Water treatment multi-agent linkage control method

Abstract

This application discloses a method for coordinated control of multiple water treatment agents, comprising the following steps: establishing a database; when the set effluent water quality parameters of the wastewater to be treated are at a set threshold, sampling and measuring the set influent water quality parameters, set effluent water quality parameters, and the parameters of the controlled agents, and recording the sampling and measurement data to form a database; the controlled agents include at least two types; establishing a matching data model; updating the database and correcting the data model; analyzing the measurement data in the updated database and correcting the data model; repeating the steps of updating the database and correcting the relationship. This application embodiment can realize the coordinated control of set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters, avoiding manual intervention and agent waste, and ensuring stable and continuous compliance of effluent standards.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, and in particular to a method for the coordinated control of multiple water treatment agents. Background Technology

[0002] In mining activities, the water generated in underground mines and the wastewater discharged from miners' domestic use are collectively referred to as mine wastewater. Mine wastewater typically contains 7%–8% solids and is characterized by large volumes, significant and irregular fluctuations in water quality. Existing treatment technologies often employ deep cone coagulation sedimentation, requiring manual adjustments to the dosage of chemicals to ensure the treated effluent turbidity meets standards when water quality fluctuates. However, manual adjustments are not timely, and the coagulation reaction is delayed, resulting in effluent turbidity (suspended solids concentration) still fluctuating between 50 and 100 mg / L, making it impossible to achieve stable and continuous compliance with discharge standards.

[0003] On-site, the common practice is to increase the dosage of chemicals to ensure stable and continuous compliance with drainage standards. This means the actual dosage is consistently higher than the theoretical or experimentally determined dosage, which easily leads to chemical waste.

[0004] Some companies use automated dosing devices to eliminate the risks associated with manual operation. These devices typically adjust the dosage of chemicals automatically based on changes in the influent flow rate, such as PAM automated dosing devices, PAC automated dosing devices, and pH automatic control systems. However, this type of automated dosing control is a one-to-one control, or unitary control mode, where one parameter corresponds to one chemical. But in water treatment, coagulation and sedimentation generally involve two or more chemicals. Using the existing unitary control mode cannot truly achieve automatic wastewater control; manual assistance is still required, and there are still instances of chemical waste or substandard effluent quality. Summary of the Invention

[0005] To address at least one of the problems mentioned in the background art, embodiments of this application provide a method for coordinated control of multiple water treatment agents, which can achieve coordinated control of setting influent water quality parameters, setting effluent water quality parameters, and parameters of the agents to be controlled, avoiding manual intervention and agent waste, and ensuring stable and continuous compliance of drainage standards.

[0006] To achieve the above objectives, the first aspect of this application provides a method for the coordinated control of multiple water treatment agents, comprising the following steps:

[0007] Establish a database: When the set effluent water quality parameters of the wastewater to be treated are at the set threshold, the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the wastewater to be treated are sampled and measured for the first time, and the sampling and measurement data are recorded to form a database; wherein, the controlled agents include at least two types;

[0008] Establish a matching data model: Analyze the sampling measurement data in the database to obtain a data model; the data model includes the correlation between the parameters of the controlled reagent and the set influent and effluent water quality parameters of the wastewater to be treated;

[0009] Feedback and Database Update: Based on the data model and the set effluent water quality parameters of the wastewater to be treated, when at least one of the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the controlled agent changes, the linkage control changes of other parameters are confirmed; after the set effluent water quality parameters of the wastewater to be treated are at the set threshold, a second sampling measurement is performed on the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the controlled agent, and the sampling measurement data is recorded to update the database;

[0010] Correcting the data model: Analyze the measurement data in the updated database and correct the data model;

[0011] Repeat the steps of updating the database and correcting the relational expressions.

[0012] In one feasible implementation, the data model includes the correlation between the parameters of various controlled agents and the set influent and effluent water quality parameters of the wastewater to be treated; or, the data model includes the correlation between the parameters of a controlled agent and the set influent and effluent water quality parameters of the wastewater to be treated, as well as the correlation between the parameters of different types of controlled agents.

