A photon quantum technology system for sewage treatment

Abstract

The invention discloses a photon technology system for sewage treatment, which relates to the technical field of sewage treatment, and is used to solve the problem that suspended matter blocks the intensity and range of light exposure to sewage, thereby reducing the efficiency of sewage treatment by photon technology. The sewage dissolved oxygen concentration and organic matter concentration are obtained by data preprocessing, and the ammonia nitrogen weight is calculated by a polynomial regression algorithm. The ammonia nitrogen weight and the ammonia nitrogen content in the sample are weighted and calculated to obtain the ammonia nitrogen coverage potential coefficient, and then the phosphate coverage potential coefficient is obtained by the same method. The equilibrium potential coefficient is calculated according to the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, and a equilibrium constraint rule is formulated to obtain a constraint difference. Through a recursive collection method, two time points are randomly selected within a period of time to collect marked parameter data, and the increase rate is calculated. The comprehensive increase rate and the constraint difference are obtained to obtain the treated material content, improve the suspended matter treatment, and improve the efficiency of sewage treatment by photon technology.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and more specifically, to a photon technology system for sewage treatment. Background Art

[0002] In recent years, with the accelerated development of industrialization and urbanization, water pollution has become increasingly serious, and sewage treatment has become one of the hot issues of global concern. The existing photon technology is used in combination with traditional coagulation and precipitation methods. Specifically, coagulants are used to remove suspended particles and colloidal substances, and heavy metals and phosphates are removed through chemical precipitation, and then photon technology is used to degrade pollutants through photocatalytic reactions.

[0003] The prior art has the following deficiencies:

[0004] At present, for different sewage conditions, for example, sewage with high suspended particles, if photon technology is used first, the suspended matter will not be treated first, blocking the intensity and range of light exposure to the sewage, reducing the efficiency of sewage treatment with photon technology. At the same time, sewage with high suspended particles usually contains two or more ions that restrict each other. Only by balancing the two ion elimination mechanisms can the suspended matter be quickly treated and the efficiency of sewage treatment with photon technology be guaranteed. Therefore, a photon technology system for sewage treatment is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a photon technology system for sewage treatment, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solution: a photon technology system for sewage treatment, comprising an ammonia nitrogen potential module, a phosphate potential module, a balance control module and an adjustment processing module; signal connections between the modules;

[0008] The ammonia nitrogen potential module is used to collect the proportion of ammonia nitrogen content in sewage samples and analyze the information of ammonia nitrogen influencing factors in multiple sewage samples. Through data preprocessing, the sewage dissolved oxygen concentration and sewage organic matter concentration are obtained, and the ammonia nitrogen weight is obtained by substituting the polynomial regression algorithm. The ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample are weighted and calculated to determine the ammonia nitrogen coverage potential coefficient, and then sent to the balance control module;

[0009] The phosphate potential module is used to collect the proportion of phosphate content in sewage samples, analyze the information of phosphate influencing factors in multiple sewage samples, obtain the hydraulic retention time and the difference in metal salt dosage, and obtain the phosphate weight through polynomial regression calculation. The phosphate weight and the proportion of phosphate content in sewage samples are subjected to logistic regression calculation to obtain the phosphate coverage potential coefficient, which is then sent to the balance control module;

[0010] The balance control module is used to obtain the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, determine the potential coefficient that needs to be balanced, formulate the balance control rules, obtain the control difference and the potential coefficient that needs to be balanced, and send them to the adjustment processing module;

[0011] The adjustment processing module is used to obtain the constraint difference and the potential coefficient that needs to be balanced, mark the potential coefficient that needs to be balanced, obtain the marked parameter, use the recursive collection method to randomly select two time points within a period of time to collect the content of the marked parameter, and calculate the increase rate of the marked parameter, and calculate the content of the processed material by comprehensively calculating the increase rate of the marked parameter and the constraint difference.

[0012] In a preferred embodiment, the ammonia nitrogen potential module directly measures the ammonia nitrogen concentration in the sewage sample by using an ammonia selective electrode to obtain the ammonia nitrogen content in each sewage sample, and then measures the total pollution content in each sewage sample by chemical oxygen demand, and calculates the ratio of the ammonia nitrogen content in each sewage sample to the total pollution content in each sewage sample to obtain the ammonia nitrogen content ratio in the sewage sample. ; Where i is the i-th sewage sample;

[0013] The dissolved oxygen concentration in sewage is detected by electrochemical reaction caused by oxygen passing through the electrode membrane. ;

[0014] Through the chemical oxygen demand determination, a strong oxidant is added and heated in a reflux device, the unreacted oxidant is titrated with a standard solution of ammonium ferrous sulfate, and the organic matter concentration of the sewage is calculated based on the titration amount. .

