Operation condition determination method, operation condition determination system, 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 an operation condition determination method and an operation condition determination system for determining operation conditions of a coke oven. The present invention also relates to a coke oven equipped with the operation condition determination system. [Background technology]

[0002] Coke used in steelworks and the like is generally produced by carbonizing coal in a chamber-hearth coke oven. Chamber-hearth coke ovens (hereinafter simply referred to as "coke ovens") can be broadly divided into single-type and double-type ovens depending on the type of fuel gas used. A single-type oven is a oven that uses a high-calorie gas called "rich gas" as fuel gas, and the rich gas generally used is 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 oven that can use both the rich gas and a low-calorie gas called "lean gas" as fuel gas. A mixed gas of coke oven gas and blast furnace gas (hereinafter referred to as M gas) is usually used as the lean gas.

[0003] Here, the structure of a coke oven will be explained using a Karl Still type double-hearth furnace as an example.

[0004] FIG. 1 is a cross-sectional perspective view showing a schematic structure of a Karl Still type coke oven. The furnace body of the coke oven 1 is constructed by stacking refractory bricks, and a plurality of coke chambers 10 and a combustion chamber 20 are provided at the top. The coke chambers 10 and the combustion chambers 20 are arranged alternately in the oven width direction indicated by the arrow W. A plurality of coal charging holes 11 are provided at the top of the coke chamber 10, and raw coal is charged into the coke chamber 10 through the coal charging holes 11. An inspection hole 21 is provided at the top of the combustion chamber 20. Note that FIG. 1 shows a cross section of the combustion chamber 20 in a plane parallel to the oven length direction indicated by the arrow L.

[0005] The carbonization chamber 10 and the combustion chamber 20 are separated by a furnace wall made of firebricks, and the carbonization chamber 10 is heated through the furnace wall by burning fuel gas in the combustion chamber 20. 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 longitudinal direction, and a fire truck and a coke dry quenching system (CDQ) are installed on the other side of the oven. The side where the extruder is installed is called the pusher side (PS) or machine side (MS), and the side where the quenching system is installed (the removal side) is called the coke side (CS). The finished coke is pushed out of the carbonization chamber 10 to the coke side by the extruder.

[0007] A heat regenerator 30 extending in the furnace length direction is provided in the lower part of the coke oven 1. The heat regenerator 30 is for utilizing the waste heat from combustion, and a plurality of heat regenerators 30 are arranged in the furnace width direction. In the heat regenerator 30, bricks with many flow paths in the vertical direction are installed for heat storage.

[0008] 2 is a schematic diagram showing the structure of a pair of coke chambers 10 and combustion chambers 20 that constitute the coke oven, and a heat regenerator connected to the coke chambers 10 and combustion chambers 20. The combustion chamber 20 is divided into multiple vertical flues 23 by partition walls 22 spaced apart in the furnace length direction. The partition walls 22 are made of firebricks, and have flow paths formed therein through which air and lean gas flow.

[0009] In addition, in the Karl Still type coke oven, the combustion chamber 20 and the regenerator 30 are usually divided into two in the furnace length direction as shown in FIG. 1, and 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 (the left side in FIG. 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 to the regenerator 30, where heat is recovered. The exhaust gas then passes through the small flue 60 and the large flue 61 and is released from the chimney 62. Usually, the operation of the coke oven is performed 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, the rich gas is supplied from the rich gas piping 40 through the horizontal rich gas piping 41 extending in the furnace length direction to the inside of the combustion chamber 20 through an opening provided at the bottom of the vertical flue 23. Meanwhile, air is supplied from all the regenerators 30 to each combustion chamber 20. At that time, the air passes through the inside of the partition wall 22 and is supplied into the combustion chamber 20 through an opening provided in the side surface of the partition wall 22.

[0011] On the other hand, when lean gas is used as the combustion gas, lean gas is once supplied from the lean gas pipe 50 to the regenerator 30 on one side (the coke side in the example shown in Figs. 1 and 2). The supplied lean gas is preheated in the regenerator 30 and then sent to the combustion chamber 20 above (solid line in Fig. 1). Similarly, air is also supplied to the regenerator 30, preheated in the regenerator 30, and then sent to the combustion chamber 20 above (broken line in Fig. 1). Here, as shown in Fig. 2, lean gas and air are alternately supplied to the multiple regenerators 30 arranged in the furnace width direction. For example, when lean gas is supplied to the odd-numbered regenerators (e.g., 30a in Fig. 2) among the multiple regenerators counted in the furnace width direction, air is supplied to the even-numbered regenerators (e.g., 30b in Fig. 2). In addition, both lean gas and air are supplied to the combustion chamber 20 through an opening provided on the side of the partition wall 22. A plurality of partition walls 22 installed at intervals in the combustion chamber 20 are alternately used to supply lean gas and air. For example, when lean gas is supplied to odd-numbered partition walls among the plurality of partition walls, counting in the furnace length direction, air is supplied to even-numbered partition walls.

