Method for determining operating conditions, system for determining operating conditions, and coke oven

Abstract

To reduce variation in the coke-making time of raw coal.SOLUTION: An operating condition determination method for determining operating conditions of a coke oven, comprising an operating condition determination step of determining operating conditions by performing a numerical analysis using a model of the coke oven. In a numerical analysis of the operation condition determination step, a pressure loss coefficient distribution in the coke oven is used, and the operating conditions are determined by using the operating conditions of the coke oven as a variable and performing optimization so that variation in the time to reach a predetermined temperature at a plurality of positions in the coke oven satisfy predetermined convergence conditions.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a method and system for determining the operating conditions of a coke oven. The present invention also relates to a coke oven equipped with the operating conditions determination system. [Background technology]

[0002] Coke used in steel mills and other industrial facilities is generally produced by carbonizing coal in a chamber-type coke oven. Chamber-type coke ovens (hereinafter simply referred to as "coke ovens") can be broadly classified into single-type and double-type ovens depending on the type of fuel gas used. A single-type oven is a furnace that uses a high-calorie gas called "rich gas" as fuel gas, and the rich gas is generally coke oven gas (hereinafter referred to as C gas) or a gas mainly composed of coke oven gas. On the other hand, a double-type oven is a furnace that can use either the aforementioned rich gas or a low-calorie gas called "poor gas" as fuel gas. The poor gas is usually a mixture of coke oven gas and blast furnace gas (hereinafter referred to as M gas).

[0003] Here, we will explain the structure of a coke oven using a Carlstil-type compound furnace as an example.

[0004] Figure 1 is a schematic cross-sectional perspective view showing the structure of a Carlstil-type coke oven. The furnace body of the coke oven 1 is constructed by stacking refractory bricks, and multiple carbonization chambers 10 and combustion chambers 20 are provided at the top. The carbonization chambers 10 and combustion chambers 20 are arranged alternately in the furnace width direction indicated by arrow W. Multiple coal charging ports 11 are provided at the top of the carbonization chambers 10, and coal, which is the raw material, is charged into the carbonization chambers 10 from the coal charging ports 11. In addition, an inspection hole 21 is provided at the top of the combustion chambers 20. Note that in Figure 1, a cross-section of the combustion chamber 20 is shown on a plane parallel to the furnace length direction indicated by arrow L.

[0005] The carbonization chamber 10 and the combustion chamber 20 are separated by a furnace wall made of refractory bricks. By burning fuel gas in the combustion chamber 20, the carbonization chamber 10 is heated through the furnace wall. In this state, the coal in the carbonization chamber 10 is carbonized for several tens of hours to become coke.

[0006] An extruder (not shown) is installed on one side of the coke oven 1 in the direction of its length, while a fire extinguishing vehicle and coke dry quenching equipment (CDQ) are installed on the other side in the direction of its length. The side where the extruder is installed is called the pusher side (PS) or machine side (MS), and the side where the quenching equipment is installed (the removal side) is called the coke side (CS). The finished coke is pushed out from the carbonization chamber 10 to the coke side by the extruder.

[0007] A heat storage chamber 30 extending in the direction of the furnace length is provided at the bottom of the coke oven 1. The heat storage chamber 30 is for utilizing the waste heat from combustion, and multiple heat storage chambers 30 are installed side by side in the direction of the furnace width. Inside the heat storage chamber 30, bricks with numerous vertically oriented channels are installed for heat storage.

[0008] Figure 2 is a schematic diagram showing the structure of a pair of carbonization chambers 10 and combustion chambers 20 that constitute the coke oven described above, and a heat storage chamber connected to the carbonization chambers 10 and combustion chambers 20. The combustion chamber 20 is divided into multiple vertical flues 23 by partition walls 22 that are spaced apart in the direction of the furnace length. The partition walls 22 are made of refractory bricks, and flow channels for air and lean gas are formed inside them.

[0009] In a Carlsstyl coke oven, the combustion chamber 20 and the heat storage chamber 30 are typically divided into two sections along the length of the oven, as shown in Figure 1, and the fuel gas is supplied to one of the combustion chambers 20. The side of the combustion chamber 20 to which the fuel gas is supplied (left side in Figure 1) is called the "combustion side," and the opposite side is called the "exhaust side." The fuel gas supplied to the combustion chamber on the combustion side is burned in the vertical flue 23, and the exhaust gas is sent to the combustion chamber on the exhaust side through the upper horizontal flue 24. The exhaust gas is then discharged into the heat storage chamber 30 for heat recovery. After that, the exhaust gas is released from the chimney 62 via the small flue 60 and the large flue 61. Normally, the coke oven is operated by alternately switching between the combustion side and the exhaust side of the combustion chamber.

[0010] When rich gas is used as the fuel gas, rich gas is supplied from the rich gas piping 40 through a horizontal rich gas piping 41 extending in the furnace length direction, and into the combustion chamber 20 through an opening provided at the bottom of the vertical flue 23. Meanwhile, air is supplied from all the heat storage chambers 30 to each combustion chamber 20. In this case, the air passes inside the partition wall 22 and is supplied into the combustion chamber 20 through an opening provided on the side of the partition wall 22.

[0011] On the other hand, when lean gas is used as the combustion gas, lean gas is supplied from the lean gas piping 50 to the heat storage chamber 30 on one side (the coke side in the example shown in Figures 1 and 2). The supplied lean gas is preheated in the heat storage chamber 30 and then sent to the combustion chamber 20 above it (solid line in Figure 1). Similarly, air is also supplied to the heat storage chamber 30, preheated in the heat storage chamber 30, and then sent to the combustion chamber 20 above it (dashed line in Figure 1). Here, as shown in Figure 2, lean gas and air are supplied alternately to the multiple heat storage chambers 30 which are arranged in a line in the furnace width direction. For example, when lean gas is supplied to the odd-numbered heat storage chamber (for example, 30a in Figure 2) in the furnace width direction, air is supplied to the even-numbered heat storage chamber (for example, 30b in Figure 2). The supply of lean gas and air into the combustion chamber 20 is carried out through openings provided on the side of the partition wall 22. Multiple partition walls 22, spaced apart within the combustion chamber 20, are alternately used for supplying lean gas and air. For example, when lean gas is supplied to the odd-numbered partition walls (counted in the furnace length direction), air is supplied to the even-numbered partition walls.

