A method for on-line evaluation of energy saving potential in steelmaking process

Abstract

This invention discloses an online assessment method for the energy-saving potential of steelmaking processes, comprising the following steps: S1. Acquiring historical production data of the steelmaking process, and constructing an energy consumption model for the steelmaking process based on the production mechanism, material flow, and energy flow of the steelmaking process, and based on an energy consumption mechanism model and a data-driven model; S2. Analyzing the historical production data to obtain energy consumption influencing factors, and determining the parameters affecting the energy consumption model and their changes in conjunction with energy-saving technologies for the steelmaking process; S3. Inputting the determined parameters and their changes into the energy consumption model of the steelmaking process, calculating the energy consumption level and related parameters of the applied energy-saving technologies, establishing an energy-saving technology library, and constructing a technology limit energy consumption model; S4. Acquiring production data of the steelmaking process online, calculating the current energy consumption level and the energy consumption level after applying energy-saving technologies; S5. Comparing and analyzing the current energy consumption level and the energy consumption level after applying energy-saving technologies, providing the changes in related parameters, the energy-saving potential of advanced technologies, and the contribution of each energy-saving technology to reducing energy consumption.

Description

Technical Field

[0001] This invention relates to the field of metallurgical energy conservation technology, and in particular to an online method for evaluating the energy-saving potential of steelmaking processes. Background Technology

[0002] Converter steelmaking is a crucial component of the long-process production in steel enterprises, and it is also the only negative-energy smelting process among the typical processes. Therefore, reducing energy consumption in steelmaking is essential for optimizing the steel industry structure and reducing overall energy consumption. Currently, a large number of energy-saving and consumption-reducing technologies have been proposed for converter steelmaking, but the energy-saving potential analysis and evaluation of each technology is still immature, and the energy-saving potential of the combined application of various technologies needs further research.

[0003] To address the aforementioned technical issues, patent application number 201510051565.7 discloses an energy efficiency assessment method for converter steelmaking processes. This method identifies and categorizes factors influencing energy consumption in converter smelting processes based on the converter smelting process and historical data; identifies the influence coefficients of key influencing factors on energy consumption and establishes benchmark energy consumption values ​​and benchmark operating conditions for converter production activities; and evaluates the converter energy efficiency level under actual operating conditions based on the benchmark operating conditions.

[0004] The two-level energy efficiency indicators in the above technical solution refer to comparable indicators obtained after adjusting for deviations under a benchmark. These indicators allow for comparison of the energy efficiency levels of different converters or heat runs, as well as the differences between various media. However, they cannot reveal the energy-saving potential of the steelmaking process.

[0005] Therefore, it is necessary to improve the existing technology to overcome the aforementioned defects. Summary of the Invention

[0006] The purpose of this invention is to provide an online assessment method for the energy-saving potential of steelmaking processes, in order to address the shortcomings of existing technologies.

[0007] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0008] An online method for assessing the energy-saving potential of a steelmaking process includes the following steps:

[0009] S1. Obtain historical production data for the steelmaking process and construct a data-driven model for the steelmaking process;

[0010] S2. Construct an energy consumption mechanism model for the steelmaking process based on the production mechanism, material flow, and energy flow of converter steelmaking; construct an energy consumption model for the steelmaking process based on the energy consumption mechanism model and the data-driven model.

[0011] S3. Analyze historical production data to obtain factors affecting energy consumption, and combine them with energy-saving technologies in the steelmaking process to determine the parameters affecting the energy consumption model and their changes.

[0012] S4. Input the determined parameters and their changes into the energy consumption model of the steelmaking process, calculate the energy consumption level and related parameters of the applied energy-saving technology; use samples to verify and evaluate the calculation results. If they are qualified, establish an energy-saving technology library; otherwise, re-analyze the factors affecting energy consumption.

