Intelligent accurate control system for dosing for sewage treatment

Abstract

The invention relates to the technical field of sewage treatment dosing, and particularly discloses an intelligent accurate control system for sewage treatment dosing, which comprises a dosing prediction model construction module, a basic dosing amount determination module, a target dosing amount determination module, a sewage treatment effect judgment module, a dosing amount adjustment module and a database. A multiple linear regression equation is adopted, a nonlinear term is introduced to generate a basic dosing amount, the dosing amount is dynamically adjusted through a water temperature compensation coefficient and a pH segmented correction coefficient, a dosing amount adjusting module is triggered based on water quality qualification rate analysis of a water outlet, and equipment parameters such as a dosing pump and a dosing pipe are combined; the influence of the operation state of the equipment on the dosage is quantitatively compensated, and the dosage of the corresponding treatment agent is specifically adjusted according to the treatment effect of specific water quality parameters, so that the dosage of the agent is saved, the dosage of the agent is accurately matched with the water quality requirement, the treatment effect is ensured to stably reach the standard, and the operation cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of sewage treatment dosing, and in particular to an intelligent and precise control system for sewage treatment dosing. Background Art

[0002] In the sewage treatment process, accurate dosing of chemical agents is a key link to ensure treatment effect and cost control. Traditional dosing methods mainly rely on manual experience or fixed proportion dosing, which cannot adapt to water quality changes in real time, resulting in agent waste or substandard treatment. Therefore, in order to ensure the best sewage treatment effect, it is necessary to make intelligent and precise control of the amount of chemical agents used in the sewage treatment process.

[0003] For example, the Chinese patent publication number CN118529801B discloses an intelligent dosing system for sewage treatment based on big data analysis. The present invention collects water quality data of the water inlet and the water outlet in real time through a water quality detector, and collects drug reserve data in real time through a liquid level detector. The monitoring platform constructs a water quality analysis model in advance, and sets a drug reserve threshold. The water quality data is analyzed according to the established water quality analysis model. If the water quality suddenly changes, an alarm is triggered immediately and the dosing plan is adjusted.

[0004] For example, the Chinese patent publication number CN119292176A discloses a PLC-based sewage treatment dosing control system, which is used to collect sewage inflow, as well as various water quality parameters during sewage treatment and discharge processes, analyze the inflow and various water quality parameters based on PLC, and calculate the dosing amount and adjust the dosing control parameters in coordination with the dosing control module based on the analysis results.

[0005] The above existing technologies still have the following problems: First, the existing technologies only adjust the dosage of the reagents according to the changes in water quality parameters, without considering the influence of linear and nonlinear factors in water quality parameters on the adjustment of the reagents, resulting in the lack of data-driven accuracy in the dosage calculation.

[0006] Secondly, during the adjustment process of the dosage, the dosing equipment will also have an impact on the adjustment. The existing technology does not take this into consideration, resulting in the actual dosage deviating from the theoretical value, resulting in a deviation between the dosage of the dosage and the actual demand.

[0007] Thirdly, the prior art ends the entire dosing adjustment process after the dosage adjustment is completed, and does not conduct subsequent monitoring and processing on the rationality of the dosage adjustment and the changes in water quality after the adjustment is completed, which cannot ensure that the treatment effect is stable and meets the standards and reduce the operating costs. Summary of the invention

[0008] In view of this, in order to solve the problems raised in the above background technology, an intelligent and precise control system for sewage treatment and drug addition is proposed.

[0009] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent and precise control system for sewage treatment dosing, including: a dosing prediction model construction module, which collects water quality parameters, process parameters and corresponding dosing amounts in historical sewage treatment processes, and constructs a dosing prediction model based on this.

[0010] The basic dosage determination module inputs the real-time collected water quality parameters of the sewage treatment inlet and the process parameters corresponding to the current sewage treatment tank into the dosage prediction model to obtain the basic dosage.

[0011] The target dosage determination module obtains the real-time water temperature and pH value in the current sewage treatment pool, dynamically adjusts the basic dosage according to the preset correction rules, and obtains the target dosage.

[0012] The sewage treatment effect determination module obtains the water quality parameters of the sewage treatment outlet and determines the sewage treatment effect of the outlet based on the sewage treatment effect determination rules.

