Intelligent dosing system and method for water treatment

Abstract

The invention relates to the technical field of sewage treatment, in particular to an intelligent dosing system and method for water treatment.The intelligent dosing system comprises a workshop material feeding data acquisition module, a control module, a raw water collecting pool, a PH adjusting pool, a flocculation reaction pool, a water outlet and a dosing module, and a first online conductivity meter is arranged in the flocculation reaction pool; the output end of the workshop material putting data acquisition module and the output end of the first online conductivity meter are electrically connected with the input end of the control module, the control module comprises an intelligent learning module, and the control module adjusts the medicine adding amount of the medicine adding module in real time according to the medicine adding amount obtained through optimization of the intelligent learning module. The water body conductivity change is monitored in real time, a feedback control system is constructed in combination with data information acquired by the workshop material putting data acquisition module, the chemical adding strategy can be optimized based on the nonlinear relation between the historical chemical adding amount and the conductivity change, the chemical adding amount can be dynamically adjusted, chemical waste is reduced, and the operation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sewage treatment, in particular to a water treatment intelligent dosing system and method. BACKGROUND

[0002] In the dosing equipment, the quality and quantity of raw water fluctuate greatly, and conductivity plays a key role in water quality monitoring. As an indicator of the ion conductivity in water, conductivity can indirectly reflect the water quality. The equipment measures the conductivity directly by immersing the electrode into the water through the built-in conductivity meter, and transmits the data to the reader to convert it into standard units. This process helps the operator to understand the changes in water quality in a timely manner, especially the increase and decrease of ion concentration. Since the increase in conductivity often indicates changes in water quality, such as an increase in pollutants such as salts and minerals, or an excessive use of water treatment chemicals leading to an increase in ion concentration, therefore, by detecting conductivity online, water quality problems can be found and solved in a timely manner.

[0003] However, in the dosing system, the change of conductivity is not a simple linear relationship with the amount of dosing. Some chemicals added to sewage first increase the conductivity and then decrease it, while some chemicals first decrease the conductivity and then increase it, and the relationship curve between the increase and the decrease is not a simple straight line relationship, so it is difficult for the staff to adjust. SUMMARY

[0004] The present application aims to at least solve the technical problems existing in the prior art. To this end, the present application proposes a water treatment intelligent dosing system and method, which can control the dosing amount in a timely and simple manner, meet the water treatment requirements, and have the effects of pollution reduction, carbon reduction and economic effect.

[0005] According to the water treatment intelligent dosing system according to some embodiments of the first aspect of the present application, the system comprises a workshop material feeding data acquisition module, a control module, a raw water collection pool, a pH adjustment pool, a flocculation reaction pool, a water outlet and a dosing module. The workshop material feeding data acquisition module is used to acquire the feeding data of the workshop materials. The raw water collection pool, the pH adjustment pool, the flocculation reaction pool and the water outlet are sequentially connected. The output end of the dosing module is connected with the flocculation reaction pool. A first online conductivity meter is arranged in the flocculation reaction pool. The output end of the workshop material feeding data acquisition module and the output end of the first online conductivity meter are both electrically connected with the input end of the control module. The output end of the control module is electrically connected with the dosing module. The control module comprises an intelligent learning module. The intelligent learning module optimizes the dosing amount according to the dosing amount of the dosing module and the real-time conductivity value acquired by the first online conductivity meter. The control module adjusts the dosing amount of the dosing module in real time according to the dosing amount optimized by the intelligent learning module.

[0006] According to some embodiments of the first aspect of the present application, the water treatment intelligent dosing system has at least the following beneficial effects: The present application monitors the change of water conductivity in real time by setting a first online conductivity meter in the flocculation reaction tank, and feeds back the conductivity data to the control module, combined with the data collected by the workshop material feeding data collection module, to build a feedback control system. The control module has an intelligent learning module built-in, which can continuously optimize the dosing strategy based on the nonlinear relationship between historical dosing amount and conductivity change, and realize dynamic adjustment of the dosing amount. Compared with traditional manual experience control or fixed parameter control method, the present system can more accurately match the change of water quality, reduce the waste of reagent, and reduce the operation cost. At the same time, through the continuous optimization of the intelligent learning module, the present application improves the adaptability of the system to complex water quality fluctuations, and improves the overall efficiency and stability of the sewage treatment.

