CN113683169A - Intelligent coagulation chemical dosing method and device for water treatment plant - Google Patents
Intelligent coagulation chemical dosing method and device for water treatment plant Download PDFInfo
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Abstract
The invention discloses an intelligent coagulation dosing method and a device for a water treatment plant, which belong to the technical field of coagulation in water purification production and aim to solve the problems of poor anti-interference capability on various water quality parameters, high requirements on water quality and process conditions, troublesome adjustment and maintenance and the like. Meanwhile, the optimized dosing method of the scheme is applied, so that the water quality guarantee rate is improved, the labor is saved, and the coagulant cost is reduced.
Description
Technical Field
The invention belongs to the technical field of water purification production coagulation, and particularly relates to an intelligent coagulation dosing method and device for a water purification plant.
Background
Coagulation is a core link in a tap water production process, directly influences the quality of effluent water, and the most important factor influencing the coagulation effect is the addition amount of a coagulant, most of the currently used coagulants are aluminum salts, research shows that the high concentration of aluminum ions in water can influence the health of people and can generate adverse effects on water quality and a water delivery system, from the viewpoint, the excessive addition of the coagulant is prevented, on the other hand, the coagulant dosage cost of purified water is the second major factor of water production cost after electricity charge, the addition amount of the coagulant directly influences the water production cost to the water price, on the premise of ensuring the treatment effect, the consumption of the coagulant is saved, the important measure is the water purification cost, the economic significance is very important, therefore, the coagulation dosage is the most important link for water purification, the efficiency of the coagulant can be fully utilized by accurately controlling the dosage, ensures the quality of the effluent and has the same significance for controlling the production cost.
Coagulation is a complex physical and chemical process, a plurality of influencing factors exist, accurate control of dosing is very complex and difficult, the technical problem to be solved at home and abroad is always solved, a complete theoretical calculation mode does not exist at present, the dosage of coagulation can only be determined according to experience or experiments, meanwhile, as the coagulation dosing process has the characteristics of nonlinearity, large time lag, time variation and uncertainty, the coagulation dosing control also constitutes a difficulty of automatic control of the process of a water plant and is a key link for improving the modernization level of the water plant, at present, a coagulation dosing automatic control system of a domestic water plant generally adopts two modes of single-factor closed-loop control or feed-forward control, the feed-forward control mainly depends on a mathematical model between a raw water parameter and a dosing amount, and as coagulation is a complex physical and chemical reaction process and has the characteristics of long time lag and large inertia, the control mode is difficult to quickly respond to the change of effluent turbidity, moreover, due to the nonlinearity of the coagulation process, the mismatch of the traditional linear model is serious, the control error is large, the single-factor closed-loop control is based on the indirect reflection of instruments such as a flow current instrument and the like on the effluent turbidity, and the change of the effluent turbidity can be quickly reflected, but the instruments have cross factors on various water quality parameters, the interference resistance is poor, the requirements on the water quality and the process conditions are high, the adjustment is troublesome, the maintenance is difficult, and the popularization and the application of the technology are limited.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an intelligent coagulation dosing method and device for a water treatment plant, which solve the problems of cross factors existing in various water quality parameters, poor anti-interference capability, high requirements on water quality and process conditions, troublesome adjustment and maintenance, and limitation on popularization and application of the technology.
In order to achieve the purpose, the invention provides the following technical scheme: an intelligent coagulation dosing method for a water treatment plant, which comprises the following steps:
s1, real-time data acquisition: wherein flow, PH, temperature, the turbidity of intaking and play water turbidity data to water are gathered in real time respectively through flowmeter, PH meter, thermometer, the turbidimeter of intaking and play water turbidity meter, and the data acquisition cycle is 1s, the cycle of data transmission to control terminal PLC is 5 min.
S2, historical data acquisition: and predicting water inflow according to historical monitoring data, extracting the historical monitoring data, generating a random forest integration algorithm training model according to a user-defined historical data range and the lag time of the dosing precipitation effect, and finally obtaining various parameters meeting the requirements.
And S3, integrating the real-time data acquisition information and the historical data acquisition information to establish an intelligent dosing model.
And S4, predicting the effluent turbidity according to the establishment of the intelligent dosing model in the S3, calculating the optimal dosing amount, and lowering the model to monitor the effluent turbidity.