[0013] The dosing amount adjustment module obtains the operation data of the dosing equipment when the judgment result is that the effect does not meet the standard, and adjusts the target dosing amount based on the operation data.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention integrates the mapping relationship between historical water quality parameters, process parameters and dosage, adopts a multivariate linear regression equation and introduces nonlinear terms, thereby significantly improving the adaptability of the model to complex water quality conditions. Compared with the traditional single-parameter model, the present model can capture the nonlinear changes of water quality parameters, reduce prediction errors, and ensure that the basic dosage is highly matched with actual needs.

[0015] (2) The present invention adjusts the basic dosage in real time through the water temperature compensation coefficient and the pH segment correction coefficient to ensure the stability of the agent reaction rate and avoid the lack of data-driven accuracy in the dosage calculation.

[0016] (3) The present invention introduces an equipment status compensation factor by comprehensively analyzing the impact of the operating parameters of the dosing pump and the drug delivery tube equipment on the actual dosage, thereby avoiding the deviation of the actual dosage from the theoretical value and reducing the deviation between the dosage of the drug and the actual demand.

[0017] (4) The present invention sets up an outlet water quality qualification rate assessment module, reversely adjusts the dosage according to the treatment effect, accurately replenishes the agent according to the unit deviation corresponding to the dosage increment, and adjusts the corresponding treatment agent dosage according to the treatment effect of the specific water quality parameters, thereby saving the agent dosage and accurately matching the agent dosage with the water quality requirements, thereby ensuring that the treatment effect is stable and meets the standards and reducing the operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the system module structure connection of the present invention.

[0020] Figure 2 This is a flow chart for determining abnormal water quality parameters of the present invention.

[0021] Figure 3 A schematic diagram of the hardware structure of a computer provided for the second embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Embodiment 1

[0024] See also Figure 1 As shown, the present invention provides an intelligent and precise control system for dosing in sewage treatment, including: a dosing prediction model building module, a basic dosing amount determination module, a target dosing amount determination module, a sewage treatment effect judgment module and a dosing amount adjustment module.

[0025] It should be noted that the present invention also includes a database for storing the standard values ​​of various process parameters, reference water temperature, reference pH value, standard values ​​of water quality parameters, water quality qualification rate intervals corresponding to various sewage treatment effects, types of treatment chemicals corresponding to various water quality parameters, the amount of treatment chemicals required to be increased corresponding to unit deviations, and the proportion of each treatment chemical amount in the total added amount.

[0026] The dosing prediction model construction module is connected to the basic dosing amount determination module, the basic dosing amount determination module is connected to the target dosing amount determination module, the target dosing amount determination module and the sewage treatment effect judgment module are both connected to the dosing amount adjustment module, and the dosing prediction model construction module, the target dosing amount determination module, the sewage treatment effect judgment module and the dosing amount adjustment module are all connected to the database.

[0027] The dosing prediction model building module collects water quality parameters, process parameters and corresponding dosing amounts in historical sewage treatment processes, and builds a dosing prediction model based on them.

[0028] In a specific embodiment of the present invention, the water quality parameters include but are not limited to suspended solids concentration, chemical oxygen demand, ammonia nitrogen and pH value, and the process parameters include but are not limited to sewage flow rate and hydraulic retention time. The suspended solids concentration, chemical oxygen demand, ammonia nitrogen and pH value are collected by a suspended solids meter, a COD analyzer, an ammonia nitrogen analyzer and a portable pH meter, respectively. The sewage flow rate is collected by an electromagnetic flowmeter installed on the sewage pipeline, and the hydraulic retention time is extracted from the background of the sewage treatment control system.

[0029] In a specific embodiment of the present invention, the specific process of constructing the dosing prediction model is: constructing a first dosing prediction model using a multivariate linear regression equation according to water quality parameters.

[0030] In a specific embodiment of the present invention, the specific process of constructing the first dosing prediction model using a multivariate linear regression equation according to water quality parameters is: extracting parameters that are linearly related to the dosage and parameters that are nonlinearly related to the dosage from the water quality parameters.