[0012] By burning the rich gas or lean gas in the combustion chamber as described above, the carbonization chambers arranged on both sides of the combustion chamber through the furnace wall are heated, and the raw coal in the carbonization chambers can be carbonized. [Prior art documents] [Patent documents]

[0013] [Patent Document 1] JP 2019-059926 A Summary of the Invention [Problem to be solved by the invention]

[0014] Here, the most ideal carbonization in a coke oven is one in which the temperature variation of the raw coal in the carbonization chamber is small and the coking of the raw coal is completed without variation at the scheduled time. If the coking time varies depending on the position in the oven, it causes variation in the quality of the produced coke. In addition, as the variation increases, the amount of uncarbonized coal increases, and there is also a problem that the amount of dust generated due to uncarbonized coal increases when removing the coke from the coke oven. Furthermore, when the variation in the coking time is large, in order to sufficiently coke the raw coal throughout the oven, it is necessary to perform carbonization for a longer time, which results in an increase in the amount of fuel used and carbon dioxide emissions. Therefore, it is necessary to reduce the variation in the coking time as much as possible.

[0015] However, as described above, coke ovens have a very complicated structure, which means that the temperature distribution inside them is prone to variation, making it difficult to promote uniform coking.

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

[0017] For example, the technology disclosed in Patent Document 1 makes it possible to identify the location of blockages in the coke oven flow passage by combining simulation and optimization technology. However, the problem of how to adjust the operating conditions to achieve ideal carbonization remained unsolved.

[0018] Therefore, currently, the operating conditions of a coke oven are determined based on the experience of the operator.

[0019] The present invention has been made to solve the above-mentioned problems, and has an object to reduce the variation in the coking time of raw coal. [Means for solving the problem]

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

[0021] 1. A method for determining operating conditions of a coke oven, comprising the steps of: An operation condition determination step of determining operation conditions by performing a numerical analysis using the coke oven model, In the numerical analysis of the operating condition determination step, a pressure loss coefficient distribution in the coke oven is used, and the operating conditions of the coke oven are used as variables, and the operating conditions are determined by optimizing the variation in the time it takes to reach a predetermined temperature at multiple positions in the coke oven so that it satisfies a predetermined convergence condition.

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

[0023] 3. The method for determining operating conditions according to item 2 above, further comprising a step of actually measuring the distribution of the characteristic values ​​prior to the pressure loss coefficient distribution calculation step.

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

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

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

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

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

[0029] 9. A coke oven equipped with the operation condition determination system according to any one of 5 to 8 above. Effect of the Invention

[0030] According to the present invention, it is possible to reduce the variation in the coking time of raw coal. [Brief description of the drawings]

[0031] [Figure 1] FIG. 2 is a cross-sectional perspective view showing a schematic structure of a Karl Still type coke oven. [Diagram 2] 1 is a schematic diagram showing the structures of a carbonization chamber, a combustion chamber, and a heat regenerator that constitute a coke oven. FIG. [Diagram 3] FIG. 2 is a flow chart showing processing in the first embodiment of the present invention. [Figure 4] FIG. 11 is a flow chart showing a process in a second embodiment of the present invention. [Diagram 5]FIG. 11 is a flow chart showing a process in a third embodiment of the present invention. [Figure 6] FIG. 13 is a flow chart showing a process in a fourth embodiment of the present invention. [Figure 7] FIG. 13 is a diagram showing the temperature distribution in one coking chamber calculated in an embodiment. [Figure 8] 4 is a graph showing the fuel gas flow rate and fuel gas calories controlled in the embodiment, and the change in temperature in the combustion chamber at that time. [Figure 9] 1 is a graph showing changes in visual smoke generation rate in the examples. [Figure 10] FIG. 13 is a flow chart showing a process in a fifth embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0032] Next, a method for carrying out the present invention will be specifically described. In the above description of the background art, a Karl Still type coke oven is used as an example, but the present invention can also be applied to other types of coke ovens. In this specification, the upper horizontal flue provided at the top of the combustion chamber is also included in the combustion chamber. Similarly, the lower horizontal flue provided at the bottom of the regenerator is also included in the regenerator.

[0033] (First embodiment) The method for determining operating conditions according to the present embodiment includes a step of determining operating conditions, which will be described below.

[0034] [Operation condition determination process] In the operation condition determination step, a numerical analysis is performed using a model of a coke oven to determine the operation conditions. In the numerical analysis, the pressure loss coefficient distribution in the coke oven is used, and the operation conditions of the coke oven are used as variables to perform optimization so that the variation in the time required to reach a predetermined temperature at multiple positions in the coke oven satisfies a predetermined convergence condition, thereby determining the operation conditions.

[0035] Model The model of the coke oven is not particularly limited, and any model can be used as long as it can perform the numerical analysis. The model typically handles relationships between temperatures, pressure loss coefficients, operating conditions, and the like in each part of the oven. The model can 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-dimensional, two-dimensional, or three-dimensional. When using a three-dimensional model, it is preferable to use a model in which the dimensions of a numerical analysis model are reduced by a machine learning model, from the viewpoint of calculation load. By using a model with reduced dimensions, a solution can be obtained quickly.

[0036] Pressure loss coefficient distribution In the numerical analysis, the pressure loss coefficient distribution in the coke oven is used. For example, when the pressure loss coefficient distribution in the coke oven is known, the known pressure loss coefficient distribution may be used. When the pressure loss coefficient distribution is unknown, the pressure loss coefficient distribution may be obtained prior to the operation condition determination step. A method for obtaining the pressure loss coefficient distribution will be described later.

