A method for reducing PN reaction dosage using intelligent control

Abstract

This invention relates to a method for reducing the dosage of chemicals in a short-cut nitrification (PN) reaction using intelligent control. The method involves real-time monitoring of the carbonate, bicarbonate, and ammonia nitrogen concentrations in the influent using sensors, as well as monitoring the pH, temperature, and effluent ammonia and nitrite nitrogen concentrations. The total alkalinity of the influent is calculated and input into a fuzzy expert system along with the ammonia nitrogen concentration to determine the influent alkalinity status. A suitable NOB suppression strategy is selected to determine the chemical dosing control strategy in the reactor, thereby reducing the required dosage. Compared to existing technologies, this invention uses a fuzzy expert system to determine the alkalinity status of influent with different water qualities, adopts a suitable NOB suppression strategy to control pH, and adjusts the amount of NaOH added in the short-cut nitrification process in a timely manner, thus saving on chemical dosage and improving wastewater treatment efficiency.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a method for reducing the dosage of chemicals used in the PN reaction using intelligent control. Background Technology

[0002] Wastewater treatment typically requires biological treatment to remove organic matter and nutrients such as nitrogen and phosphorus. The short-cut nitrification-anaerobic ammonium oxidation (PN-A) process is used to treat high NH4 levels. + -N and low C / N landfill leachate treatment is an ideal process that can save on oxygen supply and carbon source addition. Short-cut nitrification, as a pretreatment process for anaerobic ammonium oxidation, is an important step in converting ammonia nitrogen into nitrite nitrogen through nitrification. Controlling the ammonia oxidation reaction at the stage of conversion to nitrite nitrogen is the key to ensuring the efficiency and stability of the PN-A reaction.

[0003] Inhibition of nitrite-oxidizing bacteria (NOB) is crucial to preventing the further formation of nitrates after ammonia nitrogen is converted to nitrite. Ammonia oxidation is an acid-producing process with constantly fluctuating pH. Therefore, current wastewater treatment often requires the addition of chemical reagents, such as sodium hydroxide, to maintain stable short-cut nitrification by controlling the pH to a certain threshold for free ammonia (FA) or free nitrite (FNA) concentrations. Landfill leachate itself has a certain alkalinity, which buffers the pH. However, when the influent alkalinity fluctuates significantly, a single inhibition strategy can lead to excessive chemical reagent addition, and chemical consumption during water plant operation has a significant impact on water treatment costs.

[0004] Currently, there is no research on adopting different NOB inhibition strategies based on influent alkalinity. Therefore, influent alkalinity can be considered a key factor in implementing inhibition strategies for short-cut nitrification. Developing a reliable method to control the short-cut nitrification process, achieving both reduced chemical dosage and stable reaction, is a crucial issue that urgently needs to be addressed. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a method for reducing the dosage of chemicals in the PN reaction using intelligent control. By employing a fuzzy expert system, the alkalinity of the influent with different water qualities can be determined, a suitable NOB inhibition strategy can be adopted to control the pH, and the amount of NaOH added in the short-cut nitrification process can be adjusted in a timely manner to achieve the purpose of saving chemical dosage and improving wastewater treatment efficiency.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] This invention provides a method for reducing the dosage of PN reaction using intelligent control, comprising the following steps:

[0008] S1: Real-time monitoring of carbonate, bicarbonate and ammonia nitrogen concentrations in the influent water quality via sensors, as well as monitoring of pH, temperature inside the reactor and ammonia nitrogen and nitrite nitrogen concentrations in the effluent water quality.

[0009] S2: Calculate the total alkalinity of the influent based on the data monitored in S1, and determine the alkalinity status of the influent based on the ammonia nitrogen concentration and total alkalinity of the influent;

[0010] S3: Select an appropriate NOB inhibition strategy based on the influent alkalinity status determined in S2, in order to determine the dosing control strategy for the reactor and reduce the dosage.

[0011] Furthermore, in S2, the total alkalinity of the influent is expressed as calcium carbonate after pretreatment of the carbonate and bicarbonate concentrations.

[0012] Based on the ammonia nitrogen concentration and total alkalinity of the influent, a fuzzy expert system is used to determine the alkalinity status of the influent.