[0013] S5. Combining the energy consumption model of the steelmaking process and the energy-saving technology library, a technical limit energy consumption model is obtained;

[0014] S6. Obtain production data of the steelmaking process online, input the energy consumption model and technical limit energy consumption model of the steelmaking process respectively, and calculate the current energy consumption level and the energy consumption level after applying energy-saving technology;

[0015] S7. Compare and analyze the current energy consumption level and the energy consumption level after applying energy-saving technologies, and provide the changes in relevant parameters, the energy-saving potential of advanced technologies, and the contribution of each energy-saving technology to reducing energy consumption.

[0016] Furthermore, the data-driven model for the steelmaking process is constructed based on the LSTM algorithm, and the process is as follows:

[0017] When processing sequence data, the LSTM model adjusts the input x at the current time step. t The hidden state h of the previous time step t-1 Calculate the output values ​​of the forget gate, input gate, and output gate using the following formula;

[0018] f t =σ(W f [h t-1 x t ]+b f (1)

[0019] i t =σ(W i [h t-1 x t ]+b i (2)

[0020] o t =σ(W o [h t-1 x t ]+b o (3)

[0021] Among them, f t Forget gate, controls whether previous information is deleted from the cell state; i tThe input gate controls the process of updating the cell state; t The output gate controls the flow of information from the cell state to the hidden state; σ is the sigmoid activation function, and W... f W i W o and b f b i b o These are the weight matrices and bias vectors for the forget gate, input gate, and output gate, respectively; [h t-1 ,x t [h at time step t] t-1 and x t Serial vectors;

[0022] According to h t-1 and x t The expression involved in calculating the new cell state is:

[0023]

[0024] in, For the new cell state, tanh is the hyperbolic tangent activation function;

[0025] Then, using a gating mechanism, the output value f of the forget gate is determined. t The output value i of the input gate t The cell state C of the previous time step t-1 Update the current cell state C t ;

[0026]

[0027] Finally, the output value o is output through the output gate. t and the current unit state C t Calculate the new hidden state h t As the output of the current time step;

[0028] h t =o t tanh(C t (6)

[0029] By executing these steps sequentially at each time step, the LSTM model can effectively process long sequence data, maintain and update the cell state and hidden state, thereby achieving modeling and prediction of sequence data.

[0030] Furthermore, the energy consumption model for the steelmaking process is as follows:

[0031]

[0032] Among them, Q baseThe baseline energy consumption is the total energy consumption of the steelmaking process without the application of any energy-saving technologies; i represents the use of different energy-consuming items or materials in the steelmaking process; m represents the total number of energy-consuming items, that is, the total number of different energy-consuming items involved in the steelmaking process; C i E is the reduction factor; i,base This indicates the contribution or importance of each energy consumption item i to the total energy consumption in the steelmaking process.

[0033] Furthermore, the technical limit energy consumption model for the steelmaking process is as follows:

[0034]

[0035] Among them, E t The technological limit energy consumption refers to the total energy consumption of the steelmaking process after applying energy-saving technologies; m represents the total number of energy consumption items, that is, the total number of different energy consumption items involved in the steelmaking process; C i E is the reduction factor; i,t Let i be the actual energy consumption of each energy consumption item i after applying energy-saving technology.

[0036] Furthermore, the energy-saving potential of the steelmaking process is calculated using equation (9);

[0037] dQ rzgx =E base -E t (9)

[0038] Among them, dQ rzgx The energy-saving potential after applying all energy-saving technologies to the steelmaking process.

[0039] Furthermore, based on the calculation of the energy-saving potential of the steelmaking process technology, the application contribution of a single energy-saving technology is calculated using formula (10):

[0040]

[0041] Among them, gxd 技术i Let dQ be the contribution value of the application of the i-th energy-saving technology to the energy-saving potential of the steelmaking process. 技术i The energy-saving potential of applying the i-th energy-saving technology under baseline operating conditions; The summation is the energy-saving potential of each energy-saving technology; n is the total number of energy-saving technologies.