[0037] Operating conditions In the operating condition 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 related to the operation of the coke oven. Typically, the operating conditions may be at least one selected from the group consisting of (1) conditions related to flow rate, (2) conditions related to fuel gas composition, and (3) conditions related 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 condition related to the fuel gas composition literally includes 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 related 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 condition may be the pressure at any position in the furnace, and an example is the pressure in the combustion chamber. The pressure in the combustion chamber can be adjusted by controlling the opening of the pressure control valve at the exit side of the combustion chamber.

[0041] FIG. 3 is a flow diagram showing the flow of processing according to this embodiment. First, the variation in the time it takes to reach a predetermined temperature in the furnace is calculated by numerical analysis. Note that coking of raw coal in the furnace does not proceed at low temperatures, but begins to proceed once a certain temperature is reached. 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. Therefore, in the present invention, the variation in the "time it takes to reach a predetermined temperature" is used as an index.

[0042] The temperature is not particularly limited and may be determined in advance based on the raw coal used and the properties required for the product coke, but 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) for evaluating the magnitude of the calculated variation in the time it takes to reach the predetermined temperature is calculated from the variation.

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

[0045] The convergence condition is not particularly limited, but may be determined to have converged when the temperature is equal to or less than a predetermined threshold value, or when the variation in the time it takes to reach a predetermined temperature at multiple positions in the coke oven becomes minimum.

[0046] The algorithm used for the optimization is not particularly limited, and any algorithm can be used. An example of an optimization algorithm that can be suitably used is a genetic algorithm. Other examples include a gradient method.

[0047] By the above procedure, the operating conditions of the coke oven can be determined. By operating the coke oven based on the determined operating conditions, the variation in the coking time can be reduced. There is no particular limitation on the method of reflecting the determined operating conditions in the actual operation. For example, the operating conditions may be automatically reflected by a control means. Also, the operator may change the operating conditions based on the determined operating conditions.

[0048] The timing of changing the operating conditions is not particularly limited. For example, the conditions may be changed between lots. That is, the operating conditions can be changed when the coke produced in a certain lot is discharged from the carbonization chamber and the next lot is carbonized. However, in an actual coke oven, it takes time for heat to be transmitted through the oven wall bricks, so it takes time for the change in the operating conditions to be reflected in the actual environment (poor responsiveness). In addition, it is difficult to change the layer conditions such as the gas composition for each combustion chamber. Therefore, it is preferable to change the operating conditions at any time as soon as the operating conditions are determined.

[0049] Second Embodiment In the above-mentioned operation condition determination step, the pressure loss coefficient distribution in the coke oven is used. At this time, if the pressure loss coefficient distribution is known, the known pressure loss coefficient distribution may be used, but if it is unknown, it is obtained in advance. Therefore, the operation condition determination method of the second embodiment of the present invention further includes a pressure loss coefficient distribution calculation step of calculating the pressure loss coefficient distribution in the coke oven from the distribution of characteristic values ​​in the coke oven prior to the above-mentioned operation condition determination step. Hereinafter, this pressure loss coefficient distribution calculation step will be described. Note that, points not particularly mentioned can be the same as those in the above-mentioned first embodiment.

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

[0051] Model The coke oven model is not particularly limited, and any model can be used as long as it can handle the relationship between the characteristic value and the pressure loss coefficient. The model can include, for example, the law of conservation of mass, the law of conservation of momentum, the law of conservation of enthalpy, combustion calculation, etc. The analysis dimension may be one-dimensional, two-dimensional, or three-dimensional. When using a three-dimensional model, it is preferable to use a model in which the dimensions of a numerical analysis model are reduced by a machine learning model, from the viewpoint of calculation load. By using a model with reduced dimensions, 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 step, the distribution of characteristic values ​​in the coke oven is used as information for calculating the pressure loss coefficient distribution. The characteristic values ​​are not particularly limited, and any parameter that can be actually measured and can be analyzed by the numerical analysis described later can be used. Examples of the characteristic values ​​include temperature, pressure, gas (atmosphere) composition, concentration of a specific gas, and gas flow rate, and these characteristic values ​​can be used alone or in combination. From the standpoint of ease of measurement and equipment cost, it is preferable to use temperature and pressure, or only temperature, as the characteristic values. On the other hand, from the standpoint of more accurately calculating the pressure loss coefficient distribution, it is preferable to use both the temperature distribution and the flow rate distribution.

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

[0055] Fig. 4 is a flow diagram showing the flow of the process according to this embodiment. First, the distribution of characteristic values ​​in the coke oven is calculated by a numerical analysis using an arbitrary pressure loss coefficient distribution as an initial condition. At that time, the current operating conditions may be used as the operating conditions.

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

[0057] If the calculated evaluation function satisfies a predetermined convergence condition, the pressure loss coefficient distribution calculation process is terminated. On the other hand, if the convergence condition is 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, and the characteristic value distribution is calculated again. By repeating the above process, the difference between the characteristic value distribution obtained by the numerical analysis and the actual characteristic value distribution is optimized so as to satisfy the predetermined convergence condition.

