Curve sorting analysis method for energy management of iron and steel enterprises

Abstract

The invention relates to a curve sorting analysis method for energy management of iron and steel enterprises, which comprises the following steps of: acquiring a plurality of groups of data from a plurality of independent similar iron and steel systems, the data among the groups are relatively independent and have no relevance, and the groups of data are collected according to the same sampling sequence; each group of data breaks through the original sequence and is arranged according to a set sequence to form a data table, then the arrangement sequence is used as a horizontal axis, the physical quantity where the data are located is used as a longitudinal axis, the data in the table are grouped and recorded in a coordinate graph and are connected in sequence to form a curve, and the curve displays systematic differences existing among all systems; aiming at a plurality of similar systems which are relatively independent and have no relevance in energy management of the iron and steel enterprises, when similar data collected from the systems are subjected to curve analysis by utilizing the system, systematic differences existing among the systems can be revealed, and the system reliability is improved. The method is helpful for finding the cause of systematic difference and providing a method for solving the problem.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management in iron and steel enterprises, and particularly to a curve sorting analysis method for energy management in iron and steel enterprises. Background Art

[0002] The curve sorting analysis method is an important analysis method for energy management in iron and steel enterprises. Through the application of the curve analysis method, potential problems in energy management of iron and steel enterprises can be revealed. There are various data analysis means currently in use, including stratification method, cause-and-effect diagram, checklist, histogram, scatter diagram, Pareto chart, and control chart, as well as KJ method, association diagram, PDPC method, matrix diagram, matrix data analysis method, system diagram, and arrow diagram. Most of these methods are arranged and described according to the time sequence of data, and the balance law is found in the time sequence extension. For another type of mutually independent systems, the data is used independently of each other without entanglement, and it is not easy to find the variation law in the time sequence between the data of each system and within the system data. Summary of the Invention

[0003] The present invention provides a curve sorting analysis method for energy management in iron and steel enterprises, which can reveal the systematic differences existing between each system.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] A curve sorting analysis method for energy management in iron and steel enterprises collects multiple groups of data from multiple independent similar iron and steel systems. The data between each group is relatively independent and has no correlation. These groups of data are all collected in the same sampling order. Each group of data breaks the original order and is arranged according to a set order simultaneously to form a data table. Then, using the arrangement order as the horizontal axis and the physical quantity where the data is located as the vertical axis, the data in the table is grouped and recorded in the coordinate graph and connected into a curve in sequence. The curve shows the systematic differences existing between each system.

[0006] Further, the set order includes descending order and ascending order.

[0007] Further, the sampling data in the data table is divided by data time sequence or sample order and the number of sampling groups.

[0008] Further, the fact that the curve shows the systematic differences existing between each system is analyzed and shown by comparing the size of the change range of each group of data, the difference between the maximum value and the minimum value, the change of the curve slope, whether there is an intersection between the curves, the position of the intersection point on the curve, and the change of the position.

[0009] Compared with the prior art, the beneficial effects of the present invention are:

[0010] For multiple similar systems that are relatively independent and have no relevance in the energy management of iron and steel enterprises, when using the present invention to perform curve analysis on the similar data collected from these systems, the systematic differences existing between each system can be revealed, which helps to find the reasons behind the systematic differences and propose solutions to the problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the index curve of the data table described in the embodiment of the present invention.

[0012] Figure 2 It is a schematic diagram of the daily chronological curve of oxygen consumption per ton of steel described in the embodiment of the present invention.

[0013] Figure 3 It is a schematic diagram of the descending order curve of oxygen consumption per ton of steel described in the embodiment of the present invention.

[0014] Figure 4 It is a schematic diagram of the descending order curve of the iron-steel ratio described in the embodiment of the present invention.

[0015] Figure 5 It is a schematic diagram of the descending order curve of the steel production ratio described in the embodiment of the present invention.

[0016] Figure 6 It is a schematic diagram of the overall descending order curve of oxygen consumption per ton of steel described in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] The following further describes the specific embodiments of the present invention with reference to the drawings:

[0018] A curve sorting and analysis method for energy management of iron and steel enterprises according to the present invention includes collecting multiple groups of data from multiple independent similar iron and steel systems. The data between each group is relatively independent and has no relevance, and these groups of data are all collected in the same sampling order, which is the same chronological order or sample order. Each group breaks the original order and is sorted in ascending or descending order at the same time to form a new data table. Then, using the new sorting order as the horizontal axis and the physical quantity where the data is located as the vertical axis, the data in the table is grouped and recorded in the coordinate graph and connected into a line in sequence.