[0015] In a preferred embodiment, the ammonia nitrogen potential module substitutes the sewage dissolved oxygen concentration and the sewage organic matter concentration into a polynomial regression algorithm to obtain the ammonia nitrogen weight. The specific formula is:

[0016] ;

[0017] In the formula, is the ammonia nitrogen weight, is a constant term, is the first-order coefficient of dissolved oxygen, is the linear coefficient of organic matter concentration, is the quadratic coefficient of dissolved oxygen, is the quadratic coefficient of organic matter concentration, is the interaction coefficient between dissolved oxygen and organic matter concentration;

[0018] The ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample are weighted and calculated to obtain the ammonia nitrogen coverage potential coefficient. .

[0019] In a preferred embodiment, the phosphate potential module determines the phosphate content in the sewage sample by the molybdenum blue method, and then determines the total pollution content by chemical oxygen demand, and calculates the ratio of the phosphate content in the sewage sample to the total pollution content to obtain the phosphate content ratio in the sewage sample. ;

[0020] The hydraulic retention time is calculated by analyzing the ratio of the treatment facility volume and the inlet flow rate corresponding to the sewage sample. ;

[0021] The metal salt dosage in the current sewage sample is calculated by real-time measurement of the injection rate and sewage flow rate through online monitoring instruments. The ideal metal salt concentration is obtained by multiplying the phosphate concentration in the sewage by the target removal rate and calculating the ratio with the stoichiometric ratio of the metal salt to the phosphate. The metal salt dosage in the current sewage sample is calculated to be different from the ideal metal salt concentration to obtain the metal salt dosage difference. .

[0022] In a preferred embodiment, the phosphate potential module substitutes the difference between hydraulic retention time and metal salt dosage into a polynomial regression calculation to obtain the phosphate weight ;

[0023] Substituting the phosphate weight and the proportion of phosphate content in the sewage sample into the logistic regression calculation, the specific formula is expressed as follows:

[0024] ;

[0025] In the formula, is the phosphate coverage potential coefficient, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y can be set as:

[0026] ;

[0027] In the formula, is the bias term, and are the regression coefficients of phosphate weight and the proportion of phosphate content in sewage samples.

[0028] In a preferred embodiment, the balance constraint module counts the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient as a set, and assumes n is the total number of ammonia nitrogen coverage potential coefficients or phosphate coverage potential coefficients, then the set expression is as well as ;

[0029] The ammonia nitrogen coverage potential coefficient set and the phosphate coverage potential coefficient set are averaged to obtain an average ammonia nitrogen coverage potential coefficient and an average phosphate coverage potential coefficient;

[0030] If the average value of the potential coefficient of ammonia coverage is greater than the average value of the potential coefficient of phosphate coverage, the average value of the potential coefficient of ammonia coverage is marked as the potential coefficient that needs to be balanced. Conversely, if the average value of the potential coefficient of phosphate coverage is greater than the average value of the potential coefficient of ammonia coverage, the average value of the potential coefficient of phosphate coverage is marked as the potential coefficient that needs to be balanced.

[0031] Specifically, if the average value of the ammonia nitrogen coverage potential coefficient is equal to the average value of the phosphate coverage potential coefficient, the current sewage treatment is marked as being processed using a preset processing volume, and an end signal is generated;

[0032] Substitute the average value of the potential coefficient of ammonia nitrogen coverage and the average value of the potential coefficient of phosphate coverage into the equilibrium constraint rule to obtain the constraint difference;

[0033] Specifically, the equilibrium constraint rule is a logistic regression equation, which is expressed as:

[0034] ;

[0035] Where L is the constraint difference, and z is the potential coefficient that needs to be balanced minus the average value of another potential coefficient.

[0036] In a preferred embodiment, the adjustment processing module marks the potential coefficient that needs to be balanced as a marking parameter;

[0037] Specifically, the steps of randomly selecting two time points within a period of time to collect the content of the marker parameter using the recursive collection method are:

[0038] A1: Set the sampling time period and determine the termination condition;

[0039] A2: Randomly select two time points within the time period and record the content of the marker parameter;

[0040] A3: Calculate the ratio of the difference in the marker parameter content at two time points to the time length to obtain the change rate of the marker parameter;

[0041] A4: Set the change rate threshold of the marker parameter and analyze whether the recursive sampling is sufficient;

[0042] A5: If the change rate of the marking parameter is greater than the change rate threshold of the marking parameter, adjust the time interval and randomly select a time point again;

[0043] A6: Iterate steps A3 and A4 until the termination condition is met.

[0044] In a preferred embodiment, the adjustment processing module marks the change rate of the marking parameter of the termination iteration as the marking parameter increase rate;

[0045] The formula for calculating the treated material content based on the increase rate of the comprehensive marker parameters and the constraint difference is as follows:

[0046] ;

[0047] In the formula, is the treated material content, i.e. the adjusted treated material content, To constrain the difference, is the marking parameter increase rate, is the correction factor, is the potential coefficient that needs to be balanced, is the preset processing volume;

[0048] The preset processing volume is adjusted according to the calculated processing material content.