[0013] Furthermore, the process of establishing the fuzzy expert system includes the following steps:

[0014] S2-1: Construct a database that includes the relationship between alkalinity and ammonia nitrogen concentration, control strategies under different alkalinity conditions, and knowledge of short-range nitrification processes for reasoning and decision-making.

[0015] S2-2: Fuzzification. The total alkalinity and ammonia nitrogen concentration of the influent are used as input quantities. According to the size of the input data, the ammonia nitrogen concentration and total alkalinity of the influent are divided into seven fuzzy subsets and fuzzified. At the same time, the output quantity, the alkalinity state of the influent, is divided into three fuzzy subsets and fuzzified.

[0016] S2-3: Establish a fuzzy rule base. Based on the database established in S2-1, and according to the actual control strategies implemented under different alkalinity conditions, establish a set of fuzzy rules to describe the relationship between the ammonia nitrogen concentration and total alkalinity of the influent and the alkalinity state of the influent.

[0017] Furthermore, the process of using a fuzzy expert system to determine the alkalinity state of the influent is as follows: using the established fuzzy rule base and the fuzzified input quantity, the fuzzy output result is derived; the fuzzy output result is mapped to a specific output value using the defuzzified averaging method to determine the current alkalinity state of the influent.

[0018] Furthermore, in S2-2, the seven fuzzy subsets are positive large, positive medium, positive small, zero, negative small, negative medium, and negative large, respectively.

[0019] The three fuzzy subsets are positive, 0, and negative, respectively, corresponding to insufficient alkalinity, sufficient alkalinity, and excessive alkalinity.

[0020] Furthermore, in S3, the specific process of selecting a suitable NOB suppression strategy based on the determined influent alkalinity state is as follows:

[0021] When the alkalinity of the influent is determined to be insufficient, an FNA suppression strategy is adopted to maintain the FNA concentration by using a low pH value to achieve NOB suppression.

[0022] When the alkalinity of the influent is determined to be sufficient, the current suppression strategy is maintained, and the same NOB suppression strategy as the previous moment is adopted.

[0023] When the alkalinity of the influent is determined to be excessive, an FA suppression strategy is adopted, which uses a high pH value to maintain the FA concentration in order to suppress NOB.

[0024] Furthermore, when adopting the FNA suppression strategy, i.e. maintaining the FNA concentration by low pH, the threshold for FNA concentration is set to 0.1–0.15 mg / L. A reasonable pH control range is calculated based on the set FNA concentration threshold and the temperature and nitrite concentration detected in the reactor.

[0025] When using a FA suppression strategy, i.e. maintaining FA concentration through high pH, ​​the FA concentration threshold is set to 40–80 mg / L. A reasonable pH control range is calculated based on the set FA concentration threshold and the temperature and nitrite concentration detected in the reactor.

[0026] Furthermore, when the pH inside the reactor is lower than the set pH range, the amount of alkali added is adjusted until the set pH range is reached and then stopped.

[0027] When the pH inside the reactor is higher than the set pH range, the ammonia oxidation rate inside the reactor is increased by increasing the aeration coefficient to lower the pH until it reaches the set pH range.

[0028] Furthermore, the alkali dosage is achieved by drawing reagent from the alkali tank via a dosing pump. The alkali tank contains NaOH to increase the pH value inside the reactor.

[0029] Furthermore, the aeration coefficient is the ratio of aeration volume to ammonia nitrogen volume. By increasing the aeration coefficient, more ammonia nitrogen is converted into nitrite nitrogen, thereby reducing the pH value.

[0030] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0031] This invention uses a fuzzy expert system to assess the influent water quality and its alkalinity, and based on the assessment results, employs FA or FNA suppression strategies to rationally suppress NOB. Simultaneously, it automatically adjusts the NaOH dosage by calculating the pH control range, achieving the goals of saving chemical dosage, maintaining reaction process stability, and improving wastewater treatment efficiency. This system is suitable for short-cut nitrification processes and can be widely applied in wastewater treatment, environmental engineering, and other fields, demonstrating high practicality and economic benefits. Furthermore, short-cut nitrification processes can save on oxygen supply and carbon source dosage. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating the fuzzy expert system used in Example 1 to reduce the dosage of the PN reaction using intelligent control.

