Transmembrane pulse electrochemical sewage ammonia nitrogen recovery method based on machine learning

Abstract

The embodiment of the invention provides a method for recycling ammonia nitrogen in sewage by transmembrane pulse electrochemistry based on machine learning. The method comprises the following steps: acquiring pulse frequency, external voltage, initial concentration of catholyte, initial concentration of anolyte and initial concentration of saline water required by ammonia nitrogen recycling of target sewage by using a transmembrane pulse electrochemical system; for any pulse duty ratio configuration information input by a user, inputting the pulse duty ratio configuration information, the pulse frequency, the external voltage, the initial concentration of the catholyte and the like into a pre-trained ammonia nitrogen recovery efficiency prediction model to obtain corresponding ammonia nitrogen recovery efficiency information; based on the ammonia nitrogen recovery efficiency information corresponding to the multiple pieces of pulse duty ratio configuration information, selecting the pulse duty ratio configuration information in which the ammonia nitrogen removal efficiency and the ammonia nitrogen recovery efficiency are respectively not less than corresponding preset threshold values and the ammonia nitrogen recovery ratio energy consumption is minimum; and based on the selected pulse duty ratio configuration information, the pulse frequency, the external voltage, the initial concentration of the catholyte and the like, performing ammonia nitrogen recovery on the target sewage by using the transmembrane pulse electrochemical system.

Description

Technical Field

[0001] The present invention relates to the field of environmental engineering technology, and in particular to a method for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning. Background Art

[0002] Ammonia (NH 3 ) is an energy storage / conversion carrier with high energy density. The global population growth and the increasingly rapid industrial development have led to an increasing demand for ammonia. At present, ammonia production mainly relies on the energy-intensive Haber-Bosch (HB) process, which consumes 1-2% of the world's energy each year and accounts for 1.4% of global carbon emissions. At the same time, the large-scale consumption of ammonia caused by the increase in demand has also led to a continuous increase in the nitrogen content in sewage, resulting in increased eutrophication of water bodies. Under the background of dual carbon, the recovery of sewage ammonia nitrogen can effectively utilize ammonia nitrogen as a resource.

[0003] Ammonia can be recovered from wastewater through a series of methods, including gas stripping, chemical precipitation, ion exchange / adsorption, and capacitive deionization. However, the above methods all have some defects, such as the need to heat the inlet water, the additional addition of chemical agents, low ammonia recovery selectivity and efficiency, and the reactor is not easy to scale up. The electrochemical method for removing ammonia has the characteristics of simple operation and no secondary pollution, and has obvious advantages in treating high ammonia nitrogen wastewater. Among them, transmembrane electrochemical absorption technology (TMECS) is increasingly popular because it does not require additional stripping steps to reduce the complexity of the reactor and is easy to increase the processing capacity by scaling up and stacking. However, the above studies all have the problem of high energy consumption, which limits their promotion and application. Pulse technology is expected to reduce energy consumption due to the reduction of the total discharge time, and can also alleviate the H generated by the electrolysis of water on the surface of the electrodes in the anode and cathode chambers. + and OH - The accumulation of the diffusion layer reduces the energy consumption caused by the overpotential. Therefore, the low energy consumption characteristics of the pulse power supply are utilized, combined with the advantages of TMECS and pulse electric field, in order to achieve low-cost, low-energy, and high-efficiency recovery of ammonia in sewage. However, when recovering ammonia nitrogen from sewage based on transmembrane pulse electrochemistry, it is currently difficult to control the pulse duty cycle of each time period of ammonia nitrogen recovery so that it can meet the recovery efficiency requirements while maintaining low energy consumption. Summary of the invention

[0004] In a first aspect, an embodiment of the present invention provides a method for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning, the method comprising:

[0005] Obtain the pulse frequency, applied voltage, initial concentration of cathode liquid, initial concentration of anode liquid, and initial concentration of brine required for ammonia nitrogen recovery from target wastewater using a transmembrane pulse electrochemical system;

[0006] Receive multiple different pulse duty ratio configuration information for target sewage input by a user, wherein one pulse duty ratio configuration information is used to characterize the pulse duty ratio configured in each time period during the ammonia nitrogen recovery of the target sewage using the transmembrane pulse electrochemical system;

[0007] For any pulse duty cycle configuration information, input it, the pulse frequency, the applied voltage, the initial concentration of the cathode liquid, the initial concentration of the anode liquid, and the initial concentration of the brine into the pre-trained ammonia nitrogen recovery efficiency prediction model to obtain the ammonia nitrogen recovery efficiency information corresponding to the pulse duty cycle configuration information, wherein the ammonia nitrogen recovery efficiency includes ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption;

[0008] Based on the ammonia nitrogen recovery efficiency information corresponding to the multiple pulse duty cycle configuration information, select the pulse duty cycle configuration information whose ammonia nitrogen removal efficiency and ammonia nitrogen recovery efficiency are not less than the corresponding preset thresholds and whose ammonia nitrogen recovery specific energy consumption is the smallest from the multiple pulse duty cycle configuration information;

[0009] Based on the selected pulse duty cycle configuration information as well as the pulse frequency, applied voltage, initial cathode liquid concentration, initial anode liquid concentration, and initial brine concentration, a transmembrane pulse electrochemical system is used to recover ammonia nitrogen from the target wastewater.

