JP7709033B2 - Processing device, processing method, and program - Google Patents

Description

The present invention relates to a processing device, a processing method, and a program, and is particularly suitable for use in performing processing including determining the oven temperature in a coke oven.

In a coke oven called a chamber type, multiple carbonization chambers and multiple combustion chambers are arranged alternately, with furnace walls made of firebricks or the like in between. In the coke production process, first, coal is charged into the carbonization chamber (charging coal into the carbonization chamber in this manner is also called "charging", and the carbonization chamber is also called a "kiln"). After that, heat is continuously applied from the combustion chamber through the furnace wall to the carbonization chamber with the charged coal, and the coal is carbonized to produce a coke cake (in the following explanation, "coke cake" is also simply called "coke"). When the carbonization is completed, the doors on both ends of the carbonization chamber are opened, and the coke is discharged by a device called a pusher ram (such pushing of coke from the carbonization chamber is also called "unloading from the kiln").

The so-called block method is used for the loading and unloading of coke ovens. The loading and unloading work involves pushing the coke out of the coke chamber with a pusher ram, followed by loading coal into the coke chamber. In the block method, all the coke chambers are divided into Da blocks (Da is an integer equal to or greater than 2), and the loading and unloading work is carried out in units of the divided blocks. These blocks are also called "streets," and each coke oven is assigned to one of the streets so that multiple coke ovens spaced every Da spaces in the order of the coke ovens belong to the same street. For example, if each carbonization chamber is assigned a number (carbonization chamber No.) in ascending order starting from "1" in the order in which the chambers are arranged, and the number of patterns (=Da) is 5, then all the carbonization chambers are divided into patterns at intervals of 5, such as pattern 1 (carbonization chamber No. 1, 6, 11, ...), pattern 2 (carbonization chamber No. 2, 7, 12, ...), pattern 3 (carbonization chamber No. 3, 8, 13, ...), pattern 4 (carbonization chamber No. 4, 9, 14, ...), and pattern 5 (carbonization chamber No. 5, 10, 15, ...), and the unloading and loading work is carried out on a pattern basis.

Patent Document 1 discloses a technology for controlling the amount of heat input to such a coke oven, which determines the temperature of the combustion chamber (furnace temperature) to achieve a target coke temperature. Specifically, Patent Document 1 discloses a technology for calculating each set of target oven temperatures from the past to the future based on the predicted amount of future coal to be charged, predicted moisture, planned carbonization time, and actual oven temperature, and calculating the weighted average of each set of target oven temperatures as the target oven temperature for controlling the amount of heat input.

In such coke ovens, during steady-state operation, the aforementioned coal loading and unloading operations (charcoal loading and pushing) are repeated at an approximately constant cycle, but when coal loading and unloading operations are temporarily suspended for equipment maintenance or other reasons, the operation becomes non-steady.

Japanese Patent Application Publication No. 9-302350

However, the technology described in Patent Document 1 calculates the target furnace temperature using the predicted coal loading amount and predicted moisture content, and is a technology that assumes steady operation. Furthermore, the technology described in Patent Document 1 calculates the target furnace temperature for controlling the input heat amount without considering changes in the carbonization state over time. Therefore, the technology described in Patent Document 1 cannot consider how the carbonization state changes during non-steady operation. Therefore, the technology described in Patent Document 1 cannot accurately determine the target furnace temperature during non-steady operation.

The present invention was made in consideration of the above problems, and aims to accurately determine the target furnace temperature during non-steady operation.

The processing device of the present invention is a processing device that determines a target furnace temperature, which is a target value of the furnace temperature, which is the temperature of the combustion chamber in a coke oven, during non-steady operation, and includes a predicted value calculation means that calculates a predicted value of the furnace temperature during non-steady operation based on a first influencing factor, which is a factor that affects the predicted value of the furnace temperature during the non-steady operation, and calculates a predicted value of the physical quantity during the non-steady operation based on a second influencing factor, which is a factor that affects the predicted value of the physical quantity that represents the carbonization state of the coke during the non-steady operation, and a target furnace temperature determination means that determines a target furnace temperature trajectory, which is the time change of the target furnace temperature, based on the predicted value of the furnace temperature during the non-steady operation and the predicted value of the physical quantity during the non-steady operation.

The processing method of the present invention is a processing method for determining a target furnace temperature, which is a target value of the furnace temperature, which is the temperature of the combustion chamber in a coke oven, during non-steady operation, and includes a predicted value calculation process for calculating a predicted value of the furnace temperature during non-steady operation based on a first influencing factor, which is a factor that affects the predicted value of the furnace temperature during the non-steady operation, and calculating a predicted value of the physical quantity during the non-steady operation based on a second influencing factor, which is a factor that affects the predicted value of the physical quantity that represents the carbonization state of the coke during the non-steady operation, and a target furnace temperature determination process for determining a target furnace temperature trajectory, which is a time change of the target furnace temperature, based on the predicted value of the furnace temperature during the non-steady operation and the predicted value of the physical quantity during the non-steady operation.

The program of the present invention is intended to cause a computer to function as each of the means of the processing device.

According to the present invention, a predicted value of the furnace temperature during non-steady operation and a predicted value of a physical quantity representing the carbonization state of the coke during non-steady operation are calculated, and a target furnace temperature trajectory is determined based on the predicted value of the furnace temperature and the predicted value of the physical quantity. Therefore, the target furnace temperature during non-steady operation can be determined so as to reflect the predicted results of the furnace temperature during non-steady operation and the physical quantity representing the carbonization state of the coke during non-steady operation. Therefore, the target furnace temperature during non-steady operation can be determined with high accuracy.

FIG. 1 illustrates an example of a coke oven and coke making process. FIG. 2 is a diagram illustrating an example of furnace temperature. FIG. 2 is a diagram showing an example of coke being pushed out of a carbonization chamber. FIG. 2 illustrates an example of a functional configuration of a processing device. FIG. 2 is a diagram showing an example of the relationship between coke temperature, furnace temperature, input heat amount, and carbonization time and time. FIG. 4 is a diagram illustrating an example of a target furnace temperature trajectory. FIG. 13 is a diagram illustrating an example of a target furnace temperature trajectory determined at the start time of unsteady operation. FIG. 13 is a diagram illustrating an example of a target furnace temperature trajectory determined at the timing when an operating condition is changed. 11 is a flowchart illustrating an example of a processing method. FIG. 4 is a diagram showing an example of a target furnace temperature trajectory and an actual furnace temperature trajectory.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
<Outline of coke ovens and the coke manufacturing process>
First, an example of a schematic configuration of a coke oven 1 and an overview of an example of a coke production process will be described with reference to Figures 1, 2A, and 2B. Figure 1 is a diagram showing an example of a coke oven and a coke production process. Figure 2A is a diagram explaining an example of a furnace temperature. Figure 2B is a diagram showing an example of a state in which coke is pushed out of a carbonization chamber. Note that Figures 2A and 2B show a see-through view of the inside.
1 and 2A, in a coke oven 1, carbonization chambers (furnaces) 2 and combustion chambers 3 are arranged alternately with a furnace wall 4 between them. The carbonization chambers 2 carbonize charged coal to obtain coke. The combustion chambers 3 burn fuel gas to keep the carbonization chambers 2 at a high temperature.

As explained in the Background Art section, in the coke production process using the coke oven 1, the so-called block discharge method is adopted for the discharge coal loading work. The discharge coal loading work is the work of pushing coke out of the coke chamber 2 by the pusher ram 7 as shown in FIG. 2B, and then supplying coal to the coke chamber 2. In the block discharge method, all the coke chambers 2 are divided into Da numbers (Da is an integer of 2 or more), and the discharge coal loading work is performed in units of the divided numbers. Each coke chamber 2 is assigned to one of the numbers so that multiple coke chambers 2 that are Da apart in the order of the coke chambers 2 belong to the same number of numbers. In this embodiment, an example is shown in which the discharge coal loading work is performed using the block discharge method with Da set to 5. In this case, for example, the coke chambers 2 of No. 1, 6, 11, 16, ... are assigned to number 1, and the coke chambers No. 2 are assigned to number 2. The carbonization chambers 2 of No. 2, 7, 12, 17, ... are assigned to the street 3, the carbonization chambers 2 of No. 3, 8, 13, 18, ... are assigned to the street 4, the carbonization chambers 2 of No. 4, 9, 14, 19, ... are assigned to the street 5, and the carbonization chambers 2 of No. 5, 10, 15, 20, ... are assigned to the street 6. The loading work is carried out in order from the lowest numbered carbonization chamber 2 in each street. For example, the loading work of the carbonization chambers 2 assigned to the street 1 is carried out in the order of the carbonization chamber 2 of No. 1, the carbonization chamber 2 of No. 6, the carbonization chamber 2 of No. 11, the carbonization chamber 2 of No. 16, .... In addition, the order of loading the coal before unloading is, for example, 1, 3, 5, 2, and 4 to prevent a sudden drop in temperature. The time from the end of loading the coal on one street to the end of loading the coal on the next street is called the running time. The running time is generally about 3 to 6 hours. Note that this embodiment is not limited to the block unloading method. For example, if the following description treats each street (block) as an individual carbonization chamber 2, it can also be applied to cases where loading the coal before unloading is performed in units of one carbonization chamber 2.

In addition, in the coke oven manufacturing process, a furnace battery control is performed to adjust the input heat amount of all combustion chambers 3 at once and control the average carbonization state of each way. That is, the input heat amount to the coke oven 1 is controlled by operating one adjustment valve 5 installed for all combustion chambers 3. The adjustment valve 5 is a valve for adjusting the flow rate of the mixed gas of fuel gas and combustion air. The adjustment valve 5 is also operated via an actuator (not shown) under the control of the processing device 300 described later. The representative value of the temperature of all combustion chambers 3 is called the furnace battery temperature. For example, as shown in FIG. 2A, a thermometer 6 for measuring the atmospheric temperature of the combustion chamber 3 is installed in multiple combustion chambers 3 out of all combustion chambers 3, and the average temperature of the combustion chambers 3 in which the thermometer 6 is installed is the furnace battery temperature. In this embodiment, the case where the furnace temperature, which is the temperature in the combustion chamber 3 of the coke oven 1, is the furnace battery temperature is illustrated. Note that the method of this embodiment is not limited to the case where the input heat amount of all combustion chambers 3 is adjusted at once. For example, when performing the unloading and loading work for one carbonization chamber 2, a control valve and actuator can be installed in each combustion chamber 3 to control the carbonization state (amount of heat input) for each carbonization chamber 2.