[0058] The convergence condition is not particularly limited, but for example, it may be determined that the convergence has occurred when the difference between the characteristic value distribution obtained by the numerical analysis and the actual characteristic value distribution is equal to or less than a predetermined threshold value. It may also be determined that the convergence has occurred when the difference between the characteristic value distribution obtained by the numerical analysis and the actual characteristic value distribution is minimized.

[0059] The algorithm used for the optimization is not particularly limited, and any algorithm can be used. An example of an optimization algorithm that can be suitably used is a genetic algorithm. Other examples include a gradient method.

[0060] By following the above procedure, the pressure loss coefficient distribution of the coke oven can be obtained. This process can be said to be a process of reproducing the current operating conditions by numerical analysis. In other words, the pressure loss coefficient distribution that can best reproduce the actual characteristic value distribution (temperature distribution, etc.) is calculated.

[0061] The pressure loss coefficient distribution thus obtained may be used in the next operation condition determination step. The pressure loss coefficient distribution of the corner portion may vary over the long term during the continuous operation of the coke oven, but is unlikely to change suddenly in a short period of time. Therefore, it is not necessary to calculate the pressure loss coefficient distribution every time the operation condition determination step is performed. Therefore, the pressure loss coefficient distribution calculation step may be performed only once at the beginning, and thereafter, the operation condition determination step may be repeatedly performed using the obtained pressure loss coefficient distribution. Also, the pressure loss coefficient distribution calculation step may be performed at any interval.

[0062] (Third embodiment) In the above-mentioned pressure loss coefficient distribution calculation step, the distribution of characteristic values ​​in the coke oven is used when performing the numerical analysis. In this case, if the actual characteristic value distribution in the coke oven is known, the known characteristic value distribution may be used, but if it is unknown, it is obtained in advance. Therefore, the operating condition determination method of the third embodiment of the present invention further includes an actual measurement step of measuring the distribution of characteristic values ​​in the coke oven prior to the pressure loss coefficient distribution calculation step. FIG. 5 is a flow chart showing the flow of processing according to this embodiment. Note that the points not particularly mentioned can be the same as those of the first and second embodiments.

[0063] As described above, the characteristic value is not particularly limited and any value can be used. Examples of the characteristic value include temperature, pressure, gas (atmosphere) composition, concentration of a specific gas, and gas flow rate. Details are as described in the first embodiment.

[0064] The range and position for measuring the characteristic value distribution may be determined taking into consideration the required accuracy, the load of the numerical analysis, and the like. For example, one combustion chamber may be the target, or multiple combustion chambers may be the target. When one combustion chamber is the target, the characteristic value distribution within that one combustion chamber may be obtained. Also, as described above, the flow path within the partition wall may be the target. When temperature is used as the characteristic value, it is preferable to measure at least one of the temperature distribution within the combustion chamber of the coke oven and the temperature distribution within the coke chamber wall in the actual measurement process.

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

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

[0067] (Fourth embodiment) The operating condition determination method according to the fourth embodiment of the present invention further includes a display step of displaying the operating conditions determined in the operating condition determination step, as shown in Fig. 6. An operator can adjust the operating conditions of the coke oven based on the displayed operating conditions. Note that, in this embodiment, the points not specifically mentioned can be the same as those in the first to third embodiments.

[0068] In addition, when a condition related to a flow rate is determined in the above-mentioned operation condition determination step, the display step may directly display the determined flow rate, or may display the opening degree of a flow control valve for achieving the determined flow rate instead of the determined flow rate. Also, both the flow rate and the opening degree may be displayed. Similarly, when a condition related to a pressure is determined in the above-mentioned operation condition determination step, the display step may directly display the determined pressure, or may display the opening degree of a pressure control valve for achieving the determined pressure instead of the determined pressure. Also, both the pressure and the opening degree may be displayed. In addition, when the opening degree of the valve is displayed, the opening degree required to achieve a desired flow rate or pressure may be calculated based on the relationship between the opening degree and the flow rate or pressure that has been obtained in advance.

[0069] In addition, in the display step, in addition to the determined operating conditions, any other information can also be displayed. Examples of the other information include the temperature distribution in the furnace. Examples of the temperature distribution in the furnace include the temperature distribution in the combustion chamber and the temperature distribution in the coke chamber. The temperature distribution in the combustion chamber may be actually measured in the actual measurement step. Furthermore, the temperature distribution in the coke chamber may be estimated by the numerical calculation.

[0070] Fifth embodiment Next, a further embodiment of the operating condition determination method of the present invention will be described. As shown in Fig. 10, the operating condition determination method of the fifth embodiment of the present invention further includes, prior to the operating condition determination step, a thermal property value distribution calculation step of calculating a thermal property value distribution of coal in the coke oven by performing a numerical analysis using a model of the coke oven.

[0071] Although the case where the thermal property value distribution calculation step is added to the third embodiment will be described here as an example, the thermal property value distribution calculation step can be adopted in any of the first to fourth embodiments. Also, in the present embodiment, the points not specifically mentioned can be the same as any of the first to fourth embodiments.