And S5, comparing the water outlet turbidity data predicted in the S4 with the monitored water outlet turbidity data, if the data difference is small and the water outlet turbidity reaches the standard, enabling an intelligent dosing mode of the system to operate, if the data difference is large or the water outlet turbidity does not reach the standard, adjusting the water outlet turbidity in a manual dosing mode, defining a water outlet turbidity range by a user, and increasing and decreasing the dosing amount iteratively.
And S6, inputting the data of the manual dosage in the S5 into an intelligent dosage model for model training, determining whether the dosage meets a final index through a numerical value calculated by the model, finally determining the optimal dosage if the dosage meets the index, performing regression verification again according to the intelligent dosage model, and comparing the predicted water turbidity data with the monitored water turbidity data to finish the operation of the dosage mode.
As a further scheme of the invention: the establishment of the intelligent dosing model comprises the following steps:
a. according to the PAC adding strategy of the existing water plant, the process is adjusted and optimized by a strategy that the time interval is 1 day and the step length is decreased by 5 percent, the turbidity of the settled water is temporarily based on the lag time of 3 and 5.5 hours, and when the turbidity of the settled water reaches a protection threshold value, the original manual experience strategy is switched to.
b. Data preprocessing: making up for missing data; removing and compensating abnormal values; the frequency of data acquisition of different devices is uniform.
c. And (3) data standardization treatment: and (c) converting the factor values with great difference of numerical value magnitude in the step (b) into a [0,1] interval.
d. Carrying out data splitting, splitting historical data into a training set and a test set, and controlling the split numerical quantity ratio to be 8: 2 or 7: 3.
e. constructing a model: and constructing a model based on training set data by utilizing a neural network, an SVM and a random forest integrated regression algorithm.
f. And (3) testing a model: and budgeting and verifying by using the random forest integration regression algorithm through the test set data.
g. Predicted according to PAC dosing.
h. And g, issuing a model, taking the turbidity, the flow, the pH and the temperature of the water entering the plant as independent variables and the turbidity of the settled water as dependent variables based on the data generated in the step g, fitting to obtain a model function, and issuing.
As a further scheme of the invention: the predicting according to the PAC dosing amount comprises:
firstly, according to the turbidity, flow, pH and temperature of the water entering the plant monitored in real time, and historical data, the corresponding PAC adding amount is found out through the Manhattan distance.
And secondly, according to the corresponding PAC adding amount, the PAC adding amount is decreased according to a certain step length, the decreasing value is 0.05 and is used as a parameter, the parameter and the turbidity, the flow, the pH value and the temperature of the water entering the plant are used as input values, the model constructed in the step e is utilized to obtain the predicted turbidity of the settled water, and the minimum value of the PAC adding amount is obtained under the condition that the turbidity of the settled water meets the condition.
As a further scheme of the invention: the flow meter has the speed measuring range of 0.1-20 m/s, the speed measuring precision of +/-0.01 m/s, the distance measuring range of 30m or 70m, the distance measuring precision of +/-2 mm, the instantaneous flow precision of 5%, the pH meter measuring range of 0-14 and the measuring precision of +/-0.1.
As a further scheme of the invention: the water plant carries out dosing through a metering pump, wherein the metering pump Q is 400L/h, the backpressure is 16bar, and the metering repetition precision is +/-1% within the range of 10-100% of stroke length.
As a further scheme of the invention: in the establishment of the intelligent dosing model a, the protection threshold of the turbidity of the settled water is 2NTU, and the PAC dosing frequency is 5 min.
As a further scheme of the invention: in the building e of the intelligent dosing model, the turbidity, the flow, the pH, the temperature and the PAC dosage of the water entering the factory are used as independent variables, and the turbidity of the settled water is used as a dependent variable.
An intelligent coagulation medication dosing device for a water treatment plant, the device comprising:
and the acquisition unit is used for acquiring the data of the flow, the PH, the temperature, the inlet water turbidity and the outlet water turbidity of the water in real time.