The thermometer 6 may be installed in each of all the combustion chambers 3, or may be installed in only some of the combustion chambers 3. For example, the thermometer 6 may be installed in all the combustion chambers 3, and the temperature of each combustion chamber 3 may be regarded as the temperature of that combustion chamber 3 (furnace temperature).
As described above, the coke is pushed out of the carbonization chamber 2 by the pusher ram 7. In the example shown in FIG. 2B, the coke pushed out of the carbonization chamber 2 by the pusher ram 7 is discharged via the guide car 9 to a fire extinguishing car (not shown) arranged below the guide car 9, and is transported to a downstream process by the fire extinguishing car. The guide car 9 moves to the position of the carbonization chamber 2 where the coal loading work is performed. In FIG. 2B, the coke produced in the carbonization chamber 2 located at the bottom of FIG. 2B is discharged via the guide car 9 to a fire extinguishing car (not shown) to complete the coal loading work, and the guide car 9 moves to the carbonization chamber 2 located at the top of FIG. 2B. The guide car 9 after the movement is shown by a two-dot chain line. In addition, FIG. 2B illustrates a case where a thermometer 8 for measuring the temperature of the coke without contact is installed inside the guide car 9. The thermometer 8 is installed so as to face the passage path of the coke inside the guide car 9 through a window provided in the guide car 9. In this manner, the present embodiment illustrates a case where the temperature of the coke immediately after it leaves the coke chamber 2 during the coke pushing operation (at the time of pushing) is measured. Note that the coke temperature does not necessarily have to be measured in this manner as long as the temperature of the coke discharged from the coke chamber 2 is measured. In the following description, the temperature of the coke that leaves the coke chamber 2 is also referred to as the coke temperature.

<Outline of Processing Device 300>
3 is a diagram showing an example of a functional configuration of the processing device 300. The hardware of the processing device 300 is realized, for example, by using an information processing device including a central processing unit, a main storage device, an auxiliary storage device, an input device, and an output device. The hardware of the processing device 300 may be realized by a programmable logic controller (PLC) or dedicated hardware such as an application specific integrated circuit (ASIC).

In this embodiment, the processing device 300 determines a target furnace temperature, which is a target value of the furnace temperature during non-steady operation. As described above, in this embodiment, the case where the furnace temperature is the furnace battery temperature is exemplified. In steady operation, the above-mentioned kiln discharge coal loading work is repeatedly performed at an approximately constant cycle corresponding to the pass time. In contrast, in non-steady operation, the kiln discharge coal loading work is temporarily suspended due to equipment maintenance, etc. Therefore, during non-steady operation, for example, the furnace battery temperature is temporarily lowered to perform equipment maintenance, etc., and then the furnace battery temperature is raised. Non-steady operation is an operation during a period that includes a period during which the kiln discharge coal loading work is temporarily suspended and a period during which at least one kiln discharge coal loading work (charging and pushing) is performed following that period. In this embodiment, non-steady operation is an operation during a period that includes a period during which the kiln discharge coal loading work is temporarily suspended and a period during which at least one pass of the kiln discharge coal loading work is performed following that period.

In this embodiment, the start and end of the non-steady operation are the end of the kiln loading work (coke extrusion (kiln unloading) work). The end of the coke extrusion work is the timing after the timing when all the coke in the carbonization chamber 2 is pushed (discharged) from the carbonization chamber 2. The end of the coke extrusion work may be, for example, the timing when all the coke is discharged from the carbonization chamber 2, the timing when the door of the carbonization chamber 2 is closed thereafter, the timing when the temperature of the coke discharged from the carbonization chamber 2 is measured, or the timing when the coke pushed out from the carbonization chamber 2 starts to be transported to the downstream process. It may also be the timing when the coke extrusion work is said to end in the operation manual of the coke plant. In the following description, the period during which the kiln loading work is temporarily suspended is also referred to as the suspension period. In addition, if an operation abnormality occurs, the start of the non-steady operation may be the timing when the operation abnormality is detected.

In FIG. 1 , a processing device 300 that executes processes including determining a target oven temperature in a coke oven 1 includes an acquisition unit 310, a predicted value calculation unit 320, a target oven temperature determination unit 330, and a control unit 340.
The acquisition unit 310 acquires various data used in the processing device 300. The data acquired by the acquisition unit 310 includes past and present performance values of operations, future operation schedule values, operation target values, and various setting values used for calculations in the processing device 300. Examples of data acquisition modes include at least one of an operator's operation on an input device, reception from an external device, and reading from a portable storage medium. Note that each piece of data is input to the processing device 300 at any timing and acquired by the acquisition unit 310. Therefore, each piece of data does not necessarily need to be input to the processing device 300 and acquired by the acquisition unit 310 at the same timing.

The predicted value calculation unit 320 calculates a predicted value of the furnace battery temperature during non-steady operation based on a first influencing factor, which is a factor that affects the predicted value of the furnace battery temperature during non-steady operation. The predicted value calculation unit 320 also calculates a predicted value of a physical quantity that represents the coke carbonization state based on a second influencing factor, which is a factor that affects the predicted value of a physical quantity that represents the coke carbonization state during non-steady operation.

The carbonization state of the coke indicates the degree to which the coal in the produced coke has been carbonized (pyrolyzed), and is an index of the quality of the coke. The first influencing factor is not particularly limited as long as it is a factor that affects the predicted value of the furnace battery temperature during non-steady operation. In this embodiment, an example is shown in which the first influencing factor includes the predicted value of the amount of heat input to the combustion chamber 3 during non-steady operation and the furnace battery temperature at a timing prior to the predicted time of the predicted value of the furnace battery temperature during non-steady operation. Here, the furnace battery temperature at a timing prior to the predicted time of the predicted value of the furnace battery temperature may be a predicted value or an actual measured value.

The second influencing factor is not particularly limited as long as it is a factor that affects the predicted value of the physical quantity that represents the carbonization state of the coke during non-steady operation (in this embodiment, the coke temperature at the end of the non-steady operation). In this embodiment, an example is shown in which the second influencing factor includes the predicted value of the furnace temperature and the carbonization time. The carbonization time is equal to the time required to perform one coal loading operation for each of the Da (=5) combinations.

Examples of physical quantities that represent the carbonization state of the coke include the temperature of the coke produced during non-steady operation and the temperature of the furnace wall 4 during non-steady operation. The physical quantity that represents the carbonization state of the coke during non-steady operation is used to bring the carbonization state of the coke closer to the target state when the steady state is restored. From this perspective, the physical quantity that represents the carbonization state of the coke during non-steady operation is preferably the closer to the timing of returning from the non-steady operation to the steady state. Therefore, in this embodiment, a case is illustrated in which the physical quantity that represents the carbonization state of the coke during non-steady operation is the coke temperature of the coal that is first charged in the carbonization chamber 2 after the end of the pause period. As shown in FIG. 4 described later, in this embodiment, the end time of the coke extrusion work in the sixth path after the end of the pause period is set to the end of the non-steady operation (the end time t _e of the non-steady operation), so the coke temperature of the coal that is first charged in the carbonization chamber 2 after the end of the pause period is the coke temperature at the end of the non-steady operation. Therefore, in the following description, the coke temperature of the coal that is first loaded into the coke chamber 2 after the end of the pause period is also referred to as the coke temperature at the end of the non-steady operation. In addition, in this embodiment, a case where the coke temperature is a representative value for each run is illustrated. Examples of the representative value include the arithmetic mean value, the median value, the mode value, and the minimum value. As described above, the physical quantity representing the carbonization state of the coke during non-steady operation is not limited to the coke temperature, and may be, for example, the temperature of the furnace wall 4 during non-steady operation.

The coke temperature is the temperature of the coke discharged from the carbonization chamber 2, and is calculated, for example, from the measured value of the thermometer 8 shown in FIG. 2B. When the coke is pushed out of the carbonization chamber 2 by the pusher ram 7, the temperature of the coke sequentially discharged from the carbonization chamber 2 is measured by the thermometer 8, and the representative value of the temperature at each measured time and each measured position is the temperature of the coke produced in the carbonization chamber 2. Examples of the representative value include the arithmetic mean value (the sum of the temperatures at each measured time and each measured position divided by the number of measured temperatures), the median, the mode, and the minimum value. The representative value of the temperature of the coke produced in the carbonization chamber 2 belonging to one course is the coke temperature (course representative value). As described above, the course representative value is, for example, the course average value (the sum of the temperatures of the coke produced in the carbonization chambers 2 belonging to one course divided by the number of carbonization chambers 2 belonging to the course). Since the coke temperature is preferably the temperature of the coke immediately after it is discharged from the carbonization chamber 2, the coke temperature is determined as shown in FIG. 2B. However, the thermometer for measuring the coke temperature and the method for determining the coke temperature may be any of those employed in coke plants, and are not limited to the above. Even when the physical quantity representing the carbonization state of the coke during non-steady operation is the temperature of the oven wall 4 during non-steady operation, the temperature of the oven wall 4 is set as the representative value (run representative value) of the temperature of the oven wall 4 when coke is produced in the carbonization chamber 2 belonging to one run. The temperature of the oven wall 4 is measured, for example, by a thermometer (not shown) embedded in the oven wall 4.

The factors adopted as the first and second influencing factors are determined, for example, by using a known method (e.g., calculating the correlation coefficient between the objective variable and the explanatory variables using training data, eliminating explanatory variables with multicollinearity, etc.) for selecting explanatory variables (first influencing factor, second influencing factor) for the objective variable (predicted value of the furnace temperature during non-steady operation, predicted value of the coke temperature at the end of non-steady operation). In addition, the objective variable and at least one of the explanatory variables may be the same physical quantity, and the predicted value or actual value at a time prior to the predicted time of the predicted value of the objective variable may be used as the explanatory variable (first influencing factor, second influencing factor). The method itself for calculating the objective variable from the explanatory variables is realized, for example, by a known machine learning method such as regression analysis.

FIG. 4 is a diagram showing an example of the relationship between the coke temperature, the furnace temperature, the input heat amount, and the carbonization time and time.
In this embodiment, the coke temperature and the carbonization time are representative values for each run, and are obtained when the unloading coal loading work for one run is performed. That is, the coke temperature and the carbonization time are obtained in a cycle of the run time. In the graphs of the coke temperature and the carbonization time shown in FIG. 4 (the top graph and the bottom graph), the distance in the time axis direction between two plots (●) adjacent to each other in the time axis direction is the run time t _t . FIG. 4 illustrates the run time t _t (t _e ) at time t _e . Note that the run time _{t t} is generally a constant time, but may be different.

As described above, the carbonization time is equal to the time required to perform each of the Da (=5) patterns of coal loading operations. Therefore, in the graph of coke temperature and carbonization time shown in Figure 4, the distance between the two ends of the six plots (●) adjacent to each other in the time axis direction is the carbonization time. Figure 4 shows an example of the carbonization time _tk (t _e ) at time t _e .