[0042] In summary, the present invention has the following beneficial effects:

[0043] By combining material balance, heat balance, and converter production mechanisms, and utilizing advanced computer technologies such as big data, machine learning, and visualization, the energy-saving potential of applying advanced energy-saving technologies in the steelmaking process can be analyzed and evaluated online, providing objective and effective suggestions for energy conservation and consumption reduction in the steelmaking process. Attached Figure Description

[0044] Figure 1 This is a flowchart of the online assessment method for energy-saving potential in the steelmaking process described in this invention.

[0045] Figure 2 This is a schematic diagram of the data-driven model of the steelmaking process described in this invention.

[0046] Figure 3 This is a schematic diagram of the energy-saving technology library for the steelmaking process described in this invention.

[0047] Figure 4 This is a schematic diagram of the technical limit energy consumption model of the steelmaking process described in this invention. Detailed Implementation

[0048] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below with reference to the figures and specific embodiments.

[0049] like Figures 1 to 4 As shown, the present invention proposes an online method for evaluating the energy-saving potential of steelmaking processes, which includes the following steps:

[0050] S1. Obtain historical production data for the steelmaking process and construct a data-driven model for the steelmaking process;

[0051] The production history data for the steelmaking process includes the consumption of power media, the amount of molten iron charged, the amount of scrap steel charged, the amount of ore charged, the amount of lime charged, the amount of molten steel tapped, the amount of slag generated, the amount of furnace dust generated, the amount of converter gas recovered, and the amount of steam recovered.

[0052] S2. Construct an energy consumption mechanism model for the steelmaking process based on the production mechanism, material flow, and energy flow of converter steelmaking; construct an energy consumption model for the steelmaking process based on the energy consumption mechanism model and the data-driven model.

[0053] S3. Analyze historical production data to obtain factors affecting energy consumption, and combine them with energy-saving technologies in the steelmaking process to determine the parameters affecting the energy consumption model and their changes.

[0054] S4. Input the determined parameters and their changes into the energy consumption model of the steelmaking process, calculate the energy consumption level and related parameters of the applied energy-saving technology; use samples to verify and evaluate the calculation results. If they are qualified, establish an energy-saving technology library; otherwise, re-analyze the factors affecting energy consumption.

[0055] S5. Combining the energy consumption model of the steelmaking process and the energy-saving technology library, a technical limit energy consumption model is obtained;

[0056] S6. Obtain production data of the steelmaking process online, input the energy consumption model and technical limit energy consumption model of the steelmaking process respectively, and calculate the current energy consumption level and the energy consumption level after applying energy-saving technology;

[0057] S7. Compare and analyze the current energy consumption level and the energy consumption level after applying energy-saving technologies, and provide the changes in relevant parameters, the energy-saving potential of advanced technologies, and the contribution of each energy-saving technology to reducing energy consumption.

[0058] The data-driven model for the steelmaking process is constructed based on the LSTM algorithm, and the process is as follows:

[0059] When processing sequence data, the LSTM model adjusts the input x at the current time step. t The hidden state h of the previous time step t-1 Calculate the output values ​​of the forget gate, input gate, and output gate using the following formula;

[0060] f t =σ(W f [h t-1 x t ]+b f (1)

[0061] i t =σ(W i [h t-1 x t ]+b i (2)

[0062] o t =σ(W o [h t-1 x t ]+b o (3)

[0063] Among them, f t Forget gate, controls whether previous information is deleted from the cell state; i t The input gate controls the process of updating the cell state; t The output gate controls the flow of information from the cell state to the hidden state; σ is the sigmoid activation function, and W... f W i W o and b f b i b o These are the weight matrices and bias vectors for the forget gate, input gate, and output gate, respectively; [h t-1 ,xt [h at time step t] t-1 and x t Serial vectors;

[0064] According to h t-1 and x t The expression involved in calculating the new cell state is as follows:

[0065]

[0066] in, For the new cell state, tanh is the hyperbolic tangent activation function;

[0067] Then, using a gating mechanism, the output value f of the forget gate is determined. t The output value i of the input gate t The cell state C of the previous time step t-1 Update the current cell state C t ;

[0068]

[0069] Finally, the output value o is output through the output gate. t and the current unit state C t Calculate the new hidden state h t As the output of the current time step;

[0070] h t =o t tanh(C t (6)

[0071] By executing these steps sequentially at each time step, the LSTM model can effectively process long sequence data, maintain and update the cell state and hidden state, thereby achieving modeling and prediction of sequence data.