[0007] It can be understood that the method of adjusting the dosing amount is to control the opening size of the dosing pump in the dosing module through the data of the first online conductivity meter. When the data measured by the first online conductivity meter is higher than the optimal value, the control system controls the dosing pump to reduce the dosing flow; when the data measured by the first online conductivity meter is lower than the optimal value, the control system controls the dosing pump to increase the dosing flow.

[0008] According to some embodiments of the first aspect of the present application, the water treatment intelligent dosing system comprises a sedimentation tank and a filter tank, and the flocculation reaction tank, the sedimentation tank, the filter tank and the water outlet are sequentially connected.

[0009] According to some embodiments of the first aspect of the present application, the water treatment intelligent dosing system is provided with a backwash liquid reflux channel between the filter tank and the PH adjustment tank.

[0010] According to some embodiments of the first aspect of the present application, the control module calculates a predicted conductivity value according to the data collected by the workshop material feeding data collection module, and the predicted conductivity value is set as ; the water to be treated is grouped for test according to different gradients of conductivity, and the optimal conductivity value after adding the optimal amount of flocculation reaction is obtained, and the optimal conductivity value is set as ; the conductivity in the reaction tank when the dosing module is normally dosed is set as , and the dosing range of the dosing module satisfies the following formula: .

[0011] According to some embodiments of the first aspect of the present application, the calculation method of the predicted conductivity value is as follows: the residual amount of the first hour of material feeding in the workshop is set as , and there is Wherein and are the amount of material used in the first hour and the amount of material in the product in the first hour, respectively; The residence time of sewage in the raw water collection tank or the PH adjustment tank is The concentration of the first hour PH adjustment tank is The concentration of the second hour is The drainage volume of the third hour is The intelligent dosing system processing volume per hour is Then: ; ; The interval time The concentration of the raw water collection tank or the PH adjustment tank is: ; The interval time The concentration of the raw water collection tank or the PH adjustment tank is: The preliminary determination of the required amount of the reagent is the lower limit of the minimum dosing amount, and the conductivity when the minimum dosing amount is added is the predicted conductivity value

[0012] According to the water treatment intelligent dosing system of some embodiments of the first aspect of the application, the backwash reflux ratio of the backwash liquid reflux channel is 5%-25%.

[0013] According to the water treatment intelligent dosing system of some embodiments of the first aspect of the application, a water quality parameter evaluation monitor is arranged in the water outlet, and the output end of the water quality parameter evaluation monitor is connected with the input end of the control module.

[0014] According to the water treatment intelligent dosing system of some embodiments of the first aspect of the application, a second online conductivity meter is arranged in the PH adjustment tank, and the output end of the second online conductivity meter is connected with the input end of the control module.

[0015] According to the water treatment intelligent dosing system of some embodiments of the first aspect of the application, a third online conductivity meter and a fourth online conductivity meter are arranged in the flocculation reaction tank and the PH adjustment tank, respectively, and the output end of the third online conductivity meter and the output end of the fourth online conductivity meter are both connected with the input end of the control module.

[0016] According to the water treatment intelligent dosing method of some embodiments of the second aspect of the application, the method is applied to the water treatment intelligent dosing system of some embodiments of the first aspect, and the method comprises: ​The water to be treated is sampled according to different conductivity gradients, and the optimal conductivity value after the optimal flocculation reaction of the added optimal amount of the medicament is obtained, and the upper limit of the amount of the medicament, i.e., the highest amount of the medicament, is preliminarily determined according to the conductivity; The amount of the material collected on the same day is subtracted from the amount of the product, the concentration of the main pollution factor of the material to the PH adjustment tank or the raw water collection tank is predicted, the predicted conductivity value is obtained according to the predicted concentration, and the lower limit of the amount of the medicament, i.e., the lowest amount of the medicament, is preliminarily determined according to the predicted conductivity value; After the workshop material is put in, the workshop material put-in data acquisition module collects the put-in data, and the sewage generated by the put-in flows into the raw water collection tank; The water in the raw water collection tank flows to the PH adjustment tank, and the PH adjustment tank controls the PH of the sewage within a set range; The control module adjusts the amount of the medicament of the medicament adding module in real time according to the amount of the medicament optimized by the intelligent learning module, and the amount of the medicament of the medicament adding module is between the lowest amount of the medicament and the highest amount of the medicament.