In addition, since no coal loading work is performed during the downtime, the coke temperature and carbonization time cannot be obtained (see Figure 4, where there is no plot (●) on the graph of coke temperature and carbonization time during the downtime).

On the other hand, the oven temperature and input heat amount are obtained regardless of the coal loading operation (see FIG. 4, where plots (●) are added to the graph of oven temperature and input heat amount even during the idle period). In this embodiment, an example is shown in which predicted and actual values of oven temperature and input heat amount are obtained in the control period of the coke oven 1 (the output period of the control signal in the control unit 340 described later), predicted and actual values of coke temperature are obtained in the pass time period, and scheduled and actual values of carbonization time are obtained in the pass time period. In addition, the control period of the coke oven 1 is assumed to be 1 hour (hr).

In FIG. 4, time t _s is an example of the start time of the unsteady operation, and time t _e is an example of the end time of the unsteady operation.
Specifically, in this embodiment, the end time of the coke extrusion work in the way Db (Db is an integer of 1 or more) before the time when the suspension of the kiln discharge coal loading work (charging and extrusion into the coke chamber 2) begins is set as the start time _ts of the non-steady operation. More specifically, in this embodiment, the case where Db is 2 is illustrated. Therefore, in FIG. 4, the end time of the coke extrusion work in the way two ways before the time when the suspension period begins (the time of the second coke temperature plot counting backward from the start time of the suspension period) is the start time _ts of the non-steady operation. When the suspension period is known in advance, such as when equipment maintenance is performed, Db may be 1 or an integer of 2 or more. On the other hand, when the suspension period is not known in advance, such as when an operational abnormality occurs, Db is preferably 1. In FIG. 4, for convenience of notation, some plots overlap with the suspension period in the graph of the coke temperature and the carbonization time, but the plots are obtained by the kiln discharge coal loading work in the way immediately before and immediately after the suspension period. Therefore, in the above explanation, the second plot before the start of the shutdown period corresponds to the time of the second coke temperature plot counting backward from the start time of the shutdown period, including the plot that overlaps with the start time of the shutdown period.

In addition, in this embodiment, the end time of the coke extrusion work in the Da+1th run after the end of the suspension of the kiln unloading coal loading work (charging and extrusion into the coke chamber 2) is the end time t _e of the non-steady operation. The start of the coke extrusion work in a run refers to the start of the coke extrusion work in the coke chamber 2 in which the coke extrusion work is performed first among the coke chambers 2 belonging to the run, and the end of the coke extrusion work in a run refers to the end of the coke extrusion work in the coke chamber 2 in which the coke extrusion work is performed last among the coke chambers 2 belonging to the run. As described above, in this embodiment, the case where Da is 5 is exemplified. Therefore, in FIG. 4, the end time of the coke extrusion work in the 6th run after the end of the suspension period (the time of the sixth plot of the coke temperature counting from the end time of the suspension period toward the future) is the end time t _e of the non-steady operation. In the first through Da (fifth) passes after the end of the pause period, coke is produced from the coal present in the coke chamber 2 during the pause period.

On the other hand, in the Da+1 (6th) way after the end of the pause period, coal is charged into the coke chamber 2 after the pause period ends. It is preferable that the carbonization state of the coke charged into the coke chamber 2 after the pause period ends approaches the carbonization state in the steady state as soon as possible. Therefore, in this embodiment, the end time of the coke extrusion work in the Da+1 (6th) way after the pause of the kiln loading work (charging and extrusion into the coke chamber 2) ends is set as the end time t _e of the non-steady operation. That is, the end time t _e of the non-steady operation is the end time of the coke extrusion work in the way in which the kiln loading work is first performed after the pause period ends. However, the end time of the non-steady operation is not limited to the end time of the coke extrusion work in the Da+1 way after the pause of the kiln loading work (charging and extrusion into the coke chamber 2) ends. For example, the end time of the non-steady operation may be the end time of the coke extrusion operation on the Da+xth pass after the suspension of the coal loading operation (charging and extrusion into the coke chamber 2) ends, and the value of x may be selected from integers equal to or greater than 1. The values of x and Db may be appropriately adjusted so that coke of the desired quality is obtained as a result of actually executing control of the amount of heat input according to the deviation of the actual value of the oven battery temperature from the target oven temperature trajectory, as described below.
As described above, the period of unsteady operation (unsteady operation time) is the period from time t _s to time t _e .

Returning to the explanation of FIG. 3, in this embodiment, the prediction value calculation unit 320 has a furnace state calculation unit 321 and an input heat amount calculation unit 322.
As described above, in this embodiment, the first influencing factor includes a predicted value of the heat input to the combustion chamber 3. Therefore, the input heat amount calculation unit 322 calculates a predicted value of the heat input to the combustion chamber 3 during unsteady operation. The furnace state calculation unit 321 executes the following: calculating a predicted value of the furnace temperature during unsteady operation based on the above-mentioned first influencing factor; and calculating a predicted value of a physical quantity representing the carbonization state of the coke during unsteady operation based on the above-mentioned second influencing factor. In this embodiment, the furnace temperature is the furnace battery temperature, and the physical quantity representing the carbonization state of the coke is the coke temperature.

The target furnace temperature determination unit 330 determines a target furnace temperature trajectory, which is a time change of the target furnace temperature, which is a target value of the furnace temperature during non-steady operation, based on the predicted value of the furnace temperature during non-steady operation calculated by the predicted value calculation unit 320 and the predicted value of the physical quantity representing the carbonization state of the coke during non-steady operation calculated by the predicted value calculation unit 320. In this embodiment, the target furnace temperature is a target furnace battery temperature, which is a target value of the furnace battery temperature.
The control unit 340 generates and outputs a control signal to set the amount of heat input to the combustion chamber 3 to an amount of heat corresponding to the difference between the target furnace temperature trajectory determined by the target furnace temperature determination unit 330 and the actual furnace temperature value.

Here, specific examples of processing in the furnace state calculation unit 321, input heat amount calculation unit 322, target furnace temperature determination unit 330, and control unit 340 of this embodiment will be described. In the following description, as necessary, the input heat amount to the combustion chamber 3 will be abbreviated as input heat amount, the coke temperature at the end of non-steady operation will be abbreviated as coke temperature, and the furnace battery temperature during non-steady operation will be abbreviated as furnace battery temperature.

<Furnace state calculation unit 321>
First, a specific example of the process in the furnace state calculation unit 321 will be described.
In this embodiment, the furnace state calculation unit 321 calculates the predicted value of the furnace battery temperature by performing calculations using a linear time series model in which the input heat amount (GJ/hr) is input as the process model. The linear time series model is one of the statistical analysis models that calculates the predicted value of the process state at predetermined time intervals. In this embodiment, the linear time series model is a second-order lag system model, and the regression equations shown in the following equations (1) to (3) are exemplified.

Here, Tr is the furnace battery temperature (°C). Q is the input heat (GJ/hr). ΔTr(t+1), ΔTr(t), and ΔTr(t-1) are the changes (°C) in the furnace battery temperatures Tr(t+1), Tr(t), and Tr(t-1) at times t+1, t, and t-1, respectively, relative to the furnace battery temperatures Tr(t), Tr(t-1), and Tr(t-2) at times t, t-1, and t-2. That is, the relationships ΔTr(t+1) = Tr(t+1) - Tr(t), ΔTr(t) = Tr(t) - Tr(t-1), and ΔTr(t-1) = Tr(t-1) - Tr(t-2) hold. ΔQ(t+1) is the change (GJ/hr) in the amount of heat input Q(t+1) at time t+1 relative to the amount of heat input at time t. In other words, the relationship ΔQ(t+1) = Q(t+1) - Q(t) holds. As mentioned above, t+1, t-1, and t-2 are the times one hour after, one hour before, and two hours before time t, respectively.

Q(t) is the input heat amount calculated by the input heat amount calculation unit 322. ΔQ(t+1) is calculated based on the input heat amount calculated by the input heat amount calculation unit 322. When time t is the start time t _s of unsteady operation, Tr(t), Tr(t-1), and Tr(t-2) are the actual values of the furnace battery temperature at times t _s , t _s -1, and t _s -2, respectively, and ΔTr(t) and ΔTr(t-1) are calculated based on these actual values. When time t is a time later than the start time t _s of unsteady operation, ΔTr(t) and ΔTr(t-1) are calculated based on the calculation results (ΔTr(t+1)) of formulas (1) to (3) at a time prior to the time t. The furnace battery temperature Tr(t) at a time t (>t _s ) later than the start time t _s of unsteady operation becomes the predicted value of the furnace battery temperature.

The coefficients a1, a2, and b1 are coefficients for ΔTr(t), ΔTr(t-1), and ΔQ(t+1), respectively. The coefficients a1, a2, and b1 that best fit the form of equation (1) to the past operating results of the coke oven 1 are separately determined. For example, a set of data for ΔTr(t+1), ΔTr(t), ΔTr(t-1), and ΔQ(t+1) obtained from the past operating results of the coke oven 1 can be used as training data to create multiple training data, and the training data can be used to perform multiple regression analysis to determine the coefficients a1, a2, and b1.

In addition, in this embodiment, the furnace state calculation unit 321 calculates the predicted value of the coke temperature by performing a calculation using a linear model that inputs the furnace temperature (°C) and carbonization time (hr) as a process model (physical model). The linear model is one of the statistical analysis models that calculates the predicted value of the process state, and in this embodiment, the multiple regression equations shown in the following equations (4) to (9) are exemplified as the linear model.

Here, Tc is the coke temperature (°C) and _tk is the carbonization time (hr). Tc( _ts ) is the actual value of the coke temperature at the start time _ts of the non-steady operation, and Tc( _te ) is the predicted value of the coke temperature at the end time _te of the non-steady operation. Therefore, as shown in formula (5), ΔTc is the amount of change in the coke temperature from the start time _ts of the non-steady operation to the end time _te of the non-steady operation. Tr( _ts ) is the actual value of the furnace battery temperature at the start time _ts of the non-steady operation. Tr(t _e -Δt ₁ ), Tr(t _e -Δt ₂ ), and Tr(t _e -Δt ₃ ) are the predicted values of the furnace battery temperature at times t _e -Δt ₁ , t _e -Δt ₂ , and t _e -Δt ₃ , which are Δt ₁ hour, Δt ₂ hours, and Δt ₃ hours before the end time _{t e} of the unsteady operation, respectively. Tr(t _e -Δt ₁ ), Tr(t _e -Δt ₂ ), and Tr(t _e -Δt ₃ ) are calculated by formulas (1) to (3). Here, Δt ₁ , Δt ₂ , and Δt ₃ are set so that the relationships Δt ₁ ≧0, Δt ₁ < Δt ₂ < Δt ₃ , and t _e -Δt ₃ > t _s are satisfied. In addition, t _k (t _s ) is the actual value of the carbonization time at the start time t _s of the non-steady operation, and t _k (t _e ) is the scheduled value (hr) of the carbonization time at the end time t _e of the non-steady operation. The coefficients c1, c2, c3, and d1 are coefficients for ΔTr1, ΔTr2, ΔTr3, and Δt _k , respectively. The coefficients c1, c2, c3, and d1 are determined separately when the form of the formula (4) is most suitable for the past operation results of the coke oven 1. For example, a set of data of ΔTr1, ΔTr2, ΔTr3, and Δt _k obtained from the past operation results of the coke oven 1 is used as one teacher data to create a large number of teacher data, and the teacher data is used to perform multiple regression analysis to determine the coefficients c1, c2, c3, and d1.