[0072] In the numerical calculation of the above-mentioned operation condition determination step, thermal property values ​​are used for heat transfer calculation. For example, in the embodiment described later, a thermal diffusion coefficient α is used as a coefficient of a thermal diffusion equation, and this thermal diffusion coefficient α is one of the thermal property values. Here, the thermal diffusion coefficient α is a value expressed as λ / (c×ρ) using thermal conductivity λ, specific heat c, and density ρ, and the thermal conductivity λ, specific heat c, and density ρ are also thermal property values.

[0073] When the thermophysical property values ​​to be used in the numerical calculation in the operation condition determination step are known, the known thermophysical property values ​​may be used, but when the thermophysical property values ​​are unknown, they are to be obtained in advance. Even when the thermophysical property values ​​are known, the accuracy of the numerical calculation in the operation condition determination step can be further improved by recalculating the thermophysical property values.

[0074] Therefore, in the operational condition determination method of the present embodiment, prior to the operational condition determination step, a numerical analysis is performed using a model of the coke oven to calculate a thermal property value distribution of coal in the coke oven. At that time, the numerical analysis is performed using the pressure loss coefficient distribution in the coke oven and characteristic values ​​in the coke oven at multiple times. Then, the calculated thermal property value distribution is used to perform the numerical analysis in the operational condition determination step.

[0075] Model The coke oven model used in the thermophysical property distribution calculation step is not particularly limited, and any model can be used as long as it can handle the relationship between the characteristic value and the pressure loss coefficient. The model can include, for example, the law of conservation of mass, the law of conservation of momentum, the law of conservation of enthalpy, combustion calculation, etc. 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 dimensions of a numerical analysis model are reduced by a machine learning model, from the viewpoint of calculation load. By using a model with reduced dimensions, a solution can be obtained quickly.

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

[0077] Pressure loss coefficient distribution In the numerical analysis of the thermal property value distribution calculation step, the pressure loss coefficient distribution in the coke oven is used. At this time, if the pressure loss coefficient distribution is known, the known pressure loss coefficient distribution may be used. On the other hand, if the pressure loss coefficient distribution is unknown, it is to be obtained in advance. In that case, as explained in the second and third embodiments, the pressure loss coefficient distribution may be calculated in the pressure loss coefficient distribution calculation step, and used in the numerical calculation in the thermal property value distribution calculation step.

[0078] Characteristic values In the numerical analysis of the thermal property value distribution calculation step, in addition to the pressure loss coefficient distribution, characteristic values ​​in the coke oven at a plurality of times are used. Note that, although the pressure loss coefficient distribution calculation step uses the distribution of characteristic values, the characteristic value used in the pressure loss coefficient distribution calculation step may be called a first characteristic value, and the characteristic value used in the thermal property value distribution calculation step may be called a second characteristic value.

[0079] The characteristic value used in the thermal property value distribution calculation step is not particularly limited, and any parameter that can be actually measured and can be analyzed by the numerical analysis described below can be used. Examples of the characteristic value include temperature, pressure, gas (atmosphere) composition, specific gas concentration 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 thermal property value distribution calculation process, characteristic values ​​at a plurality of times are used. The interval between the plurality of times is not particularly limited, but from the viewpoint of the accuracy of the numerical calculation, it is preferable that the time interval is within 10 minutes. For example, when a large amount of coke is produced, the coke production time is about 17.5 hours. Even in such a case, if the measurement interval is within 10 minutes, it is possible to correct the characteristic values ​​at time intervals within 1% of the coke production time, and the numerical calculation can be performed with high accuracy.

[0081] The measurement position of the characteristic value may be one or more. In other words, the characteristic value distribution in the coke oven at multiple times may be used in the numerical analysis of the thermophysical property value distribution calculation step.

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

[0083] -Distribution of thermal properties In the thermal property value distribution calculation step, a numerical analysis is performed to calculate the distribution of the thermal property values ​​of coal. The thermal property values ​​may be any values ​​that are used in the next operation condition determination step. Here, the thermal property values ​​can be said to be parameters that are temperature dependent. The thermal property values ​​are typically specific heat, density, thermal conductivity, thermal diffusivity, 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-mentioned thermophysical property distribution calculation step can be performed by any method as long as it 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 can be performed using the thermophysical property values ​​of coal as variables so that the characteristic values ​​(or their distribution) in the coke oven coincide with 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 the algorithm include the ensemble Kalman filter method and the particle filter method, and among them, it is preferable to use the ensemble Kalman filter method.

[0086] In the numerical analysis, it is preferable to use the pressure loss coefficient distribution in the coke oven and the characteristic values ​​in the coke oven at a plurality of times, as well 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 as the operating conditions. The operating conditions used may be the operating conditions when the characteristic values ​​are obtained.

[0087] The above procedure makes it possible to obtain the distribution of thermal physical properties of coal. This process can be said to be a process of reproducing the current operating conditions by numerical analysis. In other words, the distribution of thermal physical properties of coal that can best reproduce the changes in the actual characteristic values ​​during a specific period is calculated.