[0049] Technical effects and advantages of the present invention:

[0050] 1. The present invention collects the proportion of ammonia nitrogen content in sewage samples, analyzes the information of ammonia nitrogen influencing factors in multiple sewage samples, obtains the sewage dissolved oxygen concentration and the sewage organic matter concentration through data preprocessing, and substitutes them into the polynomial regression algorithm to obtain the ammonia nitrogen weight, and performs weighted calculation on the ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample to determine the ammonia nitrogen coverage potential coefficient, and obtains the phosphate coverage potential coefficient in the same way, and determines the potential coefficient that needs to be balanced according to the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, formulates the balance constraint rules, obtains the constraint difference, improves the accuracy and reliability of the data, quickly handles the physical influence of suspended matter on photon technology, balances the two ion elimination mechanisms, and ensures the efficiency of sewage treatment by photon technology.

[0051] 2. The present invention obtains the constraint difference and the potential coefficient that needs to be balanced, marks the potential coefficient that needs to be balanced, obtains the marking parameter, uses the recursive collection method to randomly select two time points within a period of time to collect the content of the marking parameter, and calculates the marking parameter increase rate, and calculates the treated material content by comprehensively calculating the marking parameter increase rate and the constraint difference, thereby improving the accuracy of data collection and the treatment effect, making the adjusted treated material content more accurate, improving the suspended matter treatment, and improving the efficiency of sewage treatment by photon technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a module schematic diagram of a photon technology system for sewage treatment according to the present invention. DETAILED DESCRIPTION

[0053] 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.

[0054] The application of photon technology in environmental governance is as follows: start the ultraviolet or visible light source to ensure that the photons reach the catalyst surface, add photocatalysts (such as TiO2 suspension or fixed catalyst coating), and the catalyst generates electron-hole pairs under the action of photons. The specific chemical formula is: Hole ( ), react with water or hydroxyl ions to generate highly active free radicals: Generate highly active free radicals (such as •OH), degrade organic pollutants or kill bacteria. The chemical formula is: , photocatalytic reactions usually do not require the addition of additional chemical reagents, and the degradation products are and Mainly, avoid secondary pollution;

[0055] However, photon technology usually needs to give priority to the removal of suspended solids to ensure its treatment rate. In sewage treatment, when ammonia nitrogen, phosphate and magnesium ions exist in water at the same time, magnesium ammonium phosphate precipitation will be generated. The generated magnesium ammonium phosphate particles are small and have a high density, appearing in the form of suspended solids, especially when they are not fully precipitated in the early stage of the reaction, causing the treatment efficiency of photon technology to decrease. Furthermore, in order to adjust the removal effect of ammonia nitrogen and phosphate, it is often necessary to adjust the pH value of the water body. When the stripping method of ammonia nitrogen and the precipitation of magnesium ammonium phosphate are carried out at the same time, calcium carbonate and magnesium hydroxide will also be produced. The co-precipitation and suspension, further reducing the treatment efficiency of photon technology;

[0056] In sewage treatment, the two common mutually restrictive ions are ammonia nitrogen and phosphate. Specifically, during the treatment process, due to the need to adjust the pH or introduce chemical precipitation, there is a mutually restrictive relationship. That is, when optimizing the treatment of ammonia nitrogen, controlling the pH at a neutral pH will reduce the efficiency of phosphate precipitation. On the contrary, optimizing the alkaline conditions for phosphate precipitation will inhibit the nitrification reaction.

[0057] Example 1

[0058] The present invention discloses a photon technology system for sewage treatment, such as Figure 1 As shown, it includes an ammonia nitrogen potential module, a phosphate potential module, a balance control module and an adjustment processing module; and the modules are connected by signals;

[0059] The ammonia nitrogen potential module is used to collect the proportion of ammonia nitrogen content in sewage samples and analyze the information of ammonia nitrogen influencing factors in multiple sewage samples. Through data preprocessing, the sewage dissolved oxygen concentration and sewage organic matter concentration are obtained, and the ammonia nitrogen weight is obtained by substituting the polynomial regression algorithm. The ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample are weighted and calculated to determine the ammonia nitrogen coverage potential coefficient, and then sent to the balance control module;

[0060] There is no limit on the number of sewage samples. Generally, there are many methods for detecting sewage. For example, a series of biochemical reactions such as chemical experiments are used to determine the internal content of sewage and gradually process it. This is a common operation method and means. The method and number of sewage samples selected are not limited here, but are obtained by the experimenters through experimental data analysis, and will not be elaborated here.

[0061] The logic for obtaining the percentage of ammonia nitrogen content in sewage samples is to directly measure the ammonia nitrogen concentration in sewage samples using ammonia selective electrodes to obtain the ammonia nitrogen content in each sewage sample, and then measure the total pollution content in each sewage sample by chemical oxygen demand. The ratio of the ammonia nitrogen content in each sewage sample to the total pollution content in each sewage sample is calculated to obtain the percentage of ammonia nitrogen content in the sewage sample. ; Where i is the i-th sewage sample;