<Input heat amount calculation unit 322>
The input heat amount calculation unit 322 calculates a predicted value of the input heat amount during non-steady operation based on the candidate of the target furnace temperature trajectory and the predicted value of the furnace battery temperature calculated by the furnace state calculation unit 321. In this embodiment, the candidate of the target furnace temperature trajectory is output from the target furnace temperature determination unit 330. The input heat amount calculation unit 322 calculates a predicted value Q(t+1) one hour after the time t as a predicted value of the input heat amount that reduces the difference between the candidate of the target furnace temperature trajectory and the predicted value of the furnace battery temperature calculated by the furnace state calculation unit 321 (preferably becomes 0 (zero)). The calculation method of such a predicted value of the input heat amount itself is realized by a known technology. For example, the input heat amount calculation unit 322 uses a control simulator that computer-simulates PID control to calculate a predicted value of the input heat amount according to the deviation of the predicted value of the furnace battery temperature calculated by the furnace state calculation unit 321 from the candidate of the target furnace temperature trajectory by PID control. Moreover, other control such as PI control may be used instead of PID control.

In addition, the heat input calculation unit 322 may calculate the predicted value Q(t+1) of the heat input using the following equations (10) and (11).

Here, Tr_ref(t+m) is a candidate for the target furnace temperature trajectory (target value of furnace battery temperature) at time t+m, and in this embodiment, the case where it is given from the target furnace temperature determination unit 330 is illustrated (see the arrow line from the target furnace temperature determination unit 330 to the predicted value calculation unit 320 in FIG. 3). Tr(t+m) is the predicted value of the furnace battery temperature at time t+m, and in this embodiment, it is calculated by the furnace state calculation unit 321. Therefore, Tr_err, which is the value obtained by subtracting Tr(t+m) from Tr_ref(t+m), is the prediction error of the furnace battery temperature at time t+m. Q(t+1)_old is the input heat amount at time t+1 before the update, and Q(t+1)_new is the input heat amount at time t+1 after the update. In addition, G shown in equation (11) is a predetermined gain multiplied by Tr_err. m is a positive integer equal to or greater than 2, and is set appropriately depending on how far into the future (before time t) the furnace temperature prediction error is to be calculated.

When performing the calculations of equations (10) and (11), the input heat amount calculation unit 322 outputs the initial value of the candidate for the input heat amount Q(t+1) at time t+1 to the furnace state calculation unit 321. The initial value of the candidate for the input heat amount Q(t+1) at time t+1 may be determined in any manner. For example, the input heat amount calculation unit 322 may determine the initial value of the candidate for the input heat amount Q(t+1) at time t+1 using a random number or a preset value. The furnace state calculation unit 321 uses the initial value of the candidate for the input heat amount Q(t+1) at time t+1 to calculate the predicted value of the furnace battery temperature as described in the section <Furnace state calculation unit 321>. At this time, the furnace state calculation unit 321 calculates at least the predicted value of the furnace battery temperature for each hour from time t to t+m. The input heat calculation unit 322 calculates the prediction error Tr_err of the furnace temperature at time t+m using equation (10) based on the predicted value of the furnace temperature at time t+m calculated by the furnace state calculation unit 321 and a candidate for the target furnace temperature trajectory (target value of the furnace temperature) at time t+m.

Then, the input heat amount calculation unit 322 determines whether the prediction error Tr_err of the furnace temperature at time t+m is equal to or less than a predetermined value. The smaller the prediction error Tr_err of the furnace temperature at time t+m, the more preferable. Therefore, for example, 0 (zero) or a value close to 0 is set as the predetermined value. If the prediction error Tr_err of the furnace temperature at time t+m is not equal to or less than the predetermined value, the input heat amount calculation unit 322 assigns the initial value of the candidate for the input heat amount Q(t+1) at time t+1 to the first term (Q(t+1)_old) on the right side of equation (11), and assigns the prediction error Tr_err of the furnace temperature at time t+m to the second term on the right side of equation (11), and calculates the input heat amount Q(t+1)_new at time t+1 after updating using equation (11). The input heat amount calculation unit 322 sets the input heat amount Q(t+1)_new at time t+1 after the update calculated in this manner as a new candidate for the input heat amount Q(t+1) at time t+1. Then, using the new candidate for the input heat amount Q(t+1) at time t+1, the following steps are repeatedly performed: output of the candidate for the input heat amount Q(t+1) at time t+1 to the furnace state calculation unit 321; calculation of the predicted value of the furnace battery temperature by the furnace state calculation unit 321; calculation of the prediction error Tr_err of the furnace battery temperature at time t+m; and calculation of the input heat amount Q(t+1)_new at time t+1 after the update, until the prediction error Tr_err of the furnace battery temperature at time t+m becomes equal to or less than a predetermined value.

When the prediction error Tr_err of the furnace temperature at time t+m is equal to or less than a predetermined value, the input heat amount calculation unit 322 determines the input heat amount Q(t+1)_new at time t+1 after the update used when calculating the prediction error Tr_err of the furnace temperature at time t+m as the predicted value of the input heat amount Q(t+1) at time t+1. The furnace state calculation unit 321 calculates the predicted value of the furnace temperature Tr(t+1) at time t+1 using the predicted value of the input heat amount Q(t+1) at time t+1 determined by the input heat amount calculation unit 322, and determines the calculated predicted value as the predicted value of the furnace temperature Tr(t+1) at time t+1.

The predicted value calculation unit 320 outputs the predicted values of the input heat amount Q(t+1) and the furnace battery temperature Tr(t+1) determined as described above to the target furnace temperature determination unit 330 (see the arrow line (furnace battery temperature, input heat amount) from the predicted value calculation unit 320 to the target furnace temperature determination unit 330 in Figure 3).
Then, the time t is shifted back by one hour from time _ts +1 to time _te -1, and the predicted values of the input heat quantity Q(t+1) and the furnace battery temperature Tr(t+1) are determined as described above. This determines the predicted values of the input heat quantity Q(t+1) and the furnace battery temperature Tr(t+1) for each hour from time _ts +1 to time _te . Once the predicted value of the furnace battery temperature Tr( _te ) at the end time _te of the unsteady operation is determined, the furnace state calculation unit 321 calculates and determines the predicted value of the coke temperature Tc( _te ) at the end of the unsteady operation using equations (4) to (9). The predicted value calculation unit 320 outputs the predicted value of the coke temperature Tc (t _e ) determined as described above to the target furnace temperature determination unit 330 (see the arrow line (coke temperature) from the predicted value calculation unit 320 to the target furnace temperature determination unit 330 in Figure 3).

The number of candidates for the target furnace temperature trajectory Tr_ref(t+m) at time t+m may be one or more, but in the section <Target furnace temperature determination unit 330> described later, a case where there are multiple candidates for the target furnace temperature trajectory Tr_ref(t+m) at time t+m will be exemplified. When there are multiple candidates for the target furnace temperature trajectory Tr_ref(t+m) at time t+m, the prediction value calculation unit 320 determines the prediction values of the input heat amount Q(t+ ₁ ) and the furnace battery temperature Tr(t+1) at each hour from time ts+1 to t for each of the _multiple candidates in the above manner.

<Target Furnace Temperature Determination Unit 330>
The target furnace temperature determination unit 330 determines the target furnace temperature trajectory based on the predicted values of the input heat amount, the predicted value of the furnace battery temperature, and the predicted value of the coke temperature calculated (confirmed) by the predicted value calculation unit 320.

Fig. 5 is a diagram illustrating an example of a target furnace temperature trajectory. The graph shown at the top of Fig. 5 shows an example of a change in coke temperature over time, and the graph shown at the bottom shows an example of a change in furnace battery temperature over time. In Fig. 5, the positions (times) of the white circles on the time axis are the times at which the coke temperature and furnace battery temperature are obtained. For convenience of notation, Fig. 5 shows white circles only in the period from the start time _ts to the end time _te of unsteady operation, but the coke temperature and furnace battery temperature can be obtained outside this period as well.

In the graph shown at the top of Fig. 5, Tc(t _e ) is a predicted value of the coke temperature Tc(t _e ) calculated (confirmed) by the predicted value calculation unit 320. Tc_sv is a target value of the coke temperature at the end of the non-steady operation, and is set according to the quality required for the coke, etc. In the following description, this target value Tc_sv is also referred to as the target coke temperature.

In the graph shown at the bottom of FIG. 5, Tr( _ts ) is the actual value of the furnace battery temperature Tr( _ts ) at the start of non-steady operation. ΔTr1 is the change in furnace battery temperature (°C) from the furnace battery temperature in the steady state immediately before the start of non-steady operation (= furnace battery temperature at the start of non-steady operation) to the lowest furnace battery temperature during non-steady operation. In the following explanation, ΔTr1 is also referred to as the furnace battery temperature change at the start of non-steady operation. ΔTr2 is the change in furnace battery temperature (°C) from the lowest furnace battery temperature during non-steady operation to the furnace battery temperature in the steady state immediately after the end of non-steady operation (= furnace battery temperature at the end of non-steady operation). In the following explanation, ΔTr2 is also referred to as the furnace battery temperature change at the end of non-steady operation. Time1 is the time (hr) required for the furnace battery temperature to change from the furnace battery temperature in the steady state immediately before the start of non-steady operation to the lowest furnace battery temperature during non-steady operation. In the following description, this time time1 is also referred to as the minimum furnace temperature arrival time time1. Time2 is the time (hr) required for the furnace battery temperature to change from the minimum furnace battery temperature during unsteady operation to the furnace battery temperature in the steady state immediately after the end of unsteady operation. In the following description, this time time2 is also referred to as the minimum furnace temperature maintenance time time2. Time0 is the period (hr) of unsteady operation. The graph shown by the solid line from the start time _ts to the end time _te of unsteady operation is the target furnace temperature trajectory Tr_ref.