[0017] According to the water treatment intelligent medicament adding method according to some embodiments of the second aspect of the present application, at least the following beneficial effects are achieved: The water treatment intelligent medicament adding method provided by the present application establishes the corresponding relationship between the conductivity and the optimal amount of the medicament through the preliminary test, continuously optimizes the medicament adding strategy in combination with the actual operation data, and realizes the scientization and the intelligentization of the medicament adding control. The method firstly performs water sample test based on the conductivity gradient, determines the upper limit amount of the medicament required to achieve the optimal flocculation effect under different water quality conditions, and thus avoids resource waste and secondary pollution caused by excessive addition; meanwhile, through material balance calculation, the concentration of the main pollution factor after entering the system and the corresponding conductivity change are predicted, and then the lower limit of the minimum amount of the medicament is determined, so that the medicament addition can meet the treatment demand and is not excessively used.

[0018] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter in the description. BRIEF DESCRIPTION OF DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the description of the embodiments, given in connection with the following drawings, in which: Figure 1 The principle block diagram of the embodiments of the present application. DETAILED DESCRIPTION

[0020] Embodiments of the present application are described below in detail with reference to the accompanying drawings, wherein the same or similar components are denoted by the same or similar reference numerals throughout the drawings. The embodiments described below are exemplary only, and are intended to explain the present application, but are not to be understood as limiting the present application.

[0021] In the description of the present application, it should be understood that, in relation to orientation description, for example, the orientation or position relationship indicated by up, down, left, right, front, back, etc. is based on the orientation or position relationship shown in the drawings, and is only for the purpose of facilitating the description of the present application and simplifying the description, and does not indicate or imply that the modules or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application.

[0022] In the description of the present application, if there is a description of first, second, etc. for the purpose of distinguishing technical features, it cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the sequence of technical features indicated.

[0023] In the description of the present application, unless otherwise explicitly limited, the words such as arrangement, installation, connection, etc. should be broadly understood, and the person skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution.

[0024] As shown in FIG. 1, the first aspect of the present application provides a water treatment intelligent dosing system. Figure 1

[0025] A water treatment intelligent dosing system, comprising a workshop material feeding data acquisition module, a control module, a raw water collection pool, a pH adjustment pool, a flocculation reaction pool, a water outlet and a dosing module, the workshop material feeding data acquisition module is used for acquiring the feeding data of the workshop material, the raw water collection pool, the pH adjustment pool, the flocculation reaction pool and the water outlet are sequentially communicated, the output end of the dosing module is communicated with the flocculation reaction pool, a first online conductivity instrument is arranged in the flocculation reaction pool, the output end of the workshop material feeding data acquisition module and the output end of the first online conductivity instrument are both electrically connected with the input end of the control module, the output end of the control module is electrically connected with the dosing module, the control module comprises an intelligent learning module, the intelligent learning module optimizes the dosing amount according to the dosing amount of the dosing module and the real-time conductivity value acquired by the first online conductivity instrument, and the control module adjusts the dosing amount of the dosing module in real time according to the dosing amount optimized by the intelligent learning module.

[0026] ​The application monitors the change of water conductivity in real time by arranging a first online conductivity meter in the flocculation reaction tank, and feeds back the conductivity data to the control module, and combines the data collected by the workshop material feeding data collection module to build a feedback control system. The intelligent learning module built in the control module can continuously optimize the dosing strategy based on the nonlinear relationship between the historical dosing amount and the conductivity change, and realize the dynamic adjustment of the dosing amount. Compared with the traditional manual experience control or fixed parameter control mode, the system can more accurately match the water quality change, reduce the waste of reagent, reduce the operation cost, and at the same time, the continuous optimization of the intelligent learning module improves the adaptability of the system to complex water quality fluctuation, and improves the overall efficiency and stability of the sewage treatment.