[0012] As described above, by burning either rich or poor gas in the combustion chamber, the carbonization chambers located on both sides of the combustion chamber are heated via the furnace wall, allowing the raw coal in the carbonization chambers to be carbonized. [Prior art documents] [Patent Documents]

[0013] [Patent Document 1] Japanese Patent Publication No. 2019-059926 [Overview of the project] [Problems that the invention aims to solve]

[0014] Here, the most ideal carbonization in a coke oven means that the temperature variation of the raw coal in the carbonization chamber is small, and the coking of the raw coal is completed without variation as scheduled. If there is variation in the coking time depending on the position in the furnace, it will cause variation in the quality of the coke produced. Also, as the variation increases, the amount of uncarbonized coal increases, so there is also a problem that the amount of dust generated increases due to the uncarbonized coal when taking out the coke from the coke oven. Furthermore, when the variation in the coking time is large, in order to fully coke the raw coal throughout the furnace, it is necessary to carry out carbonization for a longer time, resulting in an increase in the fuel consumption and carbon dioxide emissions. Therefore, it is required to reduce the variation in the coking time as much as possible.

[0015] However, as described above, since the coke oven has a very complex structure, there is a problem that the temperature distribution inside is likely to vary, and it is difficult to make the carbonization proceed uniformly.

[0016] Also, in order to control the temperature distribution in the coke oven, it is conceivable to adjust various operating conditions such as the composition of the fuel gas used, the flow rate of the fuel gas, and the flow rate of the combustion air. However, the influence of changing these operating conditions on the state inside the coke oven under the actual operating environment is very complex, and it has been difficult to theoretically determine how to adjust the operating conditions to achieve ideal carbonization.

[0017] For example, in the technology disclosed in Patent Document 1, it is possible to identify the blockage points in the coke oven flow path by combining simulation and optimization techniques. However, the problem of how to adjust the operating conditions to achieve ideal carbonization has remained unsolved.

[0018] Therefore, currently, the actual situation is that the operating conditions of the coke oven are determined based on the experience of the operator.

[0019] The present invention has been made to solve the above problems, and an object thereof is to reduce the variation in the coking time of raw coal.

Means for Solving the Problems

[0020] The gist configuration of the present invention is as follows.

[0021] 1. An operating condition determination method for determining the operating conditions of a coke oven, comprising: an operating condition determination step of performing numerical analysis using the model of the coke oven to determine the operating conditions, In the numerical analysis of the operating condition determination step, the pressure loss coefficient distribution in the coke oven is used, and with the operating conditions of the coke oven as variables, optimization is performed so that the variation in the time until a predetermined temperature is reached at a plurality of positions in the coke oven satisfies a predetermined convergence condition, thereby determining the operating conditions.

[0022] 2. The operating condition determination method according to 1 above, further comprising a pressure loss coefficient distribution calculation step of performing numerical analysis using the model of the coke oven prior to the operating condition determination step, and calculating the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values in the coke oven.

[0023] 3. The operating condition determination method according to 2 above, further comprising a measurement step of actually measuring the distribution of the characteristic values prior to the pressure loss coefficient distribution calculation step.

[0024] 4. The operating condition determination method according to any one of 1 to 3 above, further comprising a display step of displaying the operating conditions determined in the operating condition determination step.

[0025] 5. An operating condition determination system for determining the operating conditions of a coke oven, comprising: an operating condition determination means for performing numerical analysis using the model of the coke oven to determine the operating conditions. An operating condition determination system that determines the operating conditions by using the pressure loss coefficient distribution in the coke oven as a variable in the numerical analysis of the operating condition determination means, and optimizing the variation in the time it takes to reach a predetermined temperature at multiple locations within the coke oven to satisfy predetermined convergence conditions.

[0026] 6. The operating condition determination system according to item 5, further comprising a pressure loss coefficient distribution calculation means for calculating the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values ​​inside the coke oven by performing numerical analysis using the coke oven model.

[0027] 7. The operating condition determination system according to item 6, further comprising a measurement means for measuring the distribution of the characteristic values.

[0028] 8. The operating condition determination system according to any one of items 5 to 7 above, further comprising a display means for displaying the operating conditions determined by the operating condition determination means.

[0029] 9. A coke oven equipped with an operating condition determination system as described in any one of items 5 to 8 above. [Effects of the Invention]

[0030] According to the present invention, it is possible to reduce variations in the coking time of raw coal. [Brief explanation of the drawing]

[0031] [Figure 1] This is a schematic cross-sectional perspective view showing the structure of a Carlstil-type coke oven. [Figure 2] This is a schematic diagram showing the structure of the carbonization chamber, combustion chamber, and heat storage chamber that make up a coke oven. [Figure 3] This is a flowchart illustrating the process in the first embodiment of the present invention. [Figure 4] This is a flowchart illustrating the process in the second embodiment of the present invention. [Figure 5]This is a flowchart illustrating the process in the third embodiment of the present invention. [Figure 6] This is a flowchart illustrating the process in the fourth embodiment of the present invention. [Figure 7] This figure shows the temperature distribution within a single carbonization chamber, as calculated in the example. [Figure 8] This graph shows the controlled fuel gas flow rate and fuel gas calorific value in the embodiment, and the resulting change in temperature in the combustion chamber. [Figure 9] This graph shows the change in the visible smoke generation rate in the example. [Figure 10] This is a flowchart illustrating the process in the fifth embodiment of the present invention. [Modes for carrying out the invention]

[0032] Next, a specific method for carrying out the present invention will be described. While the above description of the background art used the example of a Carls-Still type coke oven, the present invention is also applicable to other types of coke ovens. Furthermore, in this specification, the upper horizontal flue located above the combustion chamber is also included within the combustion chamber. Similarly, the lower horizontal flue located below the heat storage chamber is also included within the heat storage chamber.

[0033] (First Embodiment) The method for determining operating conditions in this embodiment includes an operating conditions determination step for determining the operating conditions. This operating conditions determination step will be described below.

[0034] [Process for determining operating conditions] In the process of determining operating conditions, the operating conditions are determined by performing a numerical analysis using a model of the coke oven. In this numerical analysis, the pressure loss coefficient distribution in the coke oven is used, and the operating conditions of the coke oven are used as variables to determine the operating conditions by optimizing so that the variation in the time it takes to reach a predetermined temperature at multiple locations within the coke oven satisfies predetermined convergence conditions.

[0035] Model The model of the coke oven is not particularly limited; any model capable of performing the above numerical analysis can be used. Typically, the model deals with the relationships between temperature, pressure loss coefficient, and operating conditions in various parts of the furnace. The model may include, for example, the law of conservation of mass, the law of conservation of momentum, the law of conservation of enthalpy, and combustion calculations. The analysis dimension may be one, two, or three. When using a three-dimensional model, it is preferable to use a model in which the dimension of the numerical analysis model has been reduced by a machine learning model, from the viewpoint of computational load. By using a model with reduced dimensions, a solution can be obtained quickly.