In this embodiment, the target furnace temperature determination unit 330 calculates an optimal solution for the target furnace temperature trajectory Tr_ref as shown in the bottom of Figure 5 by solving an optimization problem such as a combinatorial optimization problem, and determines the target furnace temperature trajectory Tr_ref. In this case, the furnace temperature change amount ΔTr1 at the start of the unsteady state, the furnace temperature change amount ΔTr2 at the end of the unsteady state, the time to reach the minimum furnace temperature time1, and the time to maintain the minimum furnace temperature time2 are design variables (variables to be solved).

First, the target reactor temperature determination unit 330 generates a group of candidates (multiple candidates) for the target reactor temperature trajectory Tr_ref and outputs them to the prediction value calculation unit 320. In this embodiment, an example is shown in which an optimal solution for the target reactor temperature trajectory Tr_ref is calculated using a metaheuristics method such as a genetic algorithm. Therefore, each candidate included in the group of candidates for the target reactor temperature trajectory Tr_ref is generated according to a method used in a metaheuristics method such as a genetic algorithm. Since the metaheuristics method such as a genetic algorithm itself is realized by a known technology, a detailed explanation thereof is omitted.

The predicted value calculation unit 320 calculates (determines) the predicted value of the input heat quantity Q(t), the predicted value of the furnace battery temperature Tr(t), and the predicted value of the coke temperature Tc(t _e ) as explained in the sections <Furnace state calculation unit 321> and <Input heat quantity calculation unit 322>. The predicted value of the input heat quantity Q(t) and the predicted value of the furnace battery temperature Tr(t) are values for each hour from the start time t _s of the unsteady operation to the end time t _e of the unsteady operation. The predicted value of the coke temperature Tc(t _e ) is the value at the end of the unsteady operation.

The target furnace temperature determination unit 330 uses the candidate for the target furnace temperature trajectory Tr_ref output to the prediction value calculation unit 320, and the predicted value of the input heat amount Q(t), the predicted value of the furnace battery temperature Tr(t), and the predicted value of the coke temperature Tc(t _e ) calculated (confirmed) by the prediction value calculation unit 320 for the candidate for the target furnace temperature trajectory Tr_ref, to calculate the value of the evaluation function J (fitness function) of the following equation (12) when the constraint equation of the following equation (13) is satisfied.

The range of integration of t in the first and third terms on the right side of the formula (12) is from _ts to _te ( _ts ≦t≦ _te ). The first term on the right side of the formula (12) is an example of a first evaluation index that evaluates the difference between a candidate for the target furnace temperature trajectory Tr_ref and the predicted value of the furnace battery temperature Tr(t). The second term on the right side of the formula (12) is an example of a second evaluation index that evaluates the difference between the target coke temperature Tc_sv, which is an example of a target physical quantity, and the predicted value of the coke temperature Tc( _te ), which is an example of a predicted value of a physical quantity representing the carbonization state of the coke. The third term on the right side of the formula (12) is an example of a third evaluation index that evaluates the input heat amount Q(t). w1, w2, and w3 are weighting coefficients that are set depending on how much importance is attached to each evaluation index (evaluation item) and represent the balance of evaluation between each evaluation index (note that the weighting coefficient is also called a cost coefficient, etc.). In this embodiment, the weighting coefficients w1, w2, and w3 are positive values. In this case, the weighting coefficients for important evaluation indexes are relatively large values. In the example shown in formula (12), the smaller the value of each term on the right side of formula (12), the higher the evaluation by each evaluation index. Therefore, the closer the value of the evaluation function J is to 0, the better. That is, the target furnace temperature determination unit 330 searches for a design variable that minimizes the value of the evaluation function J (within a range of 0 or more) within a range that satisfies the constraint equation of formula (13) as an optimal solution. As described above, in this embodiment, the design variables are the furnace temperature change amount ΔTr1 at the start of the unsteady state, the furnace temperature change amount ΔTr2 at the end of the unsteady state, the minimum furnace temperature arrival time time1, and the minimum furnace temperature maintenance time time2 (see FIG. 5). It is also possible to search for a design variable that maximizes the value of the evaluation function as an optimal solution. In this case, for example, the evaluation function is obtained by multiplying each term on the right side of formula (12) by (-1). The target furnace temperature determination unit 330 searches for a design variable that maximizes the value of the evaluation function within a range that satisfies the constraint equation (13), as an optimal solution.

If the generated candidate for the target reactor temperature trajectory Tr_ref does not satisfy the constraint equation (13), the target reactor temperature determination unit 330 discards the candidate for the target reactor temperature trajectory Tr_ref without outputting it to the prediction value calculation unit 320.

The target reactor temperature determination unit 330 judges whether or not the convergence condition is satisfied based on the calculation result of the value of the evaluation function J for each candidate included in the candidate group of the target reactor temperature trajectory Tr_ref. The convergence condition may be any condition that is a convergence condition used in a metaheuristic method such as a genetic algorithm. For example, the convergence condition may be a condition that the minimum value of the values of the evaluation function J calculated for each candidate included in the candidate group of the target reactor temperature trajectory Tr_ref is equal to or less than a predetermined value, or a condition that the number of calculations of the value of the evaluation function J (number of iterations) is a predetermined value.

If the convergence conditions are not satisfied, the target reactor temperature determination unit 330 regenerates and updates the group of candidates for the target reactor temperature trajectory Tr_ref. The target reactor temperature determination unit 330 then repeatedly calculates the value of the evaluation function J, determines whether the convergence conditions are satisfied, and regenerates the group of candidates for the target reactor temperature trajectory Tr_ref as described above until the convergence conditions are satisfied.

The target furnace temperature determination unit 330 determines the target furnace temperature trajectory Tr_ref, which is determined by the design variables (furnace temperature change amount ΔTr1 at the start of the non-steady state, furnace temperature change amount ΔTr2 at the end of the non-steady state, time time1 to reach the minimum furnace temperature, and time time2 to maintain the minimum furnace temperature) when the value of the evaluation function J is the smallest among the values of the evaluation function J for multiple candidate target furnace temperature trajectories Tr_ref calculated when the convergence conditions are satisfied, as the optimal solution for the target furnace temperature trajectory Tr_ref.

<Control Unit 340>
The control unit 340 calculates the input heat amount to the combustion chamber 3 at a time one hour after the current time in the same manner as the input heat amount calculation unit 322 calculates the predicted value of the input heat amount. For example, when the input heat amount calculation unit 322 calculates the predicted value of the input heat amount at a time one hour after the current time using a control simulator that computer-simulates PID control, the control unit 340 has a PID controller. The control unit 340 calculates the value of the target furnace temperature trajectory Tr_ref determined by the target furnace temperature determination unit 330 at a time one hour after the current time using the PID controller. Then, the control unit 340 generates and outputs a control signal for making the input heat amount to the combustion chamber 3 a heat amount corresponding to the difference between the calculated input heat amount to the combustion chamber 3 at a time one hour after the current time and the value of the target furnace temperature trajectory Tr_ref determined by the target furnace temperature determination unit 330 at a time one hour after the current time. An example of an output destination of the control signal is a control device of an actuator that operates the adjustment valve 5. The control device instructs the actuator to operate the regulating valve 5 so as to open to an opening degree in accordance with the control signal.

Also, for example, when the input heat amount calculation unit 322 calculates the predicted value Q(t+1) of the input heat amount using the formulas (10) and (11), the control unit 340 may calculate the predicted value Q(t+1) of the input heat amount using the formulas (10) and (11) and generate and output a control signal for setting the input heat amount to the combustion chamber 3 to the predicted value Q(t+1) of the input heat amount. In this case, it is sufficient to calculate the predicted value Q(t+1) of the input heat amount up to the time one hour after the current time. That is, in this case, in the description in the section <Input heat amount calculation unit 322>, the process of shifting the time t to a later time every hour from time t _s +1 to time t _e -1 and repeatedly executing the process is not necessary.

<When operating conditions are changed during non-steady operation>
The above-mentioned processes in the furnace state calculation unit 321, input heat amount calculation unit 322, and target furnace temperature determination unit 330 are executed at the start time _ts of the non-steady operation, and the target furnace temperature trajectory is determined. Thereafter, during the non-steady operation, the operating conditions that affect the determination of the target furnace temperature trajectory may be changed. In such a case, it is preferable to re-determine the target furnace temperature trajectory after the timing at which the operating conditions are changed. An example of such an operating condition is the schedule value of the carbonization time. When the schedule value of the carbonization time is changed, the end time t _e of the non-steady operation is changed. Therefore, hereinafter, an example of a method for re-determining the target furnace temperature trajectory when the operating conditions are changed during the non-steady operation will be described by exemplifying a case in which the schedule value of the carbonization _time is longer than the value at the start time ts of the non-steady operation. Note that the change in the schedule value of the carbonization time of the coke in the carbonization chamber 2 belonging to a certain street occurs, for example, when the kiln unloading coal loading work in the carbonization chamber 2 belonging to a street where the kiln unloading coal loading work is performed earlier than the street is delayed or advanced.

FIG. 6A is a diagram illustrating an example of a target furnace temperature trajectory determined at the start time _ts of unsteady operation, and FIG. 6B is a diagram illustrating an example of a target furnace temperature trajectory determined at the timing when the operating conditions are changed.
In Figures 6A and 6B, Tr_ref_old indicates the target furnace temperature trajectory determined at the start time _ts of unsteady operation. In Figures 6A and 6B, Tr_mes indicates the actual furnace temperature trajectory, which is the actual value of the furnace battery temperature Tr(t). Figure 6A shows that the actual value of the furnace battery temperature Tr(t) is obtained up to the start time _ts of unsteady operation. In Figure 6A, the design variables corresponding to the target furnace temperature trajectory Tr_ref_old (furnace temperature change amount ΔTr1 at the start of unsteady operation, furnace temperature change amount ΔTr2 at the end of unsteady operation, minimum furnace temperature arrival time time1, and minimum furnace temperature maintenance time time2) are denoted as ΔTr1_old, ΔTr2_old, time1_old, and time2_old.

When the operating conditions that affect the determination of the target furnace temperature trajectory are changed during the unsteady operation, the calculation (confirmation) of the predicted value of the input heat amount Q(t ₎ , the predicted value of the furnace battery temperature Tr(t ₎ , and the predicted value of the coke temperature Tc(t _e ) is performed by the predicted value calculation unit 320 (furnace state calculation unit 321 and input heat amount calculation unit 322) and the determination of the target furnace temperature trajectory Tr_ref is performed by the target furnace temperature determination unit ₃₃₀ during the period from the time when the operating conditions are changed to the time when the unsteady operation is ended, not from the start time t s of the unsteady operation to the end time t e of the unsteady operation. In this case, the design variables that cannot be changed at the time when the operating conditions are changed are set to the values in the (latest) target furnace temperature trajectory Tr_ref already determined by the target furnace temperature determination unit 330. Therefore, when the target reactor temperature determination unit 330 calculates the evaluation function J, in addition to the constraint equation shown in equation (13), a constraint equation is added indicating the constraint condition that the design variables that cannot be changed at the time when the operating conditions are changed are values on the (latest) target reactor temperature trajectory Tr_ref that has already been determined by the target reactor temperature determination unit 330.