[0031] In a specific embodiment of the present invention, the parameters having a linear relationship with the dosage include suspended matter concentration, chemical oxygen demand, and ammonia nitrogen, and the parameters having a nonlinear relationship with the dosage include pH value.

[0032] The first dosing prediction model is obtained by multi-dimensional fusion processing based on linear relationship parameters, nonlinear relationship parameters and parameter regression coefficients.

[0033] In a specific embodiment of the present invention, the specific implementation process of the multi-dimensional fusion processing to obtain the first dosing prediction model is: respectively extract the maximum and minimum values ​​corresponding to the suspended solids concentration, chemical oxygen demand, ammonia nitrogen and pH value in the historical sewage treatment process, and subtract the suspended solids concentration, chemical oxygen demand, ammonia nitrogen and pH value from their corresponding minimum values, and compare each difference with the corresponding maximum and minimum value difference to obtain the normalized suspended solids concentration, chemical oxygen demand, ammonia nitrogen and pH value, and record them as .

[0034] The first dosing prediction model is expressed as: ,in, They represent the regression coefficients corresponding to the suspended solids concentration, chemical oxygen demand, ammonia nitrogen, and pH square terms obtained by fitting the experimental data. It indicates that the intercept term is obtained by fitting the experimental data.

[0035] It should be noted that the The specific methods are obtained by fitting experimental data: Step 1 Data collection: Collect water quality parameters, process parameters and corresponding dosage in the historical sewage treatment process; Step 2 Data preprocessing: Eliminate outliers, fill missing values, and perform standard normalization on parameters of different dimensions; Step 3 Model training: Use the least squares method to solve the coefficients to minimize the sum of squares of the error between the predicted dosage and the actual dosage, where the objective function is: ,in, Indicates the historical sewage treatment numbers. , , Respectively represent the history The dosage of chemicals in the sewage treatment process and the normalized suspended solids concentration, chemical oxygen demand, ammonia nitrogen and pH value after treatment, Indicates the number of historical sewage treatments. Step 4 Coefficient determination: The optimal coefficient is obtained through mathematical calculation .

[0036] The standard value of each process parameter stored in the database is compared with its actual value to obtain the correction coefficient of each process parameter.

[0037] The correction coefficients of various process parameters are multiplied to obtain the comprehensive correction coefficient.

[0038] It should be noted that the processing logic for obtaining the correction coefficient and the comprehensive correction coefficient of each process parameter is explained as follows: the standard value of each process parameter is compared with its actual value to obtain the parameter correction coefficient, which reflects the degree of deviation between the actual value and the standard value. When the actual value is less than the standard value, the correction coefficient is greater than 1, so as to quantify the influence of a single parameter on the dosage prediction. The correction coefficients of each parameter are multiplied to obtain the comprehensive correction coefficient. The logic of the multiplication is that the deviation of each process parameter has an additive effect on the dosage prediction. The degree of deviation of multiple parameters can be comprehensively integrated through multiplication, so that the comprehensive correction coefficient can reflect the comprehensive effect of the deviation of the overall process parameters from the standard value.

[0039] The first dosing prediction model is combined with the comprehensive correction coefficient to obtain the dosing prediction model.

[0040] It should be noted that the combination of the first medication prediction model and the comprehensive correction coefficient specifically refers to: multiplying the first medication prediction model and the comprehensive correction coefficient to obtain the medication prediction model.

[0041] The embodiment of the present invention integrates the mapping relationship between historical water quality parameters, process parameters and dosage, adopts a multivariate linear regression equation and introduces nonlinear terms, thereby significantly improving the adaptability of the model to complex water quality conditions. Compared with the traditional single-parameter model, this model can capture the nonlinear changes of water quality parameters, reduce prediction errors, and ensure that the basic dosage is highly matched with actual needs.

[0042] The basic dosage determination module inputs the water quality parameters of the sewage treatment water inlet collected in real time and the process parameters corresponding to the current sewage treatment tank into the dosage prediction model to obtain the basic dosage.

[0043] The target dosage determination module obtains the real-time water temperature and pH value in the current sewage treatment pool, dynamically adjusts the basic dosage according to a preset correction rule, and obtains the target dosage.

[0044] It should be noted that the real-time water temperature and pH value in the current sewage treatment pool are collected by a thermometer and a pH value measuring instrument respectively.