[0072] The energy consumption model for the steelmaking process is as follows:

[0073]

[0074] Among them, Q base The baseline energy consumption is the total energy consumption of the steelmaking process without the application of any energy-saving technologies; i represents the use of different energy-consuming items or materials in the steelmaking process; m represents the total number of energy-consuming items, that is, the total number of different energy-consuming items involved in the steelmaking process; C i E is the reduction factor; i,base This indicates the contribution or importance of each energy consumption item i to the total energy consumption in the steelmaking process.

[0075] The technical limit energy consumption model for the steelmaking process is as follows:

[0076]

[0077] Among them, E t The technological limit energy consumption refers to the total energy consumption of the steelmaking process after applying energy-saving technologies; m represents the total number of energy consumption items, that is, the total number of different energy consumption items involved in the steelmaking process; C i E is the reduction factor; i,t Let i be the actual energy consumption of each energy consumption item i after applying energy-saving technology.

[0078] The energy-saving potential of the steelmaking process is calculated using equation (9);

[0079] dQ rzgx =E base -E t (9)

[0080] Among them, dQ rzgx The energy-saving potential after applying all energy-saving technologies to the steelmaking process.

[0081] Based on the calculation of the energy-saving potential of steelmaking process technology, the application contribution of a single energy-saving technology is calculated using formula (10):

[0082]

[0083] Among them, gxd 技术i Let dQ be the contribution value of the application of the i-th energy-saving technology to the energy-saving potential of the steelmaking process. 技术i The energy-saving potential of applying the i-th energy-saving technology under baseline operating conditions; The summation is the energy-saving potential of each energy-saving technology; n is the total number of energy-saving technologies.

[0084] Based on the aforementioned model establishment and results analysis, a visualization technology-based online assessment system platform for the energy-saving potential of steelmaking processes was designed and developed. This platform allows for interaction between the model and the technology, enabling enterprises to more intuitively understand the assessment results of the energy-saving potential. The system platform displays current production parameters of the steelmaking process, technology application status, energy consumption levels, advanced energy-saving technologies, technological energy consumption limits, the energy-saving potential of each technology, and the contribution of each technology to energy reduction.

[0085] In this document, the terms "upper," "lower," "front," "back," "left," "right," "top," "bottom," "inner," "outer," "vertical," and "horizontal," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only used for the clarity of expressing the technical solution and for the convenience of description, and therefore should not be construed as limiting the present invention.

[0086] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0087] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for online evaluation of energy-saving potential in steelmaking processes, characterized in that, Includes the following steps: S1. Obtain historical production data for the steelmaking process and construct a data-driven model for the steelmaking process; S2. Construct an energy consumption mechanism model for the steelmaking process based on the production mechanism, material flow, and energy flow of converter steelmaking; construct an energy consumption model for the steelmaking process based on the energy consumption mechanism model and the data-driven model. S3. Analyze historical production data to obtain factors affecting energy consumption, and combine them with energy-saving technologies in the steelmaking process to determine the parameters affecting the energy consumption model and their changes. S4. Input the determined parameters and their changes into the energy consumption model of the steelmaking process, calculate the energy consumption level and related parameters of the applied energy-saving technology; use samples to verify and evaluate the calculation results. If they are qualified, establish an energy-saving technology library; otherwise, re-analyze the factors affecting energy consumption. S5. Combining the energy consumption model of the steelmaking process and the energy-saving technology library, a technical limit energy consumption model is obtained; S6. Obtain production data of the steelmaking process online, input the energy consumption model and technical limit energy consumption model of the steelmaking process respectively, and calculate the current energy consumption level and the energy consumption level after applying energy-saving technology; S7. Compare and analyze the current energy consumption level and the energy consumption level after applying energy-saving technologies, and provide the changes in relevant parameters, the energy-saving potential of advanced technologies, and the contribution of each energy-saving technology to reducing energy consumption.