[0027] The water treatment intelligent dosing system described in the present example comprises a sedimentation tank and a filter tank, and the flocculation reaction tank, the sedimentation tank, the filter tank and the water outlet are sequentially communicated.

[0028] The water treatment intelligent dosing system described in the present example is provided with a backwash liquid reflux channel between the filter tank and the PH adjusting tank. Specifically, in the sedimentation tank, part of the excess reagent micro-particles and flocculation gel cannot be completely precipitated and overflow to be intercepted by the subsequent filter tank. Due to the automatic adjustment of the dosing amount, there is inevitably a phenomenon of insufficient amount of reagent, and when the wastewater flows through the filter tank, it performs secondary flocculation reaction with the micro-particle reagent flocculation body intercepted in the filter tank to form a micro-flocculation filtration effect, which avoids overflow of excess reagent and further evolves the wastewater. At the same time, when the filtered water of the filter tank rises to the set value, the backwash pump is automatically started to suck the subsequent clean water to backwash the filter tank, and the backwash water is refluxed to the front end of the PH adjusting tank to be mixed with the raw water. The excess reagent in the backwash water can continue to participate in the subsequent flocculation reaction.

[0029] In some embodiments, the filter tank adopts an active sand filter tank.

[0030] The water treatment intelligent dosing system described in the present example calculates the predicted conductivity value according to the data collected by the workshop material feeding data collection module. The water treatment intelligent dosing system described in the present example carries out grouping test on the water sample to be treated according to different gradients of conductivity, obtains the optimal conductivity value after adding the optimal amount of reagent for flocculation reaction, and sets the optimal conductivity value as The water treatment intelligent dosing system described in the present example sets the conductivity in the reaction tank when the dosing module is normally dosing as The dosing amount range of the dosing module satisfies the following formula: .

[0031] The water treatment intelligent dosing system described in the present example sets the predicted conductivity value as The calculation method is as follows: Let the residual amount of material in the first hour after the material is added to the workshop be... Then there is ,in and These are the material usage in the first hour and the material usage in the product in the first hour, respectively. Assume the retention time of wastewater in the raw water collection tank or pH adjustment tank is . The concentration in the pH adjustment tank in the first hour was... , No. hourly concentration , No. Hourly drainage volume is The intelligent dosing system has a processing capacity of [number] per hour. Then we have: ; Interval time The concentration in the subsequent raw water collection tank or pH adjustment tank is: ; By interval time The concentration in the subsequent raw water collection tank or pH adjustment tank is initially determined to determine the lower limit of the required dosage of the chemical, i.e., the minimum dosage. The conductivity at which the minimum dosage is added is the predicted conductivity value. .

[0032] Understandably, the optimal conductivity value can be obtained through a limited number of experiments.

[0033] The water treatment intelligent dosing system described in this example has a backflushing return ratio of 5%-25% in the backflushing liquid return channel.

[0034] This example describes an intelligent water treatment dosing system. A water quality parameter evaluation and monitoring instrument is installed at the outlet, and its output is connected to the input of the control module. Specifically, the water quality parameter evaluation and monitoring instrument can monitor components such as CODcr, NH4-N, total nitrogen, total phosphorus, turbidity, and suspended solids. The system uses data from the water quality parameter evaluation and monitoring instrument to determine the amount of previously added reagent. This data is then input into an intelligent learning module with learning capabilities. The system continuously learns and optimizes based on the evaluation results to determine the optimal dosage.

[0035] It is understandable that online water quality parameter evaluation and monitoring instruments have time differences in their results, so the evaluation of the dosage should be based on the effect of the dosage after deducting the time difference. For example, if there is a 1-hour time difference between the automatic water sampling and the output of the CODcr online monitoring instrument, and a subsequent 1-hour retention time in the sedimentation tank, then the evaluation result is based on the effect of the dosage applied 2 hours ago.