[0062] It should be noted that the ammonia selective electrode is a sensor based on an ion selective membrane, which is specially used to detect the concentration of ammonia ions in sewage samples. Its working principle is based on the selective interaction between the ion selective membrane and the target ion, and finally the measurement is performed through the corresponding relationship between the potential signal and the ammonia nitrogen concentration. Specifically, a standard solution with a known ammonia nitrogen concentration is used for calibration, and the logarithmic relationship between the electrode response potential and the concentration is plotted, namely the Nernst equation, which is specifically expressed as:

[0063] ;

[0064] In the formula, To measure the potential, is the reference electrode potential, is the electrode slope, is the ammonia nitrogen concentration;

[0065] Furthermore, the electrode is inserted into the sewage sample, the potential value after stabilization is read, and the concentration of ammonia nitrogen in the sample solution is calculated according to the Nernst equation;

[0066] Among them, chemical oxygen demand refers to an important indicator for evaluating the content of organic pollutants in water bodies. It is used to indicate the amount of oxygen consumed when reducing substances (mainly organic matter) in water are oxidized under the action of strong oxidants. The potassium dichromate method is usually used. Under acidic conditions, the water sample is mixed with a known amount of potassium dichromate solution. Organic matter and certain inorganic reducing substances will be oxidized by potassium dichromate, and dichromate ions will be reduced to trivalent chromium ions. The remaining potassium dichromate is determined by photometry and compared with the amount added before the reaction. The amount of oxidant consumed by the oxidized organic matter is calculated to obtain the total pollution content in each sewage sample;

[0067] Among them, data processing includes data type conversion, which refers to converting the raw data collected by the system into standardized data types; missing value processing, which refers to filling the missing data that may occur during the system collection process through interpolation and mean filling to ensure the continuity and integrity of the data, etc. The purpose of data processing is to make the data clearer and easier to understand, so that the system can calculate faster;

[0068] Specifically, the above data operation methods are all existing technologies and will not be described in detail here;

[0069] Information on factors affecting ammonia nitrogen includes the concentration of dissolved oxygen in wastewater and the concentration of organic matter in wastewater;

[0070] The dissolved oxygen concentration in sewage refers to the oxygen content dissolved in the sewage sample. Dissolved oxygen is a key environmental factor in the metabolism of aerobic microorganisms, which increases the rate of nitrification of ammonia nitrogen. The acquisition logic is to detect the dissolved oxygen concentration in sewage by electrochemical reaction caused by oxygen passing through the electrode membrane. ;

[0071] Among them, the electrochemical reaction of oxygen passing through the electrode membrane refers to the dissolved oxygen electrode method. Specifically, the environmental parameters such as temperature, air pressure, and salinity are calibrated first, and the dissolved oxygen meter probe is inserted into the sewage sample to avoid bubble interference. After stabilization, the dissolved oxygen value is recorded;

[0072] The nitrification process of ammonia nitrogen refers to the gradual oxidation of ammonia nitrogen in sewage samples into nitrates through the action of obligate autotrophic nitrifying bacteria, and finally converting it into nitrogen gas through denitrification and releasing it into the air, thereby achieving the purpose of removing ammonia nitrogen;

[0073] The organic matter concentration in sewage refers to the total content of organic matter in sewage, such as hydrocarbons, proteins, and sugars. The logic of obtaining it is to measure the chemical oxygen demand, add a strong oxidant and heat it in a reflux device, titrate the unreacted oxidant with a standard solution of ammonium ferrous sulfate, and then calculate the organic matter concentration in sewage based on the titration amount. ;

[0074] Specifically, the formula for calculating the concentration of organic matter in sewage by titration is as follows:

[0075] ;

[0076] In the formula, is the titration volume of blank solution, is the water sample titration volume, is the concentration of ammonium ferrous sulfate standard solution, is the volume of sewage sample;

[0077] Specifically, the chemical oxygen demand has been described above and will not be described here in detail;

[0078] Among them, in the above-mentioned content determination method, if an ammonia selective electrode is used to determine the ammonia nitrogen concentration, the output signal of the electrode is converted into a digital signal through a data acquisition card and transmitted to the ammonia nitrogen potential module, the dissolved oxygen electrode is connected to the dissolved oxygen meter, and the instrument is transmitted to the ammonia nitrogen potential module through wireless Bluetooth, USB or RS232, and the chemical oxygen demand analyzer has a built-in communication module (such as Ethernet or serial interface) and is transmitted to the ammonia nitrogen potential module, etc. The specific transmission means are not limited, but are determined by the experimenter through the actual data transmission time, and will not be repeated here;

[0079] Substitute the sewage dissolved oxygen concentration and sewage organic matter concentration into the polynomial regression algorithm to obtain the ammonia nitrogen weight. The specific formula is:

[0080] ;

[0081] In the formula, is the ammonia nitrogen weight, is a constant term, is the first-order coefficient of dissolved oxygen, is the linear coefficient of organic matter concentration, is the quadratic coefficient of dissolved oxygen, is the quadratic coefficient of organic matter concentration, is the interaction coefficient between dissolved oxygen and organic matter concentration;

[0082] The ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample are weighted and calculated to obtain the ammonia nitrogen coverage potential coefficient. ;

[0083] The specific weighting formula is prior art and will not be described in detail here;