And the modeling unit is used for obtaining influence factors influencing the coagulant adding amount and the coagulation effect according to a water treatment theory and analysis of a chemical reaction process after coagulant addition, wherein the factors comprise turbidity, pH value, temperature and flow, judging whether the precipitated water meets the standard or not, finally obtaining the corresponding relation between the turbidity, the flow, the pH value and the temperature of the inlet water and the PAC adding amount on the premise of meeting the turbidity of the precipitated water through a corresponding algorithm, and displaying a function formula output by the model on an interface, controlling whether the model is started or stopped or not, setting issuing frequency and issuing an instruction to a terminal node in real time by the modeling unit.
And the prediction unit is used for predicting the water outlet turbidity, calculating the optimal dosage and putting a model to monitor the water outlet turbidity, defining the water outlet turbidity range by a user, and determining whether the dosage meets the final index or not according to the value calculated by the prediction unit.
And the intelligent dosing unit is used for comparing the effluent turbidity data with the monitored effluent turbidity data, enabling the intelligent dosing mode of the system to operate if the data difference is small and the effluent turbidity reaches the standard, adjusting the effluent turbidity in a manual dosing mode if the data difference is large or the effluent turbidity does not reach the standard, and recalculating according to the adjusted PAC dosing amount to achieve the purpose of intelligent dosing.
Compared with the prior art, the invention has the beneficial effects that:
the intelligent coagulation dosing system for the water treatment plant is based on the collection of large running data of the water treatment plant, carries out nonlinear modeling and nonlinear prediction on the coagulation dosing process, reduces the influence of system time lag by using prediction control, eliminates the influence of raw water quality disturbance and model mismatch on a control system, and improves the dosing control precision and the robustness of the system so as to achieve the aim of intelligent dosing.
Drawings
FIG. 1 is a schematic block diagram of the system of the present invention;
FIG. 2 is a schematic diagram of an intelligent dosing model according to the present invention;
fig. 3 is a schematic block diagram of an intelligent coagulation administration device according to the present invention.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
As shown in fig. 1-3, the present invention provides a technical solution: an intelligent coagulation dosing method for a water treatment plant comprises the following steps:
s1, real-time data acquisition: wherein flow, PH, temperature, the turbidity of intaking and the turbidity data of play water are gathered in real time respectively through flowmeter, PH meter, thermometer, the turbidimeter of intaking and play water turbidimeter, and the data acquisition cycle is 1s, and the cycle of data transmission to control terminal PLC is 5 min.
S2, historical data acquisition: and predicting water inflow according to historical monitoring data, extracting the historical monitoring data, generating a random forest integration algorithm training model according to a user-defined historical data range and the lag time of the dosing precipitation effect, and finally obtaining various parameters meeting the requirements.
And S3, integrating the real-time data acquisition information and the historical data acquisition information to establish an intelligent dosing model.
And S4, predicting the effluent turbidity according to the establishment of the intelligent dosing model in the S3, calculating the optimal dosing amount, and lowering the model to monitor the effluent turbidity.
And S5, comparing the water outlet turbidity data predicted in the S4 with the monitored water outlet turbidity data, if the data difference is small and the water outlet turbidity reaches the standard, enabling an intelligent dosing mode of the system to operate, if the data difference is large or the water outlet turbidity does not reach the standard, adjusting the water outlet turbidity in a manual dosing mode, defining a water outlet turbidity range by a user, and increasing and decreasing the dosing amount iteratively.
And S6, inputting the data of the manual dosage in the S5 into an intelligent dosage model for model training, determining whether the dosage meets a final index through a numerical value calculated by the model, finally determining the optimal dosage if the dosage meets the index, performing regression verification again according to the intelligent dosage model, and comparing the predicted water turbidity data with the monitored water turbidity data to finish the operation of the dosage mode.
The establishment of the intelligent dosing model comprises the following steps:
a. according to the PAC adding strategy of the existing water plant, the process is adjusted and optimized by a strategy that the time interval is 1 day and the step length is decreased by 5 percent, the turbidity of the settled water is temporarily based on the lag time of 3 and 5.5 hours, and when the turbidity of the settled water reaches a protection threshold value, the original manual experience strategy is switched to.
b. Data preprocessing: making up for missing data; removing and compensating abnormal values; the frequency of data acquisition of different devices is uniform.
c. And (3) data standardization treatment: and (c) converting the factor values with great difference of numerical value magnitude in the step (b) into a [0,1] interval.
d. Carrying out data splitting, splitting historical data into a training set and a test set, and controlling the split numerical quantity ratio to be 8: 2 or 7: 3.
e. constructing a model: and constructing a model based on training set data by utilizing a neural network, an SVM and a random forest integrated regression algorithm.
f. And (3) testing a model: and budgeting and verifying by using the random forest integration regression algorithm through the test set data.
g. Predicted according to PAC dosing.
h. And g, issuing a model, taking the turbidity, the flow, the pH and the temperature of the water entering the plant as independent variables and the turbidity of the settled water as dependent variables based on the data generated in the step g, fitting to obtain a model function, and issuing.