[0088] The thermal property value calculation step may be performed at any interval. In other words, it is not necessary to perform the thermal property value distribution calculation step prior to each operation condition determination step. After performing the thermal property value distribution calculation step once to calculate the thermal property value distribution, the operation condition determination step can be performed multiple times for any period of time using the thermal property value distribution. However, the thermal property values ​​of coal vary greatly depending on the coal brand as well as the moisture contained in the coal. Therefore, it is preferable to perform the thermal property value calculation step when there is a change in brand, an increase or decrease in the coal moisture content due to a change in weather, or a change in operation conditions.

[0089] 10 is a flow diagram showing an example of the process flow according to this embodiment. In this example, after the actual measurement step and the pressure loss coefficient distribution calculation step, the thermal property value distribution is calculated by a thermal property value distribution calculation step using the ensemble Kalman filter method, and then the operating condition determination step is performed. The actual measurement step, the pressure loss coefficient distribution calculation step, and the operating condition determination step have already been described, so here, the thermal property value distribution calculation step will be described.

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

[0091] Next, (1) the multiple numerical analyses are carried out up to a time when characteristic value data exists. (2) A Kalman gain is calculated from the multiple numerical analysis data and characteristic values, and then (3) the numerical analysis results are corrected using the obtained Kalman gain. If it is the final time of the characteristic value data to be used at this point, data assimilation is terminated and the process proceeds to the next operation condition determination step. On the other hand, if it is not the final time of the characteristic value data to be used, the process returns to step (1) above and repeats the calculations.

[0092] In addition, since the thermal properties of coal vary depending on the temperature and the filling level in the coke chamber, numerical analysis can be performed assuming that the properties vary depending on the position and time.

[0093] [Data assimilation example] Next, the process in each of the above steps (1) to (3) will be described in more detail using mathematical expressions.

[0094] The ensemble Kalman filter is a method that creates a covariance matrix from multiple numerical analysis results by introducing variation into the initial values ​​and parameters of the numerical analysis, and then statistically modifies the values ​​using observed values. The observed values ​​in the process of determining the thermal properties of coal are the temperature data measured as characteristic values. Here, the thermal diffusion coefficient is determined as the thermal property value of coal.

[0095] (1) First, multiple numerical analyses with variations in the thermal diffusion coefficient distribution are performed up to a specific time when an observed value exists. Then, the results of the multiple numerical analyses are used to obtain the average value and the covariance matrix of the state vector consisting of the temperature and thermal diffusion coefficient in the numerical analyses [Equation 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 that transforms the state vector into the dimension of the observation value and the observation error covariance matrix [Equation 3]. The observation error covariance matrix is ​​estimated by the maximum likelihood method assuming a Gaussian approximation.

[0099]

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

[0101]

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[0102] Then, returning to step (1) above, multiple numerical analyses are performed using the corrected state vector, i.e., the corrected temperature and the corrected thermal diffusion coefficient, up to the time of the next observation to be assimilated.

[0103] The above steps are repeated until the final time for which characteristic value data exists, thereby calculating the thermal property value distribution.

[0104] Here, the definitions of each symbol are as follows: x t1|t2 : State vector. Data in which (temperature) + (thermal diffusion coefficient) are arranged as a vector. Subscript t1: Time in numerical analysis Subscript t2: Time of characteristic value used for data assimilation V t : Covariance matrix, showing the uncertainty of the simulation y t : Observed value H t :Linear observation matrix: Transform state variables to the dimensions of the observations K t : Kalman gain R t : Observation error covariance matrix Q t :System matrix The bars above the symbols represent the ensemble average.

[0105] The corrected state vector x t|t consists of the temperature T at the computational grid point in the numerical analysis and the modified value of the thermal diffusion coefficient α. In the reduced-dimensional numerical analysis, the time evolution coefficient a is solved based on Equation 20, so a and x t|t The temperature T contained in is converted using equation 18.

[0106] [Operation condition determination system] The operating condition determination system according to one embodiment of the present invention includes a means for implementing each step in each of the above-described embodiments. That is, the operating condition determination system includes an operating condition determination means for determining operating conditions by performing a numerical analysis using a model of a coke oven. In the numerical analysis by the operating condition determination means, a pressure loss coefficient distribution in the coke oven is used, and the operating conditions of the coke oven are used as variables, and the operating conditions are determined by optimizing the variation in the time required to reach a predetermined temperature at multiple positions in the coke oven so as to satisfy a predetermined convergence condition.

[0107] The operating condition determination means may include, for example, a calculation means for performing the numerical analysis. For example, a computer having a recording medium containing software for performing the numerical analysis may be used as the calculation means.

[0108] In addition, the operational condition determination system may further include a pressure loss coefficient distribution calculation means for performing a numerical analysis using a model of the coke oven and calculating a pressure loss coefficient distribution in the coke oven from a distribution of characteristic values ​​in the coke oven.

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

[0110] The operating condition determination system may further include a measuring means for actually measuring the distribution of the characteristic value. Any measuring means may be used as long as it can measure the characteristic value to be used. For example, when temperature is used as the characteristic value, a temperature measuring device is used as the measuring means. Examples of the temperature measuring device include a radiation thermometer and a thermocouple. Also, examples of the means for measuring the composition of the gas include various gas analyzers. Examples of the pressure measuring means include a Pitot tube.