[0010] In some implementations of the first aspect, the output voltage of the pulse power supply in the transmembrane pulse electrochemical system is 0-1000V, the output current is 0-9000A, the output power is 0-9000KVA, the frequency range is 0.1-50000HZ, and the duty cycle range is 0-100%.

[0011] In some implementations of the first aspect, the chambers in the transmembrane pulse electrochemical system include a cathode chamber, an anode chamber, two desalination chambers, a sub-cathode chamber, and a sub-anode chamber;

[0012] The auxiliary cathode chamber is connected to its corresponding desalination chamber by a cation exchange membrane, the auxiliary anode chamber is connected to its corresponding desalination chamber by an anion exchange membrane, and the cathode chamber is connected to the anode chamber by a gas-permeable and hydrophobic membrane;

[0013] The auxiliary anode is made of iridium tantalum titanium anode, and the auxiliary cathode is made of stainless steel wire mesh.

[0014] In some implementations of the first aspect, the ammonia nitrogen recovery efficiency prediction model is trained by the following steps:

[0015] Obtain data from ammonia nitrogen recovery experiments on wastewater using a transmembrane pulse electrochemical system and use it to construct a data set;

[0016] The sample data in the data set include: pulse duty cycle configured in each time period during the ammonia nitrogen recovery experiment, pulse frequency, applied voltage, initial concentration of cathode liquid, initial concentration of anode liquid, initial concentration of brine, cathode liquid concentration, and anode liquid concentration during the ammonia nitrogen recovery experiment; the label data in the data set include: ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption during the ammonia nitrogen recovery experiment;

[0017] The initial neural network was trained according to the data set to obtain an ammonia nitrogen recovery efficiency prediction model.

[0018] In some implementations of the first aspect, an initial neural network is trained according to a data set to obtain an ammonia nitrogen recovery efficiency prediction model, including:

[0019] Normalize the data in the dataset and divide the normalized dataset into a training set and a test set;

[0020] The initial neural network is trained using the training set, and at the end of the training, the performance of the current neural network is tested using the test set. If the performance test is passed, the current neural network is used as an ammonia nitrogen recovery efficiency prediction model.

[0021] In some implementations of the first aspect, using the training set to train the initial neural network includes:

[0022] Input the sample data in the training set into the initial neural network to obtain the output result, calculate the error between the output result and the label data, update the model parameters of the neural network according to the error, and continuously iterate and update until the preset stopping condition is met.

[0023] In some implementations of the first aspect, the neural network is a back propagation neural network (BPNN).

[0024] In a second aspect, an embodiment of the present invention provides a device for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning, the device comprising:

[0025] An acquisition module, used to acquire the pulse frequency, applied voltage, initial concentration of cathode liquid, initial concentration of anode liquid, and initial concentration of brine required for recovering ammonia nitrogen from target wastewater using a transmembrane pulse electrochemical system;

[0026] A receiving module, used for receiving a plurality of different pulse duty ratio configuration information for target sewage input by a user, wherein one pulse duty ratio configuration information is used for representing the pulse duty ratio configured in each time period during the ammonia nitrogen recovery of the target sewage using the transmembrane pulse electrochemical system;

[0027] A prediction module is used to input any pulse duty cycle configuration information, the pulse frequency, the applied voltage, the initial concentration of the cathode liquid, the initial concentration of the anode liquid, and the initial concentration of the brine into a pre-trained ammonia nitrogen recovery efficiency prediction model to obtain the ammonia nitrogen recovery efficiency information corresponding to the pulse duty cycle configuration information, wherein the ammonia nitrogen recovery efficiency includes ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption;

[0028] A selection module is used to select, based on the ammonia nitrogen recovery efficiency information corresponding to the multiple pulse duty cycle configuration information, the pulse duty cycle configuration information whose ammonia nitrogen removal efficiency and ammonia nitrogen recovery efficiency are not less than the corresponding preset thresholds and whose ammonia nitrogen recovery ratio energy consumption is the smallest from the multiple pulse duty cycle configuration information;

[0029] The recovery module is used to recover ammonia nitrogen from the target wastewater using a transmembrane pulse electrochemical system based on the selected pulse duty cycle configuration information, pulse frequency, applied voltage, initial cathode liquid concentration, initial anode liquid concentration, and initial brine concentration.

[0030] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0031] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable a computer to execute the method described above.

[0032] In an embodiment of the present invention, machine learning can be combined with transmembrane pulse electrochemical recovery of ammonia nitrogen from wastewater, and a prediction model can be introduced to predict the ammonia nitrogen recovery efficiency of the transmembrane pulse electrochemical system. Based on the prediction results, the pulse duty cycle of the ammonia nitrogen recovery in each time period can be controlled to achieve a level that can meet the recovery efficiency requirements while maintaining low energy consumption, thereby improving the wastewater ammonia nitrogen recovery efficiency.