[0036] The time delay compensation calculation formula is: tcompensation = tsampling + ttest + tprecipitation × (1 + Δc / creference) ; Wherein, Δc is the conductivity deviation, and crefference is the design value.

[0037] The second online conductivity instrument is arranged in the PH adjusting tank, and an output end of the second online conductivity instrument is connected with an input end of the control module. Specifically, in the embodiment, the second online conductivity instrument is arranged in the PH adjusting tank, that is, the second online conductivity instrument is arranged at the water inlet of the flocculation reaction tank. Through multiple tests, the water samples to be treated are tested in groups according to different conductivity gradients, and the conductivity value after the flocculation reaction of the optimal amount of the added reagent is obtained, so as to guide the reagent amount according to different conductivity values.

[0038] The third online conductivity instrument and the fourth online conductivity instrument are arranged in the flocculation reaction tank and the PH adjusting tank respectively, and output ends of the third online conductivity instrument and the fourth online conductivity instrument are connected with the input end of the control module. Specifically, the third online conductivity instrument and the fourth online conductivity instrument are used as backup instruments of the first online conductivity instrument and the second online conductivity instrument respectively. Under normal circumstances, the conductivity data and the viscosity data should be continuous functions with the passage of time, that is, there will be no large proportion of mutation. According to this characteristic, when the display data of the first online conductivity instrument and the third online conductivity instrument, or the display data of the second online conductivity instrument and the fourth online conductivity instrument, mutates and reaches a set proportion value within a set unit of time, it is determined that the instrument is abnormally failed, the system alarm is started to remind maintenance, and the probe or the instrument is replaced, and at the same time, the control system automatically discards the reading of the table and enables another value to maintain the stability of the system.

[0039] It can be understood that the first online conductivity instrument and the third online conductivity instrument are arranged at the outlet of the flocculation reaction tank, and the second online conductivity instrument and the fourth online conductivity instrument are arranged at the outlet of the PH adjusting tank or in the communication pipe between the PH adjusting tank and the flocculation reaction tank.

[0040] The second aspect embodiment of the present application provides a water treatment intelligent reagent adding method, which applies the water treatment intelligent reagent adding system of the first aspect embodiment, and the method comprises the following steps. The water samples to be treated are tested in groups according to different conductivity gradients, the optimal conductivity value after the flocculation reaction of the optimal amount of the added reagent is obtained, and the upper limit of the reagent amount, that is, the highest reagent amount, is preliminarily determined according to the conductivity; The amount of the material collected on the day and at the time is subtracted from the amount used in the product, the concentration of the main pollution factor of the material to the PH adjusting tank or the raw water collecting tank is predicted, the predicted conductivity value is obtained according to the predicted concentration, and the lower limit of the amount of the medicament needed to be added, that is, the minimum amount of medicament, is preliminarily determined according to the predicted conductivity value; After the workshop material is put in, the workshop material putting data acquisition module collects the putting data, and the sewage generated by putting flows into the raw water collecting tank; The raw water collecting tank outlet water flows into the PH adjusting tank, and the PH adjusting tank controls the PH of the sewage within a set range; The control module adjusts the medicament amount of the medicament adding module in real time according to the medicament amount optimized by the intelligent learning module, and the medicament amount of the medicament adding module is between the minimum medicament amount and the maximum medicament amount.

[0041] The water treatment intelligent medicament adding method provided by the application establishes the corresponding relationship between the conductivity and the optimal medicament amount through early-stage experiments, continuously optimizes the medicament adding strategy in combination with actual operation data, and realizes the scientization and the intelligentization of medicament adding control. The method firstly performs water sample testing based on the conductivity gradient, determines the upper limit amount of the medicament needed to realize the optimal flocculation effect under different water quality conditions, and thus avoids resource waste and secondary pollution caused by excessive adding; meanwhile, through material balance calculation, the concentration of the main pollution factor after entering the system and the corresponding conductivity change are predicted, and then the lower limit of the minimum medicament amount is determined, so that the medicament adding can meet the processing demand and is not excessively used.