[0036] • Pressure loss coefficient distribution The numerical analysis described above uses the pressure loss coefficient distribution in the coke oven. For example, if the pressure loss coefficient distribution in the coke oven is known, that known pressure loss coefficient distribution can be used. If the pressure loss coefficient distribution is unknown, it can be determined prior to the process of determining the operating conditions. The method for determining the pressure loss coefficient distribution will be described later.

[0037] Operating conditions In the aforementioned operating conditions determination step, the operating conditions of the coke oven are determined. The operating conditions are not particularly limited and may be one or more arbitrary conditions relating to the operation of the coke oven. Typically, the operating conditions may be at least one selected from the group consisting of (1) conditions relating to flow rate, (2) conditions relating to fuel gas composition, and (3) conditions relating to pressure.

[0038] (1) Conditions related to flow rate include the flow rate of fuel gas and the flow rate of air (combustion air).

[0039] (2) The conditions relating to the fuel gas composition include, literally, the fuel gas composition. However, since the calorific value of the fuel gas is determined by the fuel gas composition, it is preferable to use the unit calorific value of the fuel gas as the condition relating to the fuel gas composition. In this technical field, the unit calorific value of the fuel gas is expressed as the calorific value per unit volume under standard conditions, and the unit is "kcal / Nm". 3 " is conventionally used.

[0040] (3) The pressure conditions may be the pressure at any location within the furnace, for example, the pressure in the combustion chamber. The pressure in the combustion chamber can be adjusted by controlling the opening of the pressure regulating valve located on the combustion chamber outlet side.

[0041] Figure 3 is a flowchart showing the processing flow according to this embodiment. First, numerical analysis is used to calculate the variation in the time it takes to reach a predetermined temperature in the furnace. Note that coking of raw coal in the furnace does not proceed at low temperatures, but only begins after reaching a certain temperature. Therefore, in order to complete the coking of raw coal without variation, it is particularly important to reduce the variation in the time it takes to reach a temperature at which coking can proceed. Thus, in this invention, the variation in "time to reach a predetermined temperature" is used as an indicator.

[0042] The temperature is not particularly limited and may be predetermined based on the raw coal used and the required characteristics of the coke product. However, it is preferable to select the temperature from the range of 700 to 1000°C, and more preferably from the range of 750 to 950°C.

[0043] Next, an evaluation function (fitness) is calculated to assess the magnitude of the variation in the time it takes to reach a predetermined temperature.

[0044] If the calculated evaluation function satisfies the predetermined convergence conditions, the operating condition determination process is terminated. On the other hand, if the convergence conditions are not satisfied, the operating conditions used in the numerical analysis are changed, and the numerical analysis is performed again with the changed operating conditions to recalculate the variation in the time it takes to reach the predetermined temperature. By repeating the above process, the variation in the time it takes to reach the predetermined temperature is optimized to satisfy the predetermined convergence conditions.

[0045] The aforementioned convergence conditions are not particularly limited, but for example, convergence may be determined when the temperature falls below a predetermined threshold. Alternatively, convergence may be determined when the variation in the time it takes to reach a predetermined temperature at multiple locations within the coke oven becomes minimal.

[0046] The algorithm used for the optimization is not particularly limited and any algorithm can be used. A suitable example of an optimization algorithm is a genetic algorithm. Other examples include gradient descent methods.

[0047] The operating conditions for the coke oven can be determined by the above procedure. By operating the coke oven based on the determined operating conditions, variations in coking time can be reduced. The method for reflecting the determined operating conditions in actual operation is not particularly limited. For example, the operating conditions may be automatically reflected by a control means. Alternatively, the operator may change the operating conditions based on the determined operating conditions.

[0048] There are no particular limitations on the timing of changes to operating conditions. For example, conditions may be changed between batches. That is, when the coke produced in one batch is discharged from the carbonization chamber and the carbonization of the next batch is performed, the operating conditions can be changed. However, in actual coke ovens, it takes time for heat to be transferred through the furnace wall bricks, so it takes time for changes in operating conditions to be reflected in the actual environment (poor responsiveness). Furthermore, it is difficult to change the superstructure conditions such as gas composition for each combustion chamber. For this reason, it is preferable to change the operating conditions as soon as they are determined.

[0049] (Second embodiment) The above-described process for determining operating conditions uses the pressure loss coefficient distribution in the coke oven. If the pressure loss coefficient distribution is known, that known distribution can be used; however, if it is unknown, it must be determined in advance. Therefore, the method for determining operating conditions in the second embodiment of the present invention further includes a pressure loss coefficient distribution calculation step, which calculates the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values ​​inside the coke oven, prior to the above-described process for determining operating conditions. The pressure loss coefficient distribution calculation step will be described below. Points that are not specifically mentioned can be the same as in the first embodiment described above.

[0050] [Pressure loss coefficient distribution calculation process] In the pressure loss coefficient distribution calculation process, numerical analysis is performed using the coke oven model to calculate the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values ​​inside the coke oven.

[0051] Model The model of the coke oven is not particularly limited; any model that can handle the relationship between the characteristic values ​​and the pressure loss coefficient can be used. The model may include, for example, the law of conservation of mass, the law of conservation of momentum, the law of conservation of enthalpy, and combustion calculations. The analytical dimension can be one, two, or three. When using a three-dimensional model, from the viewpoint of computational load, it is preferable to use a model in which the dimensionality of the numerical analysis model has been reduced by a machine learning model. By using a model with reduced dimensionality, a solution can be obtained quickly.

[0052] The model used in the pressure loss coefficient distribution calculation step may be the same as or different from the model used in the subsequent operating condition determination step.

[0053] Distribution of characteristic values In the pressure loss coefficient distribution calculation process, the distribution of characteristic values ​​inside the coke oven is used as information for calculating the pressure loss coefficient distribution. The characteristic values ​​are not particularly limited and can be any parameters that can be measured and analyzed by the numerical analysis described later. Examples of such characteristic values ​​include temperature, pressure, gas (atmosphere) composition, concentration of a specific gas, and gas flow velocity, and these characteristic values ​​can be used individually or in combination. From the standpoint of ease of measurement and equipment cost, it is preferable to use temperature and pressure or temperature only as the characteristic values. On the other hand, from the viewpoint of calculating the pressure loss coefficient distribution more accurately, it is preferable to use both the temperature distribution and the flow velocity distribution.

[0054] The characteristic value distribution is not particularly limited, and any characteristic value distribution at any location within the coke oven can be used. Typically, a characteristic value distribution in either the combustion chamber or the heat storage chamber, or both, can be used. In particular, when using a temperature distribution, it is more preferable to use the temperature distribution in the combustion chamber. When using a velocity distribution, it is preferable to use the velocity distribution in either the combustion chamber or the heat storage chamber, or both, and in particular from the viewpoint of ease of measurement, it is preferable to use the velocity distribution at the bottom of the heat storage chamber (including the lower horizontal flue).