[0033] Figure 2 This is a schematic diagram of the dosing system for reducing the amount of drug used in the PN reaction using intelligent control in Example 1;

[0034] Figure 3 This is a flowchart illustrating a method for reducing the dosage of PN reaction using intelligent control. Detailed Implementation

[0035] The following examples illustrate specific implementations of the present invention. These examples are carried out based on the solution described in the present invention, and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following examples.

[0036] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Any structural / module names, control modes, algorithms, processes, or composition ratios not explicitly stated in this technical solution are considered common technical features disclosed in the prior art.

[0037] Example 1

[0038] This embodiment provides a method for reducing the dosage of chemicals used in short-range nitrification (PN) reactions using intelligent control, such as... Figure 3 As shown, Figure 3The diagram illustrates a process for reducing the dosage of chemicals used in the partial nitrification (PN) reaction using intelligent control. First, sensors collect various process variables from the influent, including ammonia nitrogen, bicarbonate, and carbonate concentrations. Then, the total alkalinity is calculated based on the carbonate and bicarbonate concentrations and input into a fuzzy expert system along with the ammonia nitrogen concentration to determine the alkalinity status of the influent. Based on the alkalinity status determined by the fuzzy expert system, a suitable nitrite-oxidizing bacteria (NOB) inhibition strategy is selected. The pH threshold range is calculated based on the determined NOB inhibition strategy, the current reactor internal temperature and ammonia / nitrite nitrogen concentrations, and the thresholds for free ammonia (FA) and free nitrite (FNA). Finally, by comparing the pH set range with the feedback value, alkali addition or aeration adjustment is implemented to achieve cost-effective chemical dosing and stable operation of the partial nitrification process. The specific process is as follows:

[0039] S1: Sensors monitor the concentrations of carbonate, bicarbonate, and ammonia nitrogen in the influent in real time, as well as the pH and temperature inside the reactor, and the ammonia and nitrite nitrogen concentrations in the effluent. Bicarbonate concentration is measured using a bicarbonate ion concentration meter, carbonate concentration using a carbonate ion concentration meter, and ammonia nitrogen concentration using an ammonia nitrogen detector. The sensors upload the collected data to a programmable logic controller (PLC). The influent carbonate, bicarbonate, and ammonia nitrogen concentrations detected by the online sensors serve as input parameters for the fuzzy expert system; the online monitoring of the reactor's pH and temperature, and the effluent ammonia and nitrite nitrogen concentrations, are used for subsequent pH control strategies.

[0040] S2: Calculate the total alkalinity of the influent based on the data monitored in S1, and input the ammonia nitrogen concentration and total alkalinity of the influent into the fuzzy expert system to determine the alkalinity status of the influent.

[0041] The total alkalinity of the influent is calculated as calcium carbonate after pretreatment of carbonate and bicarbonate concentrations. Specifically, 1 mol of bicarbonate can provide 1 mol of hydrogen ions during the reaction, while 1 mol of carbonate can provide 2 mol of hydrogen ions during the reaction. The concentrations of the two are converted into equal molar masses of calcium carbonate based on the amount of hydrogen ions that can be provided, and then added together to obtain the total alkalinity, so that 1 mol of calcium carbonate can provide two hydrogen ions during the reaction.

[0042] A fuzzy expert system determines the alkalinity state of the influent based on the ammonia nitrogen concentration and total alkalinity of the influent, such as... Figure 1As shown. An expert system is a computer program based on artificial intelligence technology, designed to simulate and realize the knowledge and decision-making abilities of human experts. It combines domain-specific knowledge, reasoning techniques, and problem-solving methods, providing professional-level problem-solving and decision support by simulating the thinking and decision-making processes of experts. A fuzzy expert system includes fuzzification, a rule base, and fuzzy output. The control rule set of the fuzzy expert system, i.e., the rules for alkalinity judgment, is the core of the fuzzy expert system. It is derived from the long-term experience of field personnel and the actual control patterns in the field, mainly based on empirical data from the actual control process to determine the current alkalinity state of the influent.

[0043] The fuzzy expert system was simulated and tested using MATLAB and Simulink, while the PLC was programmed using TIA Portal V17. The Siemens S7-1200 series PLC was selected for control.

