Fluorine-free casting powder raw material component optimization method, system and equipment and storage medium

Abstract

The invention discloses a fluorine-free casting powder raw material component optimization method, system and equipment and a storage medium, and relates to the technical field of continuous casting powder. The method comprises the following steps: constructing a fluoride-free casting powder raw material preparation scheme; establishing a fluoride-free casting powder raw material-performance relation model; establishing a fluorine-free casting powder raw material-performance multi-objective optimization model; and determining a multi-attribute optimal comprehensive decision result. According to the method, a modeling research and development normal form for fluorine-free casting powder raw material component design and optimization is established, the contradiction between heat transfer and lubrication of the fluorine-free casting powder can be effectively coordinated, the fluorine-free casting powder which is environmentally friendly and stable in performance is designed, then the surface quality of a casting blank is improved, and meanwhile energy consumption and production cost are reduced.

Description

Technical Field

[0001] This invention relates to the field of continuous casting protective slag technology, and in particular to a method, system, equipment and storage medium for optimizing the raw material composition of fluorine-free protective slag. Background Technology

[0002] As a key functional material in the continuous casting process, flux is crucial for ensuring smooth operation and high-quality cast billets. However, traditional fluorinated flux still faces two major challenges in practical applications. First, the use of fluorinated flux inevitably generates fluorinated waste gas and wastewater, which pose significant environmental hazards and severely impact equipment lifespan and human health. Second, there is an irreconcilable conflict between the heat transfer and lubrication functions of fluorinated flux. This conflict can lead to a series of product quality problems, such as longitudinal cracks on the billet surface and sticking / extrusion, ultimately affecting the overall performance and market competitiveness of continuously cast products.

[0003] To address the aforementioned issues, the industry has conducted a series of studies and proposed some solutions. For example, some studies have attempted to mitigate environmental harm by reducing the fluorine content in the protective slag. However, this method often leads to a decrease in the heat transfer and lubrication functions of the protective slag while reducing the fluorine content, failing to effectively solve problems such as longitudinal cracks on the billet surface and sticking / leaking steel. Other studies have attempted to improve the performance of the protective slag by adding other components to replace fluorine. However, due to the complexity of the composition and the difficulty in precisely controlling the proportions of each component, the performance of the protective slag is unstable and fails to meet the requirements of continuous casting processes. Therefore, how to design a fluorine-free protective slag that is both environmentally friendly and has stable performance has become an urgent technical problem to be solved in this field. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to provide a method for optimizing the raw material composition of fluorine-free protective slag that can accurately control the performance of fluorine-free protective slag.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for optimizing the raw material composition of fluorine-free protective slag, comprising the following steps: (1) Construct a slag blending scheme for fluorine-free protective slag; (2) Establish a raw material-performance relationship model for fluorine-free protective slag; (3) Establish a multi-objective optimization model for the raw materials and performance of fluorine-free protective slag; (4) Determine the optimal comprehensive decision result for multiple attributes.

[0006] A further technical solution is that the slag blending scheme for constructing fluorine-free protective slag includes the following steps: Blast furnace slag was selected as the main base material, supplemented with limestone and quartz sand to adjust the basicity of the fluorine-free slag. Soda ash and borax were added as the main fluxes for the fluorine-free slag. Based on the phase diagram, the chemical composition range of the boron-containing fluorine-free protective slag experimental system was calculated and analyzed. A uniform design method was used to set basicity, soda ash content, and borax content as raw material component factors. A uniform design table was used to conduct slag mixing tests.

[0007] A further technical solution is that the method for establishing the raw material-performance relationship model of fluorine-free protective slag includes the following steps: Hemispherical point temperature, viscosity, critical cooling rate for crystallization, initial temperature for crystallization, crystallization rate, and thermal conductivity were selected and measured as key indicators for evaluating the performance of fluorine-free protective slag. Using SPSS software and multiple regression analysis, a mathematical relationship model between raw material components and melting, crystallization, and heat transfer performance was established. The influence mechanism of the interaction of raw material components on each performance was analyzed, and a multiple regression model of the relationship between raw material components and performance indicators of fluorine-free protective slag was fitted.