[0013] In one feasible implementation, the set influent water quality parameters include influent flow rate, influent concentration, and influent pH value;

[0014] The set effluent water quality parameters include effluent turbidity;

[0015] The controlled reagent parameters include the dosing flow rate and the dosing concentration.

[0016] In one feasible implementation, the controlled agent includes a coagulant and a flocculant.

[0017] In one feasible implementation, based on the relationship that the charge number of the coagulant to be added is equal to the charge number of the wastewater to be treated, the correlation between the coagulant's dosing flow rate and concentration and the influent flow rate, influent concentration, influent pH value and effluent turbidity of the wastewater to be treated is obtained.

[0018] Based on the relationship that the total charge of the coagulant is equal to the total charge of the flocculant, the correlation between the coagulant's dosing flow rate and concentration and the flocculant's dosing flow rate and concentration is obtained; further, the correlation between the flocculant and coagulant's dosing flow rate and concentration and the influent flow rate, influent concentration, influent pH value and effluent turbidity of the wastewater to be treated is obtained.

[0019] In one feasible implementation, the correlation between the coagulant dosage flow rate and concentration and the influent flow rate, influent concentration, influent pH value, and effluent turbidity includes:

[0020] Q A C A =K Q (1+QC-QN)

[0021] in,

[0022] Q A C represents the dosing flow rate of the coagulant. A This refers to the concentration of the coagulant.

[0023] 'a' represents the charge density of the coagulant. pH refers to the pH value of the influent.

[0024] In one feasible implementation, the correlation between the flocculant / coagulant dosage flow rate and concentration and the influent flow rate, influent concentration, influent pH value, and effluent turbidity includes:

[0025] Q B C B =K n (1+QC-QN)

[0026] in,

[0027] Q B C represents the flocculant dosing flow rate. B This refers to the concentration of the flocculant.

[0028] b is the charge density of the flocculant, K x This represents the binding coefficient between the coagulant and the flocculant.

[0029] pH refers to the pH value of the influent.

[0030] In one feasible implementation, the relationship between the dosage flow rate and concentration of the coagulant and the dosage flow rate and concentration of the flocculant includes:

[0031] aQ A C A =K X ·bQB C B

[0032] in,

[0033] a is the charge density of the coagulant, Q A C represents the dosing flow rate of the coagulant. A This refers to the concentration of the coagulant.

[0034] b is the charge density of the coagulant, Q B C represents the flocculant dosing flow rate. B This refers to the concentration of the flocculant.

[0035] K x It represents the binding coefficient between the coagulant and the flocculant.

[0036] In one feasible implementation, the coagulant includes inorganic or organic coagulants, and the flocculant includes polyacrylamide-based organic polymeric flocculants.

[0037] This application provides a method for the coordinated control of multiple water treatment agents. This method establishes a database through extensive sampling and measurement, analyzes the database to obtain a data model, and acquires the correlation between the parameters of at least two controlled agents and the set influent and effluent water quality parameters of the wastewater to be treated. This guides the actual dosage of agents on-site, and after verification, further sampling and measurement are conducted to update the database, while simultaneously revising the data model. Through repeated feedback, database updates, and relationship revisions, an increasingly accurate data model is obtained. This allows the method to achieve coordinated control of other parameters even when any one of the parameters of the controlled agents or the set influent and effluent water quality parameters of the wastewater changes, avoiding manual intervention and agent waste, and ensuring stable and continuous compliance with discharge standards. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A schematic diagram of the steps of the water treatment multi-agent linkage control method provided in the embodiments of this application;

[0040] Figure 2 This is a schematic diagram of the relationship network of multiple parameters in the water treatment multi-agent linkage control method provided in the embodiments of this application. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. It is worth noting that the embodiments described in the accompanying drawings are only some embodiments of this application, and not all embodiments. That is, the embodiments described with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0042] The following will combine Figure 1 and Figure 2 The water treatment multi-agent linkage control method provided in the embodiments of this application is described.

[0043] This application provides a method for the coordinated control of multiple water treatment agents, including the following steps:

[0044] S1: Establish a database: When the set effluent water quality parameters of the wastewater to be treated are at the set threshold, the set influent water quality parameters, set effluent water quality parameters, and parameters of the controlled reagents of the wastewater to be treated are sampled and measured for the first time, and the sampling measurement data are recorded to form a database. The controlled reagents include at least two types.