2. The online assessment method for energy-saving potential in steelmaking processes according to claim 1, characterized in that, The data-driven model for the steelmaking process is constructed based on the LSTM algorithm, and the process is as follows: When processing sequence data, the LSTM model adjusts the input x at the current time step. t The hidden state h of the previous time step t-1 Calculate the output values ​​of the forget gate, input gate, and output gate using the following formula; f t =σ(W f [h t-1 ,x t ]+b f )) (1) i t =σ(W i [h t-1 ,x t ]+b i ) (2) the t =σ(W o [h t-1 ,x t ]+b o ) (3) Among them, f t Forget gate, controls whether previous information is deleted from the cell state; i t The input gate controls the process of updating the cell state; t The output gate controls the flow of information from the cell state to the hidden state; σ is the sigmoid activation function, and W... f W i W o and b f b i b o These are the weight matrices and bias vectors for the forget gate, input gate, and output gate, respectively; [h t-1 ,x t [h at time step t] t-1 and x t Serial vectors; According to h t-1 and x t The expression involved in calculating the new cell state is as follows: in, For the new cell state, tanh is the hyperbolic tangent activation function; Then, using a gating mechanism, the output value f of the forget gate is determined. t The output value i of the input gate t The cell state C of the previous time step t-1 Update the current cell state C t ; Finally, the output value o is output through the output gate. t and the current unit state C t Calculate the new hidden state h t As the output of the current time step; h t = no t fishy(C) t ) (6) By executing these steps sequentially at each time step, the LSTM model can effectively process long sequence data, maintain and update the cell state and hidden state, thereby achieving modeling and prediction of sequence data.

3. The online assessment method for energy-saving potential in steelmaking processes according to claim 1, characterized in that, The energy consumption model for the steelmaking process is as follows: Among them, Q base The baseline energy consumption is the total energy consumption of the steelmaking process without the application of any energy-saving technologies; 'i' represents the different energy consumption items or materials used in the steelmaking process; 'm' represents the energy consumption item. The total number, that is, the total number of different energy consumption items involved in the steelmaking process; C i E is the reduction factor; i,base This indicates the contribution or importance of each energy consumption item i to the total energy consumption in the steelmaking process.

4. The online assessment method for energy-saving potential in steelmaking processes according to claim 1, characterized in that, The technical limit energy consumption model for the steelmaking process is as follows: Among them, E t The technological limit energy consumption refers to the total energy consumption of the steelmaking process after applying energy-saving technologies; m represents the total number of energy consumption items, that is, the total number of different energy consumption items involved in the steelmaking process; C i E is the reduction factor; i,t Let i be the actual energy consumption of each energy consumption item i after applying energy-saving technology.

5. The online assessment method for energy-saving potential in steelmaking processes according to claim 1, characterized in that, The energy-saving potential of the steelmaking process is calculated using equation (9); dQ rzgx =E base -E t (9) Among them, dQ rzgx The energy-saving potential after applying all energy-saving technologies to the steelmaking process.

6. The online assessment method for energy-saving potential in steelmaking processes according to claim 5, characterized in that, Based on the calculation of the energy-saving potential of steelmaking process technology, the application contribution of a single energy-saving technology is calculated using formula (10): Among them, gxd 技术i Let dQ be the contribution value of the application of the i-th energy-saving technology to the energy-saving potential of the steelmaking process. 技术i The energy-saving potential of applying the i-th energy-saving technology under baseline operating conditions; The summation is the energy-saving potential of each energy-saving technology; n is the total number of energy-saving technologies.

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