[0033] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present invention. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0035] Figure 1 A flow chart of a method for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning provided in an embodiment of the present invention;

[0036] Figure 2 This is the structural diagram of the transmembrane pulse electrochemical system;

[0037] Figure 3 It is a schematic diagram of the ammonia nitrogen removal efficiency and ammonia nitrogen recovery efficiency under different reaction conditions in the process of treating ammonia nitrogen wastewater;

[0038] Figure 4 It is a schematic diagram of the specific energy consumption of ammonia nitrogen recovery under different reaction conditions in the process of treating nitrogen-containing wastewater;

[0039] Figure 5 This is a flow chart for building an ammonia nitrogen recovery efficiency prediction model based on BPNN;

[0040] Figure 6 is R with different numbers of hidden layers and different numbers of hidden neurons 2 and RMSE value;

[0041] Figure 7 A diagram of strategies to improve the efficiency of transmembrane pulse electrochemical system in recovering ammonia nitrogen from simulated urine;

[0042] Figure 8 This is a comparison chart of ammonia nitrogen removal and recovery efficiency under direct current conditions and boosting strategies;

[0043] Fig. 9 This is a comparison chart of specific energy consumption of ammonia nitrogen recovery under direct current conditions and boosting strategy;

[0044] Fig.10 A structural diagram of a device for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning provided in an embodiment of the present invention;

[0045] Fig.11 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0048] In order to solve the technical problems arising in the background technology, the embodiments of the present invention provide a method, device, equipment and storage medium for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning. Specifically, the pulse frequency, applied voltage, initial concentration of cathode liquid, initial concentration of anode liquid and initial concentration of brine required for recovering ammonia nitrogen from target sewage using a transmembrane pulse electrochemical system are obtained; for any pulse duty ratio configuration information input by the user, it is input into a pre-trained ammonia nitrogen recovery efficiency prediction model with pulse frequency, applied voltage, initial concentration of cathode liquid, etc., to obtain the corresponding ammonia nitrogen recovery efficiency information; based on the ammonia nitrogen recovery efficiency information corresponding to multiple pulse duty ratio configuration information, the pulse duty ratio configuration information with ammonia nitrogen removal efficiency and ammonia nitrogen recovery efficiency not less than the corresponding preset thresholds and the minimum ammonia nitrogen recovery ratio energy consumption is selected; based on the selected pulse duty ratio configuration information and pulse frequency, applied voltage, initial concentration of cathode liquid, etc., ammonia nitrogen is recovered from the target sewage using a transmembrane pulse electrochemical system.

[0049] In this way, machine learning can be combined with transmembrane pulse electrochemical recovery of ammonia nitrogen from wastewater, and a prediction model can be introduced to predict the ammonia nitrogen recovery efficiency of the transmembrane pulse electrochemical system. Based on the prediction results, the pulse duty cycle of ammonia nitrogen recovery in each time period can be controlled to achieve a level that can meet the recovery efficiency requirements while maintaining low energy consumption, thereby improving the efficiency of ammonia nitrogen recovery from wastewater.

[0050] In conjunction with the accompanying drawings, a method, device, equipment and storage medium for recovering ammonia nitrogen from wastewater by transmembrane pulse electrochemical method based on machine learning provided by an embodiment of the present invention are described in detail through specific embodiments.

[0051] Figure 1 A flow chart of a method for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning is provided in an embodiment of the present invention, such as Figure 1 As shown, the method 100 may include the following steps:

[0052] S110, obtaining the pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, and brine initial concentration required for recovering ammonia nitrogen from target wastewater using a transmembrane pulse electrochemical system.

[0053] Among them, the structure of the transmembrane pulse electrochemical system can be as follows Figure 2As shown, the chambers in the transmembrane pulse electrochemical system include a cathode chamber, an anode chamber, two desalination chambers, a sub-cathode chamber and a sub-anode chamber, and can be amplified by stacking amplification. The sub-cathode chamber is connected to its corresponding desalination chamber by a cation exchange membrane, the sub-anode chamber is connected to its corresponding desalination chamber by an anion exchange membrane, the cathode chamber and the anode chamber are connected by a gas-permeable hydrophobic membrane, the (sub-) anode uses an iridium tantalum titanium anode, and the (sub-) cathode uses a stainless steel wire mesh. The output voltage of the pulse power supply in the transmembrane pulse electrochemical system is 0-1000V, the output current is 0-9000A, the output power is 0-9000KVA, the frequency range is 0.1-50000HZ, and the duty cycle range is 0-100%.

[0054] S120, receiving a plurality of different pulse duty cycle configuration information for target sewage input by a user.

[0055] Among them, each pulse duty ratio configuration information is used to characterize the pulse duty ratio configured in each time period during the ammonia nitrogen recovery of the target sewage using the transmembrane pulse electrochemical system. For example, the ammonia nitrogen recovery period is 0-10h, and there are 5 time periods, namely 0-2h, 2-4h, 4-6h, 6-8h, and 8-10h. At this time, a pulse duty ratio configuration information can be 0-2h, 63%; 2-4h, 50%; 4-6h, 44%; 6-8h, 38%; 8-10h, 25%.

[0056] S130, for any pulse duty cycle configuration information, input it together with the pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, and brine initial concentration into a pre-trained ammonia nitrogen recovery efficiency prediction model to obtain the ammonia nitrogen recovery efficiency information corresponding to the pulse duty cycle configuration information.