[0045] In this embodiment, two types of reagents to be controlled are included: reagent A and reagent B. The parameters of the reagents to be controlled include the dosing flow rate and the dosing concentration. The set influent water quality parameters of the wastewater to be treated include the influent flow rate, the influent concentration, and the influent pH value. The set effluent water quality parameters of the wastewater to be treated include the effluent turbidity.

[0046] Specifically, technicians conduct on-site tests and collect and organize the test data.

[0047] When the influent flow rate of the wastewater to be treated is Q, the influent concentration is C, and the influent pH value is pH, the dosing parameters of the reagents are adjusted according to the characteristics of the wastewater to be treated so that the effluent turbidity N is at the set threshold. Specifically, the dosing flow rate of reagent A is Q. A The dosage concentration is C. A The dosing flow rate of agent B is Q. B The concentration of the drug added is C. B Within a specific time period T, multiple samples of the wastewater to be treated are taken and measured, resulting in multiple sampling and measurement data records that include the parameters mentioned above.

[0048] Technicians repeatedly adjusted and debugged the system based on the quality of the wastewater to be treated, and collected a large number of sampling and measurement data records, totaling hundreds of thousands, forming a sampling database set as shown in Table 1.

[0049]

[0050] Understandably, a sufficient number of sampling and measurement data records are beneficial to improving the accuracy of the data model. Moreover, the sampling and measurement data records in the database can directly guide the dosage of chemicals on site even without a data model. On-site operators can directly look up the corresponding dosage flow rate and concentration in the database based on the influent flow rate, concentration, and pH value of the wastewater to be treated, and make on-site adjustments.

[0051] S2: Establish a matching data model: Analyze the sampling measurement data in the database to obtain a data model; the data model includes the correlation between the parameters of the controlled reagent and the set influent and effluent water quality parameters of the wastewater to be treated.

[0052] Specifically, the data model can be as follows (equations ④ and ⑦), including the correlation between the parameters of various controlled reagents and the set influent and effluent water quality parameters of the wastewater to be treated. Alternatively, the data model can also be as follows (equations ④ and ⑤), including the correlation between the parameters of one controlled reagent and the set influent and effluent water quality parameters of the wastewater to be treated, as well as the correlation between the parameters of different types of controlled reagents.

[0053] The following description focuses on the controlled agents, including coagulants and flocculants. Coagulants are substances that can form charged particles in water, neutralizing the charge on colloidal substances through charge neutralization, thus disrupting colloidal stability and causing the colloidal particles to collide and form tiny flocs. The coagulant in this application does not specifically refer to any particular substance. In some embodiments, the coagulant may include inorganic coagulants, such as polyaluminum chloride and aluminum sulfate; and organic coagulants, such as Pracetam high-efficiency filter. Flocculants are substances that, through the mutual attraction between their own charge and tiny flocs, and the adsorption bridging effect of the formed flocs, adsorb surrounding suspended matter, gradually increasing their volume and weight, thereby forming flocs or clumps. The flocculant in this application also does not specifically refer to any particular substance. In some embodiments, the flocculant may include polyacrylamide-based organic polymeric flocculants.

[0054] Based on the relationship that the charge of the coagulant to be added is equal to the charge of the wastewater to be treated, the correlation between the coagulant dosing flow rate and concentration and the influent flow rate, influent concentration, influent pH value and effluent turbidity of the wastewater to be treated is obtained.

[0055] There are many wastewater quality parameters related to coagulation and sedimentation technology in water treatment. In this application, the parameters are selected based on the principle of charge conservation. Wastewater generally carries a certain amount of charge, and the amount of charge is partly determined by the influent flow rate Q and its own influent concentration C. That is, the amount of charge in wastewater is related to the value of Q × C, and this is the main determining parameter. Secondly, the amount of charge in wastewater is related to its pH value. Wastewater carries a certain amount of charge under acidic or alkaline pH conditions. Furthermore, the principle and purpose of coagulation and sedimentation is to use the network, bridging and coagulation effects of coagulants to capture and coagulate solid suspended matter and large molecular colloidal substances in wastewater, forming large flocs, thereby achieving solid-liquid separation under gravity and purifying water quality. Different influent flow rates and influent concentrations directly affect the coagulant dosing effect and effluent water quality. Influent flow rates and influent concentrations are also parameters that change in real time during water treatment operation. If the pH value of the wastewater is too high or too low, it will also affect the coagulation and sedimentation effect. Therefore, in the control method of this application embodiment, influent flow rate, influent concentration and pH value are the three parameters of wastewater quality in the linkage control.