[0008] A further technical solution involves establishing a multi-objective optimization model for the performance of fluorine-free protective slag, which includes the following steps: Based on a comprehensive understanding of the ideal performance requirements of the on-site continuous casting process for the protective slag, the third-generation genetic optimization algorithm (NSGA III) is adopted. The six multivariate regression models established above are used as multi-objective optimization functions. The optimization condition is that each performance index is as close as possible to the ideal value. A multi-objective optimization model of fluorine-free protective slag raw material-performance is established. Then, through crossover, mutation and iterative operations, the Pareto front solution set representing the optimization result is gradually approximated and finally obtained.

[0009] A further technical solution involves determining the optimal comprehensive decision result across multiple attributes, including the following steps: First, the obtained Pareto front solution set is normalized to obtain an initial scheme matrix E. ij R ij It is the j-th objective value of the i-th alternative; (1) Secondly, the information entropy of each performance index is calculated using the entropy weighting method to obtain the weight coefficient w of each index. j P ij F represents the ratio of the i-th option under the j-th indicator. j Let the information entropy of the j-th indicator be , j Information entropy redundancy; (2) (3) (4) Finally, a weighted normalized solution matrix G is constructed based on the Top-Solution Distance (TOPSIS) method. ij The comprehensive evaluation score D is obtained by calculating the Euclidean distance from each solution to the positive and negative ideal solutions. i The optimal solution is determined.

[0010] (5) (6) (7) (8) A further technical solution is that the method also includes comparative analysis and verification of the optimal solution: Based on the raw material composition conditions of the fluorine-free protective slag determined by the optimal solution, tests were conducted on its melting performance, crystallization performance, and heat transfer performance. The feasibility of the method was verified by comparing the experimental results of the optimal solution with the model predictions and by error analysis.

[0011] This invention also discloses a system for optimizing the raw material composition of fluorine-free protective slag, comprising: a raw material slag blending design module; a relationship model construction module; a multi-objective optimization model construction module; a decision result determination module; and an analysis and comparison module. The system runs on a computer to implement the method for optimizing the raw material composition of fluorine-free protective slag.

[0012] The present invention also discloses a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing the raw material composition of the fluorine-free protective slag.

[0013] The present invention also discloses a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, is used to implement the method for optimizing the raw material composition of the fluorine-free protective slag.

[0014] The beneficial effects of adopting the above technical solution are as follows: 1) This method establishes a model-based R&D paradigm for the design and optimization of raw material components of fluorine-free protective slag. Based on the slag blending experiment with uniform design of raw material components, a mathematical model of the relationship between raw material components and performance indicators is established, and environmentally friendly fluorine-free protective slag materials are prepared; 2) By adopting the multi-objective optimization comprehensive method of NSGA Ⅲ optimization algorithm combined with entropy weight-TOPSIS decision method, the boron-containing fluorine-free protective slag raw material conditions suitable for industrial continuous casting site and with optimal comprehensive performance are determined, providing an effective method for precise control of protective slag performance. Attached Figure Description

[0015] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0016] Figure 1 This is the main flowchart of the method described in Embodiment 1 of the present invention; Figure 2a The melting temperature performance test results are for the optimal solution in the method described in Embodiment 1 of the present invention. Figure 2b The viscosity-temperature curve is the optimal solution in the method described in Embodiment 1 of the present invention. Figure 3a This is a crystallization CCT curve of the optimal solution in the method described in Embodiment 1 of the present invention; Figure 3b This is a crystallization TTT curve of the optimal solution in the method described in Embodiment 1 of the present invention; Figure 4 The graph shows the test results of the crystallinity and thermal conductivity of the optimal solution. Figure 5 This is a schematic block diagram of the system described in the embodiment of the present invention; Figure 6 This is a schematic block diagram of the computer device described in the embodiments of the present invention. Detailed Implementation

[0017] 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 a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Example 1: Overall, such as Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for optimizing the raw material composition of fluorine-free protective slag, the method comprising the following steps: Step 1: Construct a slag blending scheme for fluorine-free protective slag; Step 2: Establish a raw material-performance relationship model for fluorine-free protective slag; Step 3: Establish a multi-objective optimization model for the performance of fluorine-free protective slag raw materials; Step 4: Determine the optimal comprehensive decision result for multiple attributes.