Furthermore, for the input heat quantity and furnace battery temperature during the period from the start time _ts of unsteady operation to the time when the operating conditions are changed, actual measured values are used instead of predicted values. Therefore, when the target furnace temperature determination unit 330 calculates the evaluation function J, in addition to the constraint equation shown in equation (13), a constraint equation is added indicating the constraint condition that the input heat quantity Q(t) and furnace battery temperature Tr(t) during the period from the start time _ts of unsteady operation to the time when the operating conditions are changed are actual measured values (not predicted values).

The changed operating conditions are used in the processes of the furnace state calculation unit 321, the input heat amount calculation unit 322, and the target furnace temperature determination unit 330. When the schedule value of the carbonization time _tk (t _e ) at the end time t _e of the unsteady operation is changed, the value of _tk (t _e ) in the formula (9) is changed. Also, the period time0 of the unsteady operation is changed.

6B illustrates an example in which the operating conditions are changed at time _tm . In this case, the actual value of the furnace battery temperature Tr(t) (actual furnace temperature trajectory Tr_mes) is obtained up to time _tm when the operating conditions are changed. At time _tm when the operating conditions are changed, among the design variables, the design variables that cannot be changed at time _tm when the operating conditions are changed are the furnace temperature change amount ΔTr1 at the start of the unsteady operation and the minimum furnace temperature arrival time time1. Therefore, when the target furnace temperature determination unit 330 calculates the evaluation function J, in addition to the constraint equation shown in equation (13), a constraint equation (ΔTr1=ΔTr1_old, time1=time1_old) is added in which the furnace temperature change amount ΔTr1 at the start of the non-steady state and the minimum furnace temperature arrival time time1 are set to the non-steady state start furnace temperature change amount ΔTr1_old and the minimum furnace temperature arrival time time1_old in the target furnace temperature trajectory Tr_ref_old determined at _the start time ts of the non-steady state operation. In addition, the period time0 of the non-steady state operation in equation (13) is changed from the period time0_old before the change shown in FIG. 6A to the period time0_new after the change shown in FIG. 6B. The change in the schedule value of the carbonization time is grasped, for example, during the coal loading work (coke extrusion work).

After the above changes are made, the furnace state calculation unit 321, the input heat amount calculation unit 322, and the target furnace temperature determination unit 330 execute the processes described above to determine the design variables that can be changed at the time t _m when the operating conditions are changed. In Fig. 6B, the design variables that can be changed at the time t _m when the operating conditions are changed (furnace temperature change amount ΔTr2 at the end of the non-steady state, and the minimum furnace temperature maintenance time time2) are denoted as ΔTr2_new and time2_new. Fig. 6B also shows that the part of the target furnace temperature trajectory Tr_ref_old shown in Fig. 6A at the time after the time t _m when the operating conditions are changed is changed to the target furnace temperature trajectory Tr_ref_new determined by the furnace temperature change amount ΔTr2_new at the end of the non-steady state and the minimum furnace temperature maintenance time time2_new. In addition, for comparison with the target furnace temperature trajectory Tr_ref_new, FIG. 6B also shows the portion of the target furnace temperature trajectory Tr_ref_old shown in FIG. 6A from the time _tm when the operating conditions were changed onward.

<Flowchart>
Next, an example of a processing method using the processing device 300 will be described with reference to the flow chart of FIG.

First, in step S 701 , the acquisition unit 310 acquires data to be used in the processing device 300 .
In this embodiment, the acquisition unit 310 acquires a target coke temperature Tc_sv (° C.), which is a target value of the coke temperature at the end of the unsteady operation (see FIG. 5 ).
The acquisition unit 310 also acquires a pass time t _t and a pass number Da (items). The pass time t _t is a schedule value or a performance value.
The acquisition unit 310 also acquires the actual value (hr) of the carbonization time t _k (t _s ) at the start time t _s of the non-steady operation and the scheduled value (hr) of the carbonization time t _k (t _e ) at the end time t _e of the non-steady operation.

The acquisition unit 310 also acquires the start time t _s of the non-steady operation and the end time t _e of the non-steady operation (see FIG. 5). The start time t _s of the non-steady operation is an actual value. The end time t _e of the non-steady operation is a scheduled time. The acquisition unit 310 calculates the end time t _e of the non-steady operation by adding the schedule value of the carbonization time (t _e ) at the start time t _e of the non-steady operation to the start time t _s of the non-steady operation. Instead of doing so, the acquisition unit 310 may calculate the end time t _e of the non-steady operation by adding the start time t _s of the non-steady operation and the sum of the run times t _t in each run. Then, the acquisition unit 310 calculates the period time0 (hr) of the non-steady operation based on the start time t _s of the non-steady operation and the end time t _e of the non-steady operation (see FIG. 5).

The acquisition unit 310 also acquires the actual value (° C.) of the furnace battery temperature Tr(t _s ) at the start time t _s of the unsteady operation (see FIG. 5 ).
The acquisition unit 310 acquires various actual values (operational performance data) of the coke oven 1, such as the oven battery temperature Tr(t), the coke temperature Tc(t) at the end of the coke pushing operation, and the amount of heat Q(t) input to the combustion chamber 3, in addition to the aforementioned actual values. As described above with reference to Fig. 4, the oven battery temperature Tr and the amount of heat Q input to the combustion chamber 3 are acquired at the control period of the coke oven 1. In addition, the coke temperature Tc at the end of the coke pushing operation is acquired at the pass time period.

The acquisition unit 310 also acquires weighting coefficients w1, w2, w3, coefficients a1 to a2, b1, c1 to c3, d1, times Δt ₁ , Δt ₂ , Δt ₃ , gain G, and a target value Tc_sv of the coke temperature at the end of unsteady operation as constants used in the processing in the prediction value calculation unit 320 and the target furnace temperature determination unit 330. The acquisition unit 310 may also acquire operation conditions other than the above-mentioned operation conditions (such as the target coke temperature Tc_sv and the scheduled value of the carbonization time (t _e )) as the operation conditions in the coke oven 1.
The timing of data acquisition by the acquisition unit 310 is not limited to the timing exemplified in FIG. 7, and may be any timing in FIG.

Next, in step S702, the acquisition unit 310 determines whether it is time to determine the target furnace temperature trajectory Tr_ref. In this embodiment, the acquisition unit 310 determines that it is time to determine the target furnace temperature trajectory Tr_ref at the timing when the non-steady operation starts and the timing when the operating conditions are changed. For example, in step S701, the acquisition unit 310 determines that the non-steady operation has started when the start time t _s of the non-steady operation is acquired. In addition, in step S701, the acquisition unit 310 determines that the operating conditions have been changed when, for example, in step S701, the acquisition unit 310 acquires a schedule value of the carbonization time t _k (t _e ) at the end time t _e of the non-steady operation that is different from the schedule value already acquired.

If the result of the judgment in step S702 is that it is not time to determine the target reactor temperature trajectory Tr_ref (NO in step S702), the processing in step S701 is executed again. Then, if the result of the judgment in step S702 is that it is time to determine the target reactor temperature trajectory Tr_ref (YES in step S702), the processing in step S703 is executed.

In step S703, the target reactor temperature determination unit 330 generates an initial value of a group of candidates for the target reactor temperature trajectory Tr_ref.
Next, in step S704, the processing device 300 sets the current processing time t to the time _tc at which the target furnace temperature trajectory Tr_ref is determined. In this embodiment, the time _tc at which the target furnace temperature trajectory Tr_ref is determined is the start time _ts of the unsteady operation or the time _tm at which the operating conditions are changed. Note that the current processing time t is not an actual time but a simulation time in a computer simulation.

Next, in step S705, the input heat amount calculation unit 322 calculates the predicted value of the input heat amount Q(t+1) at time t+1 for each of the multiple candidates included in the candidate group of target furnace temperature trajectory Tr_ref based on the candidates for the target furnace temperature trajectory Tr_ref and the predicted value of the furnace battery temperature calculated by the furnace state calculation unit 321. As described above, the calculation of the predicted value of the input heat amount Q(t+1) at time t+1 is realized, for example, by a control simulator that computer-simulates PID control, or by calculation using equations (10) and (11).

Next, in step S706, the furnace state calculation unit 321 calculates the predicted value of the furnace battery temperature Tr(t+1) using the predicted value of the input heat quantity Q(t+1) at time t+1 calculated in step S705 and the predicted values and/or measured values of the furnace battery temperatures Tr(t), Tr(t-1), Tr(t-2) at times t, t-1, t-2 prior to time t+1 (see formulas (1) to (3)). Note that when time t+1 is time t _s +1, the measured values of the furnace battery temperatures Tr(t), Tr(t-1), Tr(t-2) are used. When time t+1 is time t _s +4 or later, the predicted values of the furnace battery temperatures Tr(t), Tr(t-1), Tr(t-2) are used. If time t+1 is an intermediate time, then both the predicted and measured values of the furnace battery temperatures Tr(t), Tr(t-1), and Tr(t-2) are used.

Next, in step S707, the processing device 300 judges whether the current processing time t is time t _e - 1. If the result of this judgment is that the current processing time t is not time t _e - 1 (NO in step S707), the processing proceeds to step S708.

In step S708, the processing device 300 adds one hour to the current processing time t to update the current processing time t. Then, in steps S705 to S706, the predicted value of the input heat quantity Q(t+1) and the predicted value of the furnace battery temperature Tr(t+1) at time t+1, which is one hour after the updated current processing time t, are calculated for each of the multiple candidates included in the candidate group of the target furnace temperature trajectory Tr_ref. As described above, the processing in steps S705 to S708 is repeatedly executed until it is determined in step S707 that the current processing time t becomes time t _e -1.

In step S707, when it is determined that the current processing time t is time t _e -1 (YES in step S707), the predicted value of the input heat quantity _Q (t+1) and the predicted value of the furnace battery temperature Tr(t+1) at each time t+1 for each hour from the time t _c at which the target furnace temperature trajectory Tr_ref is determined (the start time t _s of the non-steady operation or the time t _m at which the operating conditions are changed) to the end time t e of the non-steady operation are calculated for each of the multiple candidates included in the candidate group for the target furnace temperature trajectory Tr_ref. In this case, the processing of step S709 is executed.