[0084] The phosphate potential module is used to collect the proportion of phosphate content in sewage samples, analyze the information of phosphate influencing factors in multiple sewage samples, obtain the hydraulic retention time and the difference in metal salt dosage, and obtain the phosphate weight through polynomial regression calculation. The phosphate weight and the proportion of phosphate content in sewage samples are subjected to logistic regression calculation to obtain the phosphate coverage potential coefficient, which is then sent to the balance control module;

[0085] Among them, the number of sewage samples and the setting of sewage samples have been perfected in the above description and will not be repeated here;

[0086] The proportion of phosphate content in sewage samples was determined by the molybdenum blue method. The total pollution content was determined by chemical oxygen demand. The ratio of phosphate content in sewage samples to total pollution content was calculated to obtain the proportion of phosphate content in sewage samples. ;

[0087] Specifically, the method and steps of using chemical oxygen demand have been mentioned and explained in the above content, and will not be repeated here;

[0088] Among them, the molybdenum blue method is to react the phosphate in the sewage sample with ammonium molybdate under acidic conditions to form phosphomolybdic heteropoly acid, and then react with a reducing agent (such as ascorbic acid) to form a blue compound. The depth of the blue color is positively correlated with the phosphate concentration. Specifically, the sewage sample is added with ammonium molybdate solution and ascorbic acid solution for mixed reaction. After color development, the absorbance value is measured at a wavelength of 880nm using a spectrophotometer, and the absorbance value is substituted into the standard curve to convert the absorbance value into phosphate content.

[0089] Specifically, the standard curve is constructed by preparing a series of phosphate standard solutions with known contents, measuring the absorbance value of each content solution, and drawing a linear relationship curve between absorbance and content. The specific standard curve equation is in the form of:

[0090] ;

[0091] In the formula, is the absorbance, is the phosphate content, and are the fitting coefficients respectively;

[0092] Read the absorbance value of the sewage sample and substitute it into the standard curve to obtain the phosphate content in the sewage sample;

[0093] Information on factors affecting phosphates includes hydraulic retention time and differences in metal salt dosage;

[0094] The logic of obtaining hydraulic retention time is to calculate the ratio of the treatment facility volume and the inlet flow rate corresponding to the sewage sample to obtain the hydraulic retention time. ;

[0095] Specifically, the calculation formula for the volume of the treatment facility corresponding to the sewage sample follows the geometric calculation of the treatment facility, and is obtained by multiplying the length, width and height of the treatment facility. The inlet flow rate is measured in real time by a flow meter to obtain the inlet flow rate;

[0096] The logic of obtaining the difference in metal salt dosage is to calculate the metal salt dosage in the current sewage sample based on the injection acceleration rate and sewage flow rate through real-time measurement by online monitoring instruments, and then calculate the ratio of the product of the phosphate concentration in the sewage and the target removal rate and the stoichiometric ratio of the metal salt and phosphate to obtain the ideal metal salt concentration, and calculate the difference between the metal salt dosage in the current sewage sample and the ideal metal salt concentration to obtain the metal salt dosage difference. ;

[0097] It should be noted that the current calculation formula for the metal salt dosage in the sewage sample is calculated by multiplying the product of the metal salt solution addition flow rate and the metal salt solution concentration by the sewage flow rate;

[0098] Specifically, the ideal metal salt concentration refers to the metal salt concentration required to achieve the target removal effect (such as phosphate removal rate) under given sewage conditions. This concentration takes into account the product of the phosphate concentration in the sewage and the target removal rate, combined with the stoichiometric ratio of the metal salt to the phosphate, and is calculated by ratio, which will not be elaborated here;

[0099] Substituting the difference between hydraulic retention time and metal salt dosage into the polynomial regression calculation, the phosphate weight is obtained. ;

[0100] Specifically, the polynomial regression calculation has been described above and will not be repeated here;

[0101] Substituting the phosphate weight and the proportion of phosphate content in the sewage sample into the logistic regression calculation, the specific formula is expressed as follows:

[0102] ;

[0103] In the formula, is the phosphate coverage potential coefficient, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y can be set as:

[0104] ;

[0105] In the formula, is the bias term, and are the regression coefficients of phosphate weight and the proportion of phosphate content in sewage samples;

[0106] The balance control module is used to obtain the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, determine the potential coefficient that needs to be balanced, formulate the balance control rules, obtain the control difference and the potential coefficient that needs to be balanced, and send them to the adjustment processing module;

[0107] The ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient are counted and taken as a set. Let n be the total number of ammonia nitrogen coverage potential coefficients or phosphate coverage potential coefficients, then the set expression is as well as ;

[0108] The ammonia nitrogen coverage potential coefficient set and the phosphate coverage potential coefficient set are averaged to obtain an average ammonia nitrogen coverage potential coefficient and an average phosphate coverage potential coefficient;

[0109] The specific average calculation formula is:

[0110] as well as ;

[0111] Obtain the average value of ammonia nitrogen coverage potential coefficient and the average value of phosphate coverage potential coefficient, and analyze the values ​​of the two potential coefficient average values;