[0055] Figure 4 is a flowchart showing the processing flow according to this embodiment. First, as initial conditions, the distribution of characteristic values ​​inside the coke oven is calculated by numerical analysis using an arbitrary pressure loss coefficient distribution. At that time, the current operating conditions can be used as the operating conditions.

[0056] Next, an evaluation function (fitness) is calculated from the calculated characteristic value distribution and the actual characteristic value distribution to assess the magnitude of the difference between the two.

[0057] If the calculated evaluation function satisfies the predetermined convergence conditions, the pressure loss coefficient distribution calculation process is terminated. On the other hand, if the convergence conditions are not satisfied, the pressure loss coefficient distribution used in the numerical analysis is changed, and the numerical analysis is performed again under the changed conditions to recalculate the characteristic value distribution. By repeating the above process, the difference between the characteristic value distribution obtained from the numerical analysis and the actual characteristic value distribution is optimized to satisfy the predetermined convergence conditions.

[0058] The aforementioned convergence conditions are not particularly limited, but for example, convergence can be determined when the difference between the characteristic value distribution obtained by numerical analysis and the actual characteristic value distribution falls below a predetermined threshold. Alternatively, convergence can be determined when the difference between the characteristic value distribution obtained by numerical analysis and the actual characteristic value distribution becomes the minimum.

[0059] The algorithm used for the optimization is not particularly limited and any algorithm can be used. A suitable example of an optimization algorithm is a genetic algorithm. Other examples include gradient descent methods.

[0060] By following the above procedure, the pressure loss coefficient distribution of the coke oven can be determined. This process can be described as a process of reproducing the current operating conditions through numerical analysis. In other words, it calculates the pressure loss coefficient distribution that best reproduces the actual characteristic value distribution (temperature distribution, etc.).

[0061] The pressure loss coefficient distribution obtained in this way can be used in the next step of determining operating conditions. Note that while the pressure loss coefficient distribution at the corner may fluctuate over the long term during continuous operation of the coke oven, it is unlikely to change drastically in the short term. Therefore, it is not always necessary to calculate the pressure loss coefficient distribution each time the operating conditions determination step is performed. Thus, the pressure loss coefficient distribution calculation step may be performed only once initially, and thereafter, the operating conditions determination step may be repeatedly performed using the obtained pressure loss coefficient distribution. Alternatively, the pressure loss coefficient distribution calculation step may be performed at arbitrary intervals.

[0062] (Third embodiment) In the pressure loss coefficient distribution calculation process described above, the distribution of characteristic values ​​inside the coke oven is used when performing numerical analysis. In this case, if the actual characteristic value distribution in the coke oven is known, that known characteristic value distribution can be used, but if it is unknown, it must be determined in advance. Therefore, the method for determining operating conditions of the third embodiment of the present invention further includes a measurement step in which the distribution of characteristic values ​​inside the coke oven is measured, prior to the pressure loss coefficient distribution calculation process. Figure 5 is a flowchart showing the processing flow according to this embodiment. Points not specifically mentioned can be the same as in the first and second embodiments described above.

[0063] As described above, the characteristic values ​​can be any values ​​without particular limitation. Examples of such characteristic values ​​include temperature, pressure, gas (atmosphere) composition, concentration of a specific gas, and gas flow rate. Further details are as described in the first embodiment above.

[0064] The range and location for measuring the characteristic value distribution should be determined considering the required accuracy, the load of numerical analysis, etc. For example, one combustion chamber may be targeted, or multiple combustion chambers may be targeted. If one combustion chamber is targeted, the characteristic value distribution within that one combustion chamber should be determined. Alternatively, as mentioned above, the flow path within the partition wall may be targeted. When temperature is used as the characteristic value, it is preferable to measure at least one of the temperature distribution inside the combustion chamber of the coke oven and the temperature distribution on the furnace wall of the carbonization chamber in the measurement process.

[0065] The measurement locations and number of measurement points for characteristic values ​​are not particularly limited and can be set arbitrarily. However, from the viewpoint of analysis accuracy, it is preferable to measure characteristic values ​​at least once in each of the multiple vertical flue pipes contained in the combustion chamber, and it is even more preferable to measure characteristic values ​​at two or more locations at different heights for each vertical flue pipe. For example, when measuring at two locations, it is preferable to measure near the bottom and near the top of the vertical flue pipe.

[0066] The measured characteristic value distribution can be used in the next step of calculating the pressure loss coefficient distribution.

[0067] (Fourth embodiment) The fourth embodiment of the present invention, as shown in Figure 6, further comprises a display step for displaying the operating conditions determined in the above operating conditions determination step. The operator can adjust the operating conditions of the coke oven based on the displayed operating conditions. In this embodiment as well, unless otherwise specified, the same can be applied as in the first to third embodiments described above.

[0068] Furthermore, if conditions related to flow rate are determined in the above-mentioned operating conditions determination process, the display process may directly display the determined flow rate, or it may display the opening degree of the flow control valve required to achieve that flow rate instead of the determined flow rate. Alternatively, both the flow rate and the opening degree may be displayed. Similarly, if conditions related to pressure are determined in the above-mentioned operating conditions determination process, the display process may directly display the determined pressure, or it may display the opening degree of the pressure control valve required to achieve that pressure instead of the determined pressure. Alternatively, both the pressure and the opening degree may be displayed. When displaying the valve opening degree, the opening degree required to achieve the desired flow rate or pressure should be calculated based on the relationship between the opening degree and the flow rate or pressure, which has been determined in advance.

[0069] Furthermore, in the display process described above, in addition to the determined operating conditions, other arbitrary information can also be displayed. Examples of such other information include the temperature distribution within the furnace. Examples of the temperature distribution within the furnace include the temperature distribution in the combustion chamber and the temperature distribution in the carbonization chamber. The temperature distribution in the combustion chamber may be measured in the measurement process. The temperature distribution in the carbonization chamber may be estimated by the numerical calculation described above.

[0070] (Fifth embodiment) Next, yet another embodiment of the method for determining operating conditions of the present invention will be described. As shown in Figure 10, the fifth embodiment of the method for determining operating conditions of the present invention further comprises a thermophysical property distribution calculation step, which is performed prior to the operating conditions determination step by performing a numerical analysis using a model of the coke oven to calculate the thermophysical property distribution of coal in the coke oven.

