A data-driven-based control method and system for a natural gas atmosphere oxidation-free furnace

Abstract

The application discloses a kind of based on data-driven natural gas atmosphere non-oxidation furnace control method and system, it includes: obtaining the process parameter sample library of different products;Based on sample library, prediction model is established, according to the temperature parameter set, the natural gas flow value and air flow value of each furnace section are predicted, and the control parameter of each furnace section is obtained;When switching process or operation, the process parameters set in each furnace section are matched with the sample data in sample library;If match, then the natural gas and air flow value corresponding to sample data is used to control the temperature of each furnace section;If not match, then the temperature value set in each furnace section in process parameter is regarded as input layer, and is input to prediction model for calculation, obtains the required natural gas and air flow value, and controls the temperature of each furnace section.Adjustment of control parameter of new product is realized self-adapting, to some extent, resource and energy consumption are saved, production efficiency is effectively improved, and product quality is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of industrial furnace control technology, and in particular to a data-driven control method and system for a natural gas atmosphere non-oxidizing furnace. Background Technology

[0002] An oxidation-free furnace is a heating furnace in which the billet does not oxidize during the heating process, and it is an important heating device in steel production. Especially when used for the heat treatment of silicon steel, an oxidation-free heating furnace rapidly heats the strip steel and is divided into two sections: a preheating section and an oxidation-free heating section. To ensure that the strip steel is not oxidized during heating, an excess of fuel gas is introduced into the oxidation-free heating section, and a micro-oxidizing atmosphere is achieved within the furnace by adjusting the air-fuel ratio. The oxidation-free heating section employs multi-stage combustion heating, with each stage using an independent control method to regulate the flow of fuel gas and combustion-supporting gases, thereby controlling the furnace temperature distribution within the oxidation-free heating area according to process requirements. Because silicon oxidizes more easily than iron, silicon steel oxidizes extremely easily in oxidizing atmospheres (containing O2, CO2, H2O, etc.), and this reaction is irreversible. Therefore, controlling the heating temperature, heating rate, and air-fuel ratio within the oxidation-free furnace is a crucial step in ensuring the quality of silicon steel products.

[0003] In existing technologies, to improve production quality and yield, reduce emissions, extend equipment life and heating capacity, natural gas atmosphere non-oxidizing furnaces use natural gas as the combustion medium. Because natural gas (35,544 kJ / m³) has a higher calorific value than coal gas (approximately 16,726–17,081 kJ / m³) as the combustion medium for non-oxidizing furnaces, and because the six sections of the non-oxidizing furnace are completely interconnected, fluctuations in the natural gas flow rate in each section have a more significant and severe impact on the furnace temperature. Therefore, a data-driven control method and system for natural gas atmosphere non-oxidizing furnaces is needed. Summary of the Invention

[0004] This application provides a data-driven natural gas atmosphere non-oxidizing furnace control method and system, which at least partially solves the technical problem in the prior art that the fluctuation of natural gas flow in each section of the furnace has a more severe impact on the furnace temperature, and achieves the technical effect of stable furnace temperature control.

[0005] Firstly, to solve the above-mentioned technical problems, embodiments of the present invention provide the following technical solutions:

[0006] A data-driven control method for a natural gas atmosphere oxidation-free furnace includes:

[0007] By using preset sensors, a sample library of process parameters for different products is obtained when natural gas is used as the combustion medium.

[0008] A predictive model is established based on the sample library. According to the temperature parameters set for each section of the non-oxidizing furnace, the natural gas flow rate and air flow rate of each section are predicted to obtain the control parameters of each section.

[0009] When switching processes or operations, the process parameters set for each furnace section are matched with the sample data in the sample library. If they match, the natural gas flow rate and air flow rate corresponding to the sample data are directly used to control the temperature of each furnace section. If they do not match, the temperature values ​​set for each furnace section in the process parameters are used as the input layer and input into the prediction model for calculation to obtain the required natural gas flow rate and air flow rate values, and the temperature of each furnace section is controlled.

[0010] Optionally, before controlling the temperature of each furnace section, the method further includes:

[0011] The natural gas flow rate and air flow rate are input into the preset cross-limit control algorithm to obtain the target natural gas flow rate and air flow rate that meet the preset fluctuation range of the furnace temperature, and the temperature of each furnace section is controlled based on the target natural gas flow rate and air flow rate.