[0111] The number of the measuring means is not particularly limited. A measuring means may be installed for each measurement position, or one measuring means may be used to measure a plurality of positions.

[0112] The operation condition determination system may further include a display means for displaying the operation conditions determined by the operation condition determination means. By including the display means, the determined operation conditions can be presented to an operator. As the display means, a display connected to a computer or the like can typically be used. As the display, it is preferable to use the display of the computer serving as the above-mentioned calculation means.

[0113] [Coke oven] The coke oven according to one embodiment of the present invention is a coke oven equipped with an operation condition determination system. The type of the 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 examples of preferred embodiments of the present invention, and the present invention is not limited to the disclosure of these examples.

[0115] Based on the temperature distribution inside one combustion chamber, we minimized the variation in the coking time of raw coal. The specific procedure is explained 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 example, 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 for the measurement, and the temperature of the bricks from the top to the bottom of the furnace was measured.

[0117] Pressure loss coefficient distribution calculation process Next, a coke oven flow path model created based on the one-dimensional model was used to perform optimization calculations to match the measured temperature distribution, and the pressure loss coefficient distribution was obtained. As the constitutive equations, the mass conservation law of the node [Equation 5], the enthalpy conservation law of the node [Equation 6], the gas state equation [Equation 7], and the momentum conservation law of the duct [Equation 8], all of which are expressed by the following formulas, were used. In addition, the combustion temperature and gas composition due to the mixture of fuel and air at each node were calculated based on the chemical equilibrium theory. In this embodiment, the "numerical analysis" used is a "simulation that can be performed by solving a physical model using a computer." This allows the pressure loss coefficient distribution to be calculated efficiently.

[0118]

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[0122] Here, the definitions of each symbol are as follows: ρ j:Density [kg / m 3 ] u j :Duct speed [m / s] A: Duct cross-sectional area [m 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] In addition, the heat transfer calculations for the heat storage chamber took into account the thermal conduction of solids [Equations 9 and 10], heat transfer between solids and fluids [Equations 11 and 12], and radiation [Equations 13 and 14], all of which are expressed by the following equations.

[0124]

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[0127]

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

[0131]

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[0132]

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[0133]

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[0134] Moreover, the definitions of other symbols are as follows: T 1 -T 2 : Temperature difference between two points taking into account heat transfer [K] K k : Thermal conductivity of solid [W / (m K)] A h : Surface area 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 trained models] In the heat transfer calculation of the coking chamber, a dimensional reduction model based on the POD-Galerkin method was used to enable the temperature distribution to be calculated in three dimensions in a short time [Equations 18-22]. In the POD-Galerkin method, the ordinary differential equation for the expansion coefficient a described in [Equation 20] is actually solved, and the number of equations is the number of selected eigenvalues ​​= the dimension of the POD basis. For example, if a heat transfer calculation model using 1,000 computational grids is dimensionally reduced by the PDO-Galerkin method, it becomes about 10 equations, which can significantly reduce the calculation time.

[0136]

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[0137]

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[0138]

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[0139]

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[0140]

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[0141] Here, the definitions of each symbol are as follows: T: Temperature [K] TIFF2025037836000024.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 coke chamber and two adjacent combustion chambers, if a normal finite element method is used, it takes about eight hours to analyze the process until the raw coal turns into coke. In contrast, if normal FEM is used to calculate the heat transfer in the coke chamber, the analysis can be completed in about 90 seconds by using the POD-Galerkin method while retaining three-dimensional temperature distribution information.

[0143] A numerical analysis was performed using the above model, and the pressure loss coefficient distribution calculation process was carried out. In the numerical analysis, the fuel gas composition, fuel gas flow rate, and air flow rate in the current operation described above were used as input values. Optimization was performed using the pressure loss coefficient for each location in the regenerator as a variable so that the temperature distribution of the bricks at the bottom of the vertical flue obtained by the numerical analysis would match the actual temperature distribution of the bricks at the bottom of the vertical flue shown in Figure 7. NSGA3, a genetic algorithm that is a type of multi-objective optimization, was used as the optimization method.

[0144] · Operation condition determination process Next, a numerical analysis was performed using the pressure loss coefficient distribution obtained in the pressure loss coefficient distribution calculation process, and an operation condition determination process was performed. In the numerical analysis, optimization was performed to minimize the variation in the time to reach a predetermined temperature at multiple positions in the coke oven, using the fuel gas composition, fuel gas flow rate, and air flow rate as variables. NSGA3, which is one of the multi-objective optimization methods of genetic algorithms, was used as the optimization method. The predetermined temperature was set to 900°C.

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

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

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

[0148] The lower part of Fig. 8 is a graph showing the change in temperature in the combustion chamber when the above control was performed. Here, the "upper flue temperature" is the temperature measured near the center of the upper part of the combustion chamber. The "average hearth temperature" is the average value of the hearth temperatures in the 4th to 32nd flues, and the "#1 flue temperature" is the hearth temperature in the 1st flue (i.e. the flue located at the extreme end).

[0149] In this embodiment, the operating conditions were controlled based on the conditions determined by the method of the present invention as described above. In particular, as shown in the upper graph of Fig. 8, the fuel gas flow rate was significantly increased and the fuel gas calories were significantly decreased around the 15th to 20th days.