[0042] The application breaks through the information barrier between the residual amount of the material affecting the sewage quality in the workshop and the medicament adding amount of the sewage station, provides a method and system capable of guiding clean production and intelligently controlling the medicament amount in a timely and simple manner, and performs reflux and recycling of the excess medicament to reduce the medicament amount and the sludge amount, reduce the influence on subsequent units, ensure obvious resource recycling, pollution reduction, carbon reduction and economic effects.

[0043] In one of the embodiments, the application is applied to one lithium battery production wastewater treatment system, the daily wastewater treatment amount of the system is about 100-210 tons / day, the raw water quality changes greatly, the main influencing factor is the large fluctuation of the residual amount of the electrolyte (discharged to the production drainage system after cleaning) in the workshop, the residual amount changes greatly during the electrolyte cleaning process, the COD of the sewage changes greatly, and the inorganic medicament polyaluminum chloride is added: because the conductivity in the water is constantly changing, the daily amount of the electrolyte is collected by the material amount acquisition system of the main pollution factor affected by the workshop, and the amount used in the product (that is, the daily product production amount multiplied by the unit product amount) is subtracted, and the concentration of the main pollution factor to the adjusting tank or the collecting tank is predicted. When the conductivity of the raw water sample was taken at values ​​near 800, 900, 1000, 1100, 1200, 1400, 1600, 1800, and 2000 µS / cm, the optimal dosage of the chemical was determined to be 220, 230, 245, 260, 267, 280, 291, 322, and 341 ppm. The corresponding conductivity values ​​were 960, 1122, 1285, 1369, 1497, 1783, 1962, 2376, and 2685 µS / cm, respectively. This data was then input into the control system, and the conductivity of the influent and effluent was used to perform curve fitting to control the dosage. Intermediate values ​​were naturally obtained using interpolation. An online CODcr water quality parameter monitoring instrument was installed at the effluent outlet, with an internal control value not exceeding 4000 mg / L.

[0044] The data before the implementation of workshop-wide clean production and intelligent dosing were as follows: After the present invention is implemented in the system: The data above shows that the more residual electrolyte is discharged into the wastewater treatment plant, the higher the amount of chemicals used, and the higher the amount of physicochemical sludge produced. This translates to higher costs per ton of water treatment, inorganic chemicals per unit of product, and sludge disposal. Raw materials are significantly saved, resulting in savings not only on wastewater treatment costs but also on raw material costs, leading to substantial economic benefits.

[0045] After adding the inorganic agent polyaluminum chloride to the above wastewater, the optimal amount of coagulant polyacrylamide was tested, and the corresponding viscosity values ​​were obtained as follows: 105, 109, 115, 121, 126, 132, 139, 148, and 153 cP. These values ​​were then input into the control system, and the dosage was controlled by curve fitting between the conductivity of the influent and the viscosity of the water after the reaction. The intermediate values ​​were naturally obtained by interpolation.

[0046] The amount of chemicals added earlier is judged by the data obtained from the online CODcr water quality parameter evaluation and monitoring instrument installed at the outlet of the subsequent sedimentation tank. This data is then connected to a control system with learning capabilities, which continuously optimizes and learns the optimal dosage based on the evaluation results.

[0047] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A smart dosing system for water treatment, characterized in that: The system includes a workshop material delivery data acquisition module, a control module, a raw water collection tank, a pH adjustment tank, a flocculation reaction tank, an outlet, and a dosing module. The workshop material delivery data acquisition module is used to collect the delivery data of workshop materials. The raw water collection tank, the pH adjustment tank, the flocculation reaction tank, and the outlet are connected in sequence. The output of the dosing module is connected to the flocculation reaction tank. A first online conductivity meter is installed in the flocculation reaction tank. The output of the workshop material delivery data acquisition module and the output of the first online conductivity meter are both electrically connected to the input of the control module. The output of the control module is electrically connected to the dosing module. The control module includes an intelligent learning module. The intelligent learning module optimizes the dosing dosage based on the dosing dosage of the dosing module and the real-time conductivity value collected by the first online conductivity meter. The control module adjusts the dosing dosage of the dosing module in real time based on the optimized dosing dosage obtained by the intelligent learning module.