[0012] Optionally, the step of obtaining the target natural gas flow rate and air flow rate values ​​that conform to the preset fluctuation range of the furnace temperature further includes:

[0013] The furnace temperature is monitored, and based on a stability threshold, it is determined whether the furnace temperature is stable.

[0014] If the furnace temperature fluctuation exceeds the stable threshold, a single cross-limiting control algorithm is used to control the furnace temperature to tend towards the set value; otherwise, a double cross-limiting control algorithm is used to control the furnace temperature within the error range of the set value.

[0015] Optionally, the step of determining whether the furnace temperature is stable based on a stability threshold may also include:

[0016] Continuously acquire furnace temperature signals for each furnace section for a preset duration, and calculate the temperature signal T of each furnace section based on a preset formula. ij Standard deviation σ j and the average temperature of each furnace section and process setpoint T s absolute difference ΔT j The preset formula is:

[0017]

[0018]

[0019] Based on the absolute difference ΔT j and standard deviation σ j Each set of stability thresholds is preset, and the furnace temperature is determined to be stable based on these thresholds.

[0020] Optionally, the steps for building a predictive model may also include:

[0021] A multi-layer feedforward BP neural network was used to establish a furnace temperature parameter prediction model, and the required temperature of each furnace section was used as input for training. The prediction model includes an input layer, a hidden layer, and an output layer. The transfer functions of the hidden layer and the output layer are tansig and purelin functions, respectively, and the network training function is traingdx function.

[0022] Optionally, the step of obtaining a sample library of process parameters for different products when natural gas is used as the combustion medium may further include:

[0023] Temperature, air flow, and gas flow data for different products are acquired using temperature sensors, air flow meters, and gas flow meters, and this data is used as a sample library of process parameters.

[0024] Optionally, the steps for controlling the temperature of each furnace section may also include:

[0025] Based on the obtained natural gas flow rate and air flow rate values, the natural gas flow rate and air flow rate are controlled through the corresponding valves.

[0026] Secondly, a data-driven natural gas atmosphere oxidation-free furnace control system is provided, the system comprising:

[0027] The sample library establishment module is used to acquire a sample library of process parameters for different products when natural gas is used as the combustion medium, through preset sensors.

[0028] The prediction module establishes a prediction model based on the sample library. According to the temperature parameters set for each section of the non-oxidizing furnace, it predicts the natural gas flow rate and air flow rate of each section to obtain the control parameters of each section.

[0029] The furnace temperature control module is used to match the process parameters set for each furnace section with the sample data in the sample library when switching processes or operations. If they match, the natural gas flow rate and air flow rate corresponding to the sample data are directly used to control the temperature of each furnace section. If they do not match, the temperature values ​​set for each furnace section in the process parameters are used as input layers and input into the prediction model for calculation to obtain the required natural gas flow rate and air flow rate values, and then the temperature of each furnace section is controlled.

[0030] Thirdly, an electronic device is provided, comprising: a temperature sensor, an air flow meter, a natural gas flow meter, an air valve, a gas valve, a PLC controller, and a computer program stored on and run on the PLC controller. When the PLC controller executes the computer program, it performs the steps corresponding to the method of the first aspect. The output terminals of the temperature sensor, the air flow meter, and the natural gas flow meter are respectively connected to the signal input terminals of the PLC controller. The signal output terminals of the PLC controller are respectively connected to the air valve and the gas valve.

[0031] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the steps corresponding to the method of the first aspect.

[0032] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0033] By utilizing sensors to acquire process parameters such as furnace temperature and the flow rates of fuel gas and combustion-supporting gas, and establishing a process parameter sample library and temperature stability criteria, a data-driven furnace temperature control parameter prediction model is built. When changing processes or operations, for matching process parameters, control parameters are directly obtained from the process parameter sample library; for new process parameters without matching samples, the established prediction model predicts the control parameters for the new process. Then, based on the obtained control parameters and the magnitude of furnace temperature fluctuations, a single / dual cross-linked furnace temperature control algorithm is selected to rapidly approach and precisely stabilize the furnace temperature at a preset value. This automates the adjustment of control parameters for new products, saving resources and energy to a certain extent, effectively improving production efficiency, and ensuring product quality. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart of a data-driven natural gas atmosphere non-oxidizing furnace control method provided in this application;

[0036] Figure 2 A data-driven control method and equipment block diagram for a natural gas atmosphere oxidation-free furnace provided in this application;

[0037] Figure 3 A schematic diagram of the system structure of a data-driven natural gas atmosphere non-oxidizing furnace control method provided in this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0040] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0041] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0042] It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. Unless otherwise specified, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0043] In the embodiments of this application, the following are provided: Figure 1 The method shown is a data-driven control method for a natural gas atmosphere oxidation-free furnace, which includes steps S101 to S103:

[0044] Step S101: Obtain a sample library of process parameters for different products when natural gas is used as the combustion medium by using a preset sensor.

