CN115010232A - Drinking water plant coagulation dosing prediction method based on weak time-lag neural network model - Google Patents
Drinking water plant coagulation dosing prediction method based on weak time-lag neural network model Download PDFInfo
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Abstract
The invention relates to a drinking water plant coagulation dosing prediction method based on a weak time-lag neural network model, wherein the structure of the neural network model is constructed to be 8-n-1, the neural network model is trained by utilizing laboratory experimental data, then the structure and parameters of the trained neural network model are migrated and used for training actual data in a production link, and finally the weak time-lag model is obtained, and the correlation coefficient between the actual measurement value and the predicted value of the weak time-lag model reaches 0.72. In addition, the method not only has the training speed, but also has high prediction accuracy.
Description
Technical Field
The invention belongs to the technical field of environmental protection monitoring, and particularly relates to a drinking water plant coagulation dosing prediction method based on a weak time-lag neural network model.
Background
Coagulation is one of the core operation units of the treatment process of a water treatment plant, and the realization of accurate control of coagulation dosing is the key for improving the automatic production level of enterprises and saving the cost of medicaments. Aiming at the problems of strong coagulation time lag, more external disturbance and the like in the production link, the model is difficult to train and predict.
Time lag exists between the dosing time point and the test water quality in the drinking water treatment plant, and the test water quality cannot reflect the real dosing effect. The traditional neural network mainly predicts the dosage through the water quality of source water, and automatically regulates and controls the dosage by comparing the effluent turbidity of a dosage point with the set turbidity, so that the time lag problem is not relieved. The overall analysis of the neural network method on the factors of the coagulation system is poor, and the sensitivity of some important factors such as phosphorus to the model is unclear. Water treatment workers and managers can estimate the approximate time of water turbidity through long-term experience, which can be significant for neural network construction and system impact analysis, as it is closer to true dosing. Theoretically, the water quality at the time point is estimated, the time lag can be reduced to a certain extent but cannot be completely eliminated, relatively weak time lag influence still exists, and the dosage for producing the water quality effect is not completely consistent with the actual dosage and is a close value.
Disclosure of Invention
Aiming at the problems in the prior art, the technical problems to be solved by the invention are as follows: how to minimize the impact of time lag on dosing prediction.
In order to solve the technical problems, the invention adopts the following technical scheme: the drinking water plant coagulation dosing prediction method based on the weak time-lag neural network model comprises the following steps:
s1 data preparation
S11: laboratory data preparation
S111: the experiment adopts a six-unit electric mixer to carry out coagulation experiment, and experimental data come from a single-factor adjustment experiment and a response surface experiment;
selecting temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254, GT and coagulation dosage as experimental factors;
when the single-factor adjustment experiment is carried out in each experiment, the turbidity of source water or the dosage of coagulation or the pH value or the temperature value is controlled, and then the values of other experiment factors which are not regulated and controlled in each experiment are measured, so that an experiment training sample is obtained in each experiment;
in each experiment of the response surface experiment, an experiment training sample is obtained in each experiment by regulating and controlling the turbidity, the pH value, the GT value and the coagulation dosage value of source water and then measuring the values of other experiment factors which are not regulated and controlled in each experiment;
the coagulant adopted in the experiment is similar liquid polyaluminium chloride in a water plant;
s112: carrying out multiple single-factor adjustment experiments and multiple response surface experiments, wherein each experiment obtains an experiment training sample to form an experiment training sample set, each experiment training sample is represented by a vector, the vector comprises temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and GT, and each experiment training sample corresponds to one experiment coagulation dosage;
s12: data preparation of actual water plant production process