[0057] Among them, the ammonia nitrogen recovery efficiency includes ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption. The above ammonia nitrogen recovery efficiency prediction model can be trained through the following steps:

[0058] The data of ammonia nitrogen recovery experiment on sewage using transmembrane pulse electrochemical system was obtained and used to construct a data set. The sample data in the data set include: pulse duty ratio configured in each time period during the ammonia nitrogen recovery experiment, pulse frequency, applied voltage, initial cathode liquid concentration, initial anode liquid concentration, initial brine concentration, cathode liquid concentration, and anode liquid concentration during the ammonia nitrogen recovery experiment. The label data in the data set include: ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption during the ammonia nitrogen recovery experiment.

[0059] The initial neural network (such as BPNN) is trained according to the data set to obtain an ammonia nitrogen recovery efficiency prediction model. Specifically, the data in the data set is normalized, and the normalized data set is divided into a training set and a test set according to a preset ratio, such as (3:2). The initial neural network is trained using the training set, and at the end of the training, the current neural network is tested for performance using the test set. If the performance test is passed, the current neural network is used as an ammonia nitrogen recovery efficiency prediction model.

[0060] Exemplarily, the above-mentioned training of the initial neural network using the training set can be as follows:

[0061] The sample data in the training set is input into the initial neural network to obtain the output result, the error between the output result and the label data is calculated, and the model parameters of the neural network are updated according to the error. The update is continuously iterated until the preset stop condition is met (for example, the error is less than the preset threshold or the number of iterations reaches the preset threshold).

[0062] S140, based on the ammonia nitrogen recovery efficiency information corresponding to the multiple pulse duty cycle configuration information, select the pulse duty cycle configuration information whose ammonia nitrogen removal efficiency and ammonia nitrogen recovery efficiency are not less than the corresponding preset thresholds and whose ammonia nitrogen recovery ratio energy consumption is minimal from the multiple pulse duty cycle configuration information.

[0063] That is to say, here, based on the ammonia nitrogen recovery efficiency information corresponding to multiple pulse duty cycle configuration information, the pulse duty cycle configuration information that optimizes the ammonia nitrogen recovery efficiency is selected from the multiple pulse duty cycle configuration information.

[0064] S150, based on the selected pulse duty cycle configuration information and pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, and brine initial concentration, a transmembrane pulse electrochemical system is used to recover ammonia nitrogen from the target wastewater.

[0065] Specifically, based on the selected pulse duty cycle configuration information as well as the pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, and brine initial concentration, the transmembrane pulse electrochemical system is configured accordingly, and the configured transmembrane pulse electrochemical system is used to recover ammonia nitrogen from the target wastewater.

[0066] In order to facilitate further understanding, the above content is described in detail below in conjunction with some specific embodiments:

[0067] Embodiment 1:

[0068] The structure of the transmembrane pulse electrochemical system involved in the present invention is as follows: Figure 2As shown, the chamber consists of a cathode chamber, an anode chamber, a desalination chamber, a sub-cathode chamber and a sub-anode chamber, and can be amplified by stacking amplification. The sub-cathode chamber is connected to its corresponding desalination chamber by a cation exchange membrane, the sub-anode chamber is connected to its corresponding desalination chamber by an anion exchange membrane, and the cathode chamber is connected to the anode chamber by a gas-permeable hydrophobic membrane. The sub-anode uses an iridium tantalum titanium anode (4.0 cm × 4.0 cm), and the sub-cathode uses a stainless steel wire mesh (200 mesh, 0.1 mm thick, 4.0 cm × 4.0 cm, model 304). The cation exchange membrane (4.0 cm × 4.0 cm) used in the system is model CMI-7000, the anion exchange membrane (4.0 cm × 4.0 cm) is model AMI-7001, and the gas-permeable hydrophobic membrane (4.0 cm × 4.0 cm) is made of polytetrafluoroethylene with a polypropylene support layer. The cathode chamber is injected with simulated ammonia nitrogen sewage (120 mL) prepared with deionized water, which contains ammonium sulfate of different concentrations. Ammonium sulfate solutions of different concentrations (120 mL) were injected into the anode chamber. 150 mM sodium sulfate solution (120 mL) was injected into each of the two desalination chambers. Sodium sulfate solutions of different concentrations (120 mL) were injected into the sub-cathode chamber and the sub-anode chamber. Experiments were conducted to explore the various factors that affect the system's ammonia nitrogen recovery effect and energy efficiency, which are mainly divided into two categories: solution parameter settings of each chamber in the reaction system (cathode liquid concentration, anode liquid concentration, brine concentration in the desalination chamber) and pulse power supply parameter settings (pulse duty cycle, pulse frequency, applied voltage). During the factor experiment, the reactor settings, system operation mode, and solution components of each chamber remained unchanged. The test cycle was 10 hours, the sampling time interval was 2 hours, the sampling volume was 1 mL each time, and the pH of each chamber was measured and recorded at the same time. All experiments were carried out in an environment with a temperature of 25±1°C. The specific experimental settings and methods are as follows:

[0069] (1) The initial experimental conditions were set as follows: the simulated sewage components and concentration in the cathode chamber were 3000 mg NH 4 + -N / L (NH 4 ) 2 SO 4 +3000mg / L Na 2 SO 4 , the concentration of the anode solution is 168 mg NH 4 + -N / L (NH 4 ) 2 SO 4 , the solution concentration of the sub-cathode chamber and the sub-anode chamber is 150mM Na 2 SO 4 The solution concentration in the two desalting chambers is 500 mM Na 2 SO 4The two desalination chambers are connected by an external hose for brine circulation at a circulation rate of 25 mL / min. The system circuit is in series, the power supply is connected to the auxiliary cathode and auxiliary anode of the reactor, and the system is pulsed with a duty cycle of 50% through a pulse power supply (SOYI-1506M) in the middle. The duty cycle of the pulse power supply is adjusted to 100% for DC mode power supply, and the system maintains a constant voltage operation mode.