[0045] In a specific embodiment of the present invention, the preset correction rule is specifically: coupling processing is performed according to the real-time water temperature in the current sewage treatment pool and the reference water temperature stored in the database to obtain a water temperature compensation coefficient.

[0046] It should be noted that the specific method of obtaining the water temperature compensation coefficient is: subtract the real-time water temperature in the current sewage treatment pool from the benchmark water temperature stored in the database, multiply the difference by the corresponding compensation coefficient, and then add the multiplication result to the compensation coefficient benchmark value under the benchmark water temperature conditions to obtain the water temperature compensation coefficient.

[0047] In a specific embodiment of the present invention, the reference value of the compensation coefficient under the reference water temperature condition is 1, and 1 is the starting point of the compensation coefficient. The corresponding compensation coefficient can be fitted by experimental data. The specific fitting method is: in a laboratory environment, other variables are controlled unchanged, only the water temperature is changed, the relationship between the reaction rate of the agent and the dosage is observed, the water temperature is gradually increased, and the dosage change when the same treatment effect is achieved is recorded. The adjustment ratio of the dosage is calculated for every 1°C change in water temperature. If it is found in the experiment that the dosage needs to be increased by 2% (i.e., 0.02 times the reference amount) for every 1°C increase in water temperature, the compensation coefficient is fitted to be 0.02.

[0048] The pH value in the current sewage treatment pool and the reference pH value stored in the database are analyzed and processed in sections to obtain the pH correction coefficient.

[0049] It should be noted that the specific process of obtaining the pH correction coefficient is: extracting the allowable fluctuation range of the pH value in the current sewage treatment pool and recording it as .

[0050] The pH correction coefficient model is constructed and expressed as: ,in, Indicates the pH correction factor, and They represent the reference pH value stored in the database and the pH value in the current sewage treatment pool, respectively. and They represent the low pH correction slope and the high pH correction slope, respectively.

[0051] In a specific embodiment of the present invention, the reference correction coefficient is 1, the technical manual of the target agent manufacturer will clearly give the pH value allowable fluctuation range for the agent to exert the best effect, the low pH correction slope and the high pH correction slope are both fitted by experimental data, and the specific fitting process is: the first step is to control the variables: fix the water quality type (such as domestic sewage), the type of agent, and the treatment process conditions, and only change the pH value of the water sample; the second step is to set the experimental group: the pH value of the low pH section can be 5.0, 5.5, and 6.0, and the pH value of the high pH section can be 9.0, 9.5, and 10.0; the third step is to perform the experiment: add the agent to each group of water samples, and record the actual dosage when the target treatment effect (such as turbidity meets the standard and pollutant removal rate meets the standard); the fourth step is to organize the experimental data: the low pH data records the corresponding pH value and dosage correction multiple, that is, the ratio of the actual dosage to the reference dosage, and the high pH data also records the pH value and dosage correction multiple; the fifth step is to use the least squares method to solve the correction coefficient that minimizes the sum of squares of the error.

[0052] The embodiment of the present invention adjusts the basic dosage in real time through the water temperature compensation coefficient and the pH segment correction coefficient, ensures the stability of the agent reaction rate, and avoids the lack of data-driven accuracy in the dosage calculation.

[0053] In a specific embodiment of the present invention, the specific method of obtaining the target dosage is: multiplying the basic dosage with the water temperature compensation coefficient and the pH value correction coefficient to obtain the target dosage.

[0054] The sewage treatment effect determination module obtains water quality parameters of the sewage treatment outlet and determines the sewage treatment effect of the outlet based on the sewage treatment effect determination rule.

[0055] In a specific embodiment of the present invention, the specific process of judging the sewage treatment effect at the outlet based on the sewage treatment effect judgment rule is: performing a pass rate analysis on the water quality parameters of the sewage treatment outlet and the standard values ​​of the water quality parameters stored in the database to obtain the water quality pass rate of the sewage treatment outlet.

[0056] In a specific embodiment of the present invention, the specific method for obtaining the water quality pass rate of the sewage treatment outlet is: analyzing the difference between each water quality parameter of the sewage treatment outlet and the corresponding standard value and the deviation ratio of the corresponding set allowable difference, obtaining the treatment pass rate corresponding to each water quality parameter, and obtaining the water quality pass rate of the sewage treatment outlet through weighted accumulation processing.