[0019] Collect the same kind of energy data from multiple similar systems in an iron and steel enterprise according to the chronological order or sample order (abbreviation: order). The data of each system is used as a group. Let the chronological order or sample order number be j, j = 1, 2, 3, 4..., n; the number of sampling groups be i, i = 1, 2, 3..., m; the sampling data be x ij , and the sampling data of each group is shown in Table 1:

[0020] Table 1

[0021] Sequence 1 Sequence 2 Sequence 3 Sequence 4 …… Sequence j …… Sequence n Group 1 <![CDATA[X 11 > <![CDATA[X 12 > <![CDATA[X 13 > <![CDATA[X 14 > …… <![CDATA[X 1j > …… <![CDATA[X 1n > Group 2 <![CDATA[X 21 > <![CDATA[X 22 > <![CDATA[X 23 > <![CDATA[X 24 > …… <![CDATA[X 2j > …… <![CDATA[X 2n > Group 3 <![CDATA[X 31 > <![CDATA[X 32 > <![CDATA[X 33 > <![CDATA[X 34 > …… <![CDATA[X 3j > …… <![CDATA[X 3n > …… …… …… …… …… …… …… …… …… Group i <![CDATA[X i1 > <![CDATA[X i2 > <![CDATA[X i3 > <![CDATA[X i4 > …… <![CDATA[X ij > …… <![CDATA[X in > …… …… …… …… …… …… …… …… …… Group m <![CDATA[X m1 > <![CDATA[X m2 > <![CDATA[X m3 > <![CDATA[X m4 > …… <![CDATA[X mj > …… <![CDATA[x mn >

[0022] Arrange each group of data in Table 1 in ascending or descending order uniformly, and form a new data table according to the order formed after arrangement; according to the new data table, use the new order as the horizontal axis and the physical quantity where the data is located as the vertical axis, record the data in groups in the coordinate graph and connect them into lines in sequence, as shown in Figure 1 ;

[0023] Through Figure 1 It is possible to find the change situation of energy data between similar systems from high to low (or from low to high) within a sampling period (or sampling sequence). By comparing the size of the change range of each group of data, the comparison between the maximum value and the minimum value, the slope of the curve, whether there is an intersection between the curves, the position of the intersection point on the curve, and the change situation of the position, a systematic comparative analysis of the energy status of each system is carried out, revealing the systematic differences existing between each system, and then finding the reasons formed behind the systematic differences and proposing solutions to the problems to improve the energy efficiency of the system.

[0024] The following embodiments are implemented on the premise of the technical solution of the present invention, and the detailed implementation manners and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments. The methods used in the following embodiments are all conventional methods unless otherwise specified.

[0025]

Embodiment

[0026] There are many processes in iron and steel enterprises that use oxygen and nitrogen. There are many characteristic parameters related to oxygen and nitrogen, such as pressure, flow rate, latent heat, entropy, enthalpy, etc. There is also a type of comprehensive parameter that describes the production and use efficiency of gas, such as comprehensive specific power consumption of gas, comprehensive oxygen consumption per ton of steel, comprehensive nitrogen consumption per ton of steel, etc. For individual processes such as ironmaking, steelmaking, coking, and rolling, there are oxygen consumption per ton of steel, nitrogen consumption per ton of steel, oxygen consumption per ton of iron, nitrogen consumption per ton of iron, nitrogen consumption per ton of coke, and so on; through the curve comparative analysis of these data, a clear understanding and judgment of the use status and use efficiency of oxygen and nitrogen in these processes can be obtained, guiding the production gas use.

[0027] Currently, the analysis of these data mostly stays on the curves formed according to time series, and based on these curves, the usage rules and characteristics of gas are studied for the gas balance of the system and guiding production; since this curve is only an instant record of the process characteristics according to time series, for those curves with frequent data changes and no regular pattern according to time series, this time series curve analysis method has certain limitations in use. If the original time series arrangement is broken and a new data arrangement and combination method is found, it will bring new breakthroughs to data analysis. This patent proposes a new data arrangement and combination method: arrange each sequence of data in descending or ascending order simultaneously, and find and explore the rules between data and between sequences through the rearrangement of data.

[0028] The curve sorting analysis method is to re - sort a set or array of data, either in ascending or descending order, or other types of sorting, to find the degree of association between these sets of data and the variation rules between groups. It is a relatively effective data comparison and analysis method.