In step S709, the furnace state calculation unit 321 calculates the predicted value of the coke temperature Tc(t _e ) at the end time _{t e} of non-steady operation for each of multiple candidates included in the candidate group of target furnace temperature trajectory Tr_ref based on the actual value of the furnace battery temperature _Tr (t _s ) at the start time t _s of non-steady operation, the predicted value of the furnace battery temperature _Tr (t _e ) at the end time _{t e} of non-steady operation, the actual value of the carbonization time t k (t _s ) at the start time t _s of non-steady operation, and the scheduled value of the carbonization time t k (t e ) at the end time t e of non-steady operation (see equations (4) to (9)). In addition, when calculating the predicted value of the coke temperature Tc(t _e ) at the end time t _e of unsteady operation, the predicted values Tr(t _e -Δt ₁ ), Tr(t _e -Δt ₂ ), Tr(t _e -Δt ₃ ) of the furnace battery temperatures during unsteady operation calculated by equations (6) to (8) are also used.

Next, in step S710, the target reactor temperature determination unit 330 calculates the value of the evaluation function J of equation (12) when the constraint equations including equation (13) are satisfied, for each of a plurality of candidates included in the candidate group of the target reactor temperature trajectory Tr_ref.
Next, in step S711, the target reactor temperature determination unit 330 judges whether or not the convergence condition is satisfied. If the result of this judgment is that the convergence condition is not satisfied (NO in step S711), the process of step S712 is executed. In step S712, the target reactor temperature determination unit 330 updates the group of candidates for the target reactor temperature trajectory Tr_ref. Then, the processes of steps S704 to S711 are executed using the candidates for the updated target reactor temperature trajectory Tr_ref. In this manner, the processes of steps S704 to S712 are repeatedly executed until the convergence condition is satisfied.

Then, in step S711, when it is determined that the convergence condition is satisfied (YES in step S711), the process of step S713 is executed. In step S713, the target furnace temperature determination unit 330 determines, as the target furnace temperature trajectory Tr_ref, the target furnace temperature trajectory Tr_ref determined by the design variables (furnace temperature change amount ΔTr1 at the start of unsteady state, furnace temperature change amount ΔTr2 at the end of unsteady state, minimum furnace temperature reaching time time1, and minimum furnace temperature maintaining time time2) when the value of the evaluation function J is the minimum value among the values of the evaluation function J for the candidates of the target furnace temperature trajectory Tr_ref calculated when the convergence condition is satisfied.
Next, in step S714, the control unit 340 calculates the input heat amount to the combustion chamber 3 at a time one hour after the current time in the same manner as the input heat amount calculation unit 322 calculates the predicted value of the input heat amount Q(t+1) in step S705. Then, the control unit 340 generates and outputs a control signal for making the input heat amount to the combustion chamber 3 a heat amount corresponding to the difference between the calculated input heat amount to the combustion chamber 3 at a time one hour after the current time and the value of the target furnace temperature trajectory Tr_ref at a time one hour after the current time determined by the target furnace temperature determination unit 330. Note that the current time is the actual time when the step S714 is executed. When the processing of step S714 is completed, the processing according to the flowchart of FIG. 7 is completed.

<Calculation example>
Fig. 8 is a diagram showing an example of the target furnace temperature trajectory Tr_ref and the actual furnace temperature trajectory Tr_mes. The actual furnace temperature trajectory Tr_mes shown in Fig. 8 is the furnace battery temperature when coke satisfying the desired quality is produced from the results of past operation. The target furnace temperature trajectory Tr_ref is the furnace battery temperature determined by the method of this embodiment using the actual values up to _the time _tc when the target furnace temperature trajectory Tr_ref is determined in the past operation, without using the actual values at times after the time _tc when the target furnace temperature trajectory Tr_ref is determined (the start time ts of unsteady operation or the time _tm when the operating conditions are changed).

Figure 8 (a) shows the calculation results when the operating conditions are not changed during unsteady operation. As shown in Figure 8 (a), it can be seen that the target furnace temperature trajectory Tr_ref can be made to follow the actual furnace temperature trajectory Tr_mes with high accuracy.

FIG. 8(b) shows the calculation result when the operation conditions (carbonization time) are changed during the non-steady operation. In FIG. 8(b), as in FIG. 6B, for comparison, the target furnace temperature trajectory Tr_ref_old determined at the start time _ts of the non-steady operation is shown even after the time _tm when the operation conditions are changed. In FIG. 8(b), from the start time _ts of the non-steady operation to the time _tm when the operation conditions are changed, the target furnace temperature trajectory Tr_ref_old determined at the start time _ts of the non-steady operation is used as the target furnace temperature trajectory. Then, from the time _tm when the operation conditions are changed to the end time _te of the non-steady operation, the target furnace temperature trajectory Tr_ref_new determined at the time _tm when the operation conditions are changed is used.

As shown in Figure 8 (b), if the target furnace temperature trajectory Tr_ref_new is re-determined at the time _tm when the operating conditions are changed, it can be seen that the target furnace temperature trajectory Tr_ref can be made to follow the actual furnace temperature trajectory Tr_mes with even greater accuracy.

<Summary>
As described above, in this embodiment, the processing device 300 calculates the predicted value of the furnace temperature during non-steady operation based on the first influencing factor, which is a factor that affects the predicted value of the furnace temperature during non-steady operation, and calculates the predicted value of the physical quantity representing the carbonization state of the coke during non-steady operation based on the second influencing factor, which is a factor that affects the predicted value of the physical quantity. Then, the processing device 300 determines the target furnace temperature trajectory Tr_ref based on the predicted value of the furnace temperature and the predicted value of the physical quantity. Therefore, it is possible to predict what the furnace temperature during non-steady operation and the carbonization state of the coke during non-steady operation will be, and dynamically determine the target furnace temperature trajectory so that the predicted result is reflected. The target furnace temperature during non-steady operation can be determined with high accuracy.

In addition, in this embodiment, the processing device 300 uses an influence factor including the amount of heat input to the combustion chamber 3 during non-steady operation and the furnace temperature at a timing prior to the predicted time of the predicted value (the time when the predicted value of the furnace temperature was obtained) as a first influence factor, which is a factor that affects the predicted value of the furnace temperature during non-steady operation. In addition, the processing device 300 uses an influence factor including the predicted value of the furnace temperature during non-steady operation as a second influence factor, which is a factor that affects the physical quantity representing the carbonization state of the coke during non-steady operation. Then, based on the first influence factor, the processing device 300 calculates a predicted value of the furnace temperature at a predicted time later than the predicted time of the predicted value of the furnace temperature already calculated (see formulas (1) to (3) as a specific example). In addition, the processing device 300 calculates a predicted value of the physical quantity representing the carbonization state of the coke during non-steady operation based on the second influence factor including the predicted value of the furnace temperature during non-steady operation calculated in this way (see formulas (4) to (8) as a specific example). Therefore, the predicted values of the furnace temperature and the physical quantities representing the coke distillation state can be calculated using influencing factors that have a large effect on the predicted values of the furnace temperature and the physical quantities representing the coke distillation state during non-steady operation.

In addition, in this embodiment, the processing device 300 calculates the amount of change for each predetermined time period determined based on the control period for the combustion chamber 3 as the predicted value of the furnace temperature. Therefore, the amount of change in the predicted value of the furnace temperature can be calculated at a timing synchronized with the control period for the combustion chamber 3. Therefore, for example, calculations for adjusting the time of the predicted value of the furnace temperature to a time that matches the control period for the combustion chamber 3 are not required, and the furnace temperature can be predicted by cumulatively adding up the predicted values of the amount of change in the furnace temperature.

In addition, in this embodiment, the processing device 300 uses an influence factor that further includes the carbonization time as the second influence factor. Therefore, the prediction accuracy of the predicted value of the physical quantity representing the carbonization state of the coke during non-steady operation can be further improved.

In addition, in this embodiment, the processing device 300 calculates the amount of change from the start of non-steady operation to the end of non-steady operation as a predicted value of the physical quantity representing the carbonization state of the coke during non-steady operation. Therefore, by predicting the physical quantity representing the carbonization state of the coke at both the start and end of non-steady operation, it is possible to predict the physical quantity representing the carbonization state of the coke.

In addition, in this embodiment, the processing device 300 performs processing by determining the end of the non-steady operation as the end of the coke extrusion work in the Da+1th pass after the suspension of loading and extrusion into the carbonization chamber 2 ends. Therefore, the end of the non-steady operation can be determined at the timing that marks the end of batch-type operation in the coke oven. Also, for example, the end of the non-steady operation can be determined to coincide with the timing at which the coke temperature is measured.

In addition, in this embodiment, the processing device 300 performs processing by setting the start of non-steady operation as the end of the coke extrusion work Db times before the start of the pause in loading and extrusion into the carbonization chamber 2. Therefore, the end of non-steady operation can be determined at the timing that marks the end of batch-type operation in the coke oven. Also, for example, the start of non-steady operation can be determined to coincide with the timing at which the coke temperature is measured.

In addition, in this embodiment, when the operating conditions are changed after the target furnace temperature trajectory is determined, the processing device 300 changes the influencing factors, among the first and second influencing factors, that are changed due to the change in the operating conditions according to the operating conditions, and then recalculates the predicted value of the furnace temperature during non-steady operation and the predicted value of the physical quantity representing the carbonization state of the coke at the time after the time _tm when the operating conditions are changed, and re-determines the target furnace temperature trajectory. Therefore, even if the operating conditions are changed after the target furnace temperature trajectory is determined, the target furnace temperature during non-steady operation can be accurately determined.

In addition, in this embodiment, the processing device 300 determines the target furnace temperature trajectory based on the results of a calculation that includes calculating the difference between the candidate target furnace temperature trajectory and the predicted furnace temperature (see the first term on the right side of equation (12) as a specific example), and the difference between the target physical quantity, which is the target value of the physical quantity representing the coke carbonization state, and the predicted value of the physical quantity representing the coke carbonization state (see the second term on the right side of equation (12) as a specific example). Therefore, it is possible to realize both a quantitative evaluation of the predicted furnace temperature and a quantitative evaluation of the predicted physical quantity representing the coke carbonization state. Therefore, the target furnace temperature in non-steady operation can be accurately determined based on these quantitative evaluations.

In addition, in this embodiment, the processing device 300 determines the target furnace temperature trajectory based on the value of an evaluation function including a first evaluation index (see the first term on the right side of equation (12) as a specific example) that evaluates the difference between a candidate target furnace temperature trajectory and the predicted value of the furnace temperature, and a second evaluation index (see the second term on the right side of equation (12) as a specific example) that evaluates the difference between a target physical quantity, which is a target value of the physical quantity representing the coke carbonization state, and the predicted value of the physical quantity representing the coke carbonization state. Therefore, a quantitative evaluation of the predicted value of the furnace temperature and a quantitative evaluation of the predicted value of the physical quantity representing the coke carbonization state can be performed by solving an optimization problem.