[0056] The specific relationship is as follows: The charge in the wastewater to be treated consists of three parts: (1) The charge carried in the influent of the wastewater to be treated, expressed by the following formula: Where K1 is the set charge constant of the material. (2) The charge number of the pH value of the wastewater to be treated is expressed by the formula 10. |7-pH|-7 (3) The number of unremoved charges remaining in the system (i.e., substrate concentration) is expressed by the following formula: According to the principle of charge conservation, to completely neutralize the charge in the wastewater to be treated, an equal amount of opposite charge needs to be added, expressed by the formula a·Q. A C A , where a is the charge density of agent A.

[0057] The comprehensive formula is:

[0058]

[0059] Calculating equation ①, we get

[0060]

[0061] Due to 10 |7-pH|-7 and All are affected by pH, etc., 10 |7-pH|-7 Values ​​in 1 and 10 -7 Depending on the actual situation, the pH value of the treated wastewater is generally between 6 and 7.5, so 10 |7-pH|-7 The value is basically 10 -7 Up to 10 -6Between; since the wastewater to be treated is mostly organic particulate colloids, and the molecular weight of particulate colloids is relatively large, with many charged groups, based on experience and long-term potential detection and evaluation, the charge carried by the particulate colloids is all within 10; 5.5 Up to 10 7 The charge constant between these two values ​​is relatively stable, and can be set as K1, which is the charge constant of the wastewater influent (material). The value is in 10 -5.5 Up to 10 -7 Between them, the values ​​of the two numbers are very close and very small, therefore... Let it be a constant K e Then equation ② simplifies to

[0062]

[0063] because K e All are constants, let For the new constant K Q Then equation ③ simplifies to

[0064] C A Q A =K Q ·(1+QC-QN)........................④

[0065] Equation ④ above is the correlation between the coagulant dosing flow rate and concentration and the influent flow rate Q, influent concentration C, pH value and effluent turbidity N of the wastewater to be treated.

[0066] At the same time, the constant relationship is obtained. two.

[0067] Based on the relationship that the total charge of the coagulant is equal to the total charge of the flocculant, the correlation between the coagulant's dosage flow rate and concentration and the flocculant's dosage flow rate and concentration is obtained. Further, the correlation between the flocculant and coagulant's dosage flow rate and concentration and the influent flow rate, influent concentration, influent pH value, and effluent turbidity of the wastewater to be treated is obtained.

[0068] The high turbidity and unclear quality of mine wastewater are due to the presence of a large number of fine particles existing in colloidal form or carrying an electrical charge. Because these fine particles generally carry a negative charge, they are difficult to aggregate and settle due to electrostatic repulsion, resulting in persistent turbidity. To clarify the water, a certain amount of a coagulant with the opposite charge (reagent A) needs to be added to neutralize the charge and destabilize the particles in the wastewater under the action of compressed electric double layers, causing a coagulation reaction. The coagulation reaction is relatively fast, and the resulting floc particles are relatively small and settle slowly. To accelerate the settling speed, a certain amount of a polymeric flocculant (reagent B) needs to be added. Under the adsorption and bridging effect of the polymeric flocculant, small floc particles aggregate into larger floc particles, which settle rapidly, thus purifying the wastewater.