[0112] Among them, if the average value of the potential coefficient of ammonia nitrogen coverage is greater than the average value of the potential coefficient of phosphate coverage, the average value of the potential coefficient of ammonia nitrogen coverage is marked as the potential coefficient that needs to be balanced; conversely, if the average value of the potential coefficient of phosphate coverage is greater than the average value of the potential coefficient of ammonia nitrogen coverage, the average value of the potential coefficient of phosphate coverage is marked as the potential coefficient that needs to be balanced;

[0113] Specifically, if the average value of the ammonia nitrogen coverage potential coefficient is equal to the average value of the phosphate coverage potential coefficient, the current sewage treatment is marked as being processed using a preset processing volume, and an end signal is generated;

[0114] Specifically, the preset treatment volume can be based on the chemical dosages prepared by the experimenter based on historical inferences, etc., to treat ammonia nitrogen and phosphate in the sewage at the same time, and will not cause the ammonia nitrogen or phosphate to be not completely removed due to mutual constraints, resulting in the generation of new suspended matter or precipitation. The specific preset treatment volume is not described in detail;

[0115] Substitute the average value of the potential coefficient of ammonia nitrogen coverage and the average value of the potential coefficient of phosphate coverage into the equilibrium constraint rule to obtain the constraint difference;

[0116] Specifically, the equilibrium constraint rule is a logistic regression equation, which is expressed as:

[0117] ;

[0118] In the formula, L is the constraint difference, z is the potential coefficient that needs to be balanced minus the average value of another potential coefficient;

[0119] Among them, the constraint difference expresses the difference of ammonia nitrogen or phosphate that needs to be balanced at present, which is used for subsequent analysis and adjustment of the preset treatment amount;

[0120] The present invention collects the proportion of ammonia nitrogen content in sewage samples, analyzes the information of ammonia nitrogen influencing factors in multiple sewage samples, obtains the sewage dissolved oxygen concentration and the sewage organic matter concentration through data preprocessing, and substitutes them into the polynomial regression algorithm to obtain the ammonia nitrogen weight, and performs weighted calculation on the ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample to determine the ammonia nitrogen coverage potential coefficient, and obtains the phosphate coverage potential coefficient in the same way, and determines the potential coefficient that needs to be balanced according to the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, formulates the balance constraint rule, obtains the constraint difference, improves the accuracy and reliability of the data, quickly handles the physical influence of suspended matter on photon technology, balances the two ion elimination mechanisms, and ensures the efficiency of sewage treatment by photon technology.

[0121] Example 2

[0122] In Example 1 of the present invention, an example is given to illustrate the proportion of ammonia nitrogen content in the collected sewage samples, and the information of ammonia nitrogen influencing factors in multiple sewage samples is analyzed. Through data preprocessing, the dissolved oxygen concentration of sewage and the organic matter concentration of sewage are obtained, and the ammonia nitrogen weight is substituted into the polynomial regression algorithm to obtain the ammonia nitrogen weight, and the ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample are weighted and calculated to determine the ammonia nitrogen coverage potential coefficient, and the phosphate coverage potential coefficient is obtained in the same way. According to the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, the potential coefficient to be balanced is determined, and a balance constraint rule is formulated to obtain an operation strategy for the constraint difference; However, in Example 1, only how to determine whether the preset treatment volume satisfies the constraint effect of balancing ammonia nitrogen and phosphate is used to complete the removal of suspended matter, but if the constraint effect of phosphate on balancing ammonia nitrogen cannot be met, the value of the treatment volume added cannot be known, resulting in imperfect removal of suspended matter, further reducing the efficiency of sewage treatment by photon quantum technology; In view of the above problems, Example 2 of the present invention is further refined;

[0123] The adjustment processing module is used to obtain the constraint difference and the potential coefficient that needs to be balanced, mark the potential coefficient that needs to be balanced, obtain the marking parameter, use the recursive collection method to randomly select two time points within a period of time to collect the content of the marking parameter, and calculate the increase rate of the marking parameter, and calculate the content of the processed material by comprehensively calculating the increase rate of the marking parameter and the constraint difference;

[0124] The potential coefficients that need to be balanced are marked and recorded as marking parameters;

[0125] Among them, the recursive collection method is a sampling method based on cyclic iteration, which aims to continuously optimize the sampling results through multiple rounds of progressive collection operations to more accurately capture the dynamic changes of data features or analysis targets. The recursive collection method is particularly suitable for data set optimization in sewage treatment or machine learning.