[0071] In this description, we will explain the case in which a thermophysical property distribution calculation step is added to the third embodiment described above, but the thermophysical property distribution calculation step can be adopted in any of the first to fourth embodiments. Furthermore, in this embodiment as well, unless otherwise specifically mentioned, the same can be used as in any of the first to fourth embodiments described above.

[0072] In the numerical calculations for the operating condition determination process described above, thermal properties are used for heat transfer calculations. For example, in the embodiment described later, the thermal diffusivity coefficient α is used as a coefficient in the thermal diffusion equation, and this thermal diffusivity coefficient α is one of the thermal properties. Here, the thermal diffusivity coefficient α is a value expressed as λ / (c×ρ) using thermal conductivity λ, specific heat c, and density ρ, and thermal conductivity λ, specific heat c, and density ρ are also thermal properties.

[0073] If the thermophysical properties used in the numerical calculations for determining operating conditions are known, those known values ​​can be used. However, if they are unknown, they must be determined beforehand. Furthermore, even if the thermophysical properties are known, recalculating them can further improve the accuracy of the numerical calculations in the operating conditions determination process.

[0074] Therefore, the method for determining operating conditions in this embodiment involves performing a numerical analysis using a model of the coke oven prior to the operating conditions determination step to calculate the distribution of thermophysical properties of coal in the coke oven. In this process, the numerical analysis is performed using the pressure loss coefficient distribution in the coke oven and characteristic values ​​inside the coke oven at multiple time points. Then, the numerical analysis in the operating conditions determination step is performed using the calculated distribution of thermophysical properties.

[0075] Model The coke oven model used in the thermophysical property distribution calculation process is not particularly limited; any model capable of handling the relationship between the characteristic values ​​and the pressure loss coefficient can be used. The model may include, for example, the law of conservation of mass, the law of conservation of momentum, the law of conservation of enthalpy, and combustion calculations. The analysis dimension can be one, two, or three. When using a three-dimensional model, it is preferable to use a model in which the dimension of the numerical analysis model has been reduced by a machine learning model, from the viewpoint of computational load. By using a model with reduced dimensions, a solution can be obtained quickly.

[0076] The model used in the thermophysical property distribution calculation step may be the same as or different from the model used in the subsequent operating condition determination step.

[0077] • Pressure loss coefficient distribution In the numerical analysis of the thermophysical property distribution calculation step, the pressure loss coefficient distribution in the coke oven is used. If the pressure loss coefficient distribution is known, that known pressure loss coefficient distribution can be used. On the other hand, if the pressure loss coefficient distribution is unknown, it must be determined in advance. In that case, as described in the second and third embodiments above, the pressure loss coefficient distribution can be calculated in the pressure loss coefficient distribution calculation step and used in the numerical calculation step of the thermophysical property distribution calculation step.

[0078] • Characteristic values In the numerical analysis of the thermophysical property distribution calculation process, in addition to the pressure loss coefficient distribution, characteristic values ​​inside the coke oven at multiple time points are also used. While the pressure loss coefficient distribution calculation process uses a distribution of characteristic values, the characteristic values ​​used in this process may be referred to as the first characteristic values, and the characteristic values ​​used in the thermophysical property distribution calculation process as the second characteristic values.

[0079] The characteristic values ​​used in the thermophysical property distribution calculation process are not particularly limited; any parameter that can be measured and analyzed by the numerical analysis described later can be used. Examples of such characteristic values ​​include temperature, pressure, gas (atmosphere) composition, concentration of a specific gas, and / or gas flow rate. From the standpoint of ease of measurement and equipment cost, it is preferable to use temperature as the characteristic value.

[0080] In the process of calculating the distribution of thermophysical properties, characteristic values ​​at multiple times are used. The interval between these multiple times is not particularly limited, but from the viewpoint of accuracy in numerical calculations, it is preferable that the time interval be within 10 minutes. For example, when producing a large quantity of coke, the coke production time is about 17.5 hours. Even in such cases, if the measurement interval is within 10 minutes, it is possible to correct the characteristic values ​​at time intervals of within 1% of the coke production time, and numerical calculations can be performed with high accuracy.

[0081] The measurement location for the aforementioned characteristic value may be one location or multiple locations. In other words, the numerical analysis of the thermophysical property distribution calculation process may use the characteristic value distribution inside the coke oven at multiple time points.

[0082] For example, when using the temperature inside the coke oven as the characteristic value, thermocouples can be embedded in the bricks of the combustion chamber, and the temperatures measured by these thermocouples at multiple times can be used. In this case, thermocouples can be embedded at multiple locations to measure the temperature distribution. In other words, it is preferable to use the temperature of the bricks at the top of the vertical flame path (upper flue temperature) or its distribution as the characteristic value.

[0083] • Distribution of thermophysical properties In the thermophysical property distribution calculation step, numerical analysis is performed to calculate the distribution of the thermophysical properties of coal. These thermophysical properties can be any properties that will be used in the subsequent operating condition determination step. Here, thermophysical properties can be defined as temperature-dependent parameters. These thermophysical properties typically include specific heat, density, thermal conductivity, thermal diffusivity, and heat transfer coefficient h. radIt may be at least one selected from the group consisting of .

[0084] Numerical analysis The numerical analysis in the above process for calculating the thermophysical property distribution can be performed using any method that can calculate the thermophysical property distribution. From the viewpoint of analytical accuracy, it is preferable to perform the numerical analysis using data assimilation. That is, data assimilation should be performed using the thermophysical properties of coal as variables so that the characteristic values ​​(or their distribution) inside the coke oven match the actual characteristic values ​​(or their distribution).

[0085] The algorithm used for data assimilation is not particularly limited, and any algorithm can be used. Examples of such algorithms include the ensemble Kalman filter method and the particle filter method, but the ensemble Kalman filter method is preferred.

[0086] In the numerical analysis described above, it is preferable to use the operating conditions in addition to the pressure loss coefficient distribution in the coke oven and the characteristic values ​​inside the coke oven at multiple times. As the operating conditions, for example, at least one selected from the group consisting of fuel gas composition, fuel gas flow rate, and air flow rate can be used. The operating conditions used are those at the time the characteristic values ​​were obtained.

[0087] By following the above procedure, the thermophysical property distribution of coal can be determined. This process can be described as a process of reproducing current operating conditions through numerical analysis. In other words, it calculates the thermophysical property distribution of coal that best reproduces the changes in characteristic values ​​over a specific period of time.

[0088] The thermophysical property calculation process may be performed at any interval. In other words, it is not necessary to perform the thermophysical property distribution calculation process before each operation condition determination process. Once the thermophysical property distribution calculation process has been performed and the thermophysical property distribution has been calculated, the operation condition determination process can be performed multiple times over any period of time using that thermophysical property distribution. However, the thermophysical properties of coal vary greatly depending on the type of coal as well as the moisture content within the coal. Therefore, it is preferable to perform the thermophysical property calculation process when there is a change in the type of coal, an increase or decrease in coal moisture content due to weather changes, or a change in operating conditions.