Collecting water quality parameters from a source water port of a water plant to a water outlet of a sedimentation tank for multiple times in a T time period to obtain an actual production data set, wherein each actual production training sample in the actual production data set is represented by a vector, the vector comprises temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and flow, and each actual production training sample corresponds to a real coagulation dosage;
s2: constructing a neural network model, wherein the structure of the neural network model is 8-n-1, and the neural network model comprises 8 input neurons, n hidden layer neurons and 1 output neuron;
s3: training the neural network model
S31: initializing a neural network model, and pre-training the neural network model by adopting an experimental training sample set;
s311: let i equal 1;
s312: inputting the ith experiment training sample into a neural network model, wherein the temperature, the source water turbidity, the effluent turbidity, the pH, the TDS, the TP, the UV254 and the GT respectively correspond to one input neuron, and the output is the predicted coagulation dosage of the ith experiment training sample;
s313: calculating the loss between the predicted coagulation dosage of the ith experiment training sample and the experimental coagulation dosage, and updating the parameters of the neural network model by back propagation by adopting a gradient descent method according to the loss;
s314: let i equal i + 1;
s315: if i is larger than the maximum iteration times, obtaining a suboptimal neural network model, executing the next step, and otherwise, returning to S312;
s32: training the suboptimal neural network model by adopting an actual production data set;
s321: let j equal 1;
s322: inputting the jth actual production training sample into a suboptimal neural network model, wherein the temperature, the source water turbidity, the outlet water turbidity, the pH, the TDS, the TP, the UV254 and the flow respectively correspond to one input neuron, and the output is the predicted coagulation dosage of the jth actual production training sample;
s323: calculating the loss between the predicted coagulation dosage and the real coagulation dosage of the jth actual production training sample, and reversely propagating and updating parameters of the suboptimal neural network model by adopting a gradient descent method according to the loss;
S324：j＝j+1；
s325: if j is larger than the maximum iteration times, obtaining an optimal neural network model, executing the next step, and otherwise, returning to the step S322;
s4: and predicting the dosage, collecting water quality parameters from a source water gap of a water plant to a water outlet of a sedimentation tank at the current time period, wherein the water quality parameters comprise temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and flow, and inputting the current water quality parameters into an optimal neural network model to obtain the coagulation dosage at the next time period.
Preferably, the response surface experiment in S11 is designed by a BOX-Behnken model.
Preferably, the neural network model in S2 has a structure of 8-n-1, where n is 3, 4, 5 or 6.
Compared with the prior art, the invention has at least the following advantages:
the structure of the laboratory model is migrated to the model training of the production scene for the first time, and the relation number of the measured value and the analog value of the weak time-lag model reaches 0.72, which is better than that of the time-lag model. Under the same condition, the quality of the weak time-lag model trained by the migration method is the same as that of the genetic algorithm, the overfitting phenomenon can be avoided, and the training speed is better than that of the genetic algorithm. Genetic algorithms consume 394 times more time than the method of the present invention.
Drawings
FIG. 1 is a diagram of a neural network model training concept.
Fig. 2 shows the results of laboratory training, wherein graphs (a) - (d) show the predicted effects of different structures of the laboratory neural network model, and graphs (e) - (f) show the training and testing results of the optimal laboratory model.
Fig. 3 is a graph showing the results of the migration learning, in which the neural network model frame of fig. (a) is 7-3-1, the neural network model frame of fig. (b) is 7-4-1, the neural network model frame of fig. (c) is 7-5-1, the neural network model frame of fig. (d) is 7-6-1, fig. (e) is the results of the laboratory training, and fig. (f) is the results of the laboratory testing.
Detailed Description
The present invention is described in further detail below.
Under the influence of weak time lag, the biggest benefit is generated in that: the outlet water quality can be introduced into the neural network model, and the real dosage can be obtained by setting the target water quality in the network, so that the optimization of dosage use is facilitated. The data is the basis for determining the quality of the network model, and the algorithm can guarantee the quality and stability of the model.
Model migration can migrate the structure or parameters of a known model to the training of models in similar fields, and improve the training quality of the model. Under the laboratory condition, the model data acquisition is easy to control, the data structure is complete, and the generated model has high precision.