[0020] The above steps will be explained in detail below with reference to specific content: Step 1: Construct a slag blending scheme for fluorine-free protective slag based on the uniform design method: Considering the wide availability and low cost of raw materials, blast furnace slag was selected as the main base material, supplemented with limestone and quartz sand to adjust the basicity of the fluorine-free slag, and then soda ash and borax were added as the main fluxes for the fluorine-free slag. Based on the phase diagram, the chemical composition range of the proposed boron-containing fluorine-free protective slag experimental system was calculated and analyzed. A uniform design method was used to set basicity (CaO / SiO2), soda ash content, and borax content as raw material component factors, and the selection was... A uniform design slag blending experiment was conducted. The uniform design slag blending scheme for fluorine-free protective slag is shown in Table 1, with the factor variables and their constraints as follows (1-1, 1-2), where... X 1 , X 2 , X 3 These represent the raw material component factors: alkalinity, soda ash content, and borax content.

[0021] Table 1 - Uniform Design Scheme for Fluorine-Free Protective Slag

[0022] Factor variables: (1) Constraints: (2) (3) (4) (5) (6) (7) (8) Multi-objective optimization function: (9) Step 2: Establish a raw material-performance relationship model for fluorine-free protective slag based on multiple regression method: Hemispherical point temperature, viscosity, critical cooling rate for crystallization, initial crystallization temperature, crystallinity, and thermal conductivity were selected and measured as key indicators for evaluating the performance of fluorine-free protective slag. Using SPSS software and multiple regression analysis, mathematical models were established to establish the relationship between raw material components and melting, crystallization, and heat transfer properties, analyzing the influence mechanism of raw material component interactions on each property. The multiple regression models that successfully fitted the relationship between raw material components and various performance indicators of the fluorine-free protective slag are shown in equations (1-3, 14, 1-5, 1-6, 1-7, 1-8), where... Y B , Y N , Y Q , Y C , Y J , Y D These represent performance indicators: hemispherical point temperature, viscosity, critical cooling rate for crystallization, initial temperature for crystallization, crystallinity, and thermal conductivity.

[0023] Step 3: Establish a multi-objective optimization model for the raw materials and performance of fluorine-free protective slag based on the NSGA III algorithm: Reconciling the contradiction between heat transfer and lubrication in fluorine-free protective slag inevitably involves a trade-off between performance indicators related to melting, crystallization, and heat transfer. Improving one aspect often comes at the cost of weakening the other two, making it a typical multi-objective optimization problem. Therefore, to address this issue, a novel NSGA III optimization algorithm was introduced to conduct multi-objective comprehensive optimization on six multiple regression models representing the relationship between raw material components and various performance indicators. For example, taking the performance requirements of the protective slag in a low-alloy peritectic steel continuous casting process as the optimization objective, the hemispherical point temperature (HMT) of the traditional fluorine-containing protective slag used in continuous casting was determined. Y B ), viscosity ( Y N ), critical cooling rate for crystallization ( Y Q ), initial crystallization temperature ( Y C ), crystallization rate ( Y J ), thermal conductivity ( Y DBased on the ideal values, and with the optimization condition that all performance indicators of the fluorine-free protective slag are close to the ideal values, the multi-objective optimization function is established as follows (1-9). In the multi-objective optimization application of the NSGA III algorithm, what is obtained is not a single optimal solution, but a set of solutions with good attributes for each objective and difficult to distinguish in terms of superiority or inferiority. This is called the Pareto front optimization solution set, which represents the set of all compromise solutions that are optimal for different objectives. The initialization parameters of the NSGA III algorithm are set as follows: initial population size is 30, maximum number of generations is 200, crossover and mutation probability is 0.8, and individual mutation probability is 0.2. After compiling the code using Python and running it, the Pareto front optimization solution set is obtained, as shown in Table 2.

[0024] Table 2 - Optimization results of Pareto front for fluorine-free protective slag

[0025] Determining the optimal comprehensive decision-making result for multiple attributes based on the entropy-weighted TOPSIS method: To effectively avoid the interference of human subjective bias in the decision-making process and ensure the balance of weight allocation, while pursuing the optimal performance of the NSGA III algorithm on various performance indicators, the entropy-weighted TOPSIS comprehensive decision-making method was selected. This method is suitable for decision problems that require simultaneous consideration of multiple interrelated or conflicting indicators, and can perform multi-attribute decision-making on the non-dominated Paretofront optimization solution set generated by multi-objective optimization of fluorine-free protective slag performance.