[0045] It should be noted that natural gas is used as the combustion medium, i.e., in a natural gas atmosphere. This embodiment employs the industrial Ethernet communication protocol to establish communication between the monitoring computer and the PLC controller of the natural gas atmosphere oxidation-free furnace; temperature, natural gas flow, and air flow data are collected through sensors. Specifically, it monitors specific parameters to achieve monitoring of the temperature, natural gas flow, and air flow in each furnace section during oxidation-free operation. In use, it reads real-time data from the required process parameter storage unit inside the oxidation-free furnace controller and stores all process parameters according to a set storage time period.

[0046] Step S102: Establish a prediction model based on the sample library, and predict the natural gas flow rate and air flow rate of each furnace section according to the temperature parameters set for each section of the non-oxidizing furnace, so as to obtain the control parameters of each furnace section.

[0047] It should be noted that in this embodiment, the prediction model employs a multi-layer feedforward BP neural network (BackPropagation Network). Furthermore, it is trained using furnace temperature as the output parameter. This results in a prediction model that forecasts natural gas and air flow rates based on temperature parameters, thereby achieving automated control of the new product.

[0048] Step S103: When switching processes or operations, the process parameters set for each furnace section are matched with the sample data in the sample library. If they match, the natural gas flow rate and air flow rate corresponding to the sample data are directly used to control the temperature of each furnace section. If they do not match, the temperature values ​​set for each furnace section in the process parameters are used as the input layer and input into the prediction model for calculation to obtain the required natural gas flow rate and air flow rate, and the temperature of each furnace section is controlled.

[0049] It should be noted that for products with prior processing experience, data from the sample library can be used directly for control without the need for prediction. For new products, a predictive model is then employed for forecasting, thereby automating the adjustment of control parameters and effectively improving production efficiency while ensuring product quality.

[0050] Furthermore, before controlling the temperature of each furnace section, the method also includes:

[0051] The natural gas flow rate and air flow rate are input into the preset cross-limit control algorithm to obtain the target natural gas flow rate and air flow rate that meet the preset fluctuation range of the furnace temperature, and the temperature of each furnace section is controlled based on the target natural gas flow rate and air flow rate.

[0052] It should be noted that since most non-oxidizing furnaces currently use coal gas as the combustion gas and employ a dual-cross control algorithm, directly using the dual-cross control algorithm for coal gas to control the furnace temperature of a non-oxidizing furnace with a natural gas combustion atmosphere presents problems such as long switching times, large temperature fluctuations, and incomplete process parameter databases when changing processes or operations. Therefore, this embodiment adopts a cross-limiting control algorithm, that is, air first, fuel second; when reducing load (cooling), fuel first, air second. This not only maintains a reasonable air-fuel ratio in steady state but also in dynamic conditions, and the dual-cross-limiting control effect is particularly better. This achieves the economic and rational requirements of the furnace combustion process, significantly improving the furnace combustion control level, which plays a positive role in saving energy, reducing pollution, and improving the environment.

[0053] The step of obtaining the target natural gas flow rate and air flow rate values ​​within the preset fluctuation range of furnace temperature also includes: detecting the furnace temperature and determining whether the furnace temperature is stable based on a stability threshold; if the furnace temperature fluctuation is greater than the stability threshold, a single cross-limiting control algorithm is used to control the furnace temperature to tend towards the set value; otherwise, a double cross-limiting control algorithm is used to control the furnace temperature within the error range of the set value.

[0054] It should be noted that different algorithms, single-cross limiting control and double-cross limiting control, are employed for different situations. The purpose is that when changing processes or operations, the single-cross furnace temperature control algorithm allows the furnace temperature to quickly approach the preset value. When the furnace temperature fluctuates near the preset value, and the fluctuation range meets the set threshold, the double-cross control algorithm is switched on to quickly stabilize the furnace temperature at the set process temperature. The single / double-cross limiting control algorithm is used cyclically based on real-time changes in furnace temperature, thereby ensuring precise and stable furnace temperature.