The drinking water plant coagulation dosing prediction method based on the weak time-lag neural network model comprises the following steps:
s1 data preparation
S11: laboratory data preparation
S111: the experiment adopts six antithetical couplet electric mixer to carry out the experiment of coagulating, and the experimental data comes from single factor and adjusts experiment and response surface experiment:
selecting temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254, GT and coagulation dosage as experimental factors;
when the single-factor adjustment experiment is carried out in each experiment, the turbidity of source water or the dosage of coagulation or the pH value or the temperature value is controlled, and then the values of other experiment factors which are not regulated and controlled in each experiment are measured, so that an experiment training sample is obtained in each experiment;
in each experiment of the response surface experiment, the GT value is controlled by regulating and controlling the source water turbidity (NTU), the pH value, the GT value and the value of the coagulation dosage (mg/L), wherein the GT value is controlled by controlling the slow stirring time and the slow stirring rotating speed. Then measuring the values of other experiment factors which are not regulated and controlled in each experiment, and obtaining an experiment training sample in each experiment; the coagulant used in the experiment is polyaluminium chloride liquid of the same type in a water plant.
For example: single factor adjustment experiment:
the values of source water turbidity (NTU), pH and coagulation dosage (mg/L) were controlled one by one. When the coagulation dosage is controlled to be variable, six water samples are arranged on a six-unit stirrer, different coagulation dosage is added into the six water samples, other factors are controlled to be unchanged, and then the required water quality parameters are measured. Other data were also measured in a similar manner.
Response surface experiment:
and adopting software Design-Expert and performing BOX-Behnken response surface experimental Design. 5 factors and 3 levels are input, the software automatically generates 46 groups of experimental schemes, experiments are carried out according to the schemes, and required water quality parameters are measured. The selected factors and levels are source water turbidity (3-21NTU), pH (6-8), coagulation dosage (2-20mg/L), slow stirring time T (12-20min), and slow stirring speed (40-120 r/min). For example, the turbidity level of the source water is 3.30, 11.53 or 21.13, the pH level is 6, 7 or 8, the amount of coagulant is 2, 11 or 20, the slow stirring time is 12, 16 or 20, and the slow stirring speed is 40, 80 or 120.
S112: carrying out multiple single-factor adjustment experiments and multiple response surface experiments, wherein each experiment obtains an experiment training sample to form an experiment training sample set, each experiment training sample is represented by a vector, the vector comprises temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and GT, and each experiment training sample corresponds to one experiment coagulation dosage;
s12: data preparation of actual water plant production process
In the T time quantum, gather the quality of water parameter of water plant source mouth of a river to sedimentation tank delivery port many times, obtain the actual production data set, every actual production training sample in the actual production data set is represented by a vector, and this vector includes temperature, source water turbidity, goes out water turbidity, pH, TDS, TP, UV254 and flow, and every actual production training sample corresponds a real amount of dosing that thoughtlessly congeals.
In the step, the flow replaces GT as a factor to establish a water plant neural network model, because the actual water inflow of the water plant exceeds 50% of the design value, the difference between the actual GT value measured by the water plant and the design value is large, and the flocculation G value and the flocculation T value in each time period can also change due to the fluctuation of the water inflow. And because GT is only one of the factors for predicting the dosage, the model migration is better realized in order to ensure the unification of laboratory data and the actual water plant model structure. Therefore, the GT value can be replaced by the flow with stable and relatively accurate data to predict the dosage.
S2: constructing a neural network model, wherein the structure of the neural network model is 8-n-1, and the neural network model comprises 8 input neurons, n hidden layer neurons and 1 output neuron;
s3: training the neural network model
S31: initializing a neural network model, and pre-training the neural network model by adopting an experimental training sample set;
s311: let i equal to 1;
s312: inputting the ith experiment training sample into a neural network model, wherein the temperature, the source water turbidity, the effluent turbidity, the pH, the TDS, the TP, the UV254 and the GT respectively correspond to one input neuron, and the output is the predicted coagulation dosage of the ith experiment training sample;
s313: calculating the loss between the predicted coagulation dosage of the ith experiment training sample and the experimental coagulation dosage, and updating the parameters of the neural network model by back propagation by adopting a gradient descent method according to the loss;
s314: let i equal i + 1;
s315: if i is greater than the maximum iteration times, obtaining a suboptimal neural network model, executing the next step, and otherwise, returning to S312;
s32: training the suboptimal neural network model by adopting an actual production data set;
s321: let j equal 1;
s322: inputting the jth actual production training sample into a suboptimal neural network model, wherein the temperature, the source water turbidity, the outlet water turbidity, the pH, the TDS, the TP, the UV254 and the flow respectively correspond to one input neuron, and the output is the predicted coagulation dosage of the jth actual production training sample;
s323: calculating the loss between the predicted coagulation dosage and the real coagulation dosage of the jth actual production training sample, and reversely propagating and updating parameters of the suboptimal neural network model by adopting a gradient descent method according to the loss;
S324：j＝j+1；
s325: if j is larger than the maximum iteration times, obtaining an optimal neural network model, namely a weak time-lag model, executing the next step, and otherwise, returning to S322;
s4: and predicting the dosage, collecting water quality parameters from a source water opening of a water plant to a water outlet of a sedimentation tank at the current time period, wherein the water quality parameters comprise temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and flow, and inputting the current water quality parameters into an optimal neural network model to obtain the coagulation dosage at the next time period.