In addition, in this embodiment, the processing device 300 determines the target furnace temperature trajectory based on the value of an evaluation function that further includes a third evaluation index that evaluates the amount of heat input (see the third term on the right side of equation (12) as a specific example). Therefore, the optimization problem for determining the target furnace temperature in non-steady operation can be an optimization problem that can obtain an optimal solution with higher accuracy.

In addition, in this embodiment, the processing device 300 calculates the value of the evaluation function by varying candidates for the target reactor temperature trajectory, and determines the target reactor temperature trajectory based on the calculated value of the evaluation function. Therefore, the target reactor temperature trajectory can be determined by a metaheuristics method without using differential equations that describe physical phenomena.

In addition, in this embodiment, the processing device 300 calculates a predicted value of the input heat amount based on the candidate target furnace temperature trajectory and the predicted value of the furnace temperature. Therefore, the predicted value of the input heat amount can be quantitatively obtained as the first influencing factor.

In addition, in this embodiment, the processing device 300 uses the temperature of the coke produced during non-steady operation or the temperature of the oven wall 4 during non-steady operation as a physical quantity representing the carbonization state of the coke during non-steady operation. Therefore, a physical quantity that can be measured during non-steady operation can be used as a physical quantity representing the carbonization state of the coke during non-steady operation. Therefore, for example, it becomes possible to verify the predicted value of the physical quantity representing the carbonization state of the coke. As described with reference to FIG. 2B, the temperature of the coke produced during non-steady operation is measured, for example, during the coke extrusion operation. Similarly, the temperature of the oven wall 4 during non-steady operation is measured, for example, during the coke extrusion operation.

In addition, in this embodiment, the processing device 300 uses the furnace battery temperature, which is a representative value of the temperatures in multiple combustion chambers 3, as the furnace temperature. Therefore, the number of variables related to the furnace temperature can be reduced. Therefore, for example, the calculation load can be further reduced.

In addition, in this embodiment, the processing device 300 generates and outputs a control signal to adjust the amount of heat input to the combustion chamber 3 to an amount of heat corresponding to the difference between the target furnace temperature trajectory and the actual furnace temperature. Therefore, the processing device 300 can execute control to achieve the target furnace temperature trajectory.

In this embodiment, the case where the furnace temperature is the furnace core temperature is exemplified. However, the furnace temperature is not limited to the furnace core temperature. For example, when the control valve and the actuator are installed in all the combustion chambers 3 as described above and the thermometer 6 is installed in all the combustion chambers 3, each formula may be changed to treat the passage (block) as an individual coke chamber 2, and the furnace temperature, which is the temperature in the combustion chamber 3 of the coke oven 1, may be the temperature of each combustion chamber 3 instead of the furnace core temperature. In this case, for example, the formulas (1) to (3) and the formulas (10) to (11) may be changed to formulas for each combustion chamber 3. In addition, depending on which coke chamber 2 the coke temperature at the start time t _s and the end time t _e of the unsteady operation is, the temperature of one or more combustion chambers 3 close to the coke chamber 2 may be set to the temperature that determines the explanatory variables ΔTr1 to ΔTr3 of the formula (4). In addition, for the first and third terms on the right-hand side of equation (12), Σ|Tr(t)-Tr_ref| and Σ|Q(t)| can be calculated for each combustion chamber 3, and the sums of Σ|Tr(t)-Tr_ref| and Σ|Q(t)| for each combustion chamber 3 can be used as evaluation indexes to be multiplied by weighting coefficients w1 and w3, respectively.

The above-described embodiment of the present invention can be realized by a computer executing a program. A computer-readable recording medium on which the program is recorded and a computer program product such as the program can also be applied as an embodiment of the present invention. Examples of the recording medium that can be used include a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a magnetic tape, a non-volatile memory card, and a ROM.
Furthermore, the above-described embodiments of the present invention are merely examples of the implementation of the present invention, and the technical scope of the present invention should not be interpreted as being limited by these. In other words, the present invention can be implemented in various forms without departing from its technical concept or main features.

REFERENCE SIGNS LIST 1 Coke oven 2 Carbonization chamber 3 Combustion chamber 4 Oven wall 5 Adjusting valve 6 Thermometer 7 Pusher ram 8 Thermometer 9 Guide car 300 Processing device 310 Acquisition unit 320 Prediction value calculation unit 321 Oven state calculation unit 322 Input heat amount calculation unit 330 Target oven temperature determination unit 340 Control unit t _k Carbonization time t _t Pass time t _s Start time of non-steady operation t _e End time of non-steady operation t _m Time when operation conditions were changed T _c Coke temperature T _r Oven battery temperature Tr_ref Target oven temperature trajectory Tr_mes Actual oven temperature trajectory time0 Period of non-steady operation time1 Time to reach minimum oven temperature time2 Time to maintain minimum oven temperature ΔTr1 Amount of change in oven temperature at start of non-steady operation ΔTr2 Amount of change in oven temperature at end of non-steady operation

Claims (18)

A processing device for determining a target furnace temperature, which is a target value of a furnace temperature, which is a temperature of a combustion chamber in a coke oven, during non-steady operation, comprising:
a prediction value calculation means for calculating a prediction value of the furnace temperature during the non-steady operation based on a first influencing factor which is a factor that influences the prediction value of the furnace temperature during the non-steady operation, and for calculating a prediction value of a physical quantity during the non-steady operation based on a second influencing factor which is a factor that influences the prediction value of the physical quantity that represents the carbonization state of the coke during the non-steady operation;
a target furnace temperature determination means for determining a target furnace temperature trajectory, which is a time change of the target furnace temperature, based on a predicted value of the furnace temperature during the unsteady operation and a predicted value of the physical quantity during the unsteady operation;
A processing device comprising: The first influencing factor is:
The amount of heat input to the combustion chamber during the non-steady operation; and
The furnace temperature at a timing prior to a prediction time of the predicted value of the furnace temperature during the unsteady operation,
The second influencing factor is:
The processing apparatus of claim 1 , further comprising the furnace temperature during the non-steady operation. The predicted value calculation means
3 . The processing apparatus according to claim 1 , further comprising: a processing unit configured to calculate, as the predicted value of the furnace temperature, an amount of change per predetermined time period determined based on a control period for the combustion chamber. The second influencing factor is:
The processing apparatus according to any one of claims 1 to 3, further comprising a dry distillation period. The predicted value calculation means
The treatment device according to any one of claims 1 to 4, further comprising: calculating an amount of change from a start of the unsteady operation to an end of the unsteady operation as the predicted value of the physical quantity during the unsteady operation. The coke oven is
A coke oven in which unloading and loading work is performed on a Da number of coke chambers, where Da is an integer of 2 or more,
At the end of the non-steady operation,
The treatment apparatus according to any one of claims 1 to 5, which is performed at the end of the coke pushing operation in the Da+1th pass after the end of the pause in loading and pushing of the coke into the coke chamber. At the start of the non-steady operation,
The treatment apparatus according to claim 6, wherein the suspension of the charging and pushing of the coke into the coke chamber is started at a time when the pushing operation of the coke in a run that is Db before the time when the suspension of the charging and pushing of the coke into the coke chamber is started is ended. When the operating conditions are changed after the target furnace temperature trajectory is determined by the target furnace temperature determination means,
the prediction value calculation means changes an influence factor, among the first influence factor and the second influence factor, that is changed due to a change in the operation conditions in accordance with the operation conditions, and then recalculates the prediction value of the furnace temperature and the prediction value of the physical quantity during the unsteady operation at a timing after the timing at which the operation conditions are changed;
The processing apparatus according to any one of claims 1 to 7, wherein the target furnace temperature determining means re-determines the target furnace temperature trajectory at a timing after the timing at which the operating conditions are changed. The target furnace temperature determination means
The processing device according to any one of claims 1 to 8, wherein the target furnace temperature trajectory is determined based on a result of a calculation including calculating a difference between the candidate for the target furnace temperature trajectory and a predicted value of the furnace temperature, and a difference between a target physical quantity that is a target value of the physical quantity and the predicted value of the physical quantity. The target furnace temperature determination means
The processing device according to any one of claims 1 to 9, wherein the target furnace temperature trajectory is determined based on a value of an evaluation function including a first evaluation index that evaluates a difference between the candidate for the target furnace temperature trajectory and a predicted value of the furnace temperature, and a second evaluation index that evaluates a difference between a target physical quantity that is a target value of the physical quantity and the predicted value of the physical quantity. The first influencing factor is:
The amount of heat input to the combustion chamber during the non-steady operation is included,
The evaluation function is
The processing apparatus according to claim 10 , further comprising a third evaluation index for evaluating the amount of input heat. The target furnace temperature determination means
The processing apparatus according to claim 10 or 11, further comprising: calculating a value of the evaluation function by varying candidates for the target furnace temperature trajectory; and determining the target furnace temperature trajectory based on the calculated value of the evaluation function. The predicted value calculation means
Calculating a predicted value of an input heat amount to the combustion chamber during the unsteady operation based on the candidate for the target furnace temperature trajectory and the predicted value of the furnace temperature;
As the first influencing factor,
The processing apparatus according to any one of claims 1 to 12, further comprising a predicted value of the input heat quantity. The physical quantity during the non-steady operation is
The treatment device according to any one of claims 1 to 13, wherein the temperature is the temperature of the coke produced during the unsteady operation, or the temperature of the wall of the coke oven during the unsteady operation. The furnace temperature is
The treatment device according to any one of claims 1 to 14, wherein the temperature is a furnace battery temperature that is a representative value of temperatures in a plurality of combustion chambers of the coke oven. The processing device according to any one of claims 1 to 15, further comprising a control means for generating and outputting a control signal for adjusting the amount of heat input to the combustion chamber of the coke oven to an amount of heat corresponding to the difference between the target oven temperature trajectory determined by the target oven temperature determination means and the actual oven temperature value. A processing method for determining a target furnace temperature, which is a target value of a furnace temperature, which is a temperature of a combustion chamber in a coke oven, during non-steady operation, comprising:
a prediction value calculation step of calculating a prediction value of the furnace temperature during the non-steady operation based on a first influencing factor which is a factor that influences the prediction value of the furnace temperature during the non-steady operation, and calculating a prediction value of the physical quantity during the non-steady operation based on a second influencing factor which is a factor that influences the prediction value of the physical quantity that represents the carbonization state of the coke during the non-steady operation;
a target furnace temperature determination step of determining a target furnace temperature trajectory, which is a time change of the target furnace temperature, based on a predicted value of the furnace temperature during the unsteady operation and a predicted value of the physical quantity during the unsteady operation;
A processing method comprising: A program for causing a computer to function as each of the means of the processing device according to any one of claims 1 to 16.