[0044] The process of establishing the fuzzy expert system includes the following steps:

[0045] S2-1: Construct a database that includes the relationship between alkalinity and ammonia nitrogen concentration, control strategies under different alkalinity conditions, and knowledge of short-range nitrification processes for reasoning and decision-making.

[0046] S2-2: Fuzzification. The total alkalinity and ammonia nitrogen concentration of the influent are used as input quantities. In actual short-cut nitrification processes, the ammonia nitrogen concentration in the influent is generally between 1000 and 2000 mg / L, and the total alkalinity (calculated as calcium carbonate) is above 2 g / L. The input universe of discourse is set to [-2, 2], and the fuzzy subset of the input variables is set to {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, i.e., {NB, NM, NS, Z, PS, PM, PB}, as shown in Table 1. Table 1 shows the fuzzy control rules for influent alkalinity, where ΔU is the influent alkalinity state, A is the influent ammonia nitrogen concentration, and B is the influent total alkalinity (calculated as calcium carbonate). Influent alkalinity can be in states of sufficiency, insufficiency, and excess; therefore, the universe of discourse is set to [-1, 1], and the fuzzy subset is {negative, zero, positive}. The triangular membership function (trimf) is used to describe the degree of membership of variables in the fuzzy set.

[0047] Table 1. Fuzzy control rules for influent alkalinity

[0048]

[0049] S2-3: Establish a fuzzy rule base. Based on the database established in S2-1, and according to the actual control strategies implemented under different alkalinity conditions, establish a set of fuzzy rules to describe the relationship between the ammonia nitrogen concentration and total alkalinity of the influent and the alkalinity state of the influent.

[0050] Using the established fuzzy rule base and the fuzzified input, the fuzzy output result is derived; the fuzzy output result is mapped to the specific output value using the defuzzified averaging method to determine the current alkalinity state of the influent.

[0051] S3: Based on the influent alkalinity status determined in S2, select a suitable NOB suppression strategy to determine the reactor's dosing control strategy, aiming to reduce the dosage. For example... Figure 2 As shown, the specific steps for selecting an appropriate NOB suppression strategy based on the determined influent alkalinity state are as follows: when the influent alkalinity state is determined to be insufficient, an FNA suppression strategy is adopted, using a low pH value to maintain the FNA concentration and achieve NOB suppression; when the influent alkalinity state is determined to be sufficient, the current suppression strategy is maintained, using the same NOB suppression strategy as the previous moment; when the influent alkalinity state is determined to be excessive, an FA suppression strategy is adopted, using a high pH value to maintain the FA concentration and achieve NOB suppression.

[0052] When employing an FNA suppression strategy, i.e., maintaining FNA concentration through low pH, the FNA concentration threshold is set at 0.1–0.15 mg / L. Ammonia nitrogen concentration, nitrite nitrogen concentration, and temperature are collected by sensors and uploaded to the PLC. These data, along with the FNA threshold, are used to calculate the upper and lower pH ranges required to reach the suppression threshold. The reaction temperature is controlled between 30°C and 35°C by a heating device. When the pH sensor inside the reactor detects a pH below the set pH range, the alkali dosage is adjusted; that is, the PLC controls the dosing pump to add NaOH to the reactor until the set pH range is reached, then stops. When the pH inside the reactor is above the set pH range, the PLC increases the aeration coefficient to increase the ammonia oxidation rate inside the reactor, thereby lowering the pH until the set pH range is reached.

[0053] When employing an FA suppression strategy, i.e., maintaining FA concentration through high pH, ​​the FA concentration threshold is set to 40–80 mg / L. The upper and lower thresholds of FA concentration are calculated by combining the ammonia nitrogen concentration measured by the sensor, temperature, and the FA concentration. The pH upper and lower threshold ranges are then calculated based on the formula for the set FA concentration. The reaction temperature is controlled between 30°C and 35°C using a heating rod. If the pH detected by the pH sensor inside the reactor is lower than the set pH range, the alkali dosage is adjusted by adding NaOH to the short-cut nitrification reactor via a PLC-controlled pump until the set pH range is reached. If the pH inside the reactor is higher than the set pH range, the aeration coefficient is increased to increase the ammonia oxidation rate inside the reactor, thereby lowering the pH until the set pH range is reached.