Specifically, the response surface experiment in the S11 is designed through a BOX-Behnken model, and the BOX-Behnken model is software which can automatically generate the best experimental scheme by inputting factors and horizontal ranges. I.e. an experimental protocol in which 5 factors and corresponding level ranges are entered into the program, the program will be automated. The five factors for regulation comprise source water turbidity (NTU), pH, coagulation dosage (mg/L), stirring time T (min) and slow stirring speed (r/min); the turbidity level range of the source water is 3-21NTU, the pH level range is 6-8, the coagulation dosage level range is 2-20mg/L, the slow stirring time level range is 12-20min, and the slow stirring speed level range is 40-120 r/min. One of the factors is regulated and controlled to carry out an experiment, and then other required water quality parameters can be measured.
Specifically, the structure of the neural network model in S2 is 8-n-1, where n ═ 3 or 4 or 5 or 6.
In the invention, coagulation experiment data is collected in a laboratory, and the constructed neural network model is trained and tested to obtain the optimum model structure and parameters, namely a suboptimal neural network model. And then, acquiring industrial production data, classifying the data to obtain weak time-lag test data delayed for 1 hour, and training a suboptimal neural network model.
Example 1:
(1) collecting laboratory model data:
this example performed the laboratory data collection work. Experiment of the inventionThe coagulation experiment was performed using a MY30006M six-gang electric mixer. The hydraulic condition range that six ally oneself with the mixer can simulate is: g value is 10-1000 s -1 And the T value is 0-99 min, so that the requirement of simulating the actual coagulation process of a Hunan pond water plant is met. The experimental data were from 60 single factor conditioning experimental groups and 46 response surface experimental groups. The single factors for adjustment include source water turbidity, coagulation dosage, pH or temperature. The hydraulic conditions comprise coagulation dosage (mg/L), rapid stirring time(s), rapid stirring speed (r/min), slow stirring time (min) and slow stirring speed (r/min). GT value is regulated by the stirring time and speed, and the result is automatically displayed by the display of the stirrer.
The experimental set of interactions between factors was designed by the BOX-Behnken model, including 5 factors, each factor including 3 levels. The 5 factors are: source water turbidity (NTU), pH, coagulation dosage (mg/L), stirring time T (min), and slow stirring speed (r/min); 3 levels of source water turbidity were 3.30, 11.53, 21.13, 3 levels of pH 6, 7, 8, 3 levels of coagulant dose were 2, 11, 20, 3 levels of slow stirring time 12, 16, 20, 3 levels of slow stirring speed 40, 80, 120.
The coagulation adopted in the experiment is similar liquid polyaluminium chloride (mass ratio (Al) in Hunan Tan certain water plant 2 O 3 %) is about 10%; the density is about 1.25g/cm 3 (ii) a The production company is Hengyang City Hengheng industry Co., Ltd.). During coagulation, the feeding mode of the coagulation system is the same as that of a Hunan pond water plant, and 1: 1 diluting with water and using. The water quality parameters tested included temperature (. degree. C.), source water turbidity (NTU), effluent turbidity (NTU), pH, TDS (mg/L), TP (μ g/L), UV254 (cm) -1 ) GT, dosage for coagulation.