[0089] Figure 10 is a flowchart showing an example of the processing flow according to this embodiment. In this example, after the measurement process and the pressure loss coefficient distribution calculation process, the thermophysical property distribution is calculated using the ensemble Kalman filter method, and then the operating conditions determination process is carried out. The measurement process, the pressure loss coefficient distribution calculation process, and the operating conditions determination process have already been explained, so here we will explain the thermophysical property distribution calculation process.

[0090] In this embodiment, multiple numerical analyses are performed under different conditions in order to perform data assimilation. Therefore, the first step is to prepare these multiple numerical analyses. For example, multiple numerical analyses with different distributions of the thermophysical properties of coal are prepared.

[0091] Next, (1) the multiple numerical analyses are carried out until the time for which characteristic value data exists. (2) After calculating the Kalman gain from the multiple numerical analysis data and characteristic values, (3) the numerical analysis results are corrected using the obtained Kalman gain. At this point, if it is the final time for the characteristic value data to be used, data assimilation is terminated and the process proceeds to the next operating condition determination step. On the other hand, if it has not yet reached the final time for the characteristic value data to be used, the process returns to step (1) above and repeats the calculation.

[0092] Furthermore, since the thermophysical properties of coal vary depending on temperature and the filling of the carbonization chamber, numerical analysis should be performed assuming that they differ depending on location and time.

[0093] [Examples of data assimilation] Next, we will explain in more detail the details of the processes in each of the above steps (1) to (3) using mathematical formulas.

[0094] The ensemble Kalman filter is a technique that introduces variability into the initial values ​​and parameters of a numerical analysis, creates a covariance matrix from the results of multiple numerical analyses, and statistically corrects the values ​​using observed values. In the process of determining the thermophysical properties of coal, the observed values ​​are the temperature data measured as characteristic values. Here, we will determine the thermal diffusivity as the thermophysical property of coal.

[0095] (1) First, multiple numerical analyses are performed to introduce variability in the thermal diffusivity distribution up to a specific time point in time for which observational values ​​exist. Then, the mean value and covariance matrix of the state vector consisting of temperature and thermal diffusivity in the numerical analysis are obtained using the results of the multiple numerical analyses [Equations 1, 2].

[0096]

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[0097]

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[0098] (2) The Kalman gain is obtained by using a linear observation matrix and an observation error covariance matrix that transform the state vector to the dimensions of the observed values ​​[Equation 3]. The observation error covariance matrix was estimated by the maximum likelihood method assuming a Gaussian approximation.

[0099]

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[0100] (3) Modify the state vector from the Kalman gain, observed values, and linear observation matrix [Equation 4].

[0101]

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[0102] After that, return to the step (1) above, and use the modified state vector, i.e., the modified temperature and the modified thermal diffusivity, to advance the multiple numerical analyses to the time of the observation value to be assimilated with data next.

[0103] By repeating the above steps until the final time when the characteristic value data exists, the distribution of thermophysical property values is calculated.

[0104] Here, the definitions of each symbol are as follows. x t1|t2 : State vector. Data obtained by arranging (temperature) + (thermal diffusivity) as a vector. Subscript t1: Time in numerical analysis Subscript t2: Time of the characteristic value used for data assimilation V t : Covariance matrix, indicating the uncertainty of the simulation y t : Observation value H t : Linear observation matrix: Converting the state variable to the dimension of the observation value K t : Kalman gain R t : Observation error covariance matrix Q t : System matrix The overline (bar) attached to the symbol represents the ensemble average value.

[0105] The modified state vector x t|t consists of the modified values of the temperature T and the thermal diffusivity α at the computational grid points in the numerical analysis. In the dimension-reduced numerical analysis, since the time evolution coefficient a is solved based on Equation 20, the temperature T included in a and x t|t is converted using Equation 18.

[0106] [Operation condition determination system] The operating condition determination system in one embodiment of the present invention comprises means for carrying out each of the steps in each of the embodiments described above. That is, the operating condition determination system comprises means for determining operating conditions by performing numerical analysis using a model of a coke oven. In the numerical analysis of the operating condition determination means, the pressure loss coefficient distribution in the coke oven is used, and the operating conditions of the coke oven are used as variables to determine the operating conditions by optimizing so that the variation in the time to reach a predetermined temperature at multiple locations in the coke oven satisfies predetermined convergence conditions.

[0107] The means for determining operating conditions may, for example, include a calculation means for performing the numerical analysis. The calculation means may, for example, be a computer equipped with a recording medium containing software for performing the numerical analysis.

[0108] Furthermore, the above operating condition determination system may further include a pressure loss coefficient distribution calculation means that performs numerical analysis using the coke oven model to calculate the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values ​​inside the coke oven.

[0109] The pressure loss coefficient distribution calculation means may include, for example, a calculation means for performing the numerical analysis. The calculation means may be, for example, a computer equipped with a recording medium containing software for performing the numerical analysis. One calculation means may also serve as both the operating condition determination means and the pressure loss coefficient distribution calculation means.

[0110] Furthermore, the above operating condition determination system may further include a measurement means for actually measuring the distribution of the characteristic values. Any measurement means capable of measuring the characteristic values ​​to be used can be used. For example, if temperature is used as the characteristic value, a temperature measuring device is used as the measurement means. Examples of temperature measuring devices include radiation thermometers and thermocouples. Examples of means for measuring the composition of gas include various gas analyzers. Examples of means for measuring pressure include Pitot tubes.

[0111] The number of measuring means is not particularly limited. Measuring means can be installed at each measurement location, but multiple measurements can also be performed with a single measuring means.

[0112] Furthermore, the above-described operating condition determination system may further include a display means for displaying the operating conditions determined by the above-described operating condition determination means. By including a display means, the determined operating conditions can be presented to the operator. Typically, the display means can be a display connected to a computer. Preferably, the display used is that of the computer used as the calculation means described above.

[0113] [Coke oven] The coke oven in this embodiment is a coke oven equipped with an operating condition determination system. The type of coke oven is not particularly limited, and the present invention can be applied to any type of coke oven. [Examples]

[0114] Next, the present invention will be described in more detail based on examples. Note that these examples are merely one example of preferred embodiments of the present invention, and the present invention is not limited to the disclosures in these examples.

[0115] We minimized the variation in coking time of raw coal based on the temperature distribution inside a single combustion chamber. The specific procedure is described below.