[0070] (2) In order to investigate the effect of cathode solution concentration on the recovery rate and energy efficiency of the transmembrane pulse electrochemical system, in addition to changing the concentration of (NH 4 ) 2 SO 4 Except for the concentration, the other conditions were kept consistent with the pulse group in the feasibility experiment. In the subsequent factor experiment, except for the individual factors to be explored in each section, the other conditions were also kept consistent with the pulse group in the feasibility experiment. 4 + -N concentration was set to 1000, 2000, 3000, 4000, 5000 mg NH 4 + -N / L.

[0071] (3) In the experiment to explore the effect of duty cycle on the recovery rate and energy efficiency of the transmembrane pulse electrochemical system, the duty cycle was set to 0%, 20%, 40%, 50%, 60%, 80%, and 100%.

[0072] (4) In the experiment to explore the effect of voltage on the recovery rate and energy efficiency of the transmembrane pulse electrochemical system, the external voltage was set to 0V, 3V, 6V, 9V, 12V, and 15V.

[0073] (5) In the experiment to investigate the effect of pulse frequency on the recovery rate and energy efficiency of the transmembrane pulse electrochemical system, the pulse frequency was set to 50 Hz, 1500 Hz, 3000 Hz, 4500 Hz, and 6000 Hz.

[0074] (6) In the experiment to investigate the effect of anolyte concentration on the recovery rate and energy efficiency of the transmembrane pulse electrochemical system, the anolyte (NH 4 ) 2 The concentration of SO4 was set to 6mM, 12mM, 18mM, 24mM, 30mM, and 50mM;

[0075] (7) In the experiment to investigate the effect of the salt water concentration in the desalination chamber on the recovery rate and energy efficiency of the transmembrane pulse electrochemical system, the Na 2 SO 4 The concentration was set to 150mM, 200mM, 300mM, 400mM, and 500mM.

[0076] The ammonia removal efficiency and recovery efficiency under different reaction conditions in the process of treating nitrogen-containing wastewater are as follows: Figure 3 As shown, the corresponding specific energy consumption is Figure 4 shown.

[0077] When the duty cycle is >20%, the removal rate and recovery rate of the system can reach more than 90% and 75% respectively after 10 hours of operation. The specific energy consumption of the system is low at low duty cycles, but the reaction time needs to be extended to achieve removal rates and recovery rates close to those at high duty cycles. Therefore, choosing a suitable duty cycle based on the specific needs of the reaction speed and energy consumption has better application prospects. In contrast, changes in frequency have almost no effect on the removal and recovery of ammonia in this system. The removal and recovery effects of ammonia improve with increasing voltage, and at the same time, the corresponding specific energy consumption also increases. NH in the anode liquid 4 + The increase in concentration will reduce the chemical potential difference between it and the adjacent cathode chamber, resulting in a gradual decrease in ammonia recovery efficiency. This system has a good treatment capacity for sewage with a wide range of ammonia nitrogen concentrations. When the cathode liquid ammonia nitrogen concentration is 1000mg / L, 3000mg / L and 5000mg / L after 10h, the ammonia removal efficiency is 96.7%, 94.4% and 92.5% respectively, and the ammonia recovery efficiency is 73.8%, 81.4% and 76.0% respectively. The corresponding specific energy consumption is 100.3kWh / kg N, 28.5kWh / kg N and 20.0kWh / kg N respectively.

[0078] Embodiment 2:

[0079] Here we provide a method for building an ammonia nitrogen recovery efficiency prediction model based on BPNN, which is used to predict the ammonia nitrogen recovery efficiency. The construction of the ammonia nitrogen recovery efficiency prediction model is divided into the following three parts: data preparation, network construction and model training.

[0080] In terms of data preparation, the data source is the experimental data in Example 1, and each set of data includes pulse duty cycle, pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, brine initial concentration, cathode liquid concentration, anode liquid concentration, pH, ammonia nitrogen removal rate, ammonia nitrogen recovery rate, and ammonia nitrogen recovery ratio energy consumption. A data set is constructed based on this data, and the sample data in the data set includes: pulse duty cycle, pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, brine concentration, cathode liquid concentration, anode liquid concentration, and pH; the label data in the data set includes: ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery ratio energy consumption. The data in the data set are normalized, and the normalized data set is divided into a training set and a test set according to a preset ratio, such as (3:2).

[0081] In the process of building the ammonia nitrogen recovery efficiency prediction model, it is necessary to complete the network structure initialization, randomly initialize the weights, set the activation function, set the learning rate and the number of iterations. After the data preprocessing and model building are completed, use the training set to train the model. The training process of data in the neural network is mainly divided into forward propagation process and back propagation process. During the forward propagation process, the data is input into the neural network, passing through the hidden layer and the output layer in sequence, and the error between the output result and the label data is calculated. Then it enters the back propagation stage, in which the model adjusts the weights of each parameter and updates the weights. Then repeat the above process until the error between the output result and the actual result reaches the target, and then use the test set to perform a performance test on the current model. If the performance test passes, the current model will be used as the final ammonia nitrogen recovery efficiency prediction model. The construction flow chart is as follows Figure 5 shown.