2. The intelligent water treatment dosing system according to claim 1, characterized in that: It includes a sedimentation tank and a filtration tank, and the flocculation reaction tank, the sedimentation tank, the filtration tank, and the outlet are connected in sequence.

3. The intelligent water treatment dosing system according to claim 2, characterized in that: A backflushing liquid return channel is provided between the filtration tank and the pH adjustment tank.

4. The intelligent water treatment dosing system according to claim 3, characterized in that: The control module calculates the predicted conductivity value based on the data collected by the workshop material delivery data acquisition module. Let the predicted conductivity value be... Water samples were taken from the water to be treated and grouped according to different conductivity gradients to obtain the optimal conductivity value after the flocculation reaction with the optimal dosage of the reagent. Let the optimal conductivity value be denoted as . ; Assume the conductivity of the reaction tank during normal dosing is... The dosage range of the dosing module then satisfies the following formula: 。 5. The intelligent water treatment dosing system according to claim 4, characterized in that: The predicted conductivity value The calculation method is as follows: Let the residual amount of material in the first hour after the material is added to the workshop be... Then there is ,in, and These are the material usage in the first hour and the material usage in the product in the first hour, respectively. Assume the retention time of wastewater in the raw water collection tank or pH adjustment tank is . The concentration in the pH adjustment tank in the first hour was... , No. hourly concentration , No. Hourly drainage volume is The intelligent dosing system has a processing capacity of [number] per hour. Then we have: ； Interval time The concentration in the subsequent raw water collection tank or pH adjustment tank is: ； By interval time The concentration in the subsequent raw water collection tank or pH adjustment tank is initially determined to determine the lower limit of the required dosage of the chemical, i.e., the minimum dosage. The conductivity at which the minimum dosage is added is the predicted conductivity value. .

6. The intelligent water treatment dosing system according to claim 3, characterized in that: The backflush return ratio of the backflush fluid return channel is 5%-25%.

7. The intelligent water treatment dosing system according to claim 1, characterized in that: A water quality parameter evaluation and monitoring instrument is installed inside the water outlet, and the output end of the water quality parameter evaluation and monitoring instrument is connected to the input end of the control module.

8. The intelligent water treatment dosing system according to claim 1, characterized in that: The pH adjustment tank is equipped with a second online conductivity meter, and the output of the second online conductivity meter is connected to the input of the control module.

9. The intelligent water treatment dosing system according to claim 7, characterized in that: A third online conductivity meter and a fourth online conductivity meter are respectively installed in the flocculation reaction tank and the pH adjustment tank. The output terminals of the third online conductivity meter and the fourth online conductivity meter are both connected to the input terminal of the control module.

10. A smart dosing method for water treatment, characterized in that: The method of using the intelligent water treatment dosing system according to any one of claims 3-9 includes: Water samples were taken from the water to be treated and grouped according to different conductivity gradients to obtain the optimal conductivity value after the flocculation reaction with the optimal dosage of the drug. The upper limit of the dosage, i.e. the maximum dosage, was initially determined based on the conductivity. Subtract the amount of material used in the product from the amount of material used on the day of collection to predict the concentration of the main pollutants of the material in the pH adjustment tank or raw water collection tank. Obtain the predicted conductivity value based on the predicted concentration. Preliminarily determine the lower limit of the dosage of the drug to be added based on the predicted conductivity value, i.e. the minimum dosage of the drug. After the materials are put into the workshop, the workshop material putting data acquisition module collects the putting data, and the wastewater generated by the putting flows into the raw water collection tank; The raw water collection tank discharges into the pH adjustment tank, which controls the pH of the wastewater within a set range. The control module adjusts the dosage of the dosing module in real time based on the dosage optimized by the intelligent learning module, and the dosage of the dosing module is between the minimum dosage and the maximum dosage.

Images