[0116] • Measurement process First, the temperature inside one combustion chamber was measured to determine the temperature distribution. The results are shown in Figure 7. In this embodiment, the temperature distribution inside a combustion chamber equipped with 32 vertical flues was measured at one point for each vertical flue. A radiation thermometer was used to measure the temperature of the bricks from the top of the furnace to the bottom.

[0117] • Pressure loss coefficient distribution calculation process Next, using a flow path model of a coke oven created based on a one-dimensional model, optimization calculations were performed to match the measured temperature distribution to obtain the pressure loss coefficient distribution. The constitutive equations used were the law of conservation of mass at nodes [Equation 5], the law of conservation of enthalpy at nodes [Equation 6], the ideal gas law [Equation 7], and the law of conservation of momentum at ducts [Equation 8], as expressed by the following equations. Furthermore, the combustion temperature and gas composition due to the mixing of fuel and air at each node were calculated based on chemical equilibrium theory. In this embodiment, "numerical analysis" is performed using "simulation that can be executed by solving a physical model using a computer." This allows for efficient calculation of the pressure loss coefficient distribution.

[0118]

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[0122] Here, the definitions of each symbol are as follows: ρ j:Density [kg / m 3 ] u j Duct velocity [m / s] A: Duct cross-sectional area [m²] 2 ] h: Enthalpy [j / m] 3 ] L: Duct length [m] K: Friction loss coefficient [-] P: Gas pressure [Pa] R: Gas constant [J / (K·mol)]

[0123] Furthermore, in the heat transfer calculations for the heat storage chamber, we considered the following solid heat conduction [Equations 9, 10], solid-fluid heat transfer [Equations 11, 12], and radiation [Equations 13, 14], which are expressed by the following formulas.

[0124]

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[0127]

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[0130] Here, the symbols [Equation 15] to [Equation 17] below represent the heat mobility in heat conduction [Equation 15], the heat mobility in heat transfer [Equation 16], and the heat mobility by radiation [Equation 13], respectively.

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[0132]

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[0133]

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[0134] Furthermore, the definitions of the other symbols are as follows: T1-T2: Temperature difference [K] between two points considering heat transfer. K k : Thermal conductivity of solids [W / (m·K)] A h : Surface area of ​​the surface related to heat transfer [m 2 ] h c : Heat transfer coefficient [W / K] h rad : Heat transfer coefficient equivalent to radiation [W / K]

[0135] [Examples of pre-trained models] In the heat transfer calculations for the carbonization chamber, a dimensionality reduction model based on the POD-Galerkin method was used to enable the calculation of the three-dimensional temperature distribution in a short time [Equations 18-22]. In the POD-Galerkin method, the ordinary differential equations for the expansion coefficient a described in [Equation 20] are actually solved, and the number of these equations is equal to the number of selected eigenvalues ​​= the dimension of the POD basis. For example, if a heat transfer calculation model using 1000 computational grids is dimensionally reduced using the POD-Galerkin method, the number of equations becomes about 10, which significantly reduces the calculation time.

[0136]

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[0137]

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[0138]

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[0139]

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[0141] Here, the definitions of each symbol are as follows: T: Temperature [K] TIFF0007859470000023.tif7170α: Thermal diffusion coefficient [m 2 / s] t: time [s] λ: Eigenvalue [-] v: Eigenvector [-] φ: Unique orthogonal basis [K] a: Time evolution coefficient [-]

[0142] For example, when analyzing one carbonization chamber and the two adjacent combustion chambers, using the standard finite element method, it takes approximately 8 hours to analyze the process until the raw coal is coked. In contrast, when using the standard FEM for heat transfer calculations in the carbonization chamber, the POD-Galerkin method allows for analysis in about 90 seconds while preserving three-dimensional temperature distribution information.

[0143] Numerical analysis was performed using the above model to calculate the pressure loss coefficient distribution. In the numerical analysis, the fuel gas composition, fuel gas flow rate, and air flow rate from the current operation were used as input values. The pressure loss coefficient for each location in the heat storage chamber was optimized as a variable so that the temperature distribution of the bricks at the bottom of the vertical flame obtained by the numerical analysis matched the actual temperature distribution of the bricks at the bottom of the vertical flame shown in Figure 7. NSGA3, a multi-objective optimization method of the genetic algorithm, was used as the optimization method.

[0144] • Process for determining operating conditions Next, a numerical analysis was performed using the pressure loss coefficient distribution obtained in the above pressure loss coefficient distribution calculation step, and the operating conditions determination step was carried out. In the numerical analysis, the fuel gas composition, fuel gas flow rate, and air flow rate were used as variables, and optimization was performed to minimize the variation in the time it takes to reach a predetermined temperature at multiple locations in the coke oven. As the optimization method, NSGA3, one of the multi-objective optimization methods of the genetic algorithm, was used. The predetermined temperature was set to 900°C.

[0145] ·Display process The operating conditions determined in the above-mentioned operating conditions determination process, namely the fuel gas flow rate, fuel gas composition, and air flow rate, were displayed on the display means. At that time, the fuel gas flow rate, fuel gas composition, and air flow rate in the current operation, as well as the temperature distribution in the combustion chamber measured in the above-mentioned measurement process and the temperature distribution in the carbonization chamber estimated by numerical analysis, were also displayed on the display means.

[0146] Using the coke oven model described above, operating conditions (fuel gas flow rate, fuel gas calorific value, and air flow rate) were determined according to the flow of the present invention. The determined operating conditions were displayed on a display device, and the operator controlled the operating conditions of the coke oven according to the display.

[0147] The upper part of Figure 8 is a graph plotting the controlled fuel gas flow rate and fuel gas calorific value against time (days). The values ​​for fuel gas flow rate and fuel gas calorific value are normalized to 1, with the value at time zero being set to 1. The time required for one coke production cycle is approximately 24 hours. During the period shown in the graph, the charging of raw coal, carbonization, and removal of carbonized coke are repeated.

[0148] Furthermore, the lower part of Figure 8 is a graph showing the temperature change in the combustion chamber when the above control is performed. Here, "upper flue temperature" refers to the temperature measured near the center of the upper part of the combustion chamber. "Average furnace bottom temperature" refers to the average value of the furnace bottom temperatures in flues 4 through 32, and "#1 flue temperature" refers to the furnace bottom temperature in flue 1 (i.e., the flue located at the very edge).

[0149] In this embodiment, as described above, the operating conditions were controlled based on the conditions determined by the method of the present invention. In particular, as shown in the upper graph of Figure 8, control was performed to significantly increase the fuel gas flow rate and significantly decrease the fuel gas calorific value around the 15th to 20th day.