[0057] In a specific embodiment of the present invention, under normal circumstances, the weight relationship is: chemical oxygen demand ≥ ammonia nitrogen > suspended solids concentration > pH value. Chemical oxygen demand characterizes the total amount of organic pollution in water and is the core indicator for measuring organic pollution in water bodies. High chemical oxygen demand will consume dissolved oxygen in water bodies, leading to hypoxia and death of aquatic organisms, and the chemical oxygen demand in industrial wastewater may contain toxic organic matter. In environmental protection supervision, chemical oxygen demand is a key assessment item, and the emission compliance rate and total amount control of sewage treatment plants are strictly controlled. The weight of ammonia nitrogen is second, but it is equally critical. The weight of suspended solids concentration varies from industry to industry, and the weight of pH value is relatively low and is a basic indicator. Therefore, the treatment qualified rates of suspended solids concentration, chemical oxygen demand, ammonia nitrogen and pH value correspond to the water quality qualified rate assessment proportion of sewage treatment outlets, which are 0.2, 0.4, 0.3 and 0.1 respectively.

[0058] The water quality pass rate of the sewage treatment outlet is compared with the water quality pass rate interval corresponding to each sewage treatment effect stored in the database. If the water quality pass rate of the sewage treatment outlet is within the water quality pass rate interval corresponding to a certain sewage treatment effect, then the sewage treatment effect is used as the sewage treatment effect of the outlet, and the sewage treatment effect of the outlet is judged accordingly.

[0059] The dosing amount adjustment module obtains the operation data of the dosing equipment when the determination result is that the effect does not meet the standard, and adjusts the target dosing amount based on the operation data.

[0060] In a specific embodiment of the present invention, the specific process of adjusting the target dosage based on the operating data is as follows: comparing the treatment pass rate corresponding to each water quality parameter with the treatment pass rate set as a reference to obtain each abnormal water quality parameter, and subtracting the treatment pass rate set as a reference for each abnormal water quality parameter from its corresponding treatment pass rate to obtain the treatment pass rate deviation corresponding to each abnormal water quality parameter.

[0061] See also Figure 2As shown, in a specific embodiment of the present invention, the specific method of obtaining each abnormal water quality parameter is: comparing the treatment pass rate corresponding to each water quality parameter with the treatment pass rate of its set reference, if the treatment pass rate corresponding to a water quality parameter is less than the treatment pass rate of its set reference, then the water quality parameter is recorded as an abnormal water quality parameter, otherwise, the water quality parameter is recorded as a normal water quality parameter, thereby obtaining each abnormal water quality parameter.

[0062] Based on the types of treatment drugs corresponding to each water quality parameter stored in the database, the types of treatment drugs corresponding to each abnormal water quality parameter are obtained, and the treatment pass rate deviation corresponding to each abnormal water quality parameter is multiplied by the required increase in the amount of treatment drugs corresponding to the unit deviation stored in the database to obtain the required increase in the amount of treatment drugs corresponding to each abnormal water quality parameter.

[0063] By comprehensively analyzing the dosing pump operation data and the drug delivery pipe operation data in the dosing equipment operation data, the influencing factor of the operation status of the dosing equipment on the dosing amount is obtained, and the factor is multiplied by the target dosing amount to obtain the actual total dosing amount.

[0064] In a specific embodiment of the present invention, the specific process of obtaining the influence factor of the operating status of the dosing equipment on the dosing amount is: quantitative analysis is performed on each key operating parameter value and its corresponding standard value in the dosing pump operation data to obtain the influence factor corresponding to each key operating parameter, and the influence factor evaluation is performed on each main operating parameter value and its corresponding normal value in the drug delivery tube operation data to obtain the influence factor corresponding to each main operating parameter.

[0065] In a specific embodiment of the present invention, the key operating parameters in the dosing pump operating data include but are not limited to the dosing pump flow rate, the dosing pump operating time, and the dosing pump speed. The main operating parameters in the drug delivery tube operating data include but are not limited to the drug delivery tube pressure and the drug delivery tube diameter.

