CN112419095A - Accurate dosing method based on historical data and real-time data feedback - Google Patents
Accurate dosing method based on historical data and real-time data feedback Download PDFInfo
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Abstract
The invention discloses an accurate dosing method based on historical data and real-time data feedback, which specifically comprises the following steps: s1, data acquisition: collecting operation data of a dosing system in real time; s2, data filtering: positioning abnormal data and removing; s3, learning and optimizing; s4, communication; s5, effect evaluation; the invention relates to the technical field of dosing methods. This accurate medicine method that adds based on historical data and real-time data feedback realize, guarantee that the system can be according to the quality of water change of intaking, temperature variation, flow variation, under the prerequisite that guarantees quality of water up to standard, realize accurate medicine, and the system can be according to historical data, real-time data carries out self-learning, optimize, guarantee that it is accurate all the time with the medicine volume, control that can be more accurate adds the medicine volume, it can guarantee system's operational effect to combine together through machine learning mode and historical data optimization mode, can solve the problem that the medicine system excessively relies on personnel's experience simultaneously.
Description
Technical Field
The invention relates to the technical field of fine adjustment of flocculant dosing of surface water plants producing water by conventional processes, in particular to a precise dosing method based on historical data and real-time data feedback.
Background
With the advance of urbanization in China, many water utilities establish and operate a plurality of surface water plants, a large water utilities have dozens of surface water plants, and the water plants generally adopt the traditional filtration process of the rage coagulation sedimentation filter.
According to actual research on site, the method includes participation in surface water plant operation to find that a plurality of problems exist in the operation of the water plant medicine adding system, and the main problems are as follows:
1) and the dosing system does not realize automatic control, and a plurality of systems in the traditional relay control mode exist.
2) The addition amount of the flocculating agent mainly depends on manual experience.
3) And the water plant operating personnel are insufficient, so that the water yield and the water quality can not be followed in real time to adjust the dosage.
4) And part of advanced water plants can adjust the dosing amount according to the water inflow of the water plants, but the dosing coefficient cannot be changed according to the change of water quality.
Inaccurate dosage control has many disadvantages, which are as follows:
1) and the fluctuation of the water quality of the discharged water is large, so that the water quality change situation of the whole pipe network is influenced.
2) And the problems of flocculant waste and electric energy exist.
3) And too much addition leads to poor flocculation effect and too much residual flocculant, thus leading to early blockage of the filter and early failure of the filter material.
At present, a flocculant adding system of a water treatment plant mainly adopts two modes of setting an adding coefficient and manually adjusting the frequency of a metering pump by means of manual experience, and an advanced system can automatically adjust according to inflow, a dosing system and inflow water quality, and the system has many defects.
Therefore, a new control system needs to be constructed to solve the above problems.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an accurate dosing method based on historical data and real-time data feedback, and solves the problems that the dosing coefficient is set by manual experience, the frequency of a metering pump is adjusted manually, the fluctuation of effluent quality is large, a flocculating agent and electric energy are wasted, the method is not environment-friendly, and the dosing coefficient cannot be changed according to the change of the water quality.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a precise dosing method based on historical data and real-time data feedback concretely comprises the following steps:
s1, data acquisition: the data acquisition module is used for acquiring relevant data of instantaneous flow, PH, turbidity, temperature, residual chlorine and conductivity of inlet water of a water plant, the data acquisition module is used for communicating with a water pump room PLC through Ethernet, acquiring relevant data of instantaneous flow, residual chlorine, PH and turbidity of outlet water, and storing the relevant data into an original table of a database in real time;
s2, data filtering: the data acquisition module reads newly added data in the original table once every one minute, positions abnormal data through normal distribution, and after the abnormal data are removed, the data in one minute are averaged and stored in a historical database of the database to prepare for other modules to call the data;
s3, learning and optimizing: the learning optimization module firstly trains by using data of a historical library, the trained model is deployed on a machine to run, once the model runs, the module continuously compares, compares the difference between the output of the model and the actual effect, continuously adjusts an output coefficient, and performs one-time incremental training on the model after the system does not run for one week;
s4, communication: establishing communication between the learning optimization module and the existing scada system of the water plant, transmitting the predicted dosage output by the model and the predicted water quality to the scada system in an opc or Ethernet mode, and displaying the output result of the system by the scada system of the water plant;
s5, evaluation of effects: the effect evaluation module compares the pH value, the turbidity and the residual chlorine of the water plant effluent obtained by the data acquisition module with target values, evaluates the accuracy of the output result of the model, and simultaneously calculates the dosage and the power consumption for energy consumption analysis.