Prediction from PAC dosing includes:
firstly, according to the turbidity, flow, pH and temperature of the water entering the plant monitored in real time, and historical data, the corresponding PAC adding amount is found out through the Manhattan distance.
And secondly, according to the corresponding PAC adding amount, the PAC adding amount is decreased according to a certain step length, the decreasing value is 0.05 and is used as a parameter, the parameter and the turbidity, the flow, the pH value and the temperature of the water entering the plant are used as input values, the model constructed in the step e is utilized to obtain the predicted turbidity of the settled water, and the minimum value of the PAC adding amount is obtained under the condition that the turbidity of the settled water meets the condition.
The flow meter speed measuring range is 0.1-20 m/s, the speed measuring precision is +/-0.01 m/s, the distance measuring range is 30m or 70m, the distance measuring precision is +/-2 mm, the instantaneous flow precision is 5%, the pH meter measuring range is 0-14pH, the metering precision is +/-0.1 pH, a water plant performs drug administration through a metering pump, a metering pump Q is 400L/h, the backpressure is 16bar, the metering repetition precision is +/-1% within a 10-100% stroke length range, in the establishment a of an intelligent drug administration model, the protection threshold of the settled water turbidity is 2NTU, the PAC dosing frequency is 5min, in the establishment e of the intelligent drug administration model, the factory water turbidity, the flow rate, the pH, the temperature and the PAC dosing amount are independent variables, and the settled water turbidity is used as a dependent variable.
An intelligent coagulation medication dosing device for a water treatment plant, which comprises:
and the acquisition unit is used for acquiring the data of the flow, the PH, the temperature, the inlet water turbidity and the outlet water turbidity of the water in real time.
And the modeling unit is used for obtaining influence factors influencing the coagulant adding amount and the coagulation effect according to a water treatment theory and analysis of a chemical reaction process after coagulant addition, wherein the factors comprise turbidity, pH value, temperature and flow, judging whether the precipitated water meets the standard or not, finally obtaining the corresponding relation between the turbidity, the flow, the pH value and the temperature of the inlet water and the PAC adding amount on the premise of meeting the turbidity of the precipitated water through a corresponding algorithm, and displaying a function formula output by the model on an interface, controlling whether the model is started or stopped or not, setting issuing frequency and issuing an instruction to a terminal node in real time by the modeling unit.
And the prediction unit is used for predicting the water outlet turbidity, calculating the optimal dosage and putting a model to monitor the water outlet turbidity, defining the water outlet turbidity range by a user, and determining whether the dosage meets the final index or not according to the value calculated by the prediction unit.
And the intelligent dosing unit is used for comparing the effluent turbidity data with the monitored effluent turbidity data, enabling the intelligent dosing mode of the system to operate if the data difference is small and the effluent turbidity reaches the standard, adjusting the effluent turbidity in a manual dosing mode if the data difference is large or the effluent turbidity does not reach the standard, and recalculating according to the adjusted PAC dosing amount to achieve the purpose of intelligent dosing.
Under the automatic control mode in the system, the control process of the metering pump is as follows:
the raw water flow meter detects the flow, when the flow reaches the starting flow of the metering pump, the starting flow is 500, the PLC controls the chemical outlet valve of the alum feeding pool to be opened, the metering pump is automatically opened, when the flow is lower than the set starting flow or the liquid level of the chemical storage tank is low, the chemical outlet valve of the alum feeding pool is not opened to the right state, the metering pump stops working, the chemical outlet valve of the alum feeding pool is closed, in order to protect the metering pump, the frequency is given firstly when the metering pump is started, the system gives a corresponding stroke again when the frequency reaches a preset value, the system keeps relatively stable after the metering pump reaches the set stroke, when the raw water changes in a certain interval, the frequency is only required to be changed correspondingly, the stroke is kept unchanged, when the metering pump needs to be stopped, the stroke is reduced to zero firstly, and when the stroke returns to zero, the frequency is given to zero again.