[0066] It should be noted that the dosing pump flow, dosing pump operating time, and dosing pump speed are all extracted from the dosing pump operation control panel. The pressure of the drug delivery tube is collected by a pressure sensor installed on the drug delivery tube. The diameter of the drug delivery tube is scanned by using a laser scanner. By measuring the information reflected by the laser beam, after data processing and algorithm calculation, the diameter of the drug delivery tube can be accurately obtained.

[0067] In a specific embodiment of the present invention, the specific process of obtaining the influencing factors corresponding to each key operating parameter is: by respectively comparing the output amount per unit time with the actual output amount, the actual operation time of the dosing pump with the set operation time, and the actual speed per unit time with the set speed, the flow deviation influencing factor, the time influencing factor and the speed influencing factor of the dosing pump are obtained. The specific process of obtaining the influencing factors corresponding to each main operating parameter is: (1) comparing the pressure of the drug delivery tube with the set normal pressure threshold to obtain the drug delivery tube pressure influencing factor, (2) taking the square of the ratio of the drug delivery tube diameter to the designed tube diameter as the drug delivery tube diameter influencing factor.

[0068] The influencing factors corresponding to each key operating parameter are multiplied by the influencing factors corresponding to each main operating parameter to obtain the influencing factors of the operating status of the dosing equipment on the dosing amount.

[0069] It should be noted that the reasons why the dosing pump and the drug delivery tube parameters affect the dosage are as follows: the output drug volume per unit time is determined by the flow rate. The larger the flow rate, the more drug volume is added per unit time, which directly affects the dosing rate; the running time determines the total duration of drug delivery. The longer the time, the more cumulative drug volume is added; the rotation speed affects the flow rate of the pump, and changes in the rotation speed will change the output drug volume per unit time; insufficient pressure will cause drug delivery to be blocked, and the actual dosage will be lower than the theoretical output of the pump; the pipe diameter determines the maximum drug delivery flow rate. A small pipe diameter or scaling will cause the inner diameter to decrease, which will limit the actual drug delivery and thus affect the dosage. The reason for obtaining the influencing factor through ratio calculation is that the ratio calculation can intuitively quantify the degree of deviation between the actual parameter and the set value. This method is simple and direct, and is convenient for quickly evaluating the proportion of the influence of a single parameter on the dosage, providing a clear quantitative basis for subsequent adjustments. The reason for obtaining the comprehensive influencing factor by accumulating the influencing factors is that the influence of each parameter on the dosage is synergistic, and the cumulative multiplication can fully reflect the comprehensive influence of multiple parameters on the dosage when they deviate at the same time, avoid viewing a single factor in isolation, make the final influencing factor more in line with the actual operating status, and ensure the accuracy and reliability of the dosage calculation.

[0070] The embodiment of the present invention comprehensively analyzes the impact of the operating parameters of the dosing pump and the drug delivery tube equipment on the actual dosage, introduces an equipment status compensation factor, avoids the actual dosage from deviating from the theoretical value, and reduces the deviation between the dosage of the drug and the actual demand.

[0071] Based on the actual dosage and the proportion of each treatment drug amount stored in the database to the total dosage, the actual dosage of the treatment drug corresponding to each abnormal water quality parameter is obtained.

[0072] The actual dosage of the treatment drug corresponding to each abnormal water quality parameter is added to the required additional dosage to obtain the final dosage of the treatment drug corresponding to each abnormal water quality parameter.

[0073] The embodiment of the present invention sets an outlet water quality qualification rate assessment module, reversely adjusts the dosage according to the treatment effect, accurately replenishes the agent according to the dosage increment corresponding to the unit deviation, and specifically adjusts the corresponding treatment agent dosage according to the treatment effect of specific water quality parameters, thereby saving the agent dosage and accurately matching the agent dosage with the water quality requirements, thereby ensuring that the treatment effect is stable and meets the standards and reducing the operating cost.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0075] Embodiment 2

[0076] like Figure 3 As shown, an embodiment of the present invention provides the following technical solution: a computer, comprising a memory 202, a processor 201, and a computer program stored in the memory 202 and executable on the processor 201; when the processor 201 executes the computer program, an intelligent and precise control system for dosing in sewage treatment as described above is implemented.