[0055] Furthermore, the step of determining whether the furnace temperature is stable based on the stability threshold also includes:

[0056] Continuously acquire furnace temperature signals for each furnace section for a preset duration, and calculate the temperature signal T of each furnace section based on a preset formula. ij Standard deviation σ j and the average temperature of each furnace section and process setpoint T s absolute difference ΔT j The preset formula is:

[0057]

[0058]

[0059] Based on the absolute difference ΔT j and standard deviation σ jEach set of stability thresholds is preset, and the furnace temperature is determined to be stable based on these thresholds.

[0060] It should be noted that a temperature sensor is used for temperature monitoring. The absolute difference ΔT is used to determine the temperature. j and standard deviation σ j Presetting stable threshold values ​​for each furnace section is based on the standard deviation σ of the furnace temperature in each section. j Sum and difference ΔT j Set the furnace temperature determination threshold S for each furnace section. σj and S Tj This means that the furnace temperature in each section is lower than the judgment threshold, i.e., when σ j ≤S σj and ΔT j ≤S Tj , indicating stability, and setting the overall stability threshold S based on this stability condition.

[0061] Furthermore, the steps in building a predictive model also include:

[0062] A multi-layer feedforward BP neural network was used to establish a furnace temperature parameter prediction model, and the required temperature of each furnace section was used as input for training. The prediction model includes an input layer, a hidden layer, and an output layer. The transfer functions of the hidden layer and the output layer are tansig and purelin functions, respectively, and the network training function is traingdx function.

[0063] It should be noted that the input layer, hidden layer, and output layer have 6, 6, and 12 units in the hidden layer, respectively. The network has 5000 epochs and an expected error of 10⁻⁶. -13 The learning rate is 0.1.

[0064] Furthermore, the step of obtaining a sample library of process parameters for different products when natural gas is used as the combustion medium also includes:

[0065] Temperature, air flow, and gas flow data for different products are acquired using temperature sensors, air flow meters, and gas flow meters, and this data is used as a sample library of process parameters.

[0066] Furthermore, the steps for controlling the temperature of each furnace section also include:

[0067] Based on the obtained natural gas flow rate and air flow rate values, the natural gas flow rate and air flow rate are controlled through the corresponding valves.

[0068] It should be noted that the gas valve is controlled based on the natural gas flow rate, while the air valve is controlled based on the air flow rate.

[0069] Based on the same inventive concept, embodiments of this application provide a data-driven natural gas atmosphere non-oxidizing furnace control system, such as... Figure 3 As shown, it includes:

[0070] The sample library establishment module is used to acquire a sample library of process parameters for different products when natural gas is used as the combustion medium, through preset sensors.

[0071] The prediction module establishes a prediction model based on the sample library. According to the temperature parameters set for each section of the non-oxidizing furnace, it predicts the natural gas flow rate and air flow rate of each section to obtain the control parameters of each section.

[0072] The furnace temperature control module is used to match the process parameters set for each furnace section with the sample data in the sample library when switching processes or operations. If they match, the natural gas flow rate and air flow rate corresponding to the sample data are directly used to control the temperature of each furnace section. If they do not match, the temperature values ​​set for each furnace section in the process parameters are used as input layers and input into the prediction model for calculation to obtain the required natural gas flow rate and air flow rate values, and then the temperature of each furnace section is controlled.

[0073] Based on the same inventive concept, embodiments of this application provide an electronic device, such as... Figure 1 As shown, it includes: a temperature sensor, an air flow meter, a natural gas flow meter, an air valve, a gas valve, a PLC controller, and a computer program stored on the PLC controller and capable of running on the PLC controller. When the PLC controller executes the computer program, it implements a data-driven control method for a natural gas atmosphere non-oxidizing furnace. The output terminals of the temperature sensor, air flow meter, and natural gas flow meter are respectively connected to the signal input terminals of the PLC controller. The signal output terminals of the PLC controller are respectively connected to the air valve and the gas valve.

[0074] Based on the same inventive concept, this embodiment provides a computer-readable storage medium storing a computer program, characterized in that the program, when executed by a processor, implements a data-driven control method for a natural gas atmosphere non-oxidizing furnace.