(2) And (4) collecting production data of a certain Hunan Tan water plant, wherein the water sample data collected by the embodiment comes from the certain Hunan Tan water plant. The sampling time is from 4 months in 2021 to 12 months in 2021. The collection is carried out for 10-16 days every month, 8 hours every day and 1 hour intervals. In a Huntan water plant, a sampling place is from a source nozzle to a water outlet of a sedimentation tank. Water quality parameters tested included temperature (OC), source turbidity (NTU), effluent turbidity (NTU)), pH, TDS (mg/L), TP (μ g/L), UV254 (cm) -1 ) Flow rate (L/h) and coagulation dosage (L/h). Quality and output of source waterThe water quality parameters are shown in tables 1 and 2. Wherein, the maximum value of the turbidity of the effluent after flocculation is 6.86, and the minimum value is 0.37. Through practical observation, the turbidity of the effluent of a sedimentation tank in a Hunan Tan water plant almost meets the requirement of the Hunan Tan water plant, is less than 3NTU, has an average value of 1.88NTU, cannot influence a subsequent filtering system, and the effluent reaches the national standard. A total of 751 samples were tested. The coagulation adopted by the water treatment engineering system is liquid polyaluminium chloride (mass ratio (Al) 2 O 3 %) is about 10%; the density is about 1.28g/cm 3 ). After being diluted by one time, the liquid polyaluminium chloride in Hunan pond in a certain water plant is conveyed to a pipeline and a flocculation tank through an automatic dosing system. In order to facilitate the deployment of the model in engineering, the dosage is calculated according to the volume concentration (L/h) of the diluted solution.
TABLE 1 distribution chart of water quality test data of source water
TABLE 2 Water quality testing data distribution chart
According to the data, a neural network model is established. The network structure is an 8-n-1 structure and comprises 8 input neurons, n hidden layer neurons and 1 output neuron. Input neuronal variables include temperature (. degree. C.), raw water turbidity (NTU), effluent turbidity (NTU), pH, TDS (mg/L), TP (. mu.g/L), UV254 and flow rate. The unit of flow in production is L/h, while in a laboratory, the coagulation volume is unchanged and is a uniform structure, and the unit of coagulation in the laboratory is converted into L/h. The output neuron is the dosage (mg/L).
Because GT has great influence on a laboratory model, GT value is introduced in the research, and the GT value of a production model of a certain Huntan water plant is a theoretical value. The study randomly drawn training samples and test samples according to an 8:2 ratio. The experiment was conducted for 500 weak lag data studies with an initial learning rate of 0.05, R of the observation model 2 The value changes. The results are shown in FIG. 2. Fig. 2 shows the laboratory training results. The simulation accuracy of the 7-3-1 structure is low (see fig. 2 a). With the increasing number of hidden layers, the training results are much better than the test results, showing some overfitting phenomena, such as the 7-6-1 structure (see FIG. 2 d). 7-4-1 is the best training structure, best training and testing R 2 The values are 0.98 and 0.97, respectively (see fig. 2 b). The 7-5-1 structure, although not showing severe overfitting (see FIG. 2 c), is less accurate than the 7-4-1 structure. Therefore, the 7-4-1 structure is the most suitable model structure. R of laboratory model 2 Values (training value 0.98, test value 0.97) (see fig. 2 e-f).