[0054] The alkali dosage is achieved by drawing reagent from the alkali tank via a dosing pump. The alkali tank contains NaOH to raise the pH value inside the reactor. NOB inhibition is achieved by adjusting the pH using the dosing pump according to different control strategies. The aeration coefficient is the ratio of aeration volume to ammonia nitrogen volume, expressed in O2 / kg NH4. + -N. By controlling the aeration blower with a frequency converter, the aeration coefficient is increased, causing more ammonia nitrogen to be converted into nitrite nitrogen, thereby lowering the pH value.

[0055] Specifically, the short-cut nitrification intelligent control process device includes a pH and ammonia nitrogen detector, a PLC, carbonate and bicarbonate ion detectors, an influent tank, an alkali tank, a dosing pump, an aeration blower, a frequency converter, and a short-cut nitrification reactor. Measurements showed that the carbonate ion concentration in the leachate from the landfill in the influent tank was 0.43 mol / L, the bicarbonate ion concentration was 0.04 mol / L, and the ammonia nitrogen concentration was 1220 mg / L; inside the reactor, the ammonia nitrogen concentration was 488 mg / L, the nitrite nitrogen concentration was 732 mg / L, the temperature was 33.2℃, and the pH was 7.1.

[0056] The PLC converts the ion concentrations of bicarbonate and carbonate into alkalinity expressed as calcium carbonate (45g of calcium carbonate). This alkalinity, along with the ammonia nitrogen concentration, is then input into the fuzzy expert system. The output water alkalinity is currently excessive. The current NOB suppression strategy is FNA suppression, with the FNA concentration controlled at 0.11mg / L. The current NOB suppression strategy will be modified to FA suppression.

[0057] The inhibition threshold for FA is 40–80 mg / L. The calculated pH setpoint is 8.0–8.2. Using PLC-controlled automatic adjustment, NaOH is added to the short-cut nitrification reactor via the dosing pump on the alkali tank, raising the pH to 8.0, reaching the set range, and then alkali addition is stopped. At this point, the FA concentration is 45.90 mg / L, achieving FA inhibition.

[0058] Example 2

[0059] This embodiment provides a method for reducing the dosage of chemicals used in the PN reaction using intelligent control. The short-cut nitrification intelligent control process device includes a pH and ammonia nitrogen detector, a PLC, carbonate and bicarbonate ion detectors, an inlet tank, an alkali tank, a dosing pump, an aeration fan, a frequency converter, and a short-cut nitrification reactor. Measurements showed that the carbonate ion concentration in the leachate in the inlet tank was 0.15 mol / L, the bicarbonate ion concentration was 0.02 mol / L, and the ammonia nitrogen concentration was 1610 mg / L; inside the reactor, the ammonia nitrogen concentration was 724.5 mg / L, the nitrite nitrogen concentration was 885.5 mg / L, the temperature was 30.4℃, the pH was 7.9, the HRT was 24 hours, the aeration rate was 7.2 L / min, the reactor volume was 25 L, and the aeration coefficient was 280 m³ / min. 3O2 / kg NH4 + -N.

[0060] The PLC converts the ion concentrations of bicarbonate and carbonate into alkalinity expressed as calcium carbonate, which is 16 g / L. This alkalinity, along with the ammonia nitrogen concentration, is then input into the fuzzy expert system. The output water alkalinity is currently insufficient. The current NOB suppression strategy is FA suppression, with the FA concentration controlled at 46.21 mg / L. The current NOB suppression strategy will be modified to FNA suppression.

[0061] The inhibition threshold for FA is 0.1–0.15 mg / L. The calculated pH setting threshold is 7.1–7.2. The PLC automatically stops alkali addition, while the frequency converter increases the aeration coefficient to 350 O2 / kg NH4+-N, gradually lowering the pH to 7.2, reaching the set range. The aeration coefficient is then adjusted back to the original setting. At this point, the nitrite nitrogen concentration rises to 1207.5 mg / L, and the FNA concentration is 0.15 mg / L, achieving FNA inhibition and ensuring NOB suppression.