In conclusion, the following results are obtained:
the control model can overcome the main problem of inaccurate prediction of the existing automatic dosing system, has strong self-adaption and self-learning capabilities, explores a new way for realizing the control of the coagulation dosing of the water plant, provides a reliable basis for the implementation of the next control of the coagulation dosing of the water plant, and simultaneously applies the scheme dosing optimization method, can improve the water quality assurance rate, saves labor and reduces the cost of a coagulant.
The points to be finally explained are: although the present invention has been described in detail with reference to the general description and the specific embodiments, on the basis of the present invention, the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. An intelligent coagulation dosing method for a water treatment plant is characterized by comprising the following steps:
s1, real-time data acquisition: the system comprises a flow meter, a PH meter, a thermometer, a water inlet turbidimeter and a water outlet turbidimeter, wherein the flow, PH, temperature, water inlet turbidity and water outlet turbidity data of water are respectively acquired in real time through the flow meter, the PH meter, the thermometer, the water inlet turbidity meter and the water outlet turbidity meter, the data acquisition period is 1s, and the period for transmitting the data to a control terminal PLC is 5 min;
s2, historical data acquisition: forecasting water inflow according to historical monitoring data, extracting the historical monitoring data, generating a random forest integration algorithm training model according to a user-defined historical data range and the lag time of the chemical dosing precipitation effect, and finally obtaining various parameters meeting the requirements;
s3, integrating the real-time data acquisition information and the historical data acquisition information to establish an intelligent dosing model;
s4, predicting the effluent turbidity according to the establishment of the intelligent dosing model in the S3, calculating the optimal dosing amount, and lowering the model to monitor the effluent turbidity;
s5, comparing the water outlet turbidity data predicted in the S4 with the monitored water outlet turbidity data, if the data difference is small and the water outlet turbidity reaches the standard, enabling an intelligent dosing mode of the system to operate, if the data difference is large or the water outlet turbidity does not reach the standard, adjusting the water outlet turbidity in a manual dosing mode, defining a water outlet turbidity range by a user, and increasing and decreasing the dosing amount;
and S6, inputting the data of the manual dosage in the S5 into an intelligent dosage model for model training, determining whether the dosage meets a final index through a numerical value calculated by the model, finally determining the optimal dosage if the dosage meets the index, performing regression verification again according to the intelligent dosage model, and comparing the predicted water turbidity data with the monitored water turbidity data to finish the operation of the dosage mode.
2. The intelligent coagulation dosing method for water treatment plants according to claim 1, wherein the establishment of the intelligent dosing model comprises the following steps:
a. according to the PAC adding strategy of the existing water plant, the process is adjusted and optimized by a strategy that the time interval is 1 day and the step length is decreased by 5 percent, the turbidity of the settled water is temporarily based on the lag time of 3 and 5.5 hours, and when the turbidity of the settled water reaches a protection threshold value, the original manual experience strategy is switched;
b. data preprocessing: making up for missing data; removing and compensating abnormal values; the frequency of data collected by different devices is uniform;
c. and (3) data standardization treatment: all the factor values with great difference of numerical value magnitude in the step b are converted into a [0,1] interval;
d. carrying out data splitting, splitting historical data into a training set and a test set, and controlling the split numerical quantity ratio to be 8: 2 or 7: 3;
e. constructing a model: carrying out model construction based on training set data by utilizing a neural network, an SVM and a random forest integration regression algorithm;
f. and (3) testing a model: budgeting and verifying by using the random forest integration regression algorithm through test set data;
g. predicting according to PAC adding amount;
h. and g, issuing a model, taking the turbidity, the flow, the pH and the temperature of the water entering the plant as independent variables and the turbidity of the settled water as dependent variables based on the data generated in the step g, fitting to obtain a model function, and issuing.