[0126] Specifically, the steps of randomly selecting two time points within a period of time to collect the content of the marker parameter using the recursive collection method are:

[0127] A1: Determine the sampling time period and set the termination condition of the collection;

[0128] A2: Use a programming tool to generate a random function within the time range to randomly select two time points, and record the content of the marker parameter at the two time points;

[0129] A3: Compare the content difference of the marker parameter at two time points, and calculate the ratio of the content difference of the marker parameter at two time points to the subtraction value of the two time points, that is, the time length, to obtain the change rate of the marker parameter;

[0130] A4: For the change rate of the marking parameter, a threshold value of the change rate of the marking parameter is set to analyze whether the recursive sampling is sufficient;

[0131] A5: Based on the analysis results, if the change rate of the marking parameter is greater than the change rate threshold of the marking parameter, the time interval is shortened and two time points are randomly selected again within the adjusted time interval;

[0132] A6: Based on the new sampling points and results, continue to iterate steps A3 and A4 until the termination condition is met;

[0133] Optionally, the experimenter can set in step A6 to stop the recursion when any of the following conditions is met, which can be when the rate of change of the marking parameter converges to a set range or falls within a set corresponding threshold range, or when the maximum number of recursions preset by the experimenter is reached, or when the characteristics of the analysis time period are fully covered, etc., which will not be elaborated here;

[0134] The threshold corresponding to the change rate of the marking parameter can be set by the experimenter according to the average value of the change rate of the historical marking parameter and the frequency of the historical processing volume change, which will not be described in detail here;

[0135] Mark the change rate of the marking parameter of the termination iteration as the marking parameter increase rate;

[0136] The formula for calculating the treated material content based on the increase rate of the comprehensive marker parameters and the constraint difference is as follows:

[0137] ;

[0138] In the formula, is the treated material content, i.e. the adjusted treated material content, To constrain the difference, is the marking parameter increase rate, is the correction factor, is the potential coefficient that needs to be balanced, is the preset processing volume;

[0139] The preset treatment volume is adjusted according to the calculated treatment material content to meet the elimination of ammonia nitrogen and phosphate and avoid the residue of suspended matter;

[0140] The present invention obtains the constraint difference and the potential coefficient that needs to be balanced, marks the potential coefficient that needs to be balanced, obtains the marking parameter, uses the recursive collection method to randomly select two time points within a period of time to collect the content of the marking parameter, and calculates the marking parameter increase rate, and calculates the treated material content by comprehensively calculating the marking parameter increase rate and the constraint difference, thereby improving the accuracy of data collection and the treatment effect, making the adjusted treated material content more accurate, improving the suspended matter treatment, and improving the efficiency of sewage treatment using the photon technology.

[0141] 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.

[0142] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0143] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0144] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0145] 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 aforementioned method embodiments and will not be repeated here.

[0146] In the 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0149] 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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0150] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A photon quantum technology system for sewage treatment, characterized in that: It includes ammonia nitrogen potential module, phosphate potential module, balance control module and adjustment processing module; signal connection between each module; The ammonia nitrogen potential module is used to collect the proportion of ammonia nitrogen content in sewage samples and analyze the information of ammonia nitrogen influencing factors in multiple sewage samples. Through data preprocessing, the sewage dissolved oxygen concentration and sewage organic matter concentration are obtained, and the ammonia nitrogen weight is obtained by substituting the polynomial regression algorithm. The ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample are weighted and calculated to determine the ammonia nitrogen coverage potential coefficient, and then sent to the balance control module; The phosphate potential module is used to collect the proportion of phosphate content in sewage samples, analyze the information of phosphate influencing factors in multiple sewage samples, obtain the hydraulic retention time and the difference in metal salt dosage, and obtain the phosphate weight through polynomial regression calculation. The phosphate weight and the proportion of phosphate content in sewage samples are subjected to logistic regression calculation to obtain the phosphate coverage potential coefficient, which is then sent to the balance control module; The balance control module is used to obtain the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient, determine the potential coefficient that needs to be balanced, formulate the balance control rules, obtain the control difference and the potential coefficient that needs to be balanced, and send them to the adjustment processing module; The adjustment processing module is used to obtain the constraint difference and the potential coefficient that needs to be balanced, mark the potential coefficient that needs to be balanced, obtain the marked parameter, use the recursive collection method to randomly select two time points within a period of time to collect the content of the marked parameter, and calculate the increase rate of the marked parameter, and calculate the content of the processed material by comprehensively calculating the increase rate of the marked parameter and the constraint difference.

2. The optical quantum technology system for sewage treatment according to claim 1, characterized in that: The ammonia nitrogen potential module directly measures the ammonia nitrogen concentration in the sewage sample by using an ammonia selective electrode to obtain the ammonia nitrogen content in each sewage sample, and then measures the total pollution content in each sewage sample by chemical oxygen demand. The ammonia nitrogen content in each sewage sample is calculated by the ratio of the total pollution content in each sewage sample to obtain the ammonia nitrogen content ratio in the sewage sample. ; Where i is the i-th sewage sample; The dissolved oxygen concentration in sewage is detected by electrochemical reaction caused by oxygen passing through the electrode membrane. ; Through the chemical oxygen demand determination, a strong oxidant is added and heated in a reflux device, the unreacted oxidant is titrated with a standard solution of ammonium ferrous sulfate, and the organic matter concentration of the sewage is calculated based on the titration amount. .