[0029] Taking the oxygen consumption data of the steel - making system from June 1st to August 12th in a certain steel plant as an example, this steel plant has 2 independent steel - making systems, simply referred to as New No.1 Steel and No.2 Steel. Here, the analysis data of these 2 steel - making systems are uniformly sorted out, and data items such as the oxygen consumption per ton of steel in New No.1 Steel, the oxygen consumption per ton of steel in No.2 Steel, the oxygen consumption per ton of steel in the steel - making system, the iron - steel ratio, and the production ratio of No.2 Steel to New No.1 Steel are listed respectively. These 5 indicators are important indicators that are crucial for the two steel - making systems through screening, related to the oxygen consumption index, and capable of comparing the oxygen - using efficiency of the 2 steel - making systems; through the trend analysis of these data, the correlations between the parameters of the steel - making system are found, and the possibility of reducing the oxygen consumption per ton of steel is explored. The data collection form of the nitrogen consumption of the steel - making system from July 1st to August 17th, 2024 is shown in Table 2 "Data Collection Form of Nitrogen Consumption of 2 Independent Steel - Making Systems of a Company from July 1st to August 17th, 2024", among which the data on July 25th is distorted and not adopted.

[0030] Table 2

[0031]

[0032]

[0033]

[0034] Make a daily chronological curve graph of the oxygen consumption per ton of steel according to the data in Table 2, as shown in Figure 2 .

[0035] It is very difficult to find the mutual comparison relationships among the oxygen consumption per ton of steel in New No.1 Steel, the oxygen consumption per ton of steel in No.2 Steel, the oxygen consumption per ton of steel, the production ratio of No.2 Steel to New No.1 Steel, and the iron - steel ratio according to the above - mentioned daily chronological curve graph. Make a descending - order curve graph of the oxygen consumption per ton of steel based on the data of these 3 quantities, as shown in Figure 3 ; Figure 3The time sequence is broken, with the oxygen consumption per ton of steel as the main line, and it is re-sorted in descending order according to the oxygen consumption per ton of steel value. At the same time, the production ratio of the second steel to the first steel and the iron-steel ratio are sorted synchronously with the time sequence of the oxygen consumption per ton of steel. From this descending curve, we can see that when the oxygen consumption per ton of steel decreases in descending order, although the production ratio of the second steel to the first steel fluctuates, the overall trend is downward, indicating that the production ratio of the second steel to the first steel has the same direction of influence on the oxygen consumption per ton of steel. The higher the production ratio, the more the oxygen consumption per ton of steel tends to increase, and the lower the production ratio, the more the oxygen consumption per ton of steel tends to decrease, indicating that the oxygen utilization efficiency of the first steel should be better than that of the second steel; while the mutual influence relationship between the iron-steel ratio and the oxygen consumption per ton of steel and the production ratio of the second steel to the first steel is not obvious from the descending curve of the oxygen consumption per ton of steel.

[0036] Make a descending curve graph of the iron-steel ratio based on the data selected above, as shown in Figure 4 , with the iron-steel ratio as the main line, re-sorted in descending order according to the iron-steel ratio value. At the same time, the production ratio of the second steel to the first steel and the oxygen consumption per ton of steel are sorted synchronously with the time sequence of the iron-steel ratio. From the descending curve of the iron-steel ratio, it is found that when the iron-steel ratio gradually decreases, although the production ratio of the second steel to the first steel and the oxygen consumption per ton of steel fluctuate, these two quantities show a gradually downward trend, indicating that the iron-steel ratio and the oxygen consumption per ton of steel also have the same direction of influence. The smaller the iron-steel ratio, the more the oxygen consumption per ton of steel tends to decrease, and the larger the iron-steel ratio, the more the oxygen consumption per ton of steel tends to increase, indicating the reverse influence of increasing scrap steel on the oxygen consumption per ton of steel during the steelmaking process; and the smaller the iron-steel ratio, the more the production ratio of the second steel to the first steel tends to decrease, and the larger the iron-steel ratio, the more the production ratio of the second steel to the first steel tends to increase, indicating the possible influence of the change in the iron-steel ratio on the steel production relationship between the second steel and the first steel.

[0037] Continue to analyze the relationship between these three quantities, and conduct a descending analysis of the production ratio of the second steel to the first steel, as shown in Figure 5 , with the production ratio of the second steel to the first steel as the main line, re-sorted in descending order according to the production ratio value of the second steel to the first steel; for comparison, the oxygen consumption per ton of the first steel and the oxygen consumption per ton of the second steel are added here, and these quantities are sorted synchronously with the time sequence of the iron-steel ratio. From the descending curve of the production ratio of the second steel to the first steel, we will find that except for the weak correlation between the oxygen consumption per ton of steel and the production ratio of the second steel to the first steel, the correlation with other quantities is not obvious.