[0077] Specifically, the processor 201 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0078] Among them, the memory 202 may include a large-capacity memory for data or instructions. For example, but not limitation, the memory 202 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 202 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 202 may be inside or outside the data processing device. In a specific embodiment, the memory 202 is a non-volatile memory. In a specific embodiment, the memory 202 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (Static Random-Access Memory, referred to as SRAM) or a dynamic random access memory (Dynamic Random Access Memory, referred to as DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (Fast Page Mode Dynamic Random Access Memory, referred to as FPMDRAM), an extended data output dynamic random access memory (Extended Date Out Dynamic Random Access Memory, referred to as EDODRAM), a synchronous dynamic random access memory (Synchronous Dynamic Random-Access Memory, referred to as SDRAM), etc.

[0079] The memory 202 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 201 .

[0080] The processor 201 reads and executes the computer program instructions stored in the memory 202 to implement the above-mentioned intelligent and precise control system for sewage treatment and dosing.

[0081] In some embodiments, the computer may further include a communication interface 203 and a bus 200. Figure 3 As shown, the processor 201, the memory 202, and the communication interface 203 are connected via a bus 200 and communicate with each other.

[0082] The communication interface 203 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication interface 203 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0083] The bus 200 includes hardware, software, or both, and couples the components of the computer to each other. The bus 200 includes, but is not limited to, at least one of the following: a data bus (DataBus), an address bus (AddressBus), a control bus (ControlBus), an expansion bus (ExpansionBus), and a local bus (LocalBus). By way of example and not limitation, the bus 200 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, or a Low Pin Count (LPC) bus. Bus, memory bus, Micro Channel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local Bus (VLB) bus or other suitable bus or a combination of two or more of these. Where appropriate, bus 200 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application considers any suitable bus or interconnect.

[0084] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent and precise control system for sewage treatment and dosing, characterized in that: include: The dosing prediction model building module collects water quality parameters, process parameters and corresponding dosing amounts in the historical sewage treatment process, and builds a dosing prediction model based on them; The basic dosage determination module inputs the real-time collected water quality parameters of the sewage treatment inlet and the process parameters corresponding to the current sewage treatment tank into the dosage prediction model to obtain the basic dosage; The target dosage determination module obtains the real-time water temperature and pH value in the current sewage treatment pool, dynamically adjusts the basic dosage according to the preset correction rules, and obtains the target dosage; The sewage treatment effect determination module obtains the water quality parameters of the sewage treatment outlet and determines the sewage treatment effect of the outlet based on the sewage treatment effect determination rules; The dosing amount adjustment module obtains the operation data of the dosing equipment when the judgment result is that the effect does not meet the standard, and adjusts the target dosing amount based on the operation data.

2. According to claim 1, the intelligent precise control system for sewage treatment and drug addition is characterized in that: The specific process of constructing the dosing prediction model is as follows: The first dosing prediction model was constructed using a multivariate linear regression equation based on water quality parameters; Compare the standard value of each process parameter stored in the database with its actual value to obtain the correction coefficient of each process parameter; Multiply the correction coefficients of each process parameter to obtain a comprehensive correction coefficient; The first dosing prediction model is combined with the comprehensive correction coefficient to obtain the dosing prediction model.

3. The intelligent precise control system for sewage treatment and dosing according to claim 2 is characterized by: The specific process of constructing the first dosing prediction model using the multivariate linear regression equation according to water quality parameters is as follows: Extract the parameters with linear relationship and nonlinear relationship with the dosage from the water quality parameters respectively; The first dosing prediction model is obtained by multi-dimensional fusion processing based on linear relationship parameters, nonlinear relationship parameters and parameter regression coefficients.

4. The intelligent precise control system for sewage treatment and dosing according to claim 1 is characterized by: The preset correction rules are specifically: The water temperature compensation coefficient is obtained by coupling the real-time water temperature in the current sewage treatment pool with the reference water temperature stored in the database; The pH value in the current sewage treatment pool and the reference pH value stored in the database are analyzed and processed in sections to obtain the pH correction coefficient.

