the memory is used to store the software program for executing the solution of the present application and is controlled by the processor for execution. The specific implementation manner may refer to the above method embodiment and will not be elaborated here.

[0078] Optionally, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processing unit through the interface circuit of the processor. The embodiments of the present application do not make specific limitations on this.

[0079] It should be noted that the structure of the processor shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0080] In addition, the technical effects of the processor may refer to the technical effects of the method described in the foregoing method embodiments, which will not be elaborated herein.

[0081] It should be understood that the processor in the embodiments of the present application may be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0082] In the present application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single item (s) or plural items (s). For example, at least one of a, b, or c may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c may be single or multiple.

[0083] It should be understood that in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution is prior or subsequent. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0084] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0085] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated herein.

[0086] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0087] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0089] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0090] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for controlling the dosing of water treatment agents, characterized in that, Applied to a water treatment system, the water treatment system includes at least one biological pond and a chemical dosing pump. The biological pond is connected to a water inlet pipe, and a plurality of sensors are arranged in the water inlet pipe and the biological pond. The plurality of sensors are used to obtain a plurality of physicochemical data and target index data; the method includes: Based on the obtained physicochemical data of the water to be treated to enter the biological pond, and performing on-line water quality detection according to the physicochemical data to obtain water quality data about the water to be treated; the physicochemical data includes any one or more of conductivity, pH value, flow rate, temperature, ammonia content, and suspended solid content, and the water quality data includes any one or more of dissolved chemical oxygen demand, phosphate content, and carbon-nitrogen ratio; Determine the predicted chemical dosing amount according to the plurality of water quality data and target index data; Based on the predicted chemical dosing amount, determine the initial frequency of the chemical dosing pump, and determine the error value between the real-time target index data and the set index data. Determine the feedback frequency of the chemical dosing pump through the error value, determine the target frequency based on the initial frequency and the feedback frequency, and control the chemical dosing pump to perform chemical dosing based on the target frequency; the real-time target index data is obtained based on the gradient time.

2. The medicament dosing control method for water treatment according to claim 1, wherein, Perform weighted summation of the corresponding weights on the obtained conductivity, pH value, temperature, flow rate, and suspended solid content to obtain the dissolved chemical oxygen demand.

3. The chemical dosing control method for water treatment according to claim 2, wherein, Perform weighted summation of the corresponding weights on the obtained conductivity, pH value, temperature, flow rate, suspended solid content, ammonia content, and dissolved chemical oxygen demand to obtain the phosphate content.

4. The chemical dosing control method for water treatment according to claim 3, wherein Perform weighted summation of the corresponding weights on the obtained conductivity, pH value, temperature, flow rate, suspended solid content, and phosphate content to obtain the carbon-nitrogen ratio.

5. The chemical dosing control method for water treatment according to claim 1, characterized in that, The determining the predicted chemical dosing amount according to the plurality of water quality data and target index data includes: determining the weight values corresponding to the plurality of water quality data according to the type of the target index data, updating the water quality data based on the weight values, and inputting the updated plurality of water quality data and the target index data into a trained and converged chemical dosing prediction model to obtain the predicted chemical dosing amount.

6. The chemical dosing control method for water treatment according to claim 5, characterized in that, The chemical dosing prediction model is a feedforward neural network model constructed by an input layer, a hidden layer, and an output layer, and the weight values between the hidden layer and the output layer and the Gaussian basis function center width vector are optimized by a genetic algorithm, and the initial network structure and parameters are adjusted based on the optimized parameters, and the chemical dosing prediction model is trained to convergence based on the adjusted model.

7. The chemical dosing control method for water treatment according to claim 1, characterized in that, The gradient time is a time setting in which the sampling interval time gradually decreases, and the real-time target index data of the water body is obtained based on the sampling interval time.

8. The chemical dosing control method for water treatment according to claim 1, characterized in that, The initial frequency of the chemical dosing pump is determined based on the chemical dosing pump flow rate and the chemical dosing pump liquid volume, and the chemical dosing pump flow rate is determined based on the chemical agent concentration, the predicted chemical dosing amount, and the flow rate.

9. The chemical dosing control method for water treatment according to claim 1, wherein Determining the target frequency based on the initial frequency and the feedback frequency includes: combining the initial frequency and the feedback frequency through a combined weight of the initial frequency and the feedback frequency; the combined weight is determined based on the change amount of water quality data per unit time and real-time target index data.

10. A drug addition control system, characterized in that, Applied to a water treatment system, the water treatment system includes at least one biological pond and a chemical dosing pump. The biological pond is connected to a water inlet pipe, and a plurality of sensors are arranged in the water inlet pipe and the biological pond; the control system is electrically connected to the plurality of sensors and the chemical dosing pump, controls the plurality of sensors and the chemical dosing pump, and obtains a plurality of physical and chemical data and target index data collected by the plurality of sensors. The control system includes: A data processing device for performing on-line water quality detection according to the physical and chemical data to obtain water quality data of the water to be treated. A prediction device for determining the predicted chemical dosing amount according to the plurality of water quality data and target index data. A control output device for obtaining an initial frequency and a feedback frequency, determining a target frequency based on the initial frequency and the feedback frequency, and using the target frequency as a control output.

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