[0069] There is an optimal range for the dosage of coagulants and flocculants (the product of the flow rate and concentration). Adding too little will have little settling effect; adding too much will cause negatively charged particles to become positively charged, increasing interparticle repulsion and reducing coagulation efficiency. Therefore, in coagulation and sedimentation processes, the dosage must be accurately controlled, and the dosing effect should be frequently monitored. The dosage of flocculants is related to the dosage of coagulants and the influent flow rate of the wastewater. According to the theory of double-layer compression and charge neutralization, the total charge released by the coagulant should be equal to or close to the total charge of the particles in the wastewater. After the coagulant is added to the wastewater, it first undergoes a hydrolysis reaction, neutralizing and adsorbing the suspended particles to form stable and relatively large flocs. The charge on these flocs is still equal to or close to the charge released by the initially added coagulant. Flocculants, acting as coagulants, adsorb and aggregate with the formed floc particles using their own negative charge. Optimal floc formation and the fastest settling speed occur when the negative charge of the flocculant is similar to or equal to the charge of the formed floc particles. Therefore, the total charge carried by the flocculant can be considered equal to the total charge released by the coagulant.

[0070] The total charge carried by the coagulant is expressed by the formula aQ. A C A Where a is the charge density of the coagulant, Q A C represents the dosing flow rate of the coagulant. A This represents the concentration of the coagulant. The total charge carried by the flocculant is expressed by the formula K. X ×bQ B C B Q B C represents the flocculant dosing flow rate. B Let b be the concentration of the flocculant, b be the charge density of the flocculant, and K be the concentration of the flocculant. X It represents the binding coefficient between the coagulant and the flocculant.

[0071] The comprehensive formula is:

[0072] aQ A C A =K X ·bQ B C B ...⑤, Calculate equation ⑤ to get...

[0073]

[0074] Because of a, b, K Q K X All are constants, let For the new constant K n Then equation ⑥ simplifies to

[0075] Q B C B =K n ·(1+QC-QN)............⑦

[0076] Equation ⑤ above represents the correlation between the controlled reagent parameters of flocculants and coagulants; Equation ⑦ represents the correlation between the dosing flow rate and concentration of flocculants and coagulants and the influent flow rate Q, influent concentration C, pH value, and effluent turbidity N of the wastewater to be treated, and also yields constant relationships.

[0077] In summary, the relationships between the parameters were identified, resulting in equations ④ and ⑦:

[0078] Q A C A =K Q (1+QC-QN)........................④

[0079] Q B C B =K n (1+QC-QN)........................⑦

[0080] It is worth noting that although the pH-related data has been converted into constants, it still has a corresponding influence in the entire relationship. Therefore, the above relationship is still considered to include the correlation between pH and the parameters of the controlled reagent.

[0081] Substituting the sampled measurement data from the sampling database set in Table 1 into equations ④ and ⑦, we obtain the corresponding K at the corresponding time. Q Value and K nThe values ​​are formed as shown in Table 2, forming K. Q Value and K n Value dataset.

[0082]

[0083] K within the corresponding time period Q and K n The data are averaged separately to obtain the average value. and

[0084] S3: Feedback and Database Update: Based on the data model and the set effluent water quality parameters of the wastewater to be treated, when at least one of the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the controlled agent changes, the linkage control changes of other parameters are confirmed; after the set effluent water quality parameters of the wastewater to be treated are at the set threshold, a second sampling measurement is performed on the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the controlled agent, and the sampling measurement data is recorded to update the database.

[0085] The "brain" is integrated around relations ④ and ⑦, and the average value is used as the core. and As the "brain," it receives and outputs signals to guide calculations. The dosage flow rate Q of the coagulant is used as an example. A The dosing flow rate Q of the flocculant B Taking the calculation as an example, that is, taking and Inputting data into the "brain" and combining it with real-time data collected on-site, including the influent flow rate Q, influent concentration C, influent pH, effluent turbidity N, and chemical concentration C of the wastewater to be treated. A C B The system automatically receives the input signal and calculates the drug delivery rate Q based on equations ④ and ⑦. A Q B .

[0086] The coagulant dosing flow rate Q calculated in the above steps is used as an example. A The dosing flow rate Q of the flocculant B By comparing the sampled measurement data with the data in the database set in Table 1, at least one sampled measurement data point will always be equal to or very close to the calculated data, verifying the feasibility of the entire process. Simultaneously, if the calculated pesticide dosing flow rate in the above steps is found to be feasible after verification by the database, a signal will be automatically output to the on-site dosing equipment to adjust the pesticide dosage. Based on experience, a 10% deviation in the on-site pesticide dosing dosage will not affect the overall operation.