[0062] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A method for reducing the dosage of a PN reaction using intelligent control, characterized in that, Includes the following steps: S1: Real-time monitoring of carbonate, bicarbonate and ammonia nitrogen concentrations in the influent water quality via sensors, as well as monitoring of pH, temperature inside the reactor and ammonia nitrogen and nitrite nitrogen concentrations in the effluent water quality. S2: Calculate the total alkalinity of the influent based on the data monitored in S1, and determine the alkalinity status of the influent based on the ammonia nitrogen concentration and total alkalinity of the influent; S3: Select an appropriate NOB inhibition strategy based on the influent alkalinity status determined in S2, in order to determine the dosing control strategy for the reactor and reduce the dosage. In S2, the total alkalinity of the influent is expressed as calcium carbonate after pretreatment of carbonate and bicarbonate concentrations. Based on the ammonia nitrogen concentration and total alkalinity of the influent, a fuzzy expert system is used to determine the alkalinity status of the influent. The process of establishing the fuzzy expert system includes the following steps: S2-1: Construct a database that includes the relationship between alkalinity and ammonia nitrogen concentration, control strategies under different alkalinity conditions, and knowledge of short-range nitrification processes for reasoning and decision-making. S2-2: Fuzzification. The total alkalinity and ammonia nitrogen concentration of the influent are used as input quantities. According to the size of the input data, the ammonia nitrogen concentration and total alkalinity of the influent are divided into seven fuzzy subsets and fuzzified. At the same time, the output quantity, the alkalinity state of the influent, is divided into three fuzzy subsets and fuzzified. S2-3: Establish a fuzzy rule base. Based on the database established in S2-1, and according to the actual control strategies implemented under different alkalinity conditions, establish a set of fuzzy rules to describe the relationship between the ammonia nitrogen concentration and total alkalinity of the influent and the alkalinity state of the influent. In S3, the specific process of selecting a suitable NOB suppression strategy based on the determined influent alkalinity state is as follows: When the alkalinity of the influent is determined to be insufficient, an FNA suppression strategy is adopted to maintain the FNA concentration by using a low pH value to achieve NOB suppression. When the alkalinity of the influent is determined to be sufficient, the current suppression strategy is maintained, and the same NOB suppression strategy as the previous moment is adopted. When the alkalinity of the influent is determined to be excessive, an FA suppression strategy is adopted, which maintains the FA concentration by using a high pH value to achieve NOB suppression. When an FNA inhibition strategy is adopted, i.e., the FNA concentration is maintained at a pH of 7.1–7.2, the threshold for FNA concentration is set at 0.1–0.15 mg / L; When a FA inhibition strategy is adopted, i.e., the FA concentration is maintained by pH 8.0 to 8.2, the FA concentration threshold is set to 40 to 80 mg / L.

2. The method for reducing the dosage of PN reaction using intelligent control according to claim 1, characterized in that, The process of using a fuzzy expert system to determine the alkalinity state of the influent is as follows: using the established fuzzy rule base and the fuzzified input, the fuzzy output result is derived; the fuzzy output result is mapped to a specific output value using the defuzzified averaging method to determine the current alkalinity state of the influent.

3. The method for reducing the dosage of PN reaction using intelligent control according to claim 1, characterized in that, In S2-2, the seven fuzzy subsets are positive large, positive medium, positive small, zero, negative small, negative medium, and negative large, respectively. The three fuzzy subsets are positive, 0, and negative, respectively, corresponding to insufficient alkalinity, sufficient alkalinity, and excessive alkalinity.

4. The method for reducing the dosage of PN reaction using intelligent control according to claim 1, characterized in that, When the pH inside the reactor is lower than the set pH range, the amount of alkali added is adjusted until the set pH range is reached and then stopped. When the pH inside the reactor is higher than the set pH range, the ammonia oxidation rate inside the reactor is increased by increasing the aeration coefficient to lower the pH until it reaches the set pH range.

5. The method for reducing the dosage of PN reaction using intelligent control according to claim 4, characterized in that, The alkali dosage is achieved by drawing reagent from the alkali tank via a dosing pump. The alkali tank contains NaOH, which is used to increase the pH value inside the reactor.

6. The method for reducing the dosage of PN reaction using intelligent control according to claim 4, characterized in that, The aeration coefficient is the ratio of aeration volume to ammonia nitrogen volume. By increasing the aeration coefficient, more ammonia nitrogen is converted into nitrite nitrogen, thereby reducing the pH value.

Images