5. The intelligent precise control system for sewage treatment and drug addition according to claim 4 is characterized by: The specific method of obtaining the target dosage is: multiplying the basic dosage with the water temperature compensation coefficient and the pH value correction coefficient to obtain the target dosage.

6. The intelligent precise control system for sewage treatment and drug addition according to claim 1 is characterized by: The specific process of determining the sewage treatment effect at the outlet based on the sewage treatment effect determination rule is as follows: Performing a pass rate analysis on the water quality parameters of the sewage treatment outlet and the standard values ​​of the water quality parameters stored in the database to obtain the pass rate of the water quality of the sewage treatment outlet; The water quality pass rate of the sewage treatment outlet is compared with the water quality pass rate interval corresponding to each sewage treatment effect stored in the database. If the water quality pass rate of the sewage treatment outlet is within the water quality pass rate interval corresponding to a certain sewage treatment effect, then the sewage treatment effect is used as the sewage treatment effect of the outlet, and the sewage treatment effect of the outlet is judged accordingly.

7. The intelligent precise control system for sewage treatment and drug addition according to claim 6 is characterized by: The specific method for obtaining the water quality pass rate of the sewage treatment outlet is: analyzing the deviation ratio of the difference between each water quality parameter of the sewage treatment outlet and the corresponding standard value and the corresponding set allowable difference, obtaining the treatment pass rate corresponding to each water quality parameter, and obtaining the water quality pass rate of the sewage treatment outlet through weighted accumulation processing.

8. The intelligent precise control system for sewage treatment and drug addition according to claim 7 is characterized by: The specific process of adjusting the target dosage based on the operation data is as follows: By comparing the qualified treatment rate corresponding to each water quality parameter with the qualified treatment rate set as a reference, each abnormal water quality parameter is obtained, and the qualified treatment rate set as a reference for each abnormal water quality parameter is subtracted from the qualified treatment rate corresponding to it to obtain the qualified treatment rate deviation corresponding to each abnormal water quality parameter; Based on the types of treatment drugs corresponding to each water quality parameter stored in the database, the types of treatment drugs corresponding to each abnormal water quality parameter are obtained, and the treatment qualified rate deviation corresponding to each abnormal water quality parameter is multiplied by the amount of treatment drugs required to be increased corresponding to the unit deviation stored in the database to obtain the required increase in the amount of treatment drugs corresponding to each abnormal water quality parameter; By comprehensively analyzing the dosing pump operation data and the drug delivery pipe operation data in the dosing equipment operation data, the influencing factor of the operation status of the dosing equipment on the dosing amount is obtained, and the influencing factor is multiplied by the target dosing amount to obtain the actual total dosing amount; Based on the actual dosage and the proportion of each treatment drug amount to the total dosage stored in the database, the actual dosage of the treatment drug corresponding to each abnormal water quality parameter is obtained; The actual dosage of the treatment drug corresponding to each abnormal water quality parameter is added to the required additional dosage to obtain the final dosage of the treatment drug corresponding to each abnormal water quality parameter.

9. The intelligent precise control system for sewage treatment and drug addition according to claim 8, characterized in that: The specific method for obtaining each abnormal water quality parameter is: compare the treatment pass rate corresponding to each water quality parameter with the treatment pass rate of its set reference. If the treatment pass rate corresponding to a water quality parameter is less than the treatment pass rate of its set reference, then the water quality parameter is recorded as an abnormal water quality parameter. Otherwise, the water quality parameter is recorded as a normal water quality parameter, thereby obtaining each abnormal water quality parameter.

10. The intelligent precise control system for sewage treatment and drug addition according to claim 8, characterized in that: The specific process of obtaining the influence factor of the operation status of the dosing equipment on the dosing amount is as follows: Quantitative analysis is performed on the key operating parameter values ​​and their corresponding standard values ​​in the dosing pump operation data to obtain the influencing factors corresponding to the key operating parameters, and the influencing factor evaluation is performed on the key operating parameter values ​​and their corresponding normal values ​​in the drug delivery tube operation data to obtain the influencing factors corresponding to the key operating parameters; The influencing factors corresponding to each key operating parameter are multiplied by the influencing factors corresponding to each main operating parameter to obtain the influencing factors of the operating status of the dosing equipment on the dosing amount.

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