[0087] At the same time, the calculated dosing flow rate is stored back into the database as the actual dosing flow rate, completing the data update in the database.

[0088] S4: Correct the data model: Analyze the measurement data in the updated database and correct the data model.

[0089] The updated database data is used as new sampling and measurement data to automatically correct the relational formula, forming a new relational formula, which is then used to guide calculations and provide feedback on the on-site operation process.

[0090] S5: Repeat the steps of updating the database and correcting the relational expressions.

[0091] This cycle continues, and the updated database and revised data model can more accurately guide on-site operations, avoiding manual intervention and waste of chemicals, and ensuring stable and continuous compliance with drainage standards.

[0092] The above is based on the drug dosing flow rate Q. A Q B Taking the calculation as an example, if any of the other parameters, such as influent flow rate Q, influent concentration C, and effluent turbidity N, are used as the control, the values ​​of the other parameters can be calculated and derived, and the above method can be used to achieve linkage control and circulation process.

[0093] For example, a threshold value for effluent turbidity N can be set. If the standard value for effluent turbidity is required to be <30mg / l, then the threshold value for effluent turbidity can be set to <20mg / l. Once the effluent turbidity value exceeds the threshold value, the dosage of flocculant and coagulant will automatically increase to match the threshold value, ensuring that the effluent water quality consistently meets the standards.

[0094] For example, when any parameter of the influent water quality parameters of the wastewater to be treated changes, such as an increase in influent flow rate, the dosage of coagulant will automatically increase to match. Based on the change in the dosage of coagulant, the dosage of flocculant will automatically increase to match, realizing the real-time adjustment of the dosage of chemicals, which not only avoids waste of chemicals but also ensures that the effluent water quality meets the standards.

[0095] When the dosage of any chemical agent changes, such as when the dosage of flocculant suddenly decreases, the dosage of coagulant will automatically decrease to match, and the influent flow rate will also be automatically adjusted to match, ensuring that the effluent water quality remains stable and consistently meets the standards.

[0096] In summary, the core purpose of the control method is to ensure that the effluent water quality is consistently up to standard. In the event of an emergency, if the effluent water quality index exceeds the standard value, the system will automatically shut down to prevent the discharge of substandard water from the system.

[0097] This application uses two controlled agents as examples for illustration. In reality, the controlled agents may include multiple agents. In this case, the above process can still be referred to to establish a matching data model based on the correlation between the charge number of the controlled agent and the charge number to be treated in the wastewater, as well as the charge number of different types of controlled agents. This will not be elaborated further.

[0098] The following is an example of an on-site test of mine wastewater treatment at a coal preparation plant in Guizhou Province to illustrate the experimental verification of the embodiments of this application.

[0099] A 10-day sampling period was selected, with 5 sampling points chosen. Within the set control parameters, the effluent turbidity N < 20 mg / L was controlled. The sampling was conducted at an influent flow rate of Q, an influent concentration of C, an influent pH value of pH, and an influent flow rate of Q for the coagulant A (polyaluminum chloride). A The dosage concentration is C. A The dosing flow rate of flocculant B, polyacrylamide, is Q. B The dosage concentration is C. B Sampling was conducted to obtain the 10-day sampling datasets for each sampling point, as shown in Tables 3-7.

[0100] It should be noted that the dosing flow rate in the table below is Q. A The actual dosing flow rate is relatively small, so a flow meter with a range of 0-500 L / h is generally selected for measurement, while the dosing flow rate is Q. B The actual dosing flow rate is relatively large, so 0-15m is generally selected. 3 The flow meter has a range of / h for measurement. To avoid introducing errors in the calculation, the sampling measurement data for both are recorded using the original measurement units.

[0101]

[0102]

[0103]

[0104]

[0105]

[0106] Using equations ④ and ⑦, calculate and determine K for each sampling point based on the sampling measurement data in Tables 3-7. Q and K N The results are shown in Tables 8-12 below.