[0082] The models and software used are all run in the Windows 10 system environment. The computer language used is Python, the integrated development environment (IDE) is PyCharm 2024.1 (Community Edition), and the model is run based on the PyTorch deep learning framework. Use Anaconda 3 to configure the environment and manage toolkits. The toolkits used mainly include scikit-learn, numpy, scipy, pandas, etc. The data prediction values ​​and corresponding model parameters in the model training process are automatically written into the Excel table for storage. The specific code construction process of the BPNN-based ammonia nitrogen recovery efficiency prediction model is as follows:

[0083] Import required libraries and modules → Define model → Set global seed → Read data → Set parameters → Use function to set seed → Dataset processing → Separate features and labels → Divide training set and test set → Data standardization → Convert to PyTorch tensor → Model initialization → Define optimizer and loss function → Set early stopping parameters → Write storage parameters → Model training → Model prediction → Calculate R 2 and RMSE → check if the performance has improved → check if it has reached the pre-set threshold → record R 2 and RMSE and write to file → End.

[0084] After the model is built, data reading and model training are performed. Data reading is done by saving the existing experimental data and the parameters corresponding to the experimental results to be predicted in a csv format file in a specific folder path. After clicking Run in the PyCharm software, the model starts running, automatically reading the data file and performing data training and prediction. The predicted results and the corresponding R 2After the model prediction is completed, parameters such as RMSE will be displayed in the PyCharm software. At the same time, all prediction data will be automatically generated by the model into a csv format file and saved in the same folder with a custom name.

[0085] In the above process, the main setting parameters involved are feature dimension, number of neural network layers, number of hidden layer neurons, label dimension, training rounds, and training set test set ratio. 2 And RMSE are used as the accuracy evaluation indicators of the model. Figure 6 Shows R under different numbers of hidden layers and different numbers of hidden neurons 2 The experiment obtained a better R when the number of hidden layers was 1. 2 Except for the RMSE value, as the number of hidden layers increases, the model accuracy cannot be improved. As the number of neurons in the hidden layer increases, the model has the best R when the number of neurons is 8. 2 (0.953) and RMSE (7.606). Therefore, in subsequent studies, the number of hidden layers and the number of neurons in the hidden layer were set to 1 and 8, respectively.

[0086] Embodiment 3:

[0087] The source separated urine is used as the practical application object of the transmembrane pulse electrochemical system for recovering ammonia nitrogen from wastewater. The ammonia nitrogen recovery efficiency prediction model trained in Example 2 is used to predict the ammonia nitrogen recovery efficiency of actual urine, and compared with the ammonia nitrogen recovery efficiency under direct current conditions to verify the ammonia nitrogen recovery efficiency advantage of the transmembrane pulse electrochemical system over the direct current system.

[0088] The collected urine was stored in a brown reagent bottle and sealed in a cool environment at room temperature. After the urine was naturally hydrolyzed for 30 days, its ammonia nitrogen content was determined. The ammonia nitrogen content was about 4750 mg NH 4 + -N / L. The urine hydrolyzate after 30 days of hydrolysis was used as the cathode chamber solution. The specific experimental setting conditions were as follows: 120 mL of 150 mM Na 2 SO 4 Solution, add 120mL of 500mM Na 2 SO 4 The solution was connected to the desalination chamber through an external pipe for brine circulation. 120 mL of 6 mM (NH 4 ) 2 SO 4Solution, add the above 120mL of actual urine after hydrolysis into the cathode chamber. The system is powered by a pulse power supply (SOYI-1506M). A series circuit is used between the six chambers of the reactor, and the system adopts a constant pressure operation mode. The reaction time is set to 12h. Sampling is performed every 2h, and the pH of each chamber is measured and recorded. Each time 1mL of the sample is stored in a centrifuge tube. The above experiments were carried out at an ambient temperature of 25±1℃.

[0089] Figure 7 The diagram shows the efficiency improvement strategy of the transmembrane pulse electrochemical system for recovering ammonia nitrogen in simulated urine. The duty ratios of the reaction time 0-2h, 2-4h, 4-6h, 6-8h, and 8-10h are set to 63%, 50%, 44%, 38%, and 25% respectively. The duty ratio under direct current conditions is set to 100% at 0-10h. Figure 8 The figure is a comparison of ammonia nitrogen removal and recovery efficiency under direct current conditions and boosting strategies. After 10 hours of operation, the ammonia removal efficiency under direct current conditions and boosting strategies was 91.8% and 77.0%, respectively, and the ammonia recovery efficiency was 74.0% and 65.2%, respectively. Fig. 9 The figure is a comparison of the specific energy consumption of ammonia nitrogen recovery efficiency under DC conditions and the boosting strategy. After 10 hours of operation, the specific energy consumption under the boosting strategy (19.5kWh / kg N) decreased by 76.9% compared with the specific energy consumption under DC conditions (84.4kWh / kg N). In summary, the transmembrane pulse electrochemical system has a greater energy-saving advantage in recovering ammonia in actual urine compared with the DC power supply. The ammonia nitrogen recovery efficiency prediction model based on BPNN can improve the efficiency of transmembrane pulse electrochemical recovery of sewage ammonia, and has good application prospects.