3. The intelligent coagulant dosing method for water treatment plant according to claim 1, wherein the prediction based on the PAC dosing amount comprises:
finding out the corresponding PAC adding amount according to the turbidity, flow, pH and temperature of the water entering a plant, which are monitored in real time, and by utilizing historical data through a Manhattan distance;
and secondly, according to the corresponding PAC adding amount, the PAC adding amount is decreased according to a certain step length, the decreasing value is 0.05 and is used as a parameter, the parameter and the turbidity, the flow, the pH value and the temperature of the water entering the plant are used as input values, the model constructed in the step e is utilized to obtain the predicted turbidity of the settled water, and the minimum value of the PAC adding amount is obtained under the condition that the turbidity of the settled water meets the condition.
4. The intelligent coagulation medication method for a water treatment plant according to claim 1, characterized in that: the flow meter has the speed measuring range of 0.1-20 m/s, the speed measuring precision of +/-0.01 m/s, the distance measuring range of 30m or 70m, the distance measuring precision of +/-2 mm, the instantaneous flow precision of 5%, the pH meter measuring range of 0-14 and the measuring precision of +/-0.1.
5. The intelligent coagulation medication method for a water treatment plant according to claim 1, characterized in that: the water plant carries out dosing through a metering pump, wherein the metering pump Q is 400L/h, the backpressure is 16bar, and the metering repetition precision is +/-1% within the range of 10-100% of stroke length.
6. The intelligent coagulation medication method for a water treatment plant according to claim 2, characterized in that: in the establishment of the intelligent dosing model a, the protection threshold of the turbidity of the settled water is 2NTU, and the PAC dosing frequency is 5 min.
7. The intelligent coagulation medication method for a water treatment plant according to claim 2, characterized in that: in the building e of the intelligent dosing model, the turbidity, the flow, the pH, the temperature and the PAC dosage of the water entering the factory are used as independent variables, and the turbidity of the settled water is used as a dependent variable.
8. The utility model provides a water treatment plant intelligence thoughtlessly congeals medication dosing device which characterized in that, the device includes:
the acquisition unit is used for acquiring the data of the flow, the PH, the temperature, the inlet water turbidity and the outlet water turbidity of water in real time;
the modeling unit is used for obtaining influence factors influencing the coagulant adding amount and the coagulation effect according to a water treatment theory and analysis of a chemical reaction process after coagulant addition, wherein the factors comprise turbidity, pH value, temperature and flow, judging whether the precipitated water meets the standard or not, finally obtaining the corresponding relation between the turbidity, the flow, the pH and the temperature of the incoming water and the PAC adding amount on the premise of meeting the turbidity of the precipitated water through a corresponding algorithm, and displaying a function formula output by the model on an interface, controlling whether the model is started or stopped or not, setting issuing frequency and issuing an instruction to a terminal node in real time by the modeling unit;
the prediction unit is used for predicting the water outlet turbidity, calculating the optimal dosage and putting a model to monitor the water outlet turbidity, defining the water outlet turbidity range by a user, and determining whether the dosage meets the final index or not according to the value calculated by the prediction unit;
and the intelligent dosing unit is used for comparing the effluent turbidity data with the monitored effluent turbidity data, enabling the intelligent dosing mode of the system to operate if the data difference is small and the effluent turbidity reaches the standard, adjusting the effluent turbidity in a manual dosing mode if the data difference is large or the effluent turbidity does not reach the standard, and recalculating according to the adjusted PAC dosing amount to achieve the purpose of intelligent dosing.
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| CN114626642A (en) * | 2022-05-16 | 2022-06-14 | 武汉华信数据系统有限公司 | Dosing system control method and device, storage medium and electronic equipment |
| CN115259318A (en) * | 2022-08-09 | 2022-11-01 | 北控水务(中国)投资有限公司 | Self-adaptive PAC dosing basic automation method |
| CN115259318B (en) * | 2022-08-09 | 2024-01-09 | 北控水务(中国)投资有限公司 | Self-adaptive PAC dosing basic automation method |
| CN115611393A (en) * | 2022-11-07 | 2023-01-17 | 中节能晶和智慧城市科技(浙江)有限公司 | Multi-end cooperative coagulant feeding method and system for multiple water plants |
| CN115611393B (en) * | 2022-11-07 | 2023-04-07 | 中节能晶和智慧城市科技(浙江)有限公司 | Multi-end cooperative coagulant feeding method and system for multiple water plants |
| CN117843155A (en) * | 2024-01-04 | 2024-04-09 | 北京新源智慧水务科技有限公司 | Softening and hardness-removing intelligent dosing control method, device and system |
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