Preferably, the data acquisition modules in the steps S1 and S2 exist in a software form, are deployed and operated on a water plant server, and communicate with the dosing PLC by using a standard industrial communication protocol in an ethernet manner.
Preferably, the learning optimization module in step S3 is configured to store the current output value and the predicted value in a parameter table of a database.
Preferably, when the learning optimization module performs training by using data in the history library in step S3, the training model is trained by using a machine learning algorithm.
Preferably, the scada system in step S4 is further configured to issue the output result to the field controller.
Preferably, the effect evaluation module in step S5 is used to comprehensively evaluate the system operation.
(III) advantageous effects
The invention provides an accurate dosing method based on historical data and real-time data feedback. Compared with the prior art, the method has the following beneficial effects: this accurate medicine method that adds based on historical data and real-time data feedback realize, through S1, data acquisition: the data acquisition module acquires the operation data of the dosing system in real time; s2, data filtering: abnormal data are located through normal distribution, after the abnormal data are removed, the data within one minute are averaged and stored in a historical database of the database, and preparation is made for other modules to call the data; s3, learning and optimizing: the learning optimization module firstly trains by using data of a historical library, the trained model is deployed on a machine to run and continuously compare, the difference between the output of the model and the actual effect is compared, the output coefficient is continuously adjusted, and after the system does not run for a week, the model is subjected to one-time incremental training; s4, communication: establishing communication between the learning optimization module and the existing scada system of the water plant, and displaying a system output result by the scada system of the water plant; s5, evaluation of effects: the accuracy of effect evaluation module evaluation model output result, calculate simultaneously with medicine volume and power consumption, carry out energy consumption analysis, can guarantee that the system can change according to the quality of water of intaking, temperature variation, flow variation, under the prerequisite of guaranteeing quality of water up to standard, realize accurate medicine, and the system can be according to historical data, real-time data carries out self-learning, optimize, it is accurate all the time to guarantee its medicine volume that adds, control that can be more accurate adds the medicine volume, it can guarantee system's operational effect to combine together through machine learning mode and historical data optimization mode, can solve the problem that the medicine system excessively relies on personnel's experience simultaneously.
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FIG. 1 is a system architecture diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a technical solution: a precise dosing method based on historical data and real-time data feedback concretely comprises the following steps:
s1, data acquisition: the method comprises the following steps of collecting relevant data of instantaneous flow, PH, turbidity, temperature, residual chlorine and conductivity of inlet water of a water plant through a data collection module, communicating with a water pump room PLC through an Ethernet, collecting relevant data of instantaneous flow, residual chlorine, PH and turbidity of outlet water, storing the relevant data into an original table of a database in real time, and collecting operation data of a dosing system in real time through the data collection module, wherein the operation data mainly comprise dosing amount, raw medicine proportion and residual medicine amount;
s2, data filtering: the data acquisition module reads newly added data in the original table once every one minute, positions abnormal data through normal distribution, and after the abnormal data are removed, the data in one minute are averaged and stored in a historical database of the database to prepare for other modules to call the data;
s3, learning and optimizing: the learning optimization module is used for training by using data of a history library, a trained model is deployed on a machine to run, once the model runs, the module continuously compares, compares the difference between the output of the model and the actual effect, and continuously adjusts an output coefficient, after the system does not run for a week, the model is subjected to incremental training once, the learning optimization module automatically trains by reading the data in the history list to obtain corresponding output values under different parameters, can automatically perform incremental learning according to the data in the newly generated history list, continuously optimizes and adapts to new conditions, and has a verification function, and the optimized output value can be continuously adjusted according to feedback data;
s4, communication: the communication function module establishes communication between the learning optimization module and the existing scada system of the water plant, transmits the predicted dosing quantity output by the model to the scada system in an opc or Ethernet mode, and then displays the result output by the system of the water plant scada system, and the communication function module is responsible for communicating with the existing automatic control system and the scada system of the water plant and is used for displaying and issuing a control command for flocculant dosing equipment;
s5, evaluation of effects: the effect evaluation module compares the pH value, the turbidity and the residual chlorine of the water plant effluent obtained by the data acquisition module with target values, evaluates the accuracy of the output result of the model, calculates the dosage and the power consumption at the same time, and performs energy consumption analysis.