(3) Migration model quality:
we applied the structure and learning parameters of the laboratory model to the training of the production data training, with the training results shown in FIGS. 3a-d and the test results shown in FIGS. 3 e-f. From the results, it was found that before migration, R of the optimum model 2 Value around 0.75, but test results below 0.6, training and test results (R) 2 ) The ratio difference is large (the mean value of 500 times of simulation is 1.36), and a certain overfitting phenomenon appears. After migration, training and test results (R) 2 ) Ratio 1.01 (average of 1.01 in 500 simulation results), R 2 The values are basically between 0.6 and 0.7, which shows that the migration can not only reduce the overfitting of the model, but also improve the performance of the model. Training and testing result R of optimum model 2 Reaching 0.71 and 0.70 respectively. The training speed for genetic optimization is slow, each training takes 7.1 minutes, and migration training only takes 0.018 minutes to achieve the same effect. The genetic algorithm is 394 times faster than the training speed of the migration algorithm. R is 2 The correlation coefficient between the measured value and the predicted value is referred to.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (3)
1. The drinking water plant coagulation dosing prediction method based on the weak time-lag neural network model is characterized by comprising the following steps of:
s1 data preparation
S11: laboratory data preparation
S111: the experiment adopts a six-unit electric mixer to carry out coagulation experiment, and experimental data come from a single-factor adjustment experiment and a response surface experiment;
selecting temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254, GT and coagulation dosage as experimental factors;
when the single-factor adjustment experiment is carried out in each experiment, the turbidity of source water or the dosage of coagulation or the pH value or the temperature value is controlled, and then the values of other experiment factors which are not regulated and controlled in each experiment are measured, so that an experiment training sample is obtained in each experiment;
in each experiment of the response surface experiment, an experiment training sample is obtained in each experiment by regulating and controlling the turbidity, the pH value, the GT value and the coagulation dosage value of source water and then measuring the values of other experiment factors which are not regulated and controlled in each experiment;
the coagulant adopted in the experiment is similar liquid polyaluminium chloride in a water plant;
s112: carrying out multiple single-factor adjustment experiments and multiple response surface experiments, wherein each experiment obtains an experiment training sample to form an experiment training sample set, each experiment training sample is represented by a vector, the vector comprises temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and GT, and each experiment training sample corresponds to one experiment coagulation dosage;
s12: data preparation of actual water plant production process
Collecting water quality parameters from a source water gap of a water plant to a water outlet of a sedimentation tank for multiple times in a T time period to obtain an actual production data set, wherein each actual production training sample in the actual production data set is represented by a vector, the vector comprises temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and flow, and each actual production training sample corresponds to a real coagulation dosage;
s2: constructing a neural network model, wherein the structure of the neural network model is 8-n-1, and the neural network model comprises 8 input neurons, n hidden layer neurons and 1 output neuron;
s3: training the neural network model
S31: initializing a neural network model, and pre-training the neural network model by adopting an experimental training sample set;
s311: let i equal to 1;
s312: inputting the ith experiment training sample into a neural network model, wherein the temperature, the source water turbidity, the effluent turbidity, the pH, the TDS, the TP, the UV254 and the GT respectively correspond to one input neuron, and the output is the predicted coagulation dosage of the ith experiment training sample;
s313: calculating the loss between the predicted coagulation dosage of the ith experiment training sample and the experimental coagulation dosage, and updating the parameters of the neural network model by back propagation by adopting a gradient descent method according to the loss;
s314: let i equal i + 1;
s315: if i is greater than the maximum iteration times, obtaining a suboptimal neural network model, executing the next step, and otherwise, returning to S312;
s32: training the suboptimal neural network model by adopting an actual production data set;
s321: let j equal 1;
s322: inputting the jth actual production training sample into a suboptimal neural network model, wherein the temperature, the source water turbidity, the outlet water turbidity, the pH, the TDS, the TP, the UV254 and the flow respectively correspond to one input neuron, and the output is the predicted coagulation dosage of the jth actual production training sample;
s323: calculating the loss between the predicted coagulation dosage and the real coagulation dosage of the jth actual production training sample, and reversely propagating and updating parameters of the suboptimal neural network model by adopting a gradient descent method according to the loss;
S324：j＝j+1；
s325: if j is larger than the maximum iteration times, obtaining an optimal neural network model, executing the next step, and otherwise, returning to the step S322;
s4: and predicting the dosage, collecting water quality parameters from a source water gap of a water plant to a water outlet of a sedimentation tank at the current time period, wherein the water quality parameters comprise temperature, source water turbidity, effluent turbidity, pH, TDS, TP, UV254 and flow, and inputting the current water quality parameters into an optimal neural network model to obtain the coagulation dosage at the next time period.
2. The method for predicting the concrete administration in the drinking water plant based on the weak time-lapse neural network model as set forth in claim 1, wherein the response surface experiment in the step S11 is designed by a BOX-Behnken model.
3. The prediction method for concrete administration in a drinking water plant based on a weak time-lapse neural network model as claimed in claim 1 or 2, wherein the structure of the neural network model in S2 is 8-n-1, where n is 3, 4, 5 or 6.
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