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] Based on Tables 8-12 above, calculate K for the 5 sampling points. Q1 and K n1 Average value as reinforced average value

[0113] A 10-day period was selected after the sampling period as the verification period. The actual wastewater quality during this period was verified by a test. The influent water quality sampling dataset for the 10-day verification period of the sampling points is shown in Table 13, and the actual dosing dataset for the 10-day verification period of the sampling points is shown in Table 14.

[0114]

[0115]

[0116] by Using this as the core, substitute equations ④ and ⑦ to calculate the theoretical dosing flow rates of agents A and B respectively, and obtain the theoretical dosing data analysis of the sampling point verification period of 10 days as shown in Table 15.

[0117]

[0118] The average theoretical dosing flow rate of coagulants and flocculants was compared with the average actual on-site dosing flow rate to calculate the error, resulting in Table 16, which presents the error analysis between theoretical and actual dosing flow rates.

[0119]

[0120] Based on the above data, under the same mine water quality conditions, the theoretically calculated average dosing flow rate for coagulants is 236.49653 L / h, and the average dosing flow rate for flocculants is 3.2329 m³ / h. However, the actual on-site dosing flow rates are 235.7818 L / h and 3.34726275 m³ / h, respectively. 3 / h, the error is within 10%, which is within the normal error range.

[0121] Based on field tests and theoretical verification, it is evident that the data model of this application's embodiments can guide the overall actual operation in the field. It is worth noting that: different mines require different constants. The values ​​are different, so each application to a particular mine will have a different value. All values ​​require the accumulation of extensive experimental data based on the embodiments of this application, and the corresponding values ​​for the mine are obtained according to the formula. The value is then used for program settings and application. However, the overall idea and calculation method are still based on the disclosure of the embodiments of this application.

[0122] Furthermore, the correlation formula between the two drugs was verified.

[0123] The charge density of coagulant A, polyaluminum chloride, is a = 30% × 50% × 1.7 = 0.255.

[0124] in,

[0125] 30% – the highest concentration of polyaluminum chloride

[0126] 50% – The proportion of aluminum ions in polyaluminum chloride

[0127] 1.7 – Aluminum ions are trivalent, carrying three positive charges, but only two of them react with charged colloidal particles in wastewater to form a coagulation reaction. One of these charges is occupied by OH- ions during hydrolysis. That is, each aluminum ion carries only 3 / 2 of a charge, or 1.7.

[0128] The charge density b of the flocculant B, polyacrylamide, is 0.25.

[0129] in,

[0130] Polyacrylamide is anionic, with 250 charged molecules per 1000 molecular segments (sodium acrylate ionizes to release its charge).

[0131] Combining the charge densities of the two agents mentioned above, and taking the average value of the daily dosage data of actual coagulants and flocculants in Table 13, calculate K according to equation ⑤. x As shown in Table 17.

[0132]

[0133] The resulting combination coefficient K x Using the average value as the core, and taking the actual water quality during the verification period as the basis for the experiment, the theoretical dosing flow rate of the flocculant was calculated based on the actual coagulant dosing flow rate and relationship ⑤. Table 18 shows the theoretical dosing data analysis of the sampling points during the 10-day verification period (K). x Law).

[0134]

[0135] The average theoretical dosing flow rate was compared with the average actual on-site dosing flow rate. Error calculations were performed, as shown in Table 19, Error Analysis of Theoretical and Actual Dosing Flow Rates for Flocculants (K). x Law).

[0136]

[0137] Using the same method, the theoretical dosing flow rate of the coagulant was calculated based on the actual flocculant dosing flow rate and formula ⑤. The average value of the theoretical dosing flow rate was then compared with the average value of the actual on-site dosing flow rate. Error calculations were performed, as shown in Table 20, Error Analysis of Theoretical and Actual Dosing Flow Rates of Coagulant (K). x Law).

[0138]

[0139] Based on the above data, the theoretical average dosing flow rate of the flocculant is 3.213793844 m³. 3 / h, actual on-site dosing flow rate: 3.34726275m³ 3 The theoretical and actual error is 4.15%; the theoretical average dosing flow rate of the coagulant is 245.5738216 L / h, and the actual dosing flow rate on site is 235.7818 L / h, with a theoretical and actual error of 3.99%. Both are less than 10% and are within the normal error range.