In the embodiment of the invention, the data acquisition modules in the steps S1 and S2 exist in a software form, are deployed and operated on the water plant server, and communicate with the PLC for dosing by adopting a standard industrial communication protocol in an Ethernet mode.
In the embodiment of the present invention, the learning optimization module in step S3 is configured to store the current output value and the predicted value in the parameter table of the database.
In the embodiment of the invention, when the learning optimization module performs training by using the data of the historical library in the step S3, the training model performs training by using a machine learning algorithm, and because the water quality is different and the water consumption characteristics are different in various places, and the mode of establishing the mathematical model is too high in cost and difficulty and is in short supply for later-period maintenance personnel, the functional module is realized by using a currently popular machine learning mode.
In this embodiment of the present invention, the scada system in step S4 is further configured to send the output result to the field controller.
In the embodiment of the present invention, the effect evaluation module in step S5 is used to comprehensively evaluate the system operating condition.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A precise dosing method based on historical data and real-time data feedback is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, data acquisition: the data acquisition module is used for acquiring relevant data of instantaneous flow, PH, turbidity, temperature, residual chlorine and conductivity of inlet water of a water plant, the data acquisition module is used for communicating with a water pump room PLC through Ethernet, acquiring relevant data of instantaneous flow, residual chlorine, PH and turbidity of outlet water, and storing the relevant data into an original table of a database in real time;
s2, data filtering: the data acquisition module reads newly added data in the original table once every one minute, positions abnormal data through normal distribution, and after the abnormal data are removed, the data in one minute are averaged and stored in a historical database of the database to prepare for other modules to call the data;
s3, learning and optimizing: the learning optimization module firstly trains by using data of a historical library, the trained model is deployed on a machine to run, once the model runs, the module continuously compares, compares the difference between the output of the model and the actual effect, continuously adjusts an output coefficient, and performs one-time incremental training on the model after the system does not run for one week;
s4, communication: establishing communication between the learning optimization module and the existing scada system of the water plant, transmitting the predicted dosage output by the model and the predicted water quality to the scada system in an opc or Ethernet mode, and displaying the output result of the system by the scada system of the water plant;
s5, evaluation of effects: the effect evaluation module compares the pH value, the turbidity and the residual chlorine of the water plant effluent obtained by the data acquisition module with target values, evaluates the accuracy of the output result of the model, and simultaneously calculates the dosage and the power consumption for energy consumption analysis.
2. The accurate dosing method based on historical data and real-time data feedback realization according to claim 1, characterized in that: the data acquisition modules in the steps S1 and S2 exist in a software form, are deployed and operated in a water plant server, and communicate with the PLC for dosing by adopting a standard industrial communication protocol in an Ethernet mode.
3. The accurate dosing method based on historical data and real-time data feedback realization according to claim 1, characterized in that: the learning optimization module in step S3 is used to store the current output value and the predicted value into the parameter table of the database.
4. The accurate dosing method based on historical data and real-time data feedback realization according to claim 1, characterized in that: when the learning optimization module performs training by using the data of the history library in the step S3, the training model performs training by using a machine learning algorithm.
5. The accurate dosing method based on historical data and real-time data feedback realization according to claim 1, characterized in that: the scada system in step S4 is further configured to issue the output result to the field controller.
6. The accurate dosing method based on historical data and real-time data feedback realization according to claim 1, characterized in that: the effect evaluation module in step S5 is used to comprehensively evaluate the system operating condition.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113484057A (en) * | 2021-07-20 | 2021-10-08 | 杭州塞博环境科技有限公司 | Method, equipment and system for calculating and evaluating energy efficiency of water treatment facility |
| CN113582309A (en) * | 2021-07-28 | 2021-11-02 | 长三角(义乌)生态环境研究中心 | Method and device for determining coagulant adding amount |
| CN114065949A (en) * | 2022-01-17 | 2022-02-18 | 心鉴智控(深圳)科技有限公司 | Intelligent water quality prediction dosing system based on historical data |
| CN114217529A (en) * | 2021-12-13 | 2022-03-22 | 北京市市政工程设计研究总院有限公司 | Intelligent dosing system and method based on mathematical model and predictive control |
| CN114386333A (en) * | 2022-01-19 | 2022-04-22 | 郑州清源智能装备科技有限公司 | Intelligent edge control method and device |
| CN114563988A (en) * | 2022-01-26 | 2022-05-31 | 浙江中控信息产业股份有限公司 | Water plant intelligent PAC adding method and system based on random forest algorithm |
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