[0038] From Figures 2 - 5 Through comprehensive analysis, the oxygen consumption per ton of steel is interrelated with the iron-steel ratio and the production ratio of the second steel to the first steel, as analyzed above; the correlation between the production ratio of the second steel to the first steel and the iron-steel ratio is not strong, unless there are special institutional arrangements for these two steelmaking systems to form an institutional correlation between the production ratio of the second steel to the first steel and the iron-steel ratio.

[0039] Why is there a correlation between the production ratio of the second steel to the first steel and the oxygen consumption per ton of steel? From the curves Figure 2 , Figure 5There is a certain reflection. For the oxygen consumption per ton of steel in the two steel plants shown in these two curves, part of it is above that of the new No.1 Steel Plant, and part of them intersect with each other. Due to fluctuations and intersections, the trend analysis of the oxygen consumption per ton of steel in these two steelmaking systems still has uncertainties, and new analysis methods are needed for such uncertainties.

[0040] Using this method, the oxygen consumption per ton of steel in the new No.1 Steel Plant, the oxygen consumption per ton of steel in the No.2 Steel Plant, and the oxygen consumption per ton of steel data are separately extracted here, and new descending curves are re-made. See Figure 6 ;

[0041] Figure 6 The time sequence of each parameter is completely disrupted. Whether it is the oxygen consumption per ton of steel in the new No.1 Steel Plant, the oxygen consumption per ton of steel in the No.2 Steel Plant, or the oxygen consumption per ton of steel, all are arranged in descending order. According to the new arrangement order, the curve graph is re-drawn to obtain three descending curves with clear trends. From the curves, the oxygen consumption per ton of steel in the new No.1 Steel Plant is between 35.51 - 56.72 Nm 3 / h, with an average of 49.87 Nm 3 / h; the oxygen consumption per ton of steel in the No.2 Steel Plant is between 48.22 - 60.23 Nm 3 / h, with an average of 54.96 Nm 3 / h; the oxygen consumption per ton of steel in the steelmaking process is between 49.83 - 58.21 Nm 3 / h, with an average of 53.01 Nm 3 / h; the average difference in the oxygen consumption per ton of steel between the new No.1 Steel Plant and the No.2 Steel Plant is 5.11 Nm 3 / h, and the overlapping interval of the oxygen consumption per ton of steel between the new No.1 Steel Plant and the No.2 Steel Plant is between 48.22 - 56.72 Nm 3 / h. Through these three descending curves, the comparison relationship of the oxygen consumption per ton of steel in the two steelmaking systems can be clearly seen. From the curves, the oxygen consumption per ton of steel in the No.2 Steel Plant is generally higher than that in the new No.1 Steel Plant, indicating that there are differences and improvement spaces in the oxygen consumption per ton of steel and oxygen utilization efficiency between the No.2 Steel Plant and the No.1 Steel Plant in terms of steelmaking equipment, facility structure, operation methods, measurement differences, etc. On the other hand, it also shows that there is also a certain improvement space in operation, etc. for the new No.1 Steel Plant in reducing the oxygen consumption per ton of steel. In this way, through the application of the full-system descending curves, the problems are exposed and the direction for problem rectification is clarified.

Claims

1. A curve sorting analysis method for energy management in steel enterprises, characterized in that: Multiple groups of data are collected from multiple independent similar steel systems. The data in each group are relatively independent and unrelated. These groups of data are collected in the same sampling order. Each group of data breaks the original order and is arranged in a set order to form a data table. The arrangement order is used as the horizontal axis and the physical quantity of the data is used as the vertical axis. The data in the table are grouped and recorded in a coordinate graph and connected in order to form a curve. The curve shows the systematic differences between the systems.

2. The curve sorting analysis method for energy management of steel enterprises according to claim 1 is characterized in that: The set order includes a descending order and an ascending order.

3. The curve sorting analysis method for energy management of steel enterprises according to claim 1 is characterized in that: The data table divides the sampled data by data time sequence or sample order and the number of sampling groups.

4. The curve sorting analysis method for energy management in a steel enterprise according to claim 1 is characterized in that: The curve shows the systematic differences between the various systems, and is displayed by analyzing the size of the range of changes in each group of data, the difference between the maximum and minimum values, the change in the slope of the curve, whether there is an intersection between the curves, the position of the intersection on the curve, and the change in position.

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