[0090] It can be seen that the present invention at least achieves the following technical effects:

[0091] The present invention aims to reduce the energy consumption of electrochemical recovery of ammonia nitrogen from sewage. Combining the low energy consumption characteristics of pulse power supply, the pulse power supply is combined with a transmembrane electrochemical system to explore the feasibility of reducing energy consumption in the process of recovering ammonia under pulse conditions. Furthermore, based on the good learning and prediction functions of BPNN for complex parameter experiments, this study proposes an operating strategy for improving the efficiency of the transmembrane pulse electrochemical recovery system for ammonia nitrogen from sewage. Finally, the strategy was verified and evaluated by treating actual human urine. In short, the present invention aims to provide a high-efficiency, no need for external agents, easy to scale up and low-energy-cost electrochemical recovery of ammonia nitrogen from sewage for recovering ammonia nitrogen from actual sewage, in order to provide technical support for the recycling of ammonia nitrogen resources and the protection of the ecology.

[0092] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0093] The above is an introduction to a method embodiment. The following is a further explanation of the solution of the present invention through an apparatus embodiment.

[0094] Fig.10 A structural diagram of a device for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemical method based on machine learning provided in an embodiment of the present invention, such as Fig.10 As shown, the apparatus 1000 may include:

[0095] The acquisition module 1010 is used to obtain the pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, and brine initial concentration required for recovering ammonia nitrogen from target wastewater using a transmembrane pulse electrochemical system.

[0096] The receiving module 1020 is used to receive multiple different pulse duty cycle configuration information for the target sewage input by the user, wherein one pulse duty cycle configuration information is used to characterize the pulse duty cycle configured in each time period during the ammonia nitrogen recovery of the target sewage using the transmembrane pulse electrochemical system.

[0097] The prediction module 1030 is used to input any pulse duty cycle configuration information, together with the pulse frequency, the external voltage, the initial concentration of the cathode liquid, the initial concentration of the anode liquid, and the initial concentration of the brine into a pre-trained ammonia nitrogen recovery efficiency prediction model to obtain the ammonia nitrogen recovery efficiency information corresponding to the pulse duty cycle configuration information, wherein the ammonia nitrogen recovery efficiency includes ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption.

[0098] The selection module 1040 is used to select, based on the ammonia nitrogen recovery efficiency information corresponding to the multiple pulse duty cycle configuration information, the pulse duty cycle configuration information with the ammonia nitrogen removal efficiency and the ammonia nitrogen recovery efficiency not less than the corresponding preset thresholds and the minimum ammonia nitrogen recovery specific energy consumption from the multiple pulse duty cycle configuration information.

[0099] The recovery module 1050 is used to recover ammonia nitrogen from the target wastewater using a transmembrane pulse electrochemical system based on the selected pulse duty cycle configuration information and pulse frequency, applied voltage, initial cathode liquid concentration, initial anode liquid concentration, and initial brine concentration.

[0100] Understandably, Fig.10The various modules / units in the device 1000 shown have the function of implementing Figure 1 The functions of the various steps in the method 100 shown can achieve their corresponding technical effects, and for the sake of brevity, they will not be repeated here.

[0101] Fig.11 1 is a block diagram of an exemplary electronic device capable of implementing an embodiment of the present invention. Electronic device 1100 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 1100 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required in the present invention.

[0102] like Fig.11 As shown, the electronic device 1100 may include a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a random access memory (RAM) 1103. In the RAM 1103, various programs and data required for the operation of the electronic device 1100 can also be stored. The computing unit 1101, the ROM 1102, and the RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0103] Multiple components in the electronic device 1100 are connected to the I / O interface 1105, including: an input unit 1106, such as a keyboard, a mouse, etc.; an output unit 1107, such as various types of displays, speakers, etc.; a storage unit 1108, such as a disk, an optical disk, etc.; and a communication unit 1109, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1109 allows the electronic device 1100 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0104] The computing unit 1101 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1101 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer program product, including a computer program, which is tangibly contained in a computer-readable medium, such as a storage unit 1108. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1100 via the ROM 1102 and / or the communication unit 1109. When the computer program is loaded into the RAM 1103 and executed by the computing unit 1101, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to execute the method 100 in any other appropriate manner (eg, by means of firmware).

[0105] The various embodiments described above in the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of the present invention, a computer-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a computer-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0108] It should be noted that the present invention also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effect achieved by executing the method in an embodiment of the present invention. For the sake of concise description, they will not be repeated here.

[0109] In addition, the present invention also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method 100 is implemented.

[0110] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and the present invention is not limited here.