3. The optical quantum technology system for sewage treatment according to claim 2 is characterized in that: The ammonia nitrogen potential module substitutes the sewage dissolved oxygen concentration and sewage organic matter concentration into the polynomial regression algorithm to obtain the ammonia nitrogen weight. The specific formula is: ； In the formula, is the ammonia nitrogen weight, is a constant term, is the first-order coefficient of dissolved oxygen, is the linear coefficient of organic matter concentration, is the quadratic coefficient of dissolved oxygen, is the quadratic coefficient of organic matter concentration, is the interaction coefficient between dissolved oxygen and organic matter concentration; The ammonia nitrogen weight and the proportion of ammonia nitrogen content in the sewage sample are weighted and calculated to obtain the ammonia nitrogen coverage potential coefficient. .

4. The optical quantum technology system for sewage treatment according to claim 3 is characterized in that: The phosphate potential module determines the phosphate content in the sewage sample by the molybdenum blue method, and then determines the total pollution content by chemical oxygen demand. The phosphate content in the sewage sample is calculated by the ratio of the total pollution content to obtain the phosphate content ratio in the sewage sample. ; The hydraulic retention time is calculated by analyzing the ratio of the treatment facility volume and the inlet flow rate corresponding to the sewage sample. ; The metal salt dosage in the current sewage sample is calculated by real-time measurement of the injection rate and sewage flow rate through online monitoring instruments. The ideal metal salt concentration is obtained by multiplying the phosphate concentration in the sewage by the target removal rate and calculating the ratio with the stoichiometric ratio of the metal salt to the phosphate. The metal salt dosage in the current sewage sample is calculated to be different from the ideal metal salt concentration to obtain the metal salt dosage difference. .

5. The optical quantum technology system for sewage treatment according to claim 4 is characterized in that: The phosphate potential module substitutes the difference between hydraulic retention time and metal salt dosage into the polynomial regression calculation to obtain the phosphate weight ; Substituting the phosphate weight and the proportion of phosphate content in the sewage sample into the logistic regression calculation, the specific formula is expressed as follows: ； In the formula, is the phosphate coverage potential coefficient, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y can be set as: ； In the formula, is the bias term, and are the regression coefficients of phosphate weight and the proportion of phosphate content in sewage samples.

6. The optical quantum technology system for sewage treatment according to claim 5, characterized in that: The balance constraint module counts the ammonia nitrogen coverage potential coefficient and the phosphate coverage potential coefficient as a set. Let n be the total number of ammonia nitrogen coverage potential coefficient or phosphate coverage potential coefficient, then the set expression is as well as ; The ammonia nitrogen coverage potential coefficient set and the phosphate coverage potential coefficient set are averaged to obtain an average ammonia nitrogen coverage potential coefficient and an average phosphate coverage potential coefficient; If the average value of the potential coefficient of ammonia coverage is greater than the average value of the potential coefficient of phosphate coverage, the average value of the potential coefficient of ammonia coverage is marked as the potential coefficient that needs to be balanced. Conversely, if the average value of the potential coefficient of phosphate coverage is greater than the average value of the potential coefficient of ammonia coverage, the average value of the potential coefficient of phosphate coverage is marked as the potential coefficient that needs to be balanced. Specifically, if the average value of the ammonia nitrogen coverage potential coefficient is equal to the average value of the phosphate coverage potential coefficient, the current sewage treatment is marked as being processed using a preset processing volume, and an end signal is generated; Substitute the average value of the potential coefficient of ammonia nitrogen coverage and the average value of the potential coefficient of phosphate coverage into the equilibrium constraint rule to obtain the constraint difference; Specifically, the equilibrium constraint rule is a logistic regression equation, which is expressed as: ； Where L is the constraint difference, and z is the potential coefficient that needs to be balanced minus the average value of another potential coefficient.

7. The optical quantum technology system for sewage treatment according to claim 6 is characterized in that: The adjustment processing module marks the potential coefficient that needs to be balanced and records it as a marking parameter; Specifically, the steps of randomly selecting two time points within a period of time to collect the content of the marker parameter using the recursive collection method are: A1: Set the sampling time period and determine the termination condition; A2: Randomly select two time points within the time period and record the content of the marker parameter; A3: Calculate the ratio of the difference in the marker parameter content at two time points to the time length to obtain the change rate of the marker parameter; A4: Set the change rate threshold of the marker parameter and analyze whether the recursive sampling is sufficient; A5: If the change rate of the marking parameter is greater than the change rate threshold of the marking parameter, adjust the time interval and randomly select a time point again; A6: Iterate steps A3 and A4 until the termination condition is met.

8. The optical quantum technology system for sewage treatment according to claim 7 is characterized in that: The adjustment processing module marks the change rate of the marking parameter of the termination iteration as the marking parameter increase rate; The formula for calculating the treated material content based on the increase rate of the comprehensive marker parameters and the constraint difference is as follows: ； In the formula, is the treated material content, i.e. the adjusted treated material content, To constrain the difference, is the marking parameter increase rate, is the correction factor, is the potential coefficient that needs to be balanced, is the preset processing volume; The preset processing volume is adjusted according to the calculated processing material content.

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