[0150] As a result, as shown in the graph at the bottom of Figure 8, the #1 hearth temperature, which was low at this time, rose and approached the average hearth temperature. From this result, it can be seen that before the above-mentioned adjustments to the flow rate and gas composition were made, the temperature near the mouth of the coke chamber was lower than that in the center of the coke chamber, and coking had not progressed sufficiently. This result coincides with the fact that the temperatures at both ends are lower in the measured temperature distribution shown in Figure 7. However, after the above adjustments, the temperature was uniformed, and it can be seen that coking began to progress even near the mouth of the coke chamber.

[0151] Here, the timing at which the temperature non-uniformity decreases in the lower part of Figure 8 is delayed compared to the timing at which the fuel gas flow rate and calories 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 furnace.

[0152] Incidentally, since it is difficult to measure the hearth temperature, in normal coke oven operation the hearth temperature of each flue is not constantly measured, and the temperature situation inside the furnace is monitored solely 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, the occurrence of temperature non-uniformity inside the furnace, as described above, cannot be grasped by merely measuring the upper flue temperature.

[0153] FIG. 9 is a graph showing the change in the dust generation state when the coke after carbonization was taken out of the coke chamber during the above operation, with the vertical axis showing the visible smoke generation rate (%) and the horizontal axis showing time (days). Here, the horizontal axis 0-30 days shows the visible smoke generation rate (%) calculated from the number of visible smoke from 0 to 30 days. Similarly, the horizontal axis 31-60 days shows the number of visible smoke from 31 to 60 days, the horizontal axis 61-91 days shows the number of visible smoke from 61 to 91 days, the horizontal axis 92-122 days shows the number of visible smoke from 92 to 122 days, and the horizontal axis 123-152 days shows the number of visible smoke from 123 to 152 days. The reference for day 0 is the same as day 0 on the horizontal axis in FIG. 8. From this result, it can be seen that the visible smoke generation rate is lower from 61 to 91 days onwards compared to 31 to 60 days. Since the amount of dust generation increases the more insufficient the coking is, this result shows that the proportion of cases where the coking was not sufficient has decreased, i.e., the variation in the coking has been reduced.

[0154] In addition, the timing at which the visual smoke generation rate decreases in Figure 9 is delayed compared to the timing at which the fuel gas flow rate and calories were adjusted, as shown in the upper part of Figure 8. This is thought to be because, as mentioned in the explanation of Figure 8, it takes time for the adjusted operating conditions to be fully reflected in the actual environment inside the furnace.

[0155] As can be seen from the above results, according to the present invention, it is possible to reduce the variation in the coking time of raw coal and perform carbonization under more ideal conditions. [Explanation of symbols]

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

Claims

1. An operating condition determination method for determining operating conditions of a coke oven, comprising the steps of: An operation condition determination step of determining operation conditions by performing a numerical analysis using the coke oven model, In the numerical analysis of the operating condition determination step, a pressure loss coefficient distribution in the coke oven is used, and the operating conditions of the coke oven are used as variables, and the operating conditions are determined by optimizing the variation in the time it takes to reach a predetermined temperature at multiple positions in the coke oven so that it satisfies a predetermined convergence condition.

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

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

4. The method further includes a thermal property distribution calculation step of calculating a thermal property distribution of coal in the coke oven by performing a numerical analysis using the coke oven model prior to the operation condition determination step, In the numerical analysis of the thermal property value distribution calculation step, a pressure loss coefficient distribution in the coke oven and characteristic values ​​in the coke oven at a plurality of times are used, The method for determining operating conditions according to any one of claims 1 to 3, wherein in the operating condition determining step, a numerical analysis is performed using the thermal property value distribution calculated in the thermal property value distribution calculating step.

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

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

7. An operating condition determination system for determining operating conditions of a coke oven, comprising: an operating condition determination means for determining operating conditions by performing a numerical analysis using the coke oven model; The numerical analysis of the operating condition determination means uses a pressure loss coefficient distribution in the coke oven, and determines the operating conditions by optimizing the variation in the time it takes to reach a predetermined temperature at multiple positions in the coke oven so that it satisfies predetermined convergence conditions, using the operating conditions of the coke oven as variables, in an operating condition determination system.

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

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

10. a thermal property distribution calculation means for calculating a thermal property distribution of coal in the coke oven by performing a numerical analysis using a model of the coke oven prior to the determination of the operating conditions by the operating condition determination means, In the numerical analysis by the thermal property value distribution calculation means, a pressure loss coefficient distribution in the coke oven and characteristic values ​​in the coke oven at a plurality of times are used, The operating condition determination system according to any one of claims 7 to 9, wherein the operating condition determination means performs a numerical analysis using the thermal property value distribution calculated by the thermal property value distribution calculation means.

11. 10. The operating condition determination system according to claim 7, further comprising a display means for displaying the operating conditions determined by the operating condition determination means.

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

13. A coke oven equipped with the operation condition determination system according to any one of claims 7 to 9.

14. A coke oven comprising the operation condition determination system according to claim 10.

15. A coke oven comprising the operation condition determination system according to claim 11.

16. A coke oven comprising the operation condition determination system according to claim 12.

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