[0111] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for recovering ammonia nitrogen from sewage by transmembrane pulse electrochemistry based on machine learning, characterized in that: The method comprises: Obtain the pulse frequency, applied voltage, initial concentration of cathode liquid, initial concentration of anode liquid, and initial concentration of brine required for ammonia nitrogen recovery from target wastewater using a transmembrane pulse electrochemical system; Receive multiple different pulse duty ratio configuration information for target sewage input by a user, wherein one pulse duty ratio configuration information is used to characterize the pulse duty ratio configured in each time period during the ammonia nitrogen recovery of the target sewage using the transmembrane pulse electrochemical system; For any pulse duty cycle configuration information, input it, the pulse frequency, the applied voltage, the initial concentration of the cathode liquid, the initial concentration of the anode liquid, and the initial concentration of the brine into the pre-trained ammonia nitrogen recovery efficiency prediction model to obtain the ammonia nitrogen recovery efficiency information corresponding to the pulse duty cycle configuration information, wherein the ammonia nitrogen recovery efficiency includes ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption; Based on the ammonia nitrogen recovery efficiency information corresponding to the multiple pulse duty cycle configuration information, select the pulse duty cycle configuration information whose ammonia nitrogen removal efficiency and ammonia nitrogen recovery efficiency are not less than the corresponding preset thresholds and whose ammonia nitrogen recovery specific energy consumption is the smallest from the multiple pulse duty cycle configuration information; Based on the selected pulse duty cycle configuration information as well as the pulse frequency, applied voltage, initial cathode liquid concentration, initial anode liquid concentration, and initial brine concentration, a transmembrane pulse electrochemical system is used to recover ammonia nitrogen from the target wastewater.

2. The method according to claim 1, characterized in that The output voltage of the pulse power supply in the transmembrane pulse electrochemical system is 0-1000V, the output current is 0-9000A, the output power is 0-9000KVA, the frequency range is 0.1-50000HZ, and the duty cycle range is 0-100%.

3. The method according to claim 1, characterized in that The chambers in the transmembrane pulse electrochemical system include a cathode chamber, an anode chamber, two desalination chambers, a secondary cathode chamber and a secondary anode chamber; The auxiliary cathode chamber is connected to its corresponding desalination chamber by a cation exchange membrane, the auxiliary anode chamber is connected to its corresponding desalination chamber by an anion exchange membrane, and the cathode chamber is connected to the anode chamber by a gas-permeable and hydrophobic membrane; The auxiliary anode is made of iridium tantalum titanium anode, and the auxiliary cathode is made of stainless steel wire mesh.

4. The method according to claim 1, characterized in that: The ammonia nitrogen recovery efficiency prediction model is trained by the following steps: Obtain data from ammonia nitrogen recovery experiments on wastewater using a transmembrane pulse electrochemical system and use it to construct a data set; The sample data in the data set include: pulse duty cycle configured in each time period during the ammonia nitrogen recovery experiment, pulse frequency, applied voltage, initial concentration of cathode liquid, initial concentration of anode liquid, initial concentration of brine, cathode liquid concentration, and anode liquid concentration during the ammonia nitrogen recovery experiment; the label data in the data set include: ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption during the ammonia nitrogen recovery experiment; The initial neural network was trained according to the data set to obtain an ammonia nitrogen recovery efficiency prediction model.

5. The method according to claim 4, characterized in that The initial neural network is trained according to the data set to obtain an ammonia nitrogen recovery efficiency prediction model, including: Normalize the data in the dataset and divide the normalized dataset into a training set and a test set; The initial neural network is trained using the training set, and at the end of the training, the performance of the current neural network is tested using the test set. If the performance test is passed, the current neural network is used as an ammonia nitrogen recovery efficiency prediction model.

6. The method according to claim 5, characterized in that Use the training set to train the initial neural network, including: Input the sample data in the training set into the initial neural network to obtain the output result, calculate the error between the output result and the label data, update the model parameters of the neural network according to the error, and continuously iterate and update until the preset stopping condition is met.

7. The method according to claim 6, characterized in that The neural network is a back propagation neural network BPNN.

8. A transmembrane pulse electrochemical device for recovering ammonia nitrogen from sewage based on machine learning, characterized in that: The device comprises: An acquisition module, used to acquire the pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, and brine initial concentration required for recovering ammonia nitrogen from target wastewater using a transmembrane pulse electrochemical system; A receiving module, used for receiving a plurality of different pulse duty ratio configuration information for target sewage input by a user, wherein one pulse duty ratio configuration information is used for representing the pulse duty ratio configured in each time period during the ammonia nitrogen recovery of the target sewage using the transmembrane pulse electrochemical system; A prediction module is used to input any pulse duty cycle configuration information, pulse frequency, applied voltage, cathode liquid initial concentration, anode liquid initial concentration, and brine initial concentration into a pre-trained ammonia nitrogen recovery efficiency prediction model to obtain ammonia nitrogen recovery efficiency information corresponding to the pulse duty cycle configuration information, wherein the ammonia nitrogen recovery efficiency includes ammonia nitrogen removal efficiency, ammonia nitrogen recovery efficiency, and ammonia nitrogen recovery specific energy consumption; A selection module is used to select, based on the ammonia nitrogen recovery efficiency information corresponding to the multiple pulse duty cycle configuration information, the pulse duty cycle configuration information whose ammonia nitrogen removal efficiency and ammonia nitrogen recovery efficiency are not less than the corresponding preset thresholds and whose ammonia nitrogen recovery ratio energy consumption is the smallest from the multiple pulse duty cycle configuration information; The recovery module is used to recover ammonia nitrogen from the target wastewater using a transmembrane pulse electrochemical system based on the selected pulse duty cycle configuration information, pulse frequency, applied voltage, initial cathode liquid concentration, initial anode liquid concentration, and initial brine concentration.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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