[0150] As a result, as shown in the lower graph of Figure 8, the low #1 furnace bottom temperature at our facility rose and approached the average furnace bottom temperature. This result indicates that before the adjustments to the flow rate and gas composition described above, the temperature near the furnace opening was lower than in the center of the carbonization chamber, and coking was not progressing sufficiently. This result is consistent with the measured temperature distribution shown in Figure 7, where the temperature at both ends was lower. However, after the above adjustments, the temperature became uniform, and coking progressed even near the furnace opening.

[0151] Here, the timing at which the temperature non-uniformity decreased, as shown in the lower part of Figure 8, is delayed compared to the timing at which the fuel gas flow rate and calorific value were adjusted, as shown in the upper part of Figure 8. This is thought to be because it takes time for the adjusted operating conditions to be fully reflected in the actual environment inside the reactor.

[0152] Furthermore, because the furnace bottom temperature is difficult to measure, the furnace bottom temperature of each flue is not constantly measured during normal coke oven operation. Instead, the temperature inside the furnace is monitored primarily by the upper flue temperature, which is easier to measure. However, as can be seen from the graph in the lower part of Figure 8, measuring only the upper flue temperature does not allow us to grasp the temperature unevenness occurring inside the furnace, as described above.

[0153] Figure 9 is a graph showing the changes in dust generation when the coke is removed from the carbonization chamber after carbonization during the above operation. The vertical axis represents the visible smoke generation rate (%), and the horizontal axis represents time (days). Here, the horizontal axis for 0-30 days shows the visible smoke generation rate (%) calculated from the number of visible smoke particles from day 0 to day 30. Similarly, 31-60 days shows the number of visible smoke particles from day 31 to day 60, 61-91 days shows the period from day 61 to day 91, 92-122 days shows the period from day 92 to day 122, and 123-152 days shows the period from day 123 to day 152. The baseline for day 0 is the same day as day 0 on the horizontal axis of Figure 8. From these results, it can be seen that the visible smoke generation rate decreases from day 61 to day 91 onwards compared to day 31 to day 60. The amount of dust generated increases when coking has not progressed sufficiently. Therefore, this result indicates that the proportion of materials where coking did not progress sufficiently decreased, that is, the variability in coking was reduced.

[0154] Furthermore, the timing at which the visible smoke generation rate decreased in Figure 9 is delayed compared to the timing at which the fuel gas flow rate and calorific value were adjusted as shown in the upper part of Figure 8. This is thought to be because, as explained in the description of Figure 8, it takes time for the adjusted operating conditions to be fully reflected in the actual environment inside the reactor.

[0155] As can be seen from the above results, the present invention reduces variations in the coking time of raw coal and allows for carbonization under more ideal conditions. [Explanation of Symbols]

[0156] 1. Coke oven 10 Carbonization Chamber 11 Coal charging port 20 Combustion chamber 21 Inspection holes 22 Partition Wall 23 Vertical flue 24 horizontal flue 30 Heat storage chamber 40. Tomi Gas Piping (C Gas Piping) 50. Low-gas piping (M gas piping) 60 Small flue 61 Great flue 62 Chimney CS Corkside PS Pusher side (machine side)

Claims

1. A method for determining the operating conditions of a coke oven, The system includes an operating conditions determination process that determines operating conditions by performing numerical analysis using the aforementioned coke oven model, A method for determining operating conditions, wherein the numerical analysis of the process for determining operating conditions uses the pressure loss coefficient distribution in the coke oven and, using the operating conditions of the coke oven as a variable, optimizes the variation in the time it takes to reach a predetermined temperature at multiple locations within the coke oven to satisfy predetermined convergence conditions.

2. The method for determining operating conditions according to claim 1, further comprising a step of calculating a pressure loss coefficient distribution, which involves performing a numerical analysis using a model of the coke oven prior to the above-mentioned operating conditions determination step to calculate the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values ​​inside the coke oven.

3. The method for determining operating conditions according to claim 2, further comprising a measurement step of measuring the distribution of the characteristic values ​​prior to the pressure loss coefficient distribution calculation step.

4. Prior to the aforementioned operating conditions determination step, the system further comprises a thermophysical property distribution calculation step, which involves performing a numerical analysis using a model of the coke oven to calculate the thermophysical property distribution of coal in the coke oven. In the numerical analysis of the thermophysical property distribution calculation process, the pressure loss coefficient distribution in the coke oven and the characteristic values ​​inside the coke oven at multiple time points are used. The method for determining operating conditions according to any one of claims 1 to 3, wherein the step for determining operating conditions is to perform a numerical analysis using the thermophysical property distribution calculated in the step for calculating the thermophysical property distribution.

5. A method for determining operating conditions according to any one of claims 1 to 3, further comprising a display step for displaying the operating conditions determined in the aforementioned operating conditions determination step.

6. The method for determining operating conditions according to claim 4, further comprising a display step for displaying the operating conditions determined in the aforementioned operating conditions determination step.

7. An operating conditions determination system for determining the operating conditions of a coke oven, The system includes an operating condition determination means that determines operating conditions by performing numerical analysis using the aforementioned coke oven model, An operating condition determination system that determines the operating conditions by using the pressure loss coefficient distribution in the coke oven as a variable in the numerical analysis of the operating condition determination means, and optimizing the variation in the time it takes to reach a predetermined temperature at multiple locations within the coke oven to satisfy predetermined convergence conditions.

8. The operating condition determination system according to claim 7, further comprising a pressure loss coefficient distribution calculation means for performing numerical analysis using the coke oven model and calculating the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values ​​inside the coke oven.

9. The operating condition determination system according to claim 8, further comprising a measurement means for measuring the distribution of the aforementioned characteristic values.

10. Prior to determining the operating conditions using the aforementioned operating conditions determination means, the system further includes a thermophysical property distribution calculation means that performs numerical analysis using the coke oven model to calculate the thermophysical property distribution of coal in the coke oven. In the numerical analysis of the thermophysical property distribution calculation means, the pressure loss coefficient distribution in the coke oven and the characteristic values ​​inside the coke oven at multiple time points are used, The operating condition determination system according to any one of claims 7 to 9, wherein the operating condition determination means performs numerical analysis using the thermophysical property distribution calculated by the thermophysical property distribution calculation means.

11. The operating condition determination system according to any one of claims 7 to 9, further comprising a display means for displaying the operating conditions determined by the operating condition determination means.

12. The operating condition determination system according to claim 10, further comprising a display means for displaying the operating conditions determined by the operating condition determination means.

13. A coke oven comprising an operating condition determination system according to any one of claims 7 to 9.

14. A coke oven comprising the operating condition determination system described in claim 10.

15. A coke oven comprising the operating condition determination system described in claim 11.

16. A coke oven comprising the operating condition determination system described in claim 12.

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