[0075] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0077] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0078] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A data-driven control method for a natural gas atmosphere non-oxidizing furnace, characterized in that, The method includes: By using preset sensors, a sample library of process parameters for different products is obtained when natural gas is used as the combustion medium. Based on the sample library, a prediction model is established. According to the temperature parameters set for each section of the non-oxidizing furnace, the natural gas flow rate and air flow rate of each furnace section are predicted to obtain the control parameters of each furnace section. When switching processes or operations, the process parameters set for each furnace section are matched with the sample data in the sample library. If they match, the natural gas flow rate and air flow rate corresponding to the sample data are directly used to control the temperature of each furnace section. If they do not match, the temperature values ​​set for each furnace section in the process parameters are used as the input layer and input into the prediction model for calculation to obtain the required natural gas flow rate and air flow rate values, and the temperature of each furnace section is controlled. Before controlling the temperature of each furnace section, the method further includes: The natural gas flow rate and air flow rate are input into a preset cross-limiting control algorithm to obtain target natural gas flow rate and air flow rate values ​​that conform to the preset fluctuation range of the furnace temperature. The temperature of each furnace section is controlled based on the target natural gas flow rate and air flow rate values. The step of obtaining target natural gas flow rate and air flow rate values ​​that conform to the preset fluctuation range of the furnace temperature further includes: detecting the furnace temperature and determining whether the furnace temperature is stable based on a stability threshold; if the furnace temperature fluctuation is greater than the stability threshold, a single cross-limiting control algorithm is used to control the furnace temperature to tend towards the set value; otherwise, a double cross-limiting control algorithm is used to control the furnace temperature within the error range of the set value.

2. The method as described in claim 1, characterized in that, The step of determining whether the furnace temperature is stable based on a stability threshold further includes: The furnace temperature signals of each furnace section are continuously acquired for a preset duration, and the temperature signals of each furnace section are calculated based on a preset formula. Standard deviation and the average temperature of each furnace section and process setting values absolute difference The preset formula is: According to absolute difference and standard deviation Each set of stability thresholds is preset, and the furnace temperature is determined to be stable based on the stability thresholds.

3. The method as described in claim 1, characterized in that, The steps for establishing the prediction model also include: A multi-layer feedforward BP neural network is used to establish a furnace temperature parameter prediction model, and the required temperature of each furnace section is used as input for training. The prediction model includes an input layer, a hidden layer, and an output layer. The transfer functions of the hidden layer and the output layer are tansig and purelin functions, respectively, and the network training function is traingdx function.

4. The method as described in claim 1, characterized in that, The step of obtaining a sample library of process parameters for different products when natural gas is used as the combustion medium further includes: Temperature, air flow, and gas flow data of different products are acquired using temperature sensors, air flow meters, and gas flow meters, and this data is used as a sample library of process parameters.

5. The method as described in claim 1, characterized in that, The step of controlling the temperature of each furnace section further includes: Based on the obtained natural gas flow rate and air flow rate, the natural gas flow rate and air flow rate are controlled by the corresponding valves.

6. A data-driven natural gas atmosphere non-oxidizing furnace control system, used to implement the data-driven natural gas atmosphere non-oxidizing furnace control method according to any one of claims 1 to 5, characterized in that, The system includes: The sample library establishment module is used to acquire a sample library of process parameters for different products when natural gas is used as the combustion medium, through preset sensors. The prediction module establishes a prediction model based on the sample library, and predicts the natural gas flow rate and air flow rate of each furnace section according to the temperature parameters set for each section of the non-oxidizing furnace, thereby obtaining the control parameters of each furnace section. The furnace temperature control module is used to match the process parameters set for each furnace section with the sample data in the sample library when switching processes or operations. If they match, the module directly uses the natural gas flow rate and air flow rate corresponding to the sample data to control the temperature of each furnace section. If they do not match, the module uses the temperature values ​​set for each furnace section in the process parameters as the input layer to the prediction model for calculation to obtain the required natural gas flow rate and air flow rate, and then controls the temperature of each furnace section.

7. An electronic device, characterized in that, The electronic device includes: a temperature sensor, an air flow meter, a natural gas flow meter, an air valve, a gas valve, a PLC controller, and a computer program stored on the PLC controller and executable on the PLC controller. When the PLC controller executes the computer program, it implements the control method according to any one of claims 1 to 5. The output terminals of the temperature sensor, the air flow meter, and the natural gas flow meter are respectively connected to the signal input terminal of the PLC controller. The signal output terminal of the PLC controller is respectively connected to the air valve and the gas valve.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps corresponding to the method as described in any one of claims 1 to 5.

Images