[0026] First, the non-dominated Pareto front solution set of the fluorine-free protective slag is organized into an initial decision matrix. Second, the information entropy of each performance index is calculated using the entropy weight method to obtain the weight coefficients of each index, as shown in Table 3. Finally, a weighted normalized scheme matrix is ​​constructed based on the TOPSIS method, and the comprehensive evaluation score D is obtained by calculating the Euclidean distance from each scheme to the positive and negative ideal solutions. i As shown in Table 4.

[0027] Specifically, the steps include the following: First, normalize the obtained Pareto front solution set to obtain an initial scheme matrix E. ij R ij It is the j-th objective value of the i-th alternative; (10) Secondly, the information entropy of each performance index is calculated using the entropy weighting method to obtain the weight coefficient w of each index. j P ij F represents the ratio of the i-th option under the j-th indicator. j Let the information entropy of the j-th indicator be , jInformation entropy redundancy; (11) (12) (13) Finally, a weighted normalized solution matrix G is constructed based on the Top-Solution Distance (TOPSIS) method. ij The comprehensive evaluation score D is obtained by calculating the Euclidean distance from each solution to the positive and negative ideal solutions. i The optimal solution is determined.

[0028] (14) (15) (16) (17) Scheme 15 is closest to the positive ideal scheme in terms of Euclidean distance, while being furthest from the negative ideal scheme. Its comprehensive evaluation score reaches 0.828, thus Scheme 15 is determined as the optimal scheme in this study.

[0029] Table 3 - Calculation Results of Index Weights Using the Entropy Weight Method

[0030] Table 4 - TOPSIS Score Calculation Results

[0031] Step 4: Comparative analysis and verification of the optimal solution: To further verify the reliability and accuracy of the optimal solution determined by the entropy weight-TOPSIS integrated decision-making method, tests were conducted on multiple performance indicators, including melting performance, crystallization performance, and heat transfer performance, based on the raw material composition conditions of the fluorine-free protective slag determined by the optimal solution. Specific test results are shown below. Figures 2a-2b , Figures 3a-3b as well as Figure 4 As shown in Table 5, the feasibility of the method was verified by comparing the experimental results of the optimal scheme with the model predictions and conducting error analysis. In summary, the results demonstrate that the NSGA III-entropy weighted TOPSIS method can effectively and accurately achieve the multi-objective optimization of raw material components for the performance of fluorine-free protective slag, and also provides an effective method for the precise control of protective slag performance.

[0032] Table 5 - Comparison of Predicted Results and Experimental Results for the Optimal Solution

[0033] Example 2 This invention discloses a system for optimizing the raw material composition of fluorine-free protective slag, such as... Figure 5 As shown, the system includes: Raw material slag blending module 101: Used to construct a raw material slag blending scheme for fluorine-free protective slag; Relationship Model Construction Module 102: Used to establish a relationship model between raw materials and performance of fluorine-free protective slag; Target optimization model construction module 103: used to establish a multi-objective optimization model for the performance of fluorine-free protective slag; Decision Result Determination Module 104: Determines the optimal comprehensive decision result across multiple attributes; Analysis and comparison module 105: Used for comparative analysis and verification of the optimal solution.

[0034] The specific implementation methods of each module in the system described in Embodiment 2 of this application can refer to the methods described in Embodiment 1, and will not be repeated here.

[0035] Example 3 In one exemplary embodiment, the present invention also provides a computer device, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements the method for optimizing the raw material composition of the fluorine-free protective slag described in Example 1.

[0036] Those skilled in the art will understand that Figure 6The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0037] In one exemplary embodiment, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0038] In one exemplary embodiment, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0039] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0040] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0041] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units, etc., and are not limited to these.

[0042] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0043] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the composition of fluorine-free protective slag raw materials, characterized in that... Includes the following steps: Construct a slag blending scheme for fluorine-free protective slag; Establish a raw material-performance relationship model for fluorine-free protective slag; Establish a multi-objective optimization model for the raw materials and performance of fluorine-free protective slag; Determine the optimal comprehensive decision result across multiple attributes.