[0140] Based on field test results and theoretical verification, it can be seen that the data model of this application embodiment can guide the adjustment and control of the dosage relationship between coagulants and flocculants.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for coordinated control of multiple water treatment agents, characterized in that... Includes the following steps: Establish a database: When the set effluent water quality parameters of the wastewater to be treated are at the set threshold, the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the wastewater to be treated are sampled and measured for the first time, and the sampling and measurement data are recorded to form a database; wherein, the controlled agents include at least two types; Establish a matching data model: Analyze the sampling measurement data in the database to obtain a data model; the data model includes the correlation between the parameters of the controlled reagent and the set influent and effluent water quality parameters of the wastewater to be treated; Feedback and database update: When at least one of the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the wastewater to be treated changes, the linkage control changes of other parameters are calculated and confirmed according to the data model; after the set effluent water quality parameters of the wastewater to be treated return to the set threshold, a second sampling measurement is performed on the set influent water quality parameters, set effluent water quality parameters, and controlled agent parameters of the controlled agent, and the sampling measurement data is recorded to update the database; Correcting the data model: Analyze the measurement data in the updated database and correct the data model; Repeat the steps of updating the database and correcting the data model; The data model includes the correlation between the parameters of a controlled agent and the set influent and effluent water quality parameters of the wastewater to be treated, as well as the correlation between the parameters of different types of controlled agents. The set influent water quality parameters include influent flow rate, influent concentration, and influent pH value; The set effluent water quality parameters include effluent turbidity; The controlled reagent parameters include the dosing flow rate and the dosing concentration; The controlled agents include coagulants and flocculants; Based on the relationship that the charge of the coagulant to be added is equal to the charge of the wastewater to be treated, the correlation between the coagulant dosing flow rate and concentration and the influent flow rate, influent concentration, influent pH value and effluent turbidity of the wastewater to be treated is obtained. Based on the relationship that the total charge of the coagulant is equal to the total charge of the flocculant, the correlation between the coagulant's dosing flow rate and concentration and the flocculant's dosing flow rate and concentration is obtained; further, the correlation between the flocculant's dosing flow rate and concentration and the influent flow rate, influent concentration, influent pH value and effluent turbidity of the wastewater to be treated is obtained. The relationships between coagulant dosing flow rate and concentration and influent flow rate, influent concentration, influent pH, and effluent turbidity include: Q A C A =K Q (1+QC-QN) in, Q A C represents the dosing flow rate of the coagulant. A This refers to the concentration of the coagulant. , where a is the charge density of the coagulant. pH refers to the pH value of the influent, which ranges from 6 to 7.

5. Q is the influent flow rate, C is the influent concentration, and N is the effluent turbidity.

2. The water treatment multi-agent linkage control method according to claim 1, characterized in that, The correlation between the dosage flow rate and concentration of coagulants and the dosage flow rate and concentration of flocculants includes: aQ A C A =K X •bQ B C B in, a is the charge density of the coagulant, Q A C represents the dosing flow rate of the coagulant. A This refers to the concentration of the coagulant. b is the charge density of the flocculant, Q B C represents the flocculant dosing flow rate. B This refers to the concentration of the flocculant. K x It represents the binding coefficient between the coagulant and the flocculant.

3. The water treatment multi-agent linkage control method according to claim 2, characterized in that, The relationships between flocculant dosage flow rate and concentration and influent flow rate, influent concentration, influent pH, and effluent turbidity include: Q B C B =K n (1+QC-QN) in, Q B C represents the flocculant dosing flow rate. B This refers to the concentration of the flocculant. b is the charge density of the flocculant, K x This represents the binding coefficient between the coagulant and the flocculant. pH refers to the pH value of the influent, which ranges from 6 to 7.

5. Q is the influent flow rate, C is the influent concentration, and N is the effluent turbidity.

4. The water treatment multi-agent linkage control method according to any one of claims 1-3, characterized in that, The coagulant includes inorganic or organic coagulants, and the flocculant includes polyacrylamide-based organic polymeric flocculants.

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