2. The method for optimizing the raw material composition of fluorine-free protective slag as described in claim 1, characterized in that, The proposed scheme for constructing a fluorine-free protective slag raw material blending method includes the following steps: Blast furnace slag was selected as the main base material, supplemented with limestone and quartz sand to adjust the basicity of the fluorine-free slag. Soda ash and borax were added as the main fluxes for the fluorine-free slag. Based on the phase diagram, the chemical composition range of the boron-containing fluorine-free protective slag experimental system was calculated and analyzed. A uniform design method was used to set basicity, soda ash content, and borax content as raw material component factors. A uniform design table was used to conduct slag mixing tests.

3. The method for optimizing the raw material composition of fluorine-free protective slag as described in claim 1, characterized in that, The method for establishing the raw material-performance relationship model of fluorine-free protective slag includes the following steps: Hemispherical point temperature, viscosity, critical cooling rate for crystallization, initial temperature for crystallization, crystallization rate, and thermal conductivity were selected and measured as key indicators for evaluating the performance of fluorine-free protective slag. Using SPSS software and multiple regression analysis, a mathematical relationship model between raw material components and melting, crystallization, and heat transfer performance was established. The influence mechanism of the interaction of raw material components on each performance was analyzed, and a multiple regression model of the relationship between raw material components and performance indicators of fluorine-free protective slag was fitted.

4. The method for optimizing the raw material composition of fluorine-free protective slag as described in claim 1, characterized in that, The method for establishing a multi-objective optimization model for the raw materials and performance of fluorine-free protective slag includes the following steps: Based on a comprehensive understanding of the ideal performance requirements of the on-site continuous casting process for the protective slag, the genetic optimization algorithm NSGA III is adopted. The six established multiple regression models are used as multi-objective optimization functions. The optimization condition is that each performance index is as close as possible to the ideal value. A multi-objective optimization model of fluorine-free protective slag raw material-performance is established. Then, through crossover, mutation and iterative operations, the Pareto front solution set representing the optimization result is gradually approximated and finally obtained.

5. The method for optimizing the raw material composition of fluorine-free protective slag as described in claim 4, characterized in that, Determining the optimal comprehensive decision result across multiple attributes includes the following steps: First, the obtained Pareto front solution set is normalized to obtain an initial scheme matrix E ij ij is the jth objective value of the ith alternative scheme;​ (1) Secondly, the information entropy of each performance index is calculated using the entropy weighting method to obtain the weight coefficient w of each index. j P ij F represents the ratio of the i-th option under the j-th indicator. j Let the information entropy of the j-th indicator be , j Information entropy redundancy; (2) (3) (4) Finally, a weighted normalized solution matrix G is constructed based on the TOPSIS (Top-Side Solution Distance) method. ij The comprehensive evaluation score D is obtained by calculating the Euclidean distance from each solution to the positive and negative ideal solutions. i The optimal solution is determined. (5) (6) (7) (8)。 6. The method for optimizing the raw material composition of fluorine-free protective slag as described in claim 1, characterized in that, The method also includes comparative analysis and verification of the optimal solution: Based on the raw material composition conditions of the fluorine-free protective slag determined by the optimal solution, tests were conducted on its melting performance, crystallization performance, and heat transfer performance. The feasibility of the method was verified by comparing the experimental results of the optimal solution with the model predictions and by error analysis.

7. A system for optimizing the raw material composition of fluorine-free protective slag, characterized in that, The system includes: Raw material slag blending module: used to construct raw material slag blending schemes for fluorine-free protective slag; Relationship Model Building Module: Used to establish a relationship model between raw materials and performance of fluorine-free protective slag; Objective optimization model construction module: used to establish a multi-objective optimization model for the performance of fluorine-free protective slag; Decision Result Determination Module: Used to determine the optimal comprehensive decision result across multiple attributes; Analysis and Comparison Module: Used for comparative analysis and verification of the optimal solution.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for optimizing the composition of the fluorine-free protective slag raw material according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program is used to implement the method for optimizing the composition of the fluorine-free protective slag raw